From 6ec9ca3a819416be5e6f0d272e616fe188d677ea Mon Sep 17 00:00:00 2001
From: Feng Zhou <100029652+fz-cambridge@users.noreply.github.com>
Date: Tue, 22 Mar 2022 13:49:34 +0000
Subject: [PATCH] Add files via upload
---
finemap-ivis/app.R | 921 +++++
finemap-ivis/data/finemap.config | 1001 +++++
finemap-ivis/data/finemap.ld | 472 +++
finemap-ivis/data/finemap.snp | 473 +++
finemap-ivis/data/finemap.z | 473 +++
flashfm-ivis/app.R | 4810 ++++++++++++++++++++++
flashfm-ivis/data/R_data_preloaded.RData | Bin 0 -> 1814920 bytes
7 files changed, 8150 insertions(+)
create mode 100644 finemap-ivis/app.R
create mode 100644 finemap-ivis/data/finemap.config
create mode 100644 finemap-ivis/data/finemap.ld
create mode 100644 finemap-ivis/data/finemap.snp
create mode 100644 finemap-ivis/data/finemap.z
create mode 100644 flashfm-ivis/app.R
create mode 100644 flashfm-ivis/data/R_data_preloaded.RData
diff --git a/finemap-ivis/app.R b/finemap-ivis/app.R
new file mode 100644
index 0000000..fc1e31d
--- /dev/null
+++ b/finemap-ivis/app.R
@@ -0,0 +1,921 @@
+# finemap-ivis version_2022_03_10
+
+####----Libraries----
+#if (!require(devtools)) install.packages("devtools")
+#devtools::install_github("gaospecial/ggVennDiagram")
+library("ggVennDiagram")
+library(dplyr)
+library(ggplot2)
+library(shiny)
+library(shinydashboard)
+library(plotly)
+library(DT)
+library(gaston)
+library(corrplot)
+library(RColorBrewer)
+#display.brewer.all(type = 'qual')
+library(randomcoloR)
+library(igraph)
+library(networkD3)
+library(stringr)
+
+# if pre-load data
+#load("data/R_data.RData")
+####----pre-loaded data as examples----
+#users will overwrite it if they upload new files
+# finemap.config <- read.csv("data/finemap.config", sep="")
+# finemap.ld <- read.table("data/finemap.ld", quote="\"", comment.char="")
+# finemap.snp <- read.csv("data/finemap.snp", sep="")
+# finemap.z <- read.csv("data/finemap.z", sep="")
+
+####----Max size of input file----
+options(shiny.maxRequestSize = 100*1024^2)
+
+#generate a vector of color based on snpGroups----
+set.seed(1)
+n <- 4
+col_vector = distinctColorPalette(n)
+pal <- c("gray", col_vector)
+pal <- setNames(pal, c("0", "cs1@99.5%+@99%+@95%+@90%", "cs1@99.5%+@99%+@95%", "cs1@99.5%+@99%", "cs1@99.5%"))
+# pal_2 <- c("gray", "dodgerblue", "dodgerblue", "dodgerblue", "dodgerblue")
+# pal_2 <- setNames(pal_2, c("0", "cs1@99.5%+@99%+@95%+@90%", "cs1@99.5%+@99%+@95%", "cs1@99.5%+@99%", "cs1@99.5%"))
+
+#Step 1: function from finemap-example.R
+format.finemap <- function(fconfig="finemap1.config",fsnp="finemap1.snp",fz="finemap1.z",fld="finemap.ld") {
+ # constuct modPP, a data.frame with models and their PPs, equivalent to mpp.pp$PP[[1]][,1]
+ fmresults <- read.table(fconfig, header = TRUE, as.is = TRUE, sep = " ")
+ modPP <- data.frame( str = fmresults$config, PP = fmresults$prob, stringsAsFactors = FALSE)
+ modsnps <- strsplit(modPP$str, ",")
+ snpmods <- sapply(modsnps, function(x) paste(x, collapse = "%"))
+ modPP$str <- snpmods
+ # gwas summary statistics and snp MPPs
+ snpPP <- read.table(fsnp, header = TRUE, as.is = TRUE, sep = " ")
+ MPP <- snpPP[,c("rsid","prob")]
+ # LD, defined as cor^2
+ r <- read.table(fld, header = FALSE, as.is = TRUE, sep = " ")
+ gwas <- read.table(fz,header = TRUE, as.is = TRUE, sep = " ") # needed to get snp names for ld matrix
+ #gwas$p <- 2*(1-pnorm(abs(gwas$beta/gwas$se)))
+ gwas$p <- 2*(pnorm(abs(gwas$beta/gwas$se), lower.tail = F))
+ r2 <- r^2
+ colnames(r2) <- gwas$rsid
+ rownames(r2) <- gwas$rsid
+ return(list(PP=modPP,MPP=MPP,LD=r2, gwas=gwas))
+}
+#fstub='finemap'
+#tt <- format.finemap(fconfig=paste0(fstub,".config"),fsnp=paste0(fstub,".snp"),fz=paste0(fstub,".z"),fld=paste0(fstub,".ld"))
+
+# Step 2: define credible sets based on a specific significant level (i.e. 0.99)----
+credset <- function(modPP,cred=0.99) {
+ tmp <- modPP[order(modPP, decreasing = TRUE)]
+ cpp <- cumsum(tmp)
+ wh <- which(cpp <= cred)
+ if (!length(wh)) wh <- 1
+ wh <- c(wh, max(wh) + 1)
+ keepmodPP <- tmp[wh]
+ mods <- names(keepmodPP)
+ usnps <- unique(unlist(strsplit(mods,"%")))
+ return(usnps)
+}
+M <- 1 # number of traits
+# #cs1_selected <- csM_selected <- vector("list",M)
+# cs1_selected <- vector("list",M)
+# for(i in 1:M){
+# #tmp <- mpp.pp$PP[[i]][,1] # trait i, single-trait fine-mapping
+# tmp <- tt$PP[,-1]
+# names(tmp) <- tt$PP[,1]
+# cs1_selected[[i]] <- credset(tmp,0.99)
+# #tmp <- mpp.pp$PP[[i]][,2] # trait i, multi-trait fine-mapping
+# #csM_selected[[i]] <- credset(tmp,0.99)
+# }
+
+
+
+####----Shiny user interface----
+ui <- dashboardPage(
+ #Dashboard header----
+ #Dashboard header----
+ dashboardHeader(
+ title = "finemap-ivis R package",
+ # Drop-down menu for messages
+ dropdownMenu(type = "messages", badgeStatus = "success",
+ headerText = 'Contact info:',
+ messageItem("Contact 1: Feng",
+ "feng.zhou[AT]mrc-bsu.cam.ac.uk"
+ ),
+ messageItem("Contact 2: Jenn",
+ "jennifer.asimit[AT]mrc-bsu.cam.ac.uk"
+ )
+ ),
+
+ # Drop-down menu for notifications
+ dropdownMenu(type = "notifications", #badgeStatus = "warning",
+ headerText = 'Update and version info:',
+ notificationItem(icon = icon("users"), status = "info",
+ "Update: this is Version 0.3"
+ ),
+ notificationItem(icon = icon("users"), status = "info",
+ "Update: modified on 10-Mar-2022"
+ )
+ ),
+
+ # Drop-down menu for tasks, with progress bar
+ dropdownMenu(type = "tasks", badgeStatus = "danger",
+ headerText = 'Source code:',
+ taskItem(value = 100, color = "aqua",
+ text = "Source code #1: flashfm",
+ href = 'https://github.com/jennasimit/flashfm'
+ ),
+ taskItem(value = 100, color = "green",
+ text = "Source code #2: flashfm-ivis",
+ href = 'https://github.com/fz-cambridge/flashfm-ivis'
+ ),
+ taskItem(value = 100, color = "yellow",
+ text = "Source code #3: finemap-ivis",
+ href = 'http://shiny.mrc-bsu.cam.ac.uk/apps/finemap-ivis/'
+ )
+ )
+ ),
+
+ #Dashboard sidebar----
+ dashboardSidebar(
+ #Data inputs----
+ radioButtons(inputId = "control_widgets_0",
+ label = h4("Select data source:"),
+ choices = list("Pre-loaded example" = 0,
+ "Upload 4 key FINEMAP outputs" = 1),
+ selected = 0),
+
+ fileInput("inFiles",
+ label="Upload FINEMAP results",
+ multiple = TRUE),
+
+ #Control widgets----
+ radioButtons(inputId = "control_widgets_1",
+ label = h4("Credible sets:"),
+ choices = list("Show all SNPs (default)" = 0,
+ "Show cs1@99.5%" = 1,
+ "Show cs1@99%" = 2,
+ "Show cs1@95%" = 3,
+ "Show cs1@90%" = 4,
+ "Show not in cs1@99%" = 5),
+ selected = 0),
+
+ sliderInput(inputId = "control_widgets_2",
+ label = h4("Define MPP value"),
+ min = 0, max = 1, value = c(0, 1)),
+
+ #Dashboards----
+ sidebarMenu(
+ menuItem("Dashboard", icon = icon("dashboard"), startExpanded = TRUE,
+ menuSubItem("Input values", tabName = "ch_0",
+ icon = icon("angle-right")),
+ menuSubItem("Output plots", tabName = "ch_1",
+ icon = icon("angle-right")),
+ menuSubItem("README", tabName = "README",
+ icon = icon("angle-right")),
+ menuSubItem(text = "Go back to flashfm-ivis",
+ href = "http://shiny.mrc-bsu.cam.ac.uk/apps/finemap-ivis/",
+ icon = icon("angle-right"))
+ )
+ )
+ ),
+
+ #Dashboard body----
+ dashboardBody(
+ tabItems(
+ #Dashboard_ch_0----
+ tabItem(tabName = "ch_0",
+ #check FINEMAP input files
+ h2("Input values"),
+ h3("Check files in the current temporary server"),
+ h4("--- (at least 4 files with ext: .config; .ld; .snp; .z)---"),
+ fluidRow(column(12, verbatimTextOutput("value"))),
+ br(),
+ h4("--- 4 key files' location in the server ---"),
+ fluidRow(column(12, DT::dataTableOutput("file_locations"))),
+ br(),
+ h4("--- tt$PP ---"),
+ fluidRow(column(12, DT::dataTableOutput("contents_tt_PP"))),
+ br(),
+ h4("--- tt$MPP ---"),
+ fluidRow(column(12, DT::dataTableOutput("contents_tt_MPP"))),
+ br(),
+ h4("--- tt$LD ---"),
+ fluidRow(column(12, DT::dataTableOutput("contents_tt_LD"))),
+ br(),
+ h4("--- tt$gwas ---"),
+ fluidRow(column(12, DT::dataTableOutput("contents_tt_gwas"))),
+ br(),
+ h4("--- credible sets at different confidence intervals ---"),
+ fluidRow(column(12, verbatimTextOutput("credible_set"))),
+ ),
+
+ #Dashboard_ch_1----
+ tabItem(tabName = "ch_1",
+ h2("Output plots"),
+ fluidRow(
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ #title = "Result_1 charts",
+ # The id lets us use input$tabset1 on the server to find the current tab
+ id = "tabset1", height = "1200px",
+ tabPanel("Regional_Association_Plot_1",
+ plotlyOutput("manhattan_1a", height = "700px")
+ ),
+ tabPanel("Regional_Association_Plot_2",
+ plotlyOutput("manhattan_2", height = "700px"),
+ sliderInput(inputId = "control_widgets_4",
+ label = h4("Define user's own credible set"),
+ min = 0.90, max = 1, step =0.001, value = 0.99),
+ sliderInput(inputId = "control_widgets_6",
+ label = h4("Define MPP value"),
+ min = 0, max = 1, step =0.005, value = c(0, 1)),
+ radioButtons(inputId = "control_widgets_5",
+ label = h4("Define the plot color:"),
+ inline=T,
+ choices = list("Blue" = 0,
+ "Red" = 1,
+ "Pink" = 2,
+ "Green" = 3,
+ "Orange" = 4,
+ "Black" = 5),
+ selected = 0)
+ ),
+ tabPanel("LD_matrix",
+ box(plotlyOutput("Plot_LD1"),
+ title = "LD Heatmap",
+ #status = "warning",
+ solidHeader = TRUE, collapsible = TRUE,
+ width=NULL),
+ box(plotOutput("Plot_LD2"),
+ title = "LD Plot",
+ solidHeader = TRUE, collapsible = TRUE,
+ width=NULL)
+ ),
+ tabPanel("Venn_diagram",
+ plotlyOutput("fig_ch4_tab2_venn_cs1"),
+ #tableOutput("table_ch4_tab2_venn_cs1"),
+ box(div(style = 'overflow-y: scroll; overflow-x: scroll; ',
+ DT::dataTableOutput('table_ch4_tab2_venn_cs1_table')),
+ title = "Table of credible sets",
+ #status = "warning",
+ solidHeader = TRUE,
+ collapsible = TRUE, width=NULL)
+ ),
+ tabPanel("SNP_network",
+ radioButtons(inputId = "control_widgets_3b_cs",
+ label = h4("Credible sets:"),
+ choices = list("Show all SNPs" = 0,
+ "Show cs1@99.5% (default)" = 1,
+ "Show cs1@99%" = 2,
+ "Show cs1@95%" = 3,
+ "Show cs1@90%" = 4),
+ selected = 1),
+ sliderInput(inputId = "control_widgets_3b_finemap",
+ label = h4("Define mpp.pp$PP value (finemap)"),
+ min = 0, max = 1, step =0.001, value = c(0.05, 1),
+ width = '800px'),
+ #textOutput("control_widgets_3b_output"),
+ forceNetworkOutput(outputId = "ch1_network_snp_finemap"),
+ downloadButton('d', 'Download network as html')
+ ),
+ width=NULL
+ )
+ )
+ )
+ ),
+
+ #Dashboard_ch_3----
+ tabItem(tabName = "README",
+ h2("README"),
+ br(),
+ h3("About this package:"),
+ br(),
+ h4("GitHub: "),
+ br(),
+ h4("flashfm R package: https://github.com/jennasimit/flashfm"),
+ h4("flashfm-ivis R package: https://github.com/fz-cambridge/flashfm-ivis"),
+ br(),
+ h4("YouTube: "),
+ #br(),
+ #HTML('')
+ fluidRow(
+ fluidRow(
+ column(6,
+ box(
+ width = NULL,
+ title = "flashfm-ivis and finemap-ivis overview",
+ HTML('')
+ )),
+ column(6,
+ box(
+ width = NULL,
+ title = "finemap-ivis user data input",
+ HTML('')
+ )),
+ )
+ )
+
+ )
+ )
+ )
+) #End of user interface
+
+
+####----Shiny server----
+server <- function(input, output) {
+
+ output$value <- renderPrint({
+ if (input$control_widgets_0 == 0){
+ print("Using pre-loaded example...")
+ }else{
+ str(input$inFiles)
+ }
+ })
+
+ tt <- reactive({
+ if (input$control_widgets_0 == 0){
+ fstub='data/finemap'
+ #format.finemap(fconfig=get(paste0(fstub,".config")),fsnp=get(paste0(fstub,".snp")),fz=get(paste0(fstub,".z")),fld=get(paste0(fstub,".ld")))
+ format.finemap(fconfig=paste0(fstub,".config"),fsnp=paste0(fstub,".snp"),fz=paste0(fstub,".z"),fld=paste0(fstub,".ld"))
+ }else{
+ file <- input$inFiles
+ req(file)
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "config"){fconfig = file$datapath[i]}
+ if (ext_check == "ld"){fld = file$datapath[i]}
+ if (ext_check == "snp"){fsnp = file$datapath[i]}
+ if (ext_check == "z"){fz = file$datapath[i]}
+ }
+ format.finemap(fconfig=fconfig,fsnp=fsnp,fz=fz,fld=fld)
+ }
+ })
+
+ output$file_locations <- DT::renderDataTable({
+ if (input$control_widgets_0 == 0){
+ fconfig = "Using pre-loaded example..."
+ fld = "Using pre-loaded example..."
+ fsnp = "Using pre-loaded example..."
+ fz = "Using pre-loaded example..."
+ location = rbind(fconfig, fld, fsnp, fz)
+ colnames(location) = c('location')
+ DT::datatable(location, options = list(dom = 't'))
+ }else{
+ file <- input$inFiles
+ req(file)
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "config"){fconfig = file$datapath[i]}
+ if (ext_check == "ld"){fld = file$datapath[i]}
+ if (ext_check == "snp"){fsnp = file$datapath[i]}
+ if (ext_check == "z"){fz = file$datapath[i]}
+ }
+ location = rbind(fconfig, fld, fsnp, fz)
+ colnames(location) = c('location')
+ DT::datatable(location, options = list(dom = 't'))
+ }
+ })
+
+ output$contents_tt_PP <- DT::renderDataTable({
+ DT::datatable(data.frame(tt()$PP),
+ extensions = 'Buttons',
+ options = list(
+ paging = TRUE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ scrollX=TRUE,
+ ordering = TRUE,
+ dom = 'lftiprBRSPQ',
+ buttons = list(
+ list(extend = 'csv', filename = "tt_PP"),
+ list(extend = 'excel', filename = "tt_PP"))
+ ),
+ class = "display")
+ })
+
+ output$contents_tt_MPP <- DT::renderDataTable({
+ DT::datatable(data.frame(tt()$MPP),
+ extensions = 'Buttons',
+ options = list(
+ paging = TRUE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ scrollX=TRUE,
+ ordering = TRUE,
+ dom = 'lftiprBRSPQ',
+ buttons = list(
+ list(extend = 'csv', filename = "tt_PP"),
+ list(extend = 'excel', filename = "tt_PP"))
+ ),
+ class = "display")
+ })
+
+ output$contents_tt_LD <- DT::renderDataTable({
+ DT::datatable(data.frame(round(tt()$LD[1:5,1:5], 6)),
+ options = list(dom = 't'))
+ })
+
+ GWAS_all_final <- reactive({
+ df_mpp = data.frame(tt()$MPP)
+ colnames(df_mpp) = c("rsid","mpp")
+ merge(data.frame(tt()$gwas),df_mpp, by.x=c("rsid"), by.y=c("rsid"))
+ })
+
+ # output$contents_tt_gwas <- DT::renderDataTable({
+ # DT::datatable(GWAS_all_final(),
+ # extensions = 'Buttons',
+ # options = list(
+ # paging = TRUE,
+ # searching = TRUE,
+ # fixedColumns = TRUE,
+ # autoWidth = TRUE,
+ # scrollX=TRUE,
+ # ordering = TRUE,
+ # dom = 'lftiprBRSPQ',
+ # buttons = list(
+ # list(extend = 'csv', filename = "tt_PP"),
+ # list(extend = 'excel', filename = "tt_PP"))
+ # ),
+ # class = "display")
+ # })
+
+ output$credible_set <- renderPrint({
+ cl = c(0.995, 0.99, 0.95, 0.90)
+ cs1_selected <- vector("list",length(cl))
+ for(i in 1:length(cl)){
+ tmp <- tt()$PP[,-1]
+ names(tmp) <- tt()$PP[,1]
+ cs1_selected[[i]] <- credset(tmp,cl[i])
+ }
+ names(cs1_selected) = c("cs1 @ 0.995","cs1 @ 0.99","cs1 @ 0.95","cs1 @ 0.90")
+ print(cs1_selected)
+ })
+
+ GWAS_all_final_cs <- reactive({
+ cl = c(0.995, 0.99, 0.95, 0.90)
+ cs1_selected <- vector("list",length(cl))
+ for(i in 1:length(cl)){
+ tmp <- tt()$PP[,-1]
+ names(tmp) <- tt()$PP[,1]
+ cs1_selected[[i]] <- credset(tmp,cl[i])
+ }
+ GWAS_all_final_cs_0 = GWAS_all_final()
+ GWAS_all_final_cs_0$p_log10 = -log10(GWAS_all_final_cs_0$p)
+ for (i in 1:length(GWAS_all_final_cs_0$rsid)){
+ if (GWAS_all_final_cs_0$rsid[i] %in% cs1_selected[[1]]){
+ GWAS_all_final_cs_0$'cs1@99.5%'[i] = 1
+ } else {GWAS_all_final_cs_0$'cs1@99.5%'[i] = 0}
+ if (GWAS_all_final_cs_0$rsid[i] %in% cs1_selected[[2]]){
+ GWAS_all_final_cs_0$'cs1@99%'[i] = 1
+ } else {GWAS_all_final_cs_0$'cs1@99%'[i] = 0}
+ if (GWAS_all_final_cs_0$rsid[i] %in% cs1_selected[[3]]){
+ GWAS_all_final_cs_0$'cs1@95%'[i] = 1
+ } else {GWAS_all_final_cs_0$'cs1@95%'[i] = 0}
+ if (GWAS_all_final_cs_0$rsid[i] %in% cs1_selected[[4]]){
+ GWAS_all_final_cs_0$'cs1@90%'[i] = 1
+ } else {GWAS_all_final_cs_0$'cs1@90%'[i] = 0}
+ }
+
+ for (i in 1:length(GWAS_all_final_cs_0$rsid)){
+ if (GWAS_all_final_cs_0$'cs1@90%'[i] == 1){
+ GWAS_all_final_cs_0$cs1_group[i] = "cs1@99.5%+@99%+@95%+@90%"
+ } else if (GWAS_all_final_cs_0$'cs1@95%'[i] == 1){
+ GWAS_all_final_cs_0$cs1_group[i] = "cs1@99.5%+@99%+@95%"
+ } else if (GWAS_all_final_cs_0$'cs1@99%'[i] == 1){
+ GWAS_all_final_cs_0$cs1_group[i] = "cs1@99.5%+@99%"
+ } else if (GWAS_all_final_cs_0$'cs1@99.5%'[i] == 1){
+ GWAS_all_final_cs_0$cs1_group[i] = "cs1@99.5%"
+ } else {GWAS_all_final_cs_0$cs1_group[i] = 0}
+ }
+
+ GWAS_all_final_cs_0 = GWAS_all_final_cs_0[GWAS_all_final_cs_0$p != 0, ] ##CHECK!!!
+ GWAS_all_final_cs_0
+ })
+
+ output$contents_tt_gwas <- DT::renderDataTable({
+ DT::datatable(GWAS_all_final_cs(),
+ extensions = 'Buttons',
+ options = list(
+ paging = TRUE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ scrollX=TRUE,
+ ordering = TRUE,
+ dom = 'lftiprBRSPQ',
+ buttons = list(
+ list(extend = 'csv', filename = "tt_PP"),
+ list(extend = 'excel', filename = "tt_PP"))
+ ),
+ class = "display")
+ })
+
+ ##CHECK!!!-done
+ # control and show credible sets in plots
+ GWAS_select_t1_a_cs <- reactive({
+ if (input$control_widgets_1 == 1){
+ GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@99.5%'==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@99%'==1, ]
+ }else if (input$control_widgets_1 == 3){
+ GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@95%'==1, ]
+ }else if (input$control_widgets_1 == 4){
+ GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@90%'==1, ]
+ }else if (input$control_widgets_1 == 5){
+ GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@99%'==0, ]
+ }else{
+ GWAS_all_final_cs()
+ }
+ })
+ GWAS_select_t1_a <- reactive({
+ GWAS_select_t1_a_cs()[GWAS_select_t1_a_cs()$mpp>=input$control_widgets_2[1] & GWAS_select_t1_a_cs()$mpp<=input$control_widgets_2[2], ]
+ })
+ # #
+ output$manhattan_1a <- renderPlotly({
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_1a <- plot_ly(GWAS_select_t1_a(), x = ~position, y = ~p_log10,
+ type = 'scatter', mode = 'markers',
+ size = ~mpp, color = ~cs1_group,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP ID:', rsid,
+ '
Allele 2:', allele2,
+ '
Allele 1:', allele1,
+ '
maf:', signif(maf,4),
+ '
ps:', position,
+ '
pval:', signif(p,4),
+ '
-log10(pval):', signif(p_log10,4),
+ '
MPP:', signif(mpp,4),
+ '
Credible_set:', cs1_group))
+ fig_1a <- fig_1a %>% layout(title = 'Interactive Regional Association Plots',
+ xaxis = list(title = paste0('Chromosome_', GWAS_select_t1_a()$chromosome[1], '_position'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t1_a()$position),
+ max(GWAS_select_t1_a()$position)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t1_a()$p_log10)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ fig_1a <- fig_1a %>% layout(shapes = list(vline(GWAS_select_t1_a()$position[which(GWAS_select_t1_a()$p_log10==max(GWAS_select_t1_a()$p_log10))]), hline(7.3)))
+ fig_1a <- fig_1a %>% layout(annotations = list(
+ list(x = 0.01 , y = 0.01, align = 'left',
+ text = "NOTE: Size = MPP, Color = Credible_sets",
+ showarrow = F, xref='paper', yref='paper')) )
+ fig_1a <- fig_1a %>% layout(legend = list(orientation = 'h'))
+ fig_1a
+ })
+
+
+ # pal_2 <- c("gray", "dodgerblue", "dodgerblue", "dodgerblue", "dodgerblue")
+ # pal_2 <- setNames(pal_2, c("0", "cs1@99.5%+@99%+@95%+@90%", "cs1@99.5%+@99%+@95%", "cs1@99.5%+@99%", "cs1@99.5%"))
+ pal_defined <- reactive({
+ if (input$control_widgets_5 == 1){
+ pal_2 = c("gray", "red")
+ }else if (input$control_widgets_5 == 2){
+ pal_2 = c("gray", "pink")
+ }else if (input$control_widgets_5 == 3){
+ pal_2 = c("gray", "darkgreen")
+ }else if (input$control_widgets_5 == 4){
+ pal_2 = c("gray", "orange")
+ }else if (input$control_widgets_5 == 5){
+ pal_2 = c("gray", "black")
+ }else{
+ pal_2 = c("gray", "dodgerblue")
+ }
+ pal_2 <- setNames(pal_2, c("0", "user defined cs"))
+ })
+
+ GWAS_all_final_cs_2 <- reactive({
+ cl = input$control_widgets_4[1]
+ cs1_selected <- vector("list",length(cl))
+ for(i in 1:length(cl)){
+ tmp <- tt()$PP[,-1]
+ names(tmp) <- tt()$PP[,1]
+ cs1_selected[[i]] <- credset(tmp,cl[i])
+ }
+ GWAS_all_final_cs_0 = GWAS_all_final()
+ GWAS_all_final_cs_0$p_log10 = -log10(GWAS_all_final_cs_0$p)
+ for (i in 1:length(GWAS_all_final_cs_0$rsid)){
+ if (GWAS_all_final_cs_0$rsid[i] %in% cs1_selected[[1]]){
+ GWAS_all_final_cs_0$'user defined cs'[i] = 1
+ } else {GWAS_all_final_cs_0$'user defined cs'[i] = 0}
+ }
+
+ for (i in 1:length(GWAS_all_final_cs_0$rsid)){
+ if (GWAS_all_final_cs_0$'user defined cs'[i] == 1){
+ GWAS_all_final_cs_0$cs1_group_2[i] = "user defined cs"
+ } else {GWAS_all_final_cs_0$cs1_group_2[i] = 0}
+ }
+ GWAS_all_final_cs_0 = GWAS_all_final_cs_0[GWAS_all_final_cs_0$p != 0, ] ##CHECK!!!
+ GWAS_all_final_cs_0
+ })
+
+ GWAS_select_t2_a_cs <- reactive({
+ GWAS_all_final_cs_2()
+ })
+ GWAS_select_t2_a <- reactive({
+ GWAS_select_t2_a_cs()[GWAS_select_t2_a_cs()$mpp>=input$control_widgets_6[1] & GWAS_select_t2_a_cs()$mpp<=input$control_widgets_6[2], ]
+ })
+
+ output$manhattan_2 <- renderPlotly({
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_2 <- plot_ly(GWAS_select_t2_a(), x = ~position, y = ~p_log10,
+ type = 'scatter', mode = 'markers',
+ size = ~mpp, color = ~cs1_group_2,
+ colors = pal_defined(),
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP ID:', rsid,
+ '
Allele 2:', allele2,
+ '
Allele 1:', allele1,
+ '
maf:', signif(maf,4),
+ '
ps:', position,
+ '
pval:', signif(p,4),
+ '
-log10(pval):', signif(p_log10,4),
+ '
MPP:', signif(mpp,4),
+ '
Credible_set:', cs1_group_2))
+ fig_2 <- fig_2 %>% layout(title = 'Interactive Regional Association Plots',
+ xaxis = list(title = paste0('Chromosome_', GWAS_select_t1_a()$chromosome[1], '_position'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t2_a()$position),
+ max(GWAS_select_t2_a()$position)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t2_a()$p_log10)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ fig_2<- fig_2 %>% layout(shapes = list(vline(GWAS_select_t2_a()$position[which(GWAS_select_t2_a()$p_log10==max(GWAS_select_t2_a()$p_log10))]), hline(7.3)))
+ fig_2 <- fig_2 %>% layout(annotations = list(
+ list(x = 0.01 , y = 0.01, align = 'left',
+ text = "NOTE: Size = MPP, Color = Credible_sets",
+ showarrow = F, xref='paper', yref='paper')) )
+ fig_2 <- fig_2 %>% layout(legend = list(orientation = 'h'))
+ fig_2
+ })
+
+ output$Plot_LD1 <- renderPlotly({
+ LD_selected_0 = as.matrix(tt()$LD)
+ LD_selected = LD_selected_0[ ,GWAS_select_t1_a()$rsid]
+ LD_selected = LD_selected[GWAS_select_t1_a()$rsid, ]
+ fig_ld <- plot_ly(x = colnames(LD_selected), y = rownames(LD_selected), z = LD_selected, colors = "Greys", type = "heatmap")
+ fig_ld
+ })
+
+ output$Plot_LD2 <- renderPlot({
+ LD_selected_0 = as.matrix(tt()$LD)
+ LD_selected = LD_selected_0[ ,GWAS_select_t1_a()$rsid]
+ LD_selected = LD_selected[GWAS_select_t1_a()$rsid, ]
+ if (input$control_widgets_1 == 0){
+ hist(LD_selected)
+ }else if (input$control_widgets_1 == 5){
+ hist(LD_selected)
+ }else{
+ # LD_selected_0 = as.matrix(tt()$LD)
+ # LD_selected = LD_selected_0[ ,GWAS_select_t1_a()$rsid]
+ # LD_selected = LD_selected[GWAS_select_t1_a()$rsid, ]
+ LD.plot(LD_selected, snp.positions = GWAS_select_t1_a()$position)
+ }
+ })
+
+ #Venn diagram cs_1----
+ fig_ch4_tab2_venn_cs1_data <- reactive({x_cs1_1 = c();
+ x_cs1_2 = c();
+ x_cs1_3 = c();
+ x_cs1_4 = c();
+ for (i in 1:length(GWAS_all_final_cs()$rsid)){
+ if (GWAS_all_final_cs()$'cs1@99.5%'[i]==1){
+ x_cs1_1 = c(x_cs1_1, GWAS_all_final_cs()$rsid[i])
+ }
+ if (GWAS_all_final_cs()$'cs1@99%'[i]==1){
+ x_cs1_2 = c(x_cs1_2, GWAS_all_final_cs()$rsid[i])
+ }
+ if (GWAS_all_final_cs()$'cs1@95%'[i]==1){
+ x_cs1_3 = c(x_cs1_3, GWAS_all_final_cs()$rsid[i])
+ }
+ if (GWAS_all_final_cs()$'cs1@90%'[i]==1){
+ x_cs1_4 = c(x_cs1_4, GWAS_all_final_cs()$rsid[i])
+ }
+ }
+ x_cs1_all <- list('cs1@99.5%' = x_cs1_1,
+ 'cs1@99%' = x_cs1_2,
+ 'cs1@95%' = x_cs1_3,
+ 'cs1@90%' = x_cs1_4)
+ })
+
+ output$fig_ch4_tab2_venn_cs1 <- renderPlotly({
+ ggVennDiagram(fig_ch4_tab2_venn_cs1_data(), show_intersect = TRUE)
+ })
+
+ output$table_ch4_tab2_venn_cs1_table <- DT::renderDataTable({
+ check_x_cs1_table = process_region_data(Venn(fig_ch4_tab2_venn_cs1_data()))[,c(1,2,5,4,3)]
+ DT::datatable(data.frame(check_x_cs1_table),
+ #options = list(dom = 't')
+ extensions = 'Buttons',
+
+ options = list(
+ paging = FALSE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ ordering = TRUE,
+ dom = 'tB',
+ buttons = list(
+ list(extend = 'csv', filename = "cs1_table"),
+ list(extend = 'excel', filename = "cs1_table"))
+ ),
+ class = "display"
+ )
+ })
+
+
+
+
+ ##CHECK!!!
+ pp_1_rownames_cs1 <- reactive({
+ if (input$control_widgets_3b_cs == 1){
+ cs1_nk = GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@99.5%'==1, ]$rsid
+ }else if (input$control_widgets_3b_cs == 2){
+ cs1_nk = GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@99%'==1, ]$rsid
+ }else if (input$control_widgets_3b_cs == 3){
+ cs1_nk = GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@95%'==1, ]$rsid
+ }else if (input$control_widgets_3b_cs == 4){
+ cs1_nk = GWAS_all_final_cs()[GWAS_all_final_cs()$'cs1@90%'==1, ]$rsid
+ }else{
+ cs1_nk = GWAS_all_final_cs()$rsid
+ }
+ pp_1_rownames = tt()$PP$str
+ pp_1_rownames = str_split(pp_1_rownames, "%", simplify = TRUE)
+ ##cs1_selected
+ fun_check_cs <- function(x,cs){
+ if (x %in% cs){
+ return(x)
+ }else{
+ return("NA")
+ }
+ }
+ pp_1_rownames_cs1 = apply(pp_1_rownames, 1:2, fun_check_cs, cs = cs1_nk)
+ pp_1_rownames_cs1 = pp_1_rownames_cs1[rowSums(pp_1_rownames_cs1 == "NA")=input$control_widgets_3b_finemap[1]*10 & plt_links_snp$l_size<=input$control_widgets_3b_finemap[2]*10, ]
+ plt_links_snp
+ })
+
+ #CHECK
+ # plt_nodes_snp_udpated <- reactive({
+ # plt_links_snp_updated = unique(c(plt_links_snp()$source,plt_links_snp()$target))
+ # plt_nodes_snp_updated = plt_nodes_snp()[plt_nodes_snp()$name %in% plt_links_snp_updated,]
+ # plt_nodes_snp_updated
+ # })
+
+ # #
+ network <- reactive({
+ forceNetwork(Links = plt_links_snp(),
+ #Nodes = plt_nodes_snp_udpated(),
+ Nodes = plt_nodes_snp(),
+ Source = 'source_id',
+ Target = 'target_id',
+ Value = 'l_size',
+ NodeID = 'name',
+ Nodesize = 'n_size',
+ Group = 'n_color',
+ #height = 500,
+ #width = 800,
+ fontSize = 10,
+ fontFamily = "serif",
+ linkDistance = 150,
+ charge = -30, #numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value).
+ linkColour = plt_links_snp()$l_color,
+ opacity = 0.8,
+ zoom = T,
+ legend = F,
+ bounded = T,
+ opacityNoHover = 1)
+ })
+
+ output$ch1_network_snp_finemap <- renderForceNetwork({
+ network()
+ })
+
+ output$d <- downloadHandler(
+ filename = function() {
+ 'snp_network.html'
+ },
+ content = function(file) {
+ saveNetwork(network(), file)
+ }
+ )
+ #
+
+} #End of Shiny server
+
+
+####----Shiny app----
+shinyApp(ui, server)
\ No newline at end of file
diff --git a/finemap-ivis/data/finemap.config b/finemap-ivis/data/finemap.config
new file mode 100644
index 0000000..a8e5cbd
--- /dev/null
+++ b/finemap-ivis/data/finemap.config
@@ -0,0 +1,1001 @@
+rank config prob log10bf odds k prob_norm_k h2 h2_0.95CI mean sd
+1 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427780 0.479159 123.75 1 5 0.488076 0.0929258 0.078737,0.10833 0.157974,0.446648,-0.135905,0.52613,0.477454 0.0174065,0.0623607,0.0196798,0.0279098,0.0970116
+2 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427780 0.28967 123.532 1.65415 5 0.295061 0.0927773 0.0786243,0.107702 0.157967,-0.135926,0.440168,0.526112,0.477429 0.017408,0.0196836,0.0620639,0.027913,0.0970199
+3 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45425212 0.0883565 123.016 5.42301 5 0.0900009 0.0923878 0.07897,0.107317 0.157641,0.449225,-0.136438,0.529988,0.498161 0.0174104,0.0623918,0.0196834,0.0279413,0.109284
+4 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425212 0.0532393 122.796 9.00008 5 0.0542302 0.0922383 0.0785321,0.10683 0.157633,-0.136459,0.442727,0.52997,0.498049 0.0174119,0.0196873,0.062095,0.0279445,0.109293
+5 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45427780 0.032896 122.587 14.5659 5 0.0335082 0.0921552 0.0782682,0.108213 0.423385,0.157625,-0.136799,0.523525,0.47914 0.0624027,0.0174139,0.0196877,0.027907,0.0970613
+6 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.0108341 119.431 44.227 4 0.592971 0.089152 0.0741312,0.103104 0.155694,0.441613,-0.140904,0.524482 0.0174363,0.0624808,0.0196939,0.0279653
+7 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439163 0.00847231 121.998 56.5558 5 0.00862999 0.0918166 0.0781765,0.107043 0.157486,0.439984,-0.145109,0.512711,0.101122 0.017418,0.0623967,0.0196943,0.0280786,0.0249282
+8 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.0065691 119.214 72.9413 4 0.359541 0.0890039 0.0749091,0.10264 0.155686,-0.140927,0.43512,0.524462 0.0174378,0.0196977,0.0621832,0.0279684
+9 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45425212 0.00537628 121.8 89.1245 5 0.00547634 0.0915831 0.0780643,0.106841 0.424996,0.157276,-0.137391,0.527337,0.497361 0.062432,0.0174181,0.0196913,0.0279388,0.109336
+10 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45439163 0.00486646 121.757 98.4615 5 0.00495702 0.0916531 0.0775763,0.107483 0.157471,-0.145136,0.433027,0.512715,0.100804 0.0174196,0.0196984,0.062101,0.0280823,0.0249309
+11 chr19_45406538,chr19_45410548,chr19_45411941,chr19_45412079,chr19_45427780 0.00185981 121.339 257.639 5 0.00189442 0.0912876 0.0774767,0.106314 0.157879,0.377523,-0.13666,0.525514,0.47732 0.017423,0.0594814,0.0197176,0.0279424,0.0971048
+12 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45429708 0.000812003 120.979 590.095 5 0.000827115 0.09117 0.0770708,0.105741 0.156515,0.43156,-0.144976,0.509121,-0.0862444 0.017421,0.0624885,0.0197101,0.0282916,0.0249668
+13 chr19_45395330,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000774825 120.959 618.409 5 0.000789245 0.0910752 0.0765213,0.106703 -0.0998376,0.162707,0.426126,-0.154138,0.507845 0.0292983,0.0175404,0.0625864,0.0200547,0.0283635
+14 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 0.000687572 118.234 696.885 4 0.0376323 0.0883568 0.0743595,0.103393 0.417266,0.155337,-0.141853,0.521873 0.0625197,0.0174439,0.0197016,0.0279628
+15 chr19_45395816,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000574793 120.829 833.62 5 0.00058549 0.0909866 0.0769346,0.104776 -0.108974,0.160054,0.431535,-0.150574,0.512181 0.0327943,0.0174694,0.0624973,0.0198898,0.028185
+16 chr19_45406538,chr19_45406673,chr19_45410273,chr19_45411941,chr19_45412079 0.000530094 120.794 903.912 5 0.00053996 0.0904928 0.0757334,0.106519 0.158218,0.0690537,0.393017,-0.184721,0.539038 0.0174396,0.0226054,0.0644164,0.0243418,0.0283413
+17 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45429708 0.000491907 120.762 974.083 5 0.000501062 0.0910217 0.077117,0.106406 0.156507,-0.145006,0.425009,0.509096,-0.0862425 0.0174225,0.019714,0.0621924,0.028295,0.0249695
+18 chr19_45395330,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.000480098 120.751 998.042 5 0.000489033 0.0909334 0.0756421,0.105492 -0.100021,0.162712,-0.154189,0.419718,0.507792 0.0293002,0.0175418,0.0200586,0.0622873,0.0283666
+19 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45439163 0.000472115 120.744 1014.92 5 0.000480901 0.0909841 0.0774494,0.106652 0.414385,0.157112,-0.14607,0.510172,0.100356 0.0624399,0.0174259,0.019703,0.0280773,0.0249415
+20 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418086 0.000434391 120.708 1103.06 5 0.000442476 0.0908975 0.0766623,0.10765 0.156567,0.442496,-0.137372,0.522202,-0.237782 0.017423,0.0624264,0.0197069,0.0279498,0.0734036
+21 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436010 0.000417649 120.69 1147.28 5 0.000425421 0.0908181 0.0766241,0.105335 0.156841,0.442199,-0.152979,0.520004,0.20092 0.0174246,0.0624262,0.0200356,0.0279763,0.0628366
+22 chr19_45406538,chr19_45409595,chr19_45410273,chr19_45411941,chr19_45412079 0.000379723 120.649 1261.86 5 0.00038679 0.0906553 0.0756191,0.105353 0.157972,0.134168,0.444844,-0.164206,0.524519 0.0174365,0.043266,0.062435,0.0210617,0.027941
+23 chr19_45401666,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000378626 120.648 1265.52 5 0.000385673 0.0908833 0.0765516,0.104411 -0.078103,0.160494,0.42261,-0.158429,0.50403 0.0244272,0.0174861,0.0627117,0.0204272,0.0286673
+24 chr19_45395816,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.000355675 120.621 1347.18 5 0.000362295 0.0908444 0.0768219,0.105324 -0.109171,0.160055,-0.150614,0.425153,0.512139 0.0327963,0.0174709,0.0198936,0.0621983,0.028188
+25 chr19_45406538,chr19_45406673,chr19_45411941,chr19_45412040,chr19_45412079 0.000337531 120.598 1419.6 5 0.000343813 0.0903565 0.076688,0.105132 0.158222,0.0693942,-0.184979,0.386426,0.539077 0.0174409,0.02261,0.0243499,0.0641173,0.0283436
+26 chr19_45406538,chr19_45410548,chr19_45411941,chr19_45412079,chr19_45425212 0.000326201 120.583 1468.9 5 0.000332272 0.0907348 0.0779463,0.106102 0.157541,0.379794,-0.137203,0.529357,0.496768 0.017427,0.059512,0.0197214,0.0279744,0.10939
+27 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45438575 0.000271603 120.504 1764.19 5 0.000276658 0.0907832 0.0760066,0.10583 0.157443,0.441645,-0.153186,0.508133,0.14012 0.0174316,0.0624314,0.0200742,0.0284393,0.0452375
+28 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418086 0.000266213 120.495 1799.91 5 0.000271167 0.0907525 0.0761916,0.106742 0.15656,-0.137387,0.436103,0.522182,-0.238024 0.0174245,0.0197107,0.0621292,0.0279529,0.0734098
+29 chr19_45385759,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000258161 120.482 1856.05 5 0.000262965 0.0906911 0.0765605,0.10507 -0.0707274,0.159348,0.424973,-0.157255,0.515405 0.0232066,0.0174636,0.0626704,0.0203967,0.0281024
+30 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436010 0.000253033 120.473 1893.66 5 0.000257742 0.0906698 0.0760965,0.105179 0.156834,-0.153001,0.435705,0.519984,0.200911 0.0174261,0.0200394,0.0621289,0.0279794,0.0628419
+31 chr19_45401666,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.000234498 120.44 2043.34 5 0.000238862 0.0907413 0.0769957,0.104998 -0.078263,0.160495,-0.158494,0.416167,0.503965 0.0244292,0.0174875,0.0204314,0.0624129,0.0286706
+32 chr19_45406538,chr19_45409595,chr19_45411941,chr19_45412040,chr19_45412079 0.000228781 120.429 2094.4 5 0.000233039 0.0905056 0.0773381,0.10499 0.157963,0.13409,-0.164215,0.438317,0.524499 0.017438,0.0432695,0.0210654,0.0621377,0.0279442
+33 chr19_45406538,chr19_45407326,chr19_45410273,chr19_45411941,chr19_45412079 0.000211918 120.396 2261.06 5 0.000215862 0.0905066 0.0758088,0.105171 0.157614,0.123107,0.445162,-0.161965,0.523818 0.0174351,0.042135,0.0624443,0.0209565,0.0279448
+34 chr19_45395909,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000210822 120.394 2272.81 5 0.000214746 0.0905505 0.0780647,0.104811 0.0838043,0.157783,0.449591,-0.144874,0.530839 0.0283877,0.017437,0.0624905,0.0197244,0.0280259
+35 chr19_45406538,chr19_45407409,chr19_45410273,chr19_45411941,chr19_45412079 0.000205725 120.383 2329.12 5 0.000209554 0.0905021 0.0764995,0.105469 0.157485,0.124444,0.445054,-0.161414,0.523933 0.0174337,0.0427019,0.062444,0.0208986,0.0279446
+36 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45438575 0.000164888 120.287 2905.97 5 0.000167956 0.0906355 0.0754752,0.105594 0.157436,-0.153211,0.435165,0.508111,0.14014 0.0174331,0.0200779,0.0621341,0.0284424,0.0452413
+37 chr19_45405181,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000163061 120.282 2938.52 5 0.000166096 0.0904329 0.0766106,0.104905 0.119362,0.157575,0.445049,-0.161331,0.523882 0.0421232,0.0174362,0.0624471,0.0209574,0.0279459
+38 chr19_45385759,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.000158266 120.269 3027.55 5 0.000161212 0.0905461 0.0763746,0.105512 -0.0708124,0.159344,-0.157306,0.418469,0.515369 0.0232093,0.017465,0.0204011,0.0623739,0.0281057
+39 chr19_45406538,chr19_45411941,chr19_45412079,chr19_45420079 0.000136935 117.533 3499.17 4 0.00749475 0.0878505 0.0741789,0.103134 0.156208,-0.145362,0.522552,0.359285 0.0174508,0.0196603,0.0279781,0.0559235
+40 chr19_45406538,chr19_45407326,chr19_45411941,chr19_45412040,chr19_45412079 0.000127706 120.176 3752.06 5 0.000130082 0.0903569 0.0763833,0.105357 0.157605,0.123027,-0.161974,0.438636,0.523799 0.0174366,0.0421385,0.0209602,0.062147,0.0279479
+41 chr19_45395909,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 0.00012759 120.175 3755.46 5 0.000129965 0.0904019 0.0769389,0.104752 0.0837909,0.157775,-0.144892,0.443123,0.530821 0.0283905,0.0174385,0.0197281,0.0621938,0.0280291
+42 chr19_45406538,chr19_45407409,chr19_45411941,chr19_45412040,chr19_45412079 0.000124 120.163 3864.18 5 0.000126308 0.0903525 0.076249,0.105063 0.157476,0.124366,-0.161424,0.43853,0.523914 0.0174352,0.0427054,0.0209023,0.0621466,0.0279478
+43 chr19_45398462,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000120671 120.151 3970.78 5 0.000122917 0.0903496 0.0755544,0.104732 0.114432,0.157689,0.445035,-0.159904,0.524271 0.0419086,0.0174397,0.062451,0.0208735,0.0279466
+44 chr19_45403182,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000116839 120.137 4101.03 5 0.000119013 0.0903423 0.076721,0.104226 0.114103,0.157501,0.444942,-0.160508,0.523611 0.0419622,0.0174372,0.0624508,0.0209587,0.0279485
+45 chr19_45398465,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000116548 120.136 4111.27 5 0.000118717 0.0903399 0.075365,0.105642 0.113901,0.15768,0.44502,-0.159818,0.524272 0.041903,0.0174398,0.0624514,0.0208736,0.0279467
+46 chr19_45405521,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000112971 120.123 4241.42 5 0.000115074 0.0905417 0.0765005,0.105735 -0.0609271,0.157603,0.415478,-0.165934,0.496993 0.021832,0.0174389,0.0631431,0.0216324,0.0296388
+47 chr19_45398201,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000111357 120.116 4302.92 5 0.000113429 0.0903358 0.0757318,0.104876 0.111781,0.157523,0.445158,-0.159499,0.523583 0.0412975,0.0174378,0.0624531,0.0208446,0.0279489
+48 chr19_45399526,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 0.000107481 120.101 4458.1 5 0.000109481 0.090324 0.0772792,0.105022 0.110806,0.157539,0.445193,-0.159514,0.523681 0.0411466,0.0174382,0.0624538,0.0208581,0.0279487
+49 chr19_45405181,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 9.829e-05 120.062 4874.94 5 0.000100119 0.0902832 0.076997,0.104344 0.119283,0.157566,-0.16134,0.438525,0.523863 0.0421266,0.0174377,0.0209611,0.0621497,0.027949
+50 chr19_45393742,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.44422e-05 120.045 5073.57 5 9.61998e-05 0.0903242 0.0767065,0.104857 0.0757787,0.157399,0.448875,-0.144531,0.530174 0.0283168,0.0174367,0.0624994,0.0197278,0.0280277
+51 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418749 8.90103e-05 120.019 5383.18 5 9.06669e-05 0.0903604 0.0765257,0.103988 0.162728,0.443523,-0.141647,0.522333,-0.177406 0.0176217,0.0624459,0.0196836,0.0279596,0.0661986
+52 chr19_45403192,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.89388e-05 120.019 5387.51 5 9.05941e-05 0.0904208 0.077448,0.104661 -0.193733,0.156291,0.442146,-0.138281,0.52281 0.0714696,0.0174269,0.0624426,0.0197056,0.0279551
+53 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45425066 8.78565e-05 120.013 5453.87 5 8.94916e-05 0.0904027 0.0768038,0.105844 0.163719,0.444965,-0.119896,0.523397,-0.1013 0.0176815,0.0624561,0.0211892,0.0279517,0.0378219
+54 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45422160 7.64844e-05 119.953 6264.79 5 7.79078e-05 0.0904027 0.0763835,0.10556 0.161254,0.440223,-0.126537,0.520228,-0.0816421 0.0175514,0.062447,0.0204133,0.0279954,0.0307378
+55 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420079 7.52452e-05 119.946 6367.96 5 7.66456e-05 0.0903874 0.0758367,0.105946 0.156348,0.312808,-0.139687,0.526056,0.18532 0.0174288,0.0797809,0.019689,0.0279569,0.0713558
+56 chr19_45398462,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.27559e-05 119.932 6585.84 5 7.41099e-05 0.0902 0.0759812,0.104877 0.114353,0.15768,-0.159913,0.438513,0.524252 0.0419121,0.0174412,0.0208772,0.0621536,0.0279497
+57 chr19_45393144,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 7.23855e-05 119.929 6619.54 5 7.37327e-05 0.090358 0.0759512,0.105247 -0.187761,0.156439,0.437805,-0.144326,0.520177 0.0712958,0.0174284,0.0624614,0.0197253,0.0279973
+58 chr19_45396144,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 7.20721e-05 119.927 6648.32 5 7.34135e-05 0.0902888 0.0761463,0.104566 0.069597,0.157561,0.448343,-0.145362,0.524657 0.0267889,0.0174408,0.0624974,0.0197569,0.0279489
+59 chr19_45405521,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.16319e-05 119.925 6689.18 5 7.2965e-05 0.0904068 0.0760739,0.105199 -0.0612633,0.157606,-0.166098,0.409107,0.496819 0.0218311,0.0174402,0.021636,0.0628345,0.0296416
+60 chr19_45403182,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.04431e-05 119.918 6802.07 5 7.17541e-05 0.0901927 0.0766905,0.104723 0.114022,0.157493,-0.160517,0.438418,0.523592 0.0419657,0.0174387,0.0209624,0.0621534,0.0279517
+61 chr19_45398465,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.02718e-05 119.916 6818.65 5 7.15796e-05 0.0901903 0.0763834,0.104195 0.113822,0.157671,-0.159827,0.438498,0.524253 0.0419064,0.0174413,0.0208773,0.062154,0.0279499
+62 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418743 6.98425e-05 119.914 6860.56 5 7.11423e-05 0.0902914 0.0754964,0.105367 0.162521,0.443528,-0.141575,0.522202,-0.172201 0.017624,0.0624488,0.0196841,0.0279631,0.0664724
+63 chr19_45398201,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 6.71367e-05 119.897 7137.06 5 6.83862e-05 0.0901861 0.0763484,0.105042 0.111701,0.157514,-0.159508,0.438635,0.523564 0.0413009,0.0174393,0.0208482,0.0621556,0.027952
+64 chr19_45391454,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.6347e-05 119.892 7222.01 5 6.75818e-05 0.0901993 0.0762433,0.105155 0.109929,0.156897,0.44452,-0.157481,0.523789 0.0436044,0.0174328,0.0624555,0.0207511,0.0279507
+65 chr19_45399526,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 6.47971e-05 119.881 7394.75 5 6.6003e-05 0.0901743 0.0756865,0.104542 0.110725,0.15753,-0.159523,0.43867,0.523662 0.04115,0.0174397,0.0208618,0.0621564,0.0279518
+66 chr19_45396318,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.47202e-05 119.881 7403.54 5 6.59247e-05 0.0902774 0.0765386,0.105659 0.136707,0.157681,0.442291,-0.135796,0.528454 0.0532586,0.0174435,0.0624458,0.0197832,0.0279919
+67 chr19_45403048,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.3666e-05 119.874 7526.13 5 6.48509e-05 0.0902781 0.0765926,0.104299 0.16817,0.15662,0.442392,-0.137117,0.528028 0.0655859,0.0174301,0.0624462,0.0197382,0.0279834
+68 chr19_45383037,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.00519e-05 119.848 7979.07 5 6.11696e-05 0.0902173 0.0777628,0.104506 0.067371,0.1562,0.441251,-0.142492,0.530486 0.0267464,0.0174276,0.0624459,0.0196929,0.0280505
+69 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433148 5.82406e-05 119.835 8227.22 5 5.93245e-05 0.0902456 0.076875,0.104527 0.15652,0.446131,-0.138243,0.527635,0.211943 0.0174297,0.0624718,0.0197112,0.0279775,0.0838774
+70 chr19_45393742,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.71681e-05 119.827 8381.57 5 5.8232e-05 0.0901756 0.0767249,0.105307 0.0757642,0.157391,-0.144549,0.442405,0.530156 0.0283196,0.0174382,0.0197315,0.0622028,0.0280309
+71 chr19_45393473,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.68934e-05 119.825 8422.05 5 5.79522e-05 0.0901801 0.0762494,0.104166 0.0698509,0.157357,0.448698,-0.143937,0.529912 0.0281024,0.0174394,0.0625108,0.0197207,0.0280345
+72 chr19_45382984,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.5091e-05 119.811 8697.59 5 5.61163e-05 0.0901929 0.075987,0.104863 0.0663005,0.156171,0.441379,-0.142413,0.530409 0.0266749,0.0174278,0.0624467,0.0196924,0.028051
+73 chr19_45403192,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.44109e-05 119.805 8806.29 5 5.54236e-05 0.0902753 0.0763009,0.104552 -0.193963,0.156285,-0.138297,0.435735,0.522791 0.0714756,0.0174284,0.0197094,0.0621453,0.0279581
+74 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418749 5.34987e-05 119.798 8956.45 5 5.44944e-05 0.0902098 0.0766418,0.106267 0.162712,-0.141671,0.436964,0.522315,-0.177199 0.0176232,0.0196874,0.0621484,0.0279628,0.066204
+75 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414125 5.26264e-05 119.791 9104.91 5 5.36058e-05 0.0902232 0.076267,0.105787 0.156534,0.447119,-0.141719,0.524753,0.152402 0.0174302,0.0624863,0.0196861,0.0279506,0.061173
+76 chr19_45398014,chr19_45406538,chr19_45406673,chr19_45411941,chr19_45412079 5.1697e-05 119.783 9268.59 5 5.26592e-05 0.0898007 0.0758249,0.104566 0.367618,0.157997,0.0718197,-0.187381,0.53727 0.0643778,0.0174463,0.022588,0.0243324,0.0283499
+77 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425066 5.07456e-05 119.775 9442.37 5 5.169e-05 0.0902407 0.0754935,0.104662 0.163656,-0.120071,0.438054,0.523375,-0.100629 0.017683,0.0211912,0.0621565,0.0279551,0.0378237
+78 chr19_45397343,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.06609e-05 119.774 9458.15 5 5.16037e-05 0.0901409 0.0760054,0.105359 0.120017,0.157518,0.441595,-0.154083,0.524227 0.0494063,0.0174431,0.0624477,0.0204169,0.0279508
+79 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45420079 4.81136e-05 119.752 9958.9 5 4.90091e-05 0.0902528 0.0756941,0.104945 0.156341,-0.139779,0.303996,0.525995,0.187738 0.0174302,0.019692,0.0798984,0.0279594,0.0718089
+80 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45429708 4.79319e-05 119.75 9996.65 5 4.8824e-05 0.0903523 0.0770094,0.104692 0.405971,0.156155,-0.145944,0.506621,-0.0857417 0.0625472,0.0174288,0.0197189,0.0282891,0.0249862
+81 chr19_45395330,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 4.69236e-05 119.741 10211.5 5 4.77969e-05 0.0902649 0.0769118,0.105333 -0.0994767,0.400429,0.16233,-0.155085,0.50534 0.0293268,0.0626575,0.0175485,0.0200658,0.0283599
+82 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432504 4.51455e-05 119.724 10613.6 5 4.59857e-05 0.0901939 0.0760166,0.104629 0.156762,0.444064,-0.138063,0.525213,0.220943 0.0174329,0.0624572,0.0197185,0.0279527,0.0906217
+83 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45422160 4.46258e-05 119.719 10737.3 5 4.54563e-05 0.0902434 0.0763796,0.104764 0.161215,-0.12665,0.433396,0.520221,-0.0812071 0.017553,0.0204159,0.0621513,0.0279989,0.0307412
+84 chr19_45393144,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.39189e-05 119.712 10910.1 5 4.47362e-05 0.09021 0.076781,0.103746 -0.18778,0.156431,-0.144352,0.431298,0.520155 0.0713021,0.0174299,0.0197291,0.0621642,0.0280004
+85 chr19_45396144,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.36523e-05 119.71 10976.7 5 4.44647e-05 0.0901404 0.0757917,0.103924 0.0695876,0.157554,-0.14538,0.441874,0.52464 0.0267916,0.0174422,0.0197605,0.0622007,0.027952
+86 chr19_45406538,chr19_45410548,chr19_45411941,chr19_45412079 4.31353e-05 117.031 11108.3 4 0.00236089 0.0875161 0.0744201,0.1026 0.155596,0.37218,-0.141692,0.523842 0.0174526,0.0595936,0.0197312,0.0279977
+87 chr19_45395619,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.2208e-05 119.695 11352.3 5 4.29935e-05 0.0901224 0.075458,0.1057 0.0561594,0.156764,0.451528,-0.140908,0.528223 0.0235736,0.0174333,0.0625876,0.019684,0.027995
+88 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418743 4.19896e-05 119.693 11411.4 5 4.2771e-05 0.090141 0.0758793,0.105259 0.162505,-0.141598,0.436971,0.522184,-0.171991 0.0176255,0.019688,0.0621513,0.0279663,0.0664778
+89 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427353 4.15963e-05 119.689 11519.2 5 4.23705e-05 0.0902121 0.0771225,0.105871 0.158969,0.429602,-0.154255,0.509178,-0.0602606 0.0174807,0.06265,0.020448,0.0286647,0.0249681
+90 chr19_45391454,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.0043e-05 119.672 11966.1 5 4.07883e-05 0.0900499 0.0757087,0.104471 0.109855,0.156888,-0.157492,0.438002,0.52377 0.043608,0.0174343,0.0207548,0.062158,0.0279538
+91 chr19_45396318,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.954e-05 119.667 12118.3 5 4.02759e-05 0.0901314 0.0762568,0.106897 0.136859,0.157676,-0.13581,0.435867,0.52844 0.0532631,0.017445,0.0197871,0.0621485,0.027995
+92 chr19_45403048,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.86443e-05 119.657 12399.2 5 3.93635e-05 0.0901303 0.0763862,0.104797 0.168197,0.156613,-0.137138,0.435913,0.528009 0.0655914,0.0174316,0.019742,0.0621488,0.0279865
+93 chr19_45383037,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.75005e-05 119.644 12777.4 5 3.81984e-05 0.0900773 0.0771523,0.105127 0.0676696,0.156196,-0.142513,0.435007,0.530499 0.0267484,0.017429,0.0196965,0.062148,0.0280537
+94 chr19_45406538,chr19_45411941,chr19_45412079,chr19_45427780,chr19_45435661 3.58473e-05 119.624 13366.6 5 3.65145e-05 0.0900793 0.0761776,0.104579 0.159871,-0.149186,0.521476,0.485427,0.289251 0.0174444,0.0195891,0.0279338,0.0972115,0.0508447
+95 chr19_45405062,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.57908e-05 119.623 13387.8 5 3.64569e-05 0.0897328 0.0761145,0.104532 -0.0490306,0.156517,0.453703,-0.175171,0.533669 0.0245115,0.017433,0.0627431,0.0260729,0.0283262
+96 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433148 3.52393e-05 119.617 13597.3 5 3.58951e-05 0.0900969 0.0770584,0.106212 0.156512,-0.138265,0.439642,0.527616,0.211877 0.0174312,0.0197151,0.0621746,0.0279806,0.0838848
+97 chr19_45393473,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.44201e-05 119.606 13920.9 5 3.50607e-05 0.0900314 0.0760294,0.104788 0.0698284,0.15735,-0.143955,0.44222,0.529893 0.0281052,0.0174409,0.0197244,0.0622141,0.0280378
+98 chr19_45382984,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.4383e-05 119.606 13935.9 5 3.50229e-05 0.0900527 0.0756222,0.105193 0.0665964,0.156167,-0.142434,0.43513,0.530422 0.026677,0.0174292,0.019696,0.0621489,0.0280542
+99 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420086 3.42931e-05 119.605 13972.4 5 3.49314e-05 0.090122 0.0766001,0.104195 0.162846,0.444673,-0.119611,0.523447,-0.0868267 0.0177046,0.062468,0.0217608,0.0279569,0.0377929
+100 chr19_45395816,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.31803e-05 119.591 14441.1 5 3.37978e-05 0.090163 0.076169,0.104434 -0.108085,0.405771,0.159663,-0.151494,0.509688 0.0328225,0.0625612,0.0174774,0.0199004,0.0281828
+101 chr19_45395714,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.27122e-05 119.584 14647.7 5 3.3321e-05 0.0902341 0.0766168,0.104849 0.0480883,0.155732,0.409441,-0.162922,0.492709 0.0206636,0.0174294,0.0639753,0.0218496,0.0311239
+102 chr19_45397466,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.25829e-05 119.583 14705.8 5 3.31893e-05 0.0900812 0.0763274,0.104036 0.266276,0.15564,0.445436,-0.138431,0.527437 0.116106,0.0174284,0.0624746,0.0197145,0.0279821
+103 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414125 3.16593e-05 119.57 15134.9 5 3.22485e-05 0.0900729 0.0760574,0.105481 0.156526,-0.141739,0.44058,0.524734,0.152215 0.0174317,0.0196899,0.0621887,0.0279537,0.061178
+104 chr19_45406538,chr19_45407788,chr19_45410273,chr19_45411941,chr19_45412079 3.16242e-05 119.57 15151.6 5 3.22128e-05 0.0898293 0.0763605,0.104859 0.15851,-0.0423088,0.422158,-0.160033,0.538579 0.0174802,0.0202713,0.0631428,0.0217085,0.0287543
+105 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421650 3.0806e-05 119.558 15554.1 5 3.13793e-05 0.0900997 0.0768922,0.104102 0.161702,0.442352,-0.1252,0.523084,-0.0793465 0.0176289,0.0624552,0.0208683,0.0279604,0.0349689
+106 chr19_45397343,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.02721e-05 119.551 15828.4 5 3.08355e-05 0.0899888 0.0755455,0.105101 0.119735,0.157506,-0.154079,0.434979,0.524205 0.0494106,0.0174447,0.0204212,0.0621505,0.0279539
+107 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45415640,chr19_45427780 2.98646e-05 119.545 16044.4 5 3.04204e-05 0.0889242 0.0754361,0.10472 0.156325,0.474681,0.633385,-0.102284,0.475392 0.017429,0.0621867,0.0307503,0.0203284,0.0972984
+108 chr19_45383562,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.97859e-05 119.544 16086.8 5 3.03402e-05 0.0900329 0.0752218,0.104463 0.0993771,0.155704,0.444917,-0.144548,0.526455 0.0443392,0.0174286,0.0624705,0.0197522,0.0279667
+109 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420225 2.95193e-05 119.54 16232 5 3.00687e-05 0.0900591 0.0764547,0.105109 0.156877,0.44768,-0.139679,0.524695,-0.169096 0.0174365,0.0625113,0.0196928,0.0279533,0.0749919
+110 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421204 2.83264e-05 119.522 16915.6 5 2.88536e-05 0.090061 0.0768217,0.104369 0.162555,0.444881,-0.122561,0.523695,-0.0856209 0.0177018,0.0624728,0.0213559,0.0279563,0.0386184
+111 chr19_45388588,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.75948e-05 119.511 17364.1 5 2.81083e-05 0.090139 0.0767605,0.104389 0.0442812,0.155557,0.412069,-0.160191,0.501851 0.0197284,0.0174297,0.0638342,0.0214849,0.0297244
+112 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432504 2.74158e-05 119.508 17477.5 5 2.7926e-05 0.0900462 0.0761832,0.105194 0.156755,-0.138083,0.437597,0.525195,0.220998 0.0174344,0.0197223,0.06216,0.0279558,0.0906296
+113 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45436010 2.65445e-05 119.494 18051.2 5 2.70385e-05 0.0900229 0.0768978,0.105524 0.417899,0.156484,-0.153927,0.517393,0.200947 0.0624652,0.0174322,0.0200434,0.0279739,0.0628657
+114 chr19_45395619,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.55417e-05 119.477 18759.8 5 2.60171e-05 0.0899737 0.0763731,0.104705 0.0561419,0.156757,-0.140925,0.445064,0.528206 0.0235764,0.0174348,0.0196878,0.0622918,0.0279983
+115 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427353 2.54827e-05 119.476 18803.3 5 2.59569e-05 0.0900669 0.0760352,0.103988 0.158967,-0.154306,0.423114,0.509128,-0.0603644 0.0174822,0.0204523,0.0623526,0.0286681,0.0249705
+116 chr19_45393247,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.54805e-05 119.476 18804.9 5 2.59547e-05 0.0899492 0.0761073,0.105532 0.102602,0.157085,0.442149,-0.151875,0.524665 0.0479357,0.0174411,0.0624554,0.0203416,0.0279539
+117 chr19_45403412,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.53376e-05 119.473 18910.9 5 2.58092e-05 0.0896677 0.0765203,0.104308 -0.0444987,0.155928,0.452901,-0.171232,0.533663 0.0239764,0.0174297,0.0627505,0.0255628,0.0283871
+118 chr19_45404431,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.53046e-05 119.473 18935.7 5 2.57755e-05 0.0896672 0.0757353,0.104134 -0.0444956,0.155933,0.452893,-0.171237,0.53367 0.0239871,0.0174298,0.0627504,0.0255696,0.0283882
+119 chr19_45406538,chr19_45409113,chr19_45410273,chr19_45411941,chr19_45412079 2.52118e-05 119.471 19005.3 5 2.5681e-05 0.0899977 0.076982,0.103873 0.157453,0.112225,0.443757,-0.136049,0.528259 0.0174478,0.0516176,0.0624629,0.0198122,0.0280075
+120 chr19_45402262,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.5027e-05 119.468 19145.7 5 2.54928e-05 0.0894828 0.0750974,0.103455 -0.0653209,0.155061,0.441165,-0.140317,0.574906 0.0407467,0.0174334,0.0624551,0.0196888,0.0419522
+121 chr19_45388879,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.44277e-05 119.458 19615.4 5 2.48823e-05 0.0899373 0.0769235,0.103707 0.101396,0.157076,0.442178,-0.151765,0.524673 0.0477997,0.0174413,0.0624559,0.0203403,0.0279541
+122 chr19_45383830,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.39165e-05 119.448 20034.7 5 2.43616e-05 0.0899569 0.0764594,0.104613 0.0637536,0.156206,0.43796,-0.144638,0.52833 0.0298596,0.0174309,0.062479,0.0197633,0.0280117
+123 chr19_45398014,chr19_45401666,chr19_45406538,chr19_45411941,chr19_45412079 2.38877e-05 119.448 20058.8 5 2.43323e-05 0.0900839 0.0761255,0.10467 0.397005,-0.0780786,0.160143,-0.159413,0.501491 0.06279,0.0244537,0.017494,0.0204397,0.028662
+124 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45413576 2.34255e-05 119.439 20454.6 5 2.38614e-05 0.0903624 0.0774024,0.105146 0.15689,0.448347,-0.145766,0.458824,0.0744969 0.01744,0.0625377,0.0198134,0.0408327,0.0336331
+125 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426458 2.30953e-05 119.433 20747 5 2.35251e-05 0.0900099 0.0744322,0.104496 0.155219,0.441719,-0.143166,0.522274,-0.257992 0.0174309,0.0624563,0.019714,0.0279733,0.119502
+126 chr19_45403858,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.3051e-05 119.432 20786.9 5 2.348e-05 0.0896476 0.075252,0.103829 -0.043412,0.155925,0.452636,-0.170503,0.533382 0.0239708,0.01743,0.0627519,0.025564,0.028382
+127 chr19_45396219,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.29664e-05 119.431 20863.5 5 2.33938e-05 0.0901144 0.0762451,0.103916 0.0443147,0.155835,0.412175,-0.160713,0.496266 0.0204476,0.0174305,0.0639267,0.0217131,0.0308516
+128 chr19_45388581,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.2933e-05 119.43 20893.8 5 2.33598e-05 0.090013 0.0765108,0.105879 -0.112943,0.154219,0.442828,-0.134136,0.526666 0.0524021,0.017443,0.0624591,0.0199354,0.0279727
+129 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45418086 2.27772e-05 119.427 21036.8 5 2.32011e-05 0.0900477 0.0763557,0.104889 0.416465,0.156188,-0.138442,0.519594,-0.233541 0.0624674,0.0174311,0.0197142,0.0279486,0.07344
+130 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433892 2.20688e-05 119.413 21712 5 2.24795e-05 0.0899818 0.0759805,0.106346 0.158116,0.438855,-0.142498,0.522582,-0.183314 0.0174666,0.0624702,0.0197005,0.0279689,0.0861074
+131 chr19_45398014,chr19_45406538,chr19_45409595,chr19_45411941,chr19_45412079 2.18039e-05 119.408 21975.8 5 2.22097e-05 0.0898331 0.0761981,0.104552 0.419785,0.157588,0.132842,-0.164948,0.52188 0.0624718,0.0174443,0.0432843,0.0210732,0.027939
+132 chr19_45405062,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.11049e-05 119.394 22703.7 5 2.14977e-05 0.0895788 0.0755327,0.104424 -0.0487322,0.156504,-0.174985,0.446989,0.533593 0.0245119,0.0174346,0.0260758,0.0624403,0.0283294
+133 chr19_45383240,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.10125e-05 119.392 22803.5 5 2.14036e-05 0.0899429 0.0760093,0.105344 0.136791,0.156704,0.440106,-0.145414,0.524427 0.0654434,0.0174363,0.0624612,0.0198042,0.0279547
+134 chr19_45395714,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.07458e-05 119.387 23096.6 5 2.11319e-05 0.0900999 0.0764217,0.10493 0.0484796,0.155724,-0.163131,0.402927,0.492426 0.0206626,0.0174308,0.0218539,0.0636621,0.0311271
+135 chr19_45402477,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.07339e-05 119.386 23109.9 5 2.11197e-05 0.0899914 0.0762801,0.105615 -0.0448194,0.157201,0.416765,-0.165385,0.500805 0.0217585,0.0174459,0.0636192,0.0230045,0.0302355
+136 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45428459 2.01645e-05 119.374 23762.5 5 2.05398e-05 0.0899847 0.0771021,0.104384 0.159178,0.429465,-0.152958,0.511258,-0.0576469 0.0175098,0.06273,0.020517,0.0286661,0.0276088
+137 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45420086 1.99476e-05 119.37 24020.8 5 2.03189e-05 0.0899619 0.0752461,0.105083 0.162782,-0.119811,0.437816,0.523428,-0.0861532 0.0177061,0.0217624,0.062168,0.0279603,0.0377945
+138 chr19_45397466,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.97098e-05 119.364 24310.6 5 2.00766e-05 0.0899324 0.0763252,0.104912 0.266157,0.155633,-0.138454,0.43894,0.527417 0.116116,0.0174299,0.0197184,0.0621773,0.0279852
+139 chr19_45391654,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.97081e-05 119.364 24312.8 5 2.00748e-05 0.0898772 0.0768317,0.105332 0.0980343,0.156961,0.441911,-0.151467,0.524543 0.0485078,0.0174411,0.0624578,0.020368,0.027955
+140 chr19_45404972,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.9658e-05 119.363 24374.7 5 2.00239e-05 0.0896153 0.075467,0.103934 -0.0414801,0.155886,0.452177,-0.169059,0.533007 0.023909,0.0174304,0.0627539,0.0254904,0.0283827
+141 chr19_45406538,chr19_45407788,chr19_45411941,chr19_45412040,chr19_45412079 1.93899e-05 119.357 24711.8 5 1.97507e-05 0.0896838 0.0756395,0.105521 0.158509,-0.0424107,-0.160112,0.415603,0.538586 0.0174817,0.0202755,0.0217147,0.0628499,0.0287568
+142 chr19_45406538,chr19_45407720,chr19_45410273,chr19_45411941,chr19_45412079 1.8627e-05 119.34 25723.8 5 1.89737e-05 0.0895639 0.0756422,0.10474 0.159851,0.0541896,0.441823,-0.177669,0.524326 0.0176153,0.0332768,0.0624609,0.0299152,0.0279567
+143 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45420079 1.84117e-05 119.335 26024.7 5 1.87544e-05 0.0899878 0.0759447,0.103674 0.276182,0.156204,-0.140178,0.524531,0.212981 0.07758,0.0174329,0.0196942,0.0279548,0.0693763
+144 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45412955 1.83269e-05 119.333 26145.2 5 1.86679e-05 0.0899337 0.0762016,0.10499 0.161364,0.442112,-0.122167,0.524416,-0.0761995 0.0176574,0.0624608,0.0217863,0.0279562,0.0379137
+145 chr19_45383562,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.80562e-05 119.326 26537.1 5 1.83922e-05 0.0898846 0.0765616,0.105239 0.0993712,0.155697,-0.144569,0.438438,0.526437 0.0443432,0.0174301,0.0197559,0.0621733,0.0279698
+146 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416291 1.80311e-05 119.326 26574 5 1.83667e-05 0.0899476 0.0751002,0.103524 0.158404,0.428918,-0.154763,0.51064,-0.0499604 0.0174816,0.0627742,0.0208417,0.0287801,0.0246404
+147 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421650 1.80008e-05 119.325 26618.8 5 1.83358e-05 0.0899409 0.0756798,0.105181 0.161651,-0.125345,0.435538,0.523066,-0.078786 0.0176304,0.0208705,0.0621576,0.0279637,0.0349717
+148 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45420225 1.76624e-05 119.317 27128.7 5 1.79911e-05 0.0899072 0.0759546,0.104667 0.156866,-0.139706,0.441089,0.524674,-0.168663 0.0174381,0.0196967,0.0622127,0.0279564,0.0749971
+149 chr19_45398817,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.73898e-05 119.31 27554.1 5 1.77134e-05 0.0899404 0.0759457,0.1055 -0.0510866,0.158806,0.429048,-0.153248,0.511283 0.0253526,0.0174988,0.0627719,0.0206198,0.0287157
+150 chr19_45388588,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.73284e-05 119.308 27651.7 5 1.76509e-05 0.0900015 0.0761634,0.104687 0.0445837,0.155548,-0.160355,0.405532,0.501671 0.0197295,0.0174311,0.02149,0.0635286,0.0297283
+151 chr19_45406538,chr19_45412040,chr19_45412079,chr19_45415640,chr19_45427780 1.69571e-05 119.299 28257.1 5 1.72727e-05 0.0887592 0.0756242,0.10359 0.156313,0.467888,0.633222,-0.102065,0.47545 0.0174306,0.0618932,0.0307531,0.0203332,0.097308
+152 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421204 1.64617e-05 119.286 29107.5 5 1.67681e-05 0.0899007 0.075445,0.105223 0.162488,-0.122751,0.438013,0.523673,-0.0848919 0.0177033,0.0213576,0.0621724,0.0279597,0.0386197
+153 chr19_45389381,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.64405e-05 119.286 29145.1 5 1.67464e-05 0.0899765 0.0761318,0.105322 0.125602,0.155632,0.443588,-0.139235,0.515724 0.0617285,0.0174307,0.0624681,0.0197046,0.0282869
+154 chr19_45385759,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.63495e-05 119.283 29307.2 5 1.66538e-05 0.0898935 0.0758825,0.104398 -0.0707276,0.399563,0.158998,-0.158238,0.512853 0.023229,0.0627411,0.0174714,0.0204085,0.0280983
+155 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439947 1.63347e-05 119.283 29333.7 5 1.66387e-05 0.0898685 0.0762889,0.104909 0.157817,0.449302,-0.13614,0.526997,0.0454865 0.0174637,0.0625817,0.0198358,0.0279849,0.0231312
+156 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433122 1.6278e-05 119.281 29436 5 1.65809e-05 0.0898573 0.0757309,0.104087 0.157516,0.451738,-0.139281,0.526504,0.0527062 0.0174551,0.0626725,0.0197047,0.0279747,0.026896
+157 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433699 1.61169e-05 119.277 29730.1 5 1.64169e-05 0.0899189 0.0761162,0.105362 0.155056,0.437881,-0.145089,0.519898,-0.118831 0.0174335,0.0624885,0.0197988,0.0280508,0.0595365
+158 chr19_45405931,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.61122e-05 119.277 29738.8 5 1.64121e-05 0.0898997 0.0762082,0.105757 0.0901389,0.157436,0.360026,-0.136346,0.528082 0.0469573,0.0174549,0.0755782,0.0198311,0.02802
+159 chr19_45406538,chr19_45409113,chr19_45411941,chr19_45412040,chr19_45412079 1.53629e-05 119.256 31189.4 5 1.56488e-05 0.0898509 0.0749987,0.104168 0.157448,0.112336,-0.136064,0.437318,0.528245 0.0174493,0.0516223,0.0198161,0.0621658,0.0280106
+160 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45438575 1.52523e-05 119.253 31415.5 5 1.55362e-05 0.0899514 0.0764501,0.105571 0.416169,0.15706,-0.154018,0.505709,0.138336 0.0624728,0.0174395,0.0200851,0.0284393,0.0452601
+161 chr19_45393247,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.52502e-05 119.253 31419.9 5 1.5534e-05 0.0897975 0.075385,0.104894 0.102323,0.157073,-0.151872,0.435547,0.524642 0.0479397,0.0174426,0.0203458,0.062158,0.0279571
+162 chr19_45403412,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.4993e-05 119.246 31958.8 5 1.5272e-05 0.0895145 0.0763739,0.103617 -0.0442236,0.155919,-0.171067,0.446212,0.533587 0.023977,0.0174313,0.0255657,0.0624481,0.0283904
+163 chr19_45404431,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.49735e-05 119.245 32000.4 5 1.52522e-05 0.0895139 0.0755308,0.105112 -0.0442204,0.155924,-0.171072,0.446204,0.533593 0.0239877,0.0174313,0.0255725,0.062448,0.0283915
+164 chr19_45383830,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.49361e-05 119.244 32080.5 5 1.52141e-05 0.0898168 0.0761909,0.104469 0.0641429,0.156202,-0.144677,0.431679,0.528339 0.0298607,0.0174322,0.0197667,0.0621785,0.0280148
+165 chr19_45402262,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.49287e-05 119.244 32096.5 5 1.52065e-05 0.0893324 0.0759036,0.104331 -0.0650082,0.155057,-0.140347,0.434564,0.574642 0.0407503,0.017435,0.0196926,0.0621583,0.0419549
+166 chr19_45388879,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.46168e-05 119.235 32781.3 5 1.48889e-05 0.0897855 0.0771264,0.103838 0.10111,0.157064,-0.151761,0.435574,0.52465 0.0478037,0.0174428,0.0203445,0.0621585,0.0279573
+167 chr19_45396219,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.45184e-05 119.232 33003.4 5 1.47886e-05 0.0899792 0.0769626,0.10581 0.0447012,0.155829,-0.160915,0.405651,0.495995 0.0204465,0.0174318,0.0217173,0.0636135,0.0308548
+168 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416831 1.41787e-05 119.221 33794.3 5 1.44425e-05 0.0898094 0.0751207,0.104871 0.155778,0.440638,-0.178021,0.481936,0.049722 0.0174328,0.0624713,0.0293628,0.0374918,0.0291009
+169 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45413576 1.41162e-05 119.219 33943.9 5 1.43789e-05 0.0902118 0.0762258,0.105233 0.156881,-0.14578,0.441811,0.458906,0.0743838 0.0174415,0.0198171,0.0622399,0.0408361,0.0336358
+170 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426458 1.39524e-05 119.214 34342.4 5 1.4212e-05 0.0898607 0.076145,0.104322 0.155212,-0.143188,0.435194,0.522255,-0.257785 0.0174324,0.0197178,0.0621589,0.0279764,0.119512
+171 chr19_45388581,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.39393e-05 119.214 34374.6 5 1.41988e-05 0.0898655 0.07617,0.105526 -0.112996,0.154211,-0.134155,0.436362,0.526649 0.0524066,0.0174445,0.0199393,0.0621618,0.0279758
+172 chr19_45396665,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.38044e-05 119.21 34710.7 5 1.40613e-05 0.0900551 0.0759615,0.105107 0.0406661,0.157509,0.412029,-0.141839,0.49899 0.0204749,0.0174558,0.064226,0.0196947,0.030778
+173 chr19_45401579,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.37872e-05 119.209 34753.9 5 1.40438e-05 0.0899036 0.07562,0.105466 0.153118,0.156219,0.445129,-0.139638,0.517301 0.0791004,0.0174332,0.0624886,0.0196989,0.0282029
+174 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45434662 1.37034e-05 119.207 34966.4 5 1.39584e-05 0.0898702 0.0753161,0.104058 0.15508,0.437963,-0.144936,0.520036,-0.113262 0.017434,0.0624913,0.0198004,0.0280538,0.0591864
+175 chr19_45403858,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.36477e-05 119.205 35109.1 5 1.39017e-05 0.0894946 0.0750851,0.102808 -0.043137,0.155916,-0.170338,0.44595,0.533306 0.0239714,0.0174316,0.0255669,0.0624496,0.0283852
+176 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433112 1.35346e-05 119.201 35402.5 5 1.37865e-05 0.089837 0.0763446,0.104906 0.156369,0.44317,-0.141259,0.523979,0.136658 0.0174346,0.0624671,0.0196888,0.0279581,0.0723047
+177 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433892 1.34546e-05 119.199 35613 5 1.3705e-05 0.0898352 0.0754987,0.104315 0.158111,-0.142523,0.432391,0.52256,-0.183521 0.017468,0.0197043,0.0621725,0.027972,0.0861143
+178 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440940 1.30442e-05 119.185 36733.4 5 1.3287e-05 0.0898387 0.0767478,0.104011 0.155268,0.451646,-0.139774,0.522573,-0.0746285 0.0174326,0.0626902,0.0196973,0.0279757,0.0397445
+179 chr19_45402477,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.30286e-05 119.185 36777.5 5 1.3271e-05 0.0898538 0.0764283,0.104411 -0.0451832,0.157205,-0.165612,0.410259,0.500589 0.021758,0.0174473,0.0230088,0.0633095,0.0302387
+180 chr19_45383240,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.27025e-05 119.174 37721.5 5 1.29389e-05 0.0897939 0.0747988,0.104083 0.136697,0.156696,-0.145436,0.43358,0.524406 0.0654491,0.0174378,0.0198081,0.0621641,0.0279578
+181 chr19_45406538,chr19_45410273,chr19_45410444,chr19_45411941,chr19_45412079 1.26964e-05 119.173 37739.7 5 1.29327e-05 0.089854 0.076965,0.104075 0.156879,0.422858,-0.0411502,-0.159096,0.505782 0.0174438,0.0633025,0.0224732,0.0220595,0.0297718
+182 chr19_45399580,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.25888e-05 119.17 38062.4 5 1.28231e-05 0.0898011 0.075543,0.105338 -0.190618,0.158627,0.438167,-0.143594,0.523187 0.103614,0.0175038,0.0624907,0.0197423,0.0279661
+183 chr19_45395909,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.24876e-05 119.166 38370.6 5 1.272e-05 0.0897371 0.0754703,0.105263 0.0831781,0.425051,0.157404,-0.145799,0.528142 0.0284003,0.0625283,0.0174448,0.0197326,0.0280228
+184 chr19_45383061,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.24588e-05 119.165 38459.6 5 1.26906e-05 0.0898203 0.0766851,0.105903 -0.0349977,0.156506,0.437676,-0.150396,0.51704 0.0190245,0.017437,0.0625001,0.0203537,0.0282496
+185 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45428459 1.23633e-05 119.162 38756.7 5 1.25933e-05 0.0898397 0.0757028,0.104568 0.159179,-0.153016,0.422974,0.511203,-0.0577885 0.0175112,0.0205214,0.0624324,0.0286696,0.0276116
+186 chr19_45398014,chr19_45406538,chr19_45407326,chr19_45411941,chr19_45412079 1.22743e-05 119.159 39037.5 5 1.25027e-05 0.0896865 0.0755691,0.104831 0.420186,0.157233,0.121844,-0.162718,0.521187 0.0624807,0.017443,0.0421527,0.0209676,0.0279428
+187 chr19_45394326,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.2216e-05 119.157 39223.8 5 1.24434e-05 0.089793 0.0754888,0.104746 -0.189396,0.15855,0.437932,-0.143558,0.523214 0.103827,0.0175013,0.0624955,0.0197419,0.027966
+188 chr19_45401587,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45427780 1.21987e-05 119.156 39279.6 5 1.24257e-05 0.0897903 0.0751347,0.103884 0.308521,0.158547,-0.139141,0.524719,0.478403 0.0560494,0.0174409,0.0197238,0.0279736,0.097196
+189 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45425066,chr19_45427780 1.21414e-05 119.154 39464.8 5 1.23674e-05 0.0894142 0.0760433,0.104358 0.169462,0.488092,0.551254,-0.178354,0.506233 0.0176816,0.0620803,0.027533,0.0351433,0.0970549
+190 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433135 1.20931e-05 119.152 39622.4 5 1.23182e-05 0.0897855 0.0762727,0.103012 0.156341,0.442934,-0.140509,0.526501,0.156913 0.0174349,0.0624669,0.0196894,0.0279791,0.0863445
+191 chr19_45398014,chr19_45406538,chr19_45407409,chr19_45411941,chr19_45412079 1.19106e-05 119.146 40229.6 5 1.21323e-05 0.0896819 0.0761963,0.10364 0.420068,0.157105,0.123154,-0.162172,0.521301 0.0624804,0.0174415,0.0427198,0.0209097,0.0279427
+192 chr19_45398265,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.19101e-05 119.146 40231.2 5 1.21318e-05 0.0898234 0.0759006,0.104697 -0.181657,0.155471,0.438398,-0.143506,0.521248 0.0988435,0.0174319,0.062488,0.0197392,0.0280132
+193 chr19_45391654,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.18024e-05 119.142 40598.4 5 1.2022e-05 0.0897257 0.0755387,0.104639 0.0977479,0.156949,-0.151463,0.435314,0.524521 0.0485119,0.0174426,0.0203722,0.0621604,0.0279582
+194 chr19_45382966,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.17958e-05 119.141 40621.3 5 1.20153e-05 0.0898043 0.076594,0.105 -0.0344512,0.156534,0.437619,-0.150199,0.51714 0.0190308,0.0174377,0.062503,0.0203474,0.0282513
+195 chr19_45404972,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.16496e-05 119.136 41130.9 5 1.18664e-05 0.0894625 0.0746308,0.103855 -0.0412054,0.155877,-0.168895,0.445496,0.532931 0.0239096,0.017432,0.0254933,0.0624515,0.028386
+196 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436817 1.13555e-05 119.125 42196.2 5 1.15668e-05 0.089783 0.0764769,0.103372 0.157871,0.439346,-0.142096,0.523129,-0.15942 0.0174736,0.0624765,0.0196997,0.0279679,0.0888713
+197 chr19_45384105,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.13116e-05 119.123 42360 5 1.15221e-05 0.0897877 0.0755634,0.103438 -0.03349,0.156075,0.437328,-0.151521,0.516758 0.0188072,0.017433,0.0625109,0.0205723,0.0282935
+198 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435661 1.12043e-05 119.119 42765.6 5 1.14128e-05 0.0897886 0.0755539,0.104759 0.156735,0.364666,-0.142703,0.525057,0.107564 0.0174428,0.0768713,0.0197174,0.0279609,0.0625465
+199 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435458 1.11342e-05 119.116 43034.8 5 1.13414e-05 0.089959 0.0765286,0.105126 0.155377,0.436681,-0.143632,0.500201,0.0608086 0.0174332,0.0625232,0.0197443,0.0308572,0.0326139
+200 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435774 1.10625e-05 119.114 43313.8 5 1.12684e-05 0.0897601 0.0769553,0.106136 0.156083,0.446568,-0.138255,0.52707,0.138334 0.0174329,0.0625264,0.0197455,0.0279958,0.0782477
+201 chr19_45406538,chr19_45407720,chr19_45411941,chr19_45412040,chr19_45412079 1.10605e-05 119.113 43321.7 5 1.12663e-05 0.0894117 0.0748482,0.104878 0.159815,0.053825,-0.17745,0.435173,0.524303 0.0176168,0.0332794,0.0299216,0.0621636,0.0279599
+202 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416291 1.10346e-05 119.112 43423.4 5 1.12399e-05 0.0898021 0.0771478,0.105371 0.158402,-0.154821,0.422409,0.510586,-0.0500687 0.0174831,0.0208465,0.0624777,0.0287838,0.0246434
+203 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45412955 1.08783e-05 119.106 44047.1 5 1.10808e-05 0.0897794 0.0749255,0.106037 0.161326,-0.122294,0.435433,0.524392,-0.0758017 0.0176589,0.0217887,0.0621632,0.0279594,0.0379168
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+205 chr19_45405931,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.07829e-05 119.102 44437.1 5 1.09835e-05 0.0897809 0.0762761,0.104892 0.0924637,0.157472,-0.136275,0.351885,0.528139 0.0469467,0.017456,0.0198331,0.0751951,0.028022
+206 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45437100 1.07188e-05 119.1 44702.6 5 1.09183e-05 0.0897832 0.0748298,0.103978 0.155248,0.441726,-0.142393,0.522624,-0.209441 0.0174335,0.0624644,0.0197065,0.0279777,0.118002
+207 chr19_45398817,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.06648e-05 119.098 44928.9 5 1.08633e-05 0.0897956 0.0762249,0.103202 -0.0512245,0.158807,-0.153309,0.422553,0.511224 0.0253552,0.0175003,0.0206243,0.0624743,0.0287192
+208 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45420086,chr19_45427780 1.05345e-05 119.092 45484.9 5 1.07305e-05 0.0893881 0.075339,0.103631 0.169709,0.486349,0.549637,-0.172732,0.504954 0.0176928,0.0620912,0.0275643,0.0341961,0.0970608
+209 chr19_45388255,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 1.02836e-05 119.082 46594.4 5 1.0475e-05 0.0897466 0.0753322,0.104429 0.182181,0.156568,0.443233,-0.140132,0.525456 0.10506,0.017439,0.0624715,0.0196938,0.0279636
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+211 chr19_45389381,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 9.98172e-06 119.069 48003.6 5 1.01675e-05 0.0898287 0.0757406,0.104069 0.125633,0.155625,-0.139256,0.437115,0.515703 0.0617339,0.0174322,0.0197085,0.0621708,0.0282899
+212 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45415640,chr19_45425212 9.9148e-06 119.066 48327.6 5 1.00993e-05 0.0885201 0.0747948,0.105057 0.156082,0.477037,0.639295,-0.104952,0.507257 0.0174313,0.0622087,0.0307402,0.0203009,0.109438
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+214 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433122 9.8308e-06 119.062 48740.5 5 1.00138e-05 0.0897081 0.0762654,0.105082 0.157508,-0.1393,0.445249,0.526486,0.0526527 0.0174566,0.0197086,0.0623768,0.0279779,0.0268995
+215 chr19_45398457,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.67429e-06 119.055 49529.1 5 9.85434e-06 0.089704 0.0755686,0.105008 0.0606546,0.156578,0.446038,-0.134545,0.529914 0.0362691,0.0174399,0.0625211,0.0200528,0.0281459
+216 chr19_45388130,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.64822e-06 119.054 49662.9 5 9.82778e-06 0.0897348 0.0758138,0.104736 0.163499,0.156306,0.442971,-0.138765,0.52556 0.0960447,0.0174357,0.0624703,0.0197291,0.0279655
+217 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45439947 9.61675e-06 119.053 49825.4 5 9.79572e-06 0.0897122 0.076069,0.10458 0.157793,-0.136203,0.442543,0.526955,0.0451472 0.0174653,0.0198394,0.0622785,0.0279881,0.0231312
+218 chr19_45382398,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.54657e-06 119.05 50191.7 5 9.72424e-06 0.0897484 0.07521,0.10578 -0.0760628,0.155464,0.441874,-0.136011,0.52562 0.0445965,0.0174326,0.0624658,0.0198973,0.0279664
+219 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45422846 9.4781e-06 119.046 50554.3 5 9.65449e-06 0.089779 0.0766717,0.105789 0.15715,0.43543,-0.133821,0.522142,-0.0372767 0.0174528,0.0625702,0.0201172,0.0279922,0.0216997
+220 chr19_45398014,chr19_45405181,chr19_45406538,chr19_45411941,chr19_45412079 9.47384e-06 119.046 50577 5 9.65016e-06 0.0896136 0.0760549,0.103537 0.420091,0.118101,0.157194,-0.162084,0.521251 0.0624834,0.0421407,0.017444,0.0209685,0.0279439
+221 chr19_45400956,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.30308e-06 119.038 51505.4 5 9.47622e-06 0.0897199 0.0758961,0.103875 0.138967,0.156451,0.442716,-0.138604,0.526027 0.0828316,0.0174379,0.0624691,0.0197368,0.0279736
+222 chr19_45381272,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 9.14184e-06 119.031 52413.8 5 9.31197e-06 0.089721 0.0756345,0.104086 0.100057,0.154911,0.431083,-0.144311,0.524862 0.0599213,0.0174385,0.062784,0.0197947,0.0279596
+223 chr19_45382034,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.95453e-06 119.022 53510.2 5 9.12118e-06 0.0897145 0.0758152,0.103802 -0.0299968,0.155227,0.431542,-0.151233,0.518826 0.01827,0.0174347,0.062768,0.0206708,0.0281713
+224 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416831 8.82933e-06 119.016 54269 5 8.99365e-06 0.0896691 0.0748726,0.104513 0.155772,-0.178365,0.434363,0.481544,0.0501621 0.0174342,0.0293628,0.0621724,0.0374927,0.0291024
+225 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433139 8.81985e-06 119.015 54327.3 5 8.98399e-06 0.0896973 0.0766064,0.105431 0.156417,0.441878,-0.140782,0.525866,0.175761 0.0174377,0.0624663,0.0196894,0.0279713,0.107066
+226 chr19_45397924,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.76869e-06 119.013 54644.3 5 8.93188e-06 0.0897071 0.0770244,0.104062 -0.134856,0.157779,0.438631,-0.140788,0.523967 0.0819146,0.0174781,0.0624927,0.0196894,0.0279606
+227 chr19_45396665,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 8.76565e-06 119.012 54663.2 5 8.92879e-06 0.0899226 0.075692,0.104448 0.0411456,0.157523,-0.141877,0.405469,0.498666 0.0204711,0.0174572,0.0196984,0.0639028,0.0307803
+228 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440428 8.75827e-06 119.012 54709.3 5 8.92127e-06 0.0896994 0.076236,0.104638 0.156695,0.445617,-0.137926,0.52594,0.0797004 0.0174429,0.0625139,0.0197731,0.0279729,0.0486305
+229 chr19_45402546,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.67049e-06 119.008 55263.1 5 8.83186e-06 0.0896975 0.0750848,0.105069 0.132092,0.156464,0.442702,-0.138707,0.526141 0.0808098,0.0174386,0.0624699,0.0197352,0.0279771
+230 chr19_45397229,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.65428e-06 119.007 55366.7 5 8.81534e-06 0.0893548 0.0747295,0.103676 -0.0420311,0.1554,0.441791,-0.14052,0.555707 0.0420479,0.017435,0.0624673,0.0196928,0.0417222
+231 chr19_45406538,chr19_45408628,chr19_45410273,chr19_45411941,chr19_45412079 8.64151e-06 119.006 55448.4 5 8.80234e-06 0.0895634 0.0765755,0.104409 0.15564,-0.0839857,0.44826,-0.140853,0.538284 0.0174321,0.0562413,0.0626232,0.0196892,0.0294403
+232 chr19_45397171,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.63994e-06 119.006 55458.5 5 8.80074e-06 0.0896698 0.0770862,0.103218 0.0568249,0.15666,0.446341,-0.134583,0.529806 0.0354461,0.0174427,0.0625358,0.0200803,0.0281549
+233 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431453 8.53551e-06 119.001 56137 5 8.69437e-06 0.0896866 0.0759479,0.103799 0.156597,0.436227,-0.146333,0.52304,-0.0415822 0.0174413,0.0625557,0.019974,0.0279734,0.025734
+234 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45434662 8.36809e-06 118.992 57260.2 5 8.52383e-06 0.089724 0.0758789,0.10324 0.155071,-0.144967,0.431505,0.520008,-0.113468 0.0174355,0.0198042,0.0621932,0.0280569,0.059191
+235 chr19_45398014,chr19_45405521,chr19_45406538,chr19_45411941,chr19_45412079 8.34619e-06 118.991 57410.5 5 8.50152e-06 0.0897874 0.0757461,0.103675 0.390422,-0.06205,0.157297,-0.167346,0.493998 0.0631928,0.0218455,0.0174462,0.0216416,0.0296235
+236 chr19_45401579,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 8.33575e-06 118.991 57482.4 5 8.49088e-06 0.0897545 0.0759189,0.105416 0.152989,0.156211,-0.139661,0.438623,0.517288 0.0791072,0.0174347,0.0197028,0.0621911,0.028206
+237 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431542 8.33298e-06 118.99 57501.4 5 8.48807e-06 0.0899369 0.0751649,0.105694 0.15562,0.434393,-0.140818,0.490625,0.0791402 0.0174338,0.0626198,0.0196911,0.0344607,0.0468966
+238 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433112 8.22249e-06 118.985 58274.2 5 8.37551e-06 0.0896894 0.0764473,0.104486 0.156362,-0.141281,0.4367,0.52396,0.136721 0.0174361,0.0196925,0.0621698,0.0279612,0.0723109
+239 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45423078 8.18455e-06 118.983 58544.2 5 8.33688e-06 0.0896812 0.0752747,0.104829 0.156409,0.442724,-0.140443,0.525237,0.137364 0.0174381,0.0624707,0.0196916,0.027963,0.0857956
+240 chr19_45405740,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 8.17441e-06 118.982 58616.9 5 8.32655e-06 0.0896809 0.0753099,0.105165 0.186136,0.155494,0.444134,-0.139149,0.526309 0.116354,0.0174329,0.0624868,0.0197201,0.0279823
+241 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439261 8.122e-06 118.979 58995.1 5 8.27316e-06 0.0897177 0.0755048,0.104567 0.155745,0.437826,-0.141779,0.521286,0.0608076 0.0174326,0.062511,0.019697,0.0280287,0.0374014
+242 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435776 8.00297e-06 118.973 59872.6 5 8.15191e-06 0.0896667 0.075333,0.104545 0.156037,0.445979,-0.138385,0.52683,0.127404 0.0174338,0.0625282,0.0197542,0.0279985,0.0806782
+243 chr19_45406538,chr19_45408659,chr19_45410273,chr19_45411941,chr19_45412079 7.91298e-06 118.968 60553.5 5 8.06024e-06 0.0896967 0.0758201,0.104778 0.155151,-0.172393,0.441149,-0.142035,0.522596 0.0174359,0.107726,0.0624682,0.0197024,0.0279844
+244 chr19_45406538,chr19_45410444,chr19_45411941,chr19_45412040,chr19_45412079 7.91117e-06 118.968 60567.3 5 8.05841e-06 0.089714 0.0751605,0.1039 0.15688,-0.0414744,-0.159266,0.416362,0.505612 0.0174452,0.0224733,0.0220637,0.062996,0.0297751
+245 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440940 7.84779e-06 118.964 61056.5 5 7.99384e-06 0.0896882 0.075427,0.104401 0.155261,-0.139795,0.445111,0.522561,-0.0744629 0.0174341,0.0197012,0.0623931,0.0279788,0.0397487
+246 chr19_45406538,chr19_45408077,chr19_45410273,chr19_45411941,chr19_45412079 7.83075e-06 118.963 61189.3 5 7.97649e-06 0.0896767 0.0759911,0.104442 0.156999,0.0714667,0.376634,-0.13743,0.526874 0.0174553,0.0478571,0.0761659,0.0198278,0.0280064
+247 chr19_45399580,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.68643e-06 118.955 62338.2 5 7.82948e-06 0.0896549 0.0751293,0.104875 -0.19098,0.158625,-0.143622,0.431708,0.523165 0.103622,0.0175053,0.0197461,0.0621926,0.0279692
+248 chr19_45384063,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 7.68157e-06 118.955 62377.7 5 7.82453e-06 0.0896377 0.0749796,0.104012 -0.0963444,0.159589,0.440244,-0.142479,0.524549 0.0627084,0.0176157,0.0624739,0.0197163,0.0279594
+249 chr19_45383061,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.54327e-06 118.947 63521.4 5 7.68365e-06 0.0896717 0.0757418,0.105361 -0.0349804,0.156497,-0.150418,0.431149,0.517022 0.0190264,0.0174384,0.020358,0.0622035,0.0282529
+250 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433123 7.52542e-06 118.946 63672 5 7.66548e-06 0.0896442 0.075217,0.10382 0.156848,0.447859,-0.140347,0.525405,0.0408805 0.0174488,0.0625995,0.0196931,0.0279658,0.026593
+251 chr19_45394326,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.46164e-06 118.943 64216.2 5 7.60051e-06 0.0896469 0.0765583,0.105525 -0.189782,0.158548,-0.143587,0.431474,0.523191 0.103835,0.0175028,0.0197456,0.0621974,0.0279691
+252 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435661 7.4503e-06 118.942 64314 5 7.58895e-06 0.0896682 0.076237,0.104021 0.156757,-0.142803,0.356275,0.525039,0.110808 0.017444,0.0197215,0.0764832,0.0279635,0.0625338
+253 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45437608 7.40401e-06 118.939 64716.1 5 7.5418e-06 0.0896447 0.0757157,0.104745 0.156013,0.445841,-0.13842,0.526728,0.123632 0.017434,0.0625289,0.0197566,0.0279978,0.0807659
+254 chr19_45396276,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 7.37153e-06 118.937 65001.2 5 7.50872e-06 0.0896467 0.0752487,0.103044 -0.133377,0.159631,0.438537,-0.142949,0.521968 0.087342,0.0176223,0.0625008,0.0197354,0.0280083
+255 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433135 7.3656e-06 118.937 65053.6 5 7.50268e-06 0.0896385 0.0759265,0.103375 0.156334,-0.140529,0.436486,0.526485,0.157106 0.0174363,0.0196932,0.0621697,0.0279823,0.0863522
+256 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45424351 7.33751e-06 118.935 65302.6 5 7.47407e-06 0.0896048 0.0756688,0.104375 0.156819,0.44621,-0.144674,0.526585,0.0563866 0.017449,0.0625439,0.0198517,0.027995,0.037881
+257 chr19_45398265,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.23413e-06 118.929 66235.8 5 7.36876e-06 0.0896757 0.0767049,0.105716 -0.181733,0.155464,-0.143532,0.4319,0.521225 0.0988522,0.0174333,0.0197431,0.0621907,0.0280163
+258 chr19_45382966,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 7.14269e-06 118.924 67083.7 5 7.27563e-06 0.0896557 0.0757588,0.104454 -0.0344347,0.156526,-0.15022,0.431093,0.517121 0.0190327,0.0174392,0.0203517,0.0622064,0.0282547
+259 chr19_45398014,chr19_45398462,chr19_45406538,chr19_45411941,chr19_45412079 7.02031e-06 118.916 68253.2 5 7.15096e-06 0.0895307 0.0753657,0.104648 0.420085,0.113147,0.157306,-0.160659,0.521636 0.0624871,0.041926,0.0174475,0.0208845,0.0279446
+260 chr19_45384332,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 7.01464e-06 118.916 68308.4 5 7.14518e-06 0.0896492 0.075692,0.104029 -0.0281744,0.155894,0.437673,-0.149636,0.517723 0.0188331,0.0174336,0.0625251,0.0205381,0.028324
+261 chr19_45406538,chr19_45408564,chr19_45410273,chr19_45411941,chr19_45412079 6.92163e-06 118.91 69226.3 5 7.05045e-06 0.089756 0.0767239,0.105267 0.153939,-0.0298349,0.43217,-0.132806,0.516608 0.0174698,0.0190957,0.0627634,0.0203638,0.0284132
+262 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436817 6.91995e-06 118.91 69243.1 5 7.04874e-06 0.0896362 0.0758023,0.10409 0.157866,-0.14212,0.43288,0.523108,-0.159646 0.0174751,0.0197034,0.0621787,0.027971,0.0888783
+263 chr19_45384105,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 6.86365e-06 118.906 69811 5 6.99139e-06 0.0896397 0.0760347,0.105408 -0.0334945,0.156067,-0.151548,0.430816,0.516735 0.0188091,0.0174345,0.0205765,0.0622141,0.0282968
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+268 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435774 6.69019e-06 118.895 71621.1 5 6.8147e-06 0.0896112 0.0756774,0.10415 0.156075,-0.138278,0.440071,0.527049,0.138212 0.0174344,0.0197494,0.0622293,0.027999,0.0782551
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+270 chr19_45406513,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 6.63739e-06 118.892 72190.8 5 6.76092e-06 0.0895389 0.0761958,0.104504 -0.100426,0.167566,-0.141581,0.431348,0.523022 0.0596855,0.0188068,0.0196963,0.0622072,0.0279742
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+272 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435458 6.57205e-06 118.887 72908.5 5 6.69437e-06 0.0898016 0.0761865,0.103837 0.155371,-0.143645,0.429948,0.500371,0.0603133 0.0174347,0.0197486,0.062233,0.0308633,0.0326206
+273 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436657 6.52824e-06 118.884 73397.8 5 6.64974e-06 0.089608 0.0751356,0.104111 0.156544,0.437593,-0.145928,0.522972,-0.0369289 0.0174431,0.0625317,0.0199955,0.0279799,0.0255816
+274 chr19_45406538,chr19_45408475,chr19_45410273,chr19_45412079,chr19_45427780 6.51616e-06 118.884 73533.9 5 6.63743e-06 0.0894502 0.0749775,0.104673 0.148449,-0.124306,0.477552,0.546266,0.490954 0.0174704,0.0248123,0.0621748,0.0276444,0.0971521
+275 chr19_45398014,chr19_45398201,chr19_45406538,chr19_45411941,chr19_45412079 6.50255e-06 118.883 73687.7 5 6.62357e-06 0.0895177 0.0754965,0.104715 0.420244,0.110553,0.157142,-0.160262,0.520956 0.0624891,0.0413146,0.0174456,0.0208554,0.027947
+276 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45437100 6.4877e-06 118.882 73856.5 5 6.60844e-06 0.0896345 0.0757747,0.104802 0.155241,-0.142415,0.435216,0.522605,-0.209306 0.017435,0.0197103,0.0621669,0.0279808,0.118012
+277 chr19_45402104,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.33817e-06 118.872 75598.9 5 6.45613e-06 0.0895841 0.0749538,0.10444 -0.124903,0.155171,0.442135,-0.141653,0.527852 0.0883512,0.0174371,0.0624705,0.0196974,0.0280613
+278 chr19_45382433,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.29767e-06 118.869 76085 5 6.41488e-06 0.0896208 0.0753773,0.104308 -0.0541881,0.15556,0.440714,-0.138362,0.525217 0.0375117,0.0174335,0.062473,0.0197691,0.027965
+279 chr19_45398014,chr19_45399526,chr19_45406538,chr19_45411941,chr19_45412079 6.27616e-06 118.867 76345.8 5 6.39297e-06 0.089506 0.0758835,0.103677 0.42028,0.109576,0.157157,-0.160275,0.521053 0.0624899,0.0411636,0.017446,0.020869,0.0279467
+280 chr19_45388255,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 6.23928e-06 118.865 76797.1 5 6.35539e-06 0.0895985 0.0768986,0.104434 0.1822,0.156561,-0.140154,0.436751,0.525437 0.105069,0.0174405,0.0196976,0.0621741,0.0279667
+281 chr19_45384338,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.21535e-06 118.863 77092.7 5 6.33103e-06 0.0896124 0.0759157,0.104989 -0.0266424,0.155837,0.437907,-0.149203,0.518087 0.0188545,0.0174337,0.0625259,0.0205488,0.0283258
+282 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436753 6.10362e-06 118.855 78504 5 6.21721e-06 0.0895933 0.0751177,0.104954 0.156909,0.446907,-0.141358,0.523666,0.0397577 0.0174546,0.062583,0.0196931,0.0279666,0.028258
+283 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427727 6.06897e-06 118.853 78952.2 5 6.18192e-06 0.0896291 0.0764755,0.106317 0.154867,0.438939,-0.142886,0.521303,-0.0891843 0.017443,0.0624984,0.019739,0.0280487,0.062132
+284 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432135 6.04794e-06 118.851 79226.7 5 6.1605e-06 0.0895977 0.0745873,0.105696 0.155649,0.437446,-0.142793,0.524541,-0.0600896 0.0174334,0.0625405,0.0197362,0.0279606,0.0427008
+285 chr19_45404926,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 6.04483e-06 118.851 79267.6 5 6.15732e-06 0.0896231 0.0766086,0.105682 -0.135454,0.15503,0.441671,-0.136839,0.524388 0.0950012,0.0174397,0.0624705,0.0198963,0.0279608
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+287 chr19_45398457,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.8737e-06 118.839 81577 5 5.98301e-06 0.0895562 0.07499,0.103558 0.0606767,0.156571,-0.134563,0.439577,0.529898 0.0362729,0.0174414,0.020057,0.0622247,0.0281492
+288 chr19_45388130,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.85851e-06 118.837 81788.4 5 5.96754e-06 0.089587 0.0753955,0.105078 0.16356,0.156299,-0.138786,0.436496,0.525542 0.096053,0.0174371,0.0197329,0.062173,0.0279686
+289 chr19_45381272,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.85587e-06 118.837 81825.3 5 5.96485e-06 0.0895885 0.0753565,0.103956 0.101911,0.15489,-0.144387,0.424827,0.524857 0.0599045,0.0174398,0.0197975,0.0624622,0.0279625
+290 chr19_45406538,chr19_45412040,chr19_45412079,chr19_45420086,chr19_45427780 5.81395e-06 118.834 82415.3 5 5.92215e-06 0.0892132 0.0749963,0.104294 0.169648,0.479349,0.54967,-0.172148,0.504956 0.0176945,0.0617945,0.0275677,0.0342021,0.0970706
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+292 chr19_45382398,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.7774e-06 118.831 82936.7 5 5.88492e-06 0.0895996 0.0762346,0.103818 -0.0760056,0.155457,-0.136038,0.435363,0.525599 0.0446002,0.0174341,0.019901,0.0621684,0.0279695
+293 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45422846 5.69679e-06 118.825 84110.3 5 5.80281e-06 0.0896282 0.0760919,0.104608 0.157137,-0.133873,0.428841,0.522124,-0.0371644 0.0174543,0.0201202,0.0622772,0.0279955,0.0217033
+294 chr19_45394592,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.67929e-06 118.824 84369.5 5 5.78499e-06 0.0895804 0.0761289,0.104616 0.13253,0.156162,0.442546,-0.139141,0.52523 0.0971612,0.0174369,0.0624746,0.0197332,0.0279662
+295 chr19_45396434,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.65376e-06 118.822 84750.5 5 5.75898e-06 0.0895794 0.0757013,0.104692 0.13394,0.156152,0.442513,-0.139118,0.525211 0.0984071,0.0174368,0.0624744,0.0197345,0.027966
+296 chr19_45400956,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.64683e-06 118.821 84854.4 5 5.75192e-06 0.089572 0.0751927,0.103652 0.139003,0.156443,-0.138626,0.436235,0.526009 0.0828387,0.0174394,0.0197407,0.0621717,0.0279767
+297 chr19_45393742,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 5.63658e-06 118.821 85008.8 5 5.74148e-06 0.0895126 0.0764617,0.104022 0.0751572,0.424352,0.157023,-0.145455,0.527481 0.0283294,0.0625372,0.0174444,0.0197359,0.0280246
+298 chr19_45392093,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.62516e-06 118.82 85181.4 5 5.72984e-06 0.0896181 0.0759696,0.105304 0.0719869,0.156033,0.4437,-0.139234,0.520246 0.0517813,0.0174354,0.0624894,0.0197276,0.0281272
+299 chr19_45406538,chr19_45412040,chr19_45412079,chr19_45415640,chr19_45425212 5.60019e-06 118.818 85561.1 5 5.70442e-06 0.0883536 0.0749083,0.103137 0.156069,0.470212,0.639132,-0.104734,0.507203 0.017433,0.0619152,0.030743,0.0203057,0.109449
+300 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45430417 5.54494e-06 118.814 86413.6 5 5.64814e-06 0.0895832 0.0757782,0.104242 0.157716,0.438141,-0.144876,0.519793,-0.05496 0.0174981,0.0625247,0.0199102,0.0281772,0.0407591
+301 chr19_45401159,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.54162e-06 118.813 86465.4 5 5.64476e-06 0.0895722 0.0745117,0.103996 0.0931929,0.156392,0.441235,-0.138855,0.525486 0.0693019,0.0174413,0.0624717,0.0197498,0.0279709
+302 chr19_45386654,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.50977e-06 118.811 86965.2 5 5.61231e-06 0.0895712 0.0755745,0.104591 0.132169,0.15614,0.442688,-0.139112,0.525257 0.0985101,0.0174368,0.0624763,0.0197361,0.0279669
+303 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45425066 5.50439e-06 118.81 87050.3 5 5.60683e-06 0.0896042 0.0755533,0.10417 0.420686,0.163345,-0.120881,0.520776,-0.101118 0.0624958,0.0176891,0.021198,0.0279494,0.0378397
+304 chr19_45387713,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.49438e-06 118.81 87208.9 5 5.59663e-06 0.0895706 0.0749947,0.103981 0.137204,0.156106,0.443214,-0.139059,0.525193 0.102408,0.0174363,0.0624826,0.019739,0.027966
+305 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45424237 5.48281e-06 118.809 87392.8 5 5.58485e-06 0.0895966 0.0757925,0.104303 0.154937,0.439248,-0.142797,0.521523,-0.0850914 0.0174426,0.0624958,0.01974,0.0280458,0.0624828
+306 chr19_45382034,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.44203e-06 118.805 88047.7 5 5.54331e-06 0.089567 0.0758923,0.104417 -0.0300195,0.155219,-0.15127,0.425005,0.518798 0.018273,0.0174362,0.0206759,0.0624742,0.0281747
+307 chr19_45382305,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.43839e-06 118.805 88106.6 5 5.53961e-06 0.0895794 0.0755556,0.104488 -0.0594978,0.155475,0.441833,-0.13676,0.525416 0.0444379,0.0174345,0.0624717,0.0199331,0.0279698
+308 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433139 5.3743e-06 118.8 89157.4 5 5.47432e-06 0.0895505 0.0755044,0.104227 0.15641,-0.140803,0.435427,0.525849,0.17605 0.0174392,0.0196932,0.0621688,0.0279744,0.107075
+309 chr19_45397924,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.34934e-06 118.798 89573.4 5 5.44889e-06 0.0895606 0.0757306,0.104278 -0.135132,0.157775,-0.140811,0.432167,0.523946 0.0819209,0.0174796,0.0196932,0.0621947,0.0279637
+310 chr19_45385061,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.34031e-06 118.797 89724.9 5 5.43969e-06 0.0895733 0.0758205,0.105863 -0.149156,0.155793,0.440355,-0.142435,0.522777 0.112181,0.0174339,0.0624788,0.0197246,0.0279908
+311 chr19_45406538,chr19_45407251,chr19_45410273,chr19_45411941,chr19_45412079 5.33607e-06 118.797 89796.1 5 5.43538e-06 0.0895617 0.0748015,0.104613 0.156146,0.128681,0.442518,-0.139211,0.525233 0.0174371,0.0976314,0.0624752,0.0197329,0.0279669
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+314 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433002 5.29096e-06 118.793 90561.7 5 5.38943e-06 0.0895736 0.075488,0.105792 0.156227,0.438453,-0.143856,0.521359,-0.0623716 0.0174384,0.0625176,0.0198172,0.0280612,0.047157
+315 chr19_45398633,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.28967e-06 118.793 90583.9 5 5.38811e-06 0.0895392 0.0760865,0.104148 0.0733491,0.156452,0.443437,-0.138046,0.527515 0.0569276,0.0174436,0.0624875,0.0198156,0.0280597
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+318 chr19_45397171,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.2436e-06 118.789 91379.7 5 5.34119e-06 0.0895218 0.0758037,0.104529 0.0568385,0.156653,-0.134601,0.439878,0.52979 0.03545,0.0174441,0.0200845,0.0622395,0.0281583
+319 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431453 5.21757e-06 118.787 91835.6 5 5.31467e-06 0.0895407 0.0751392,0.10464 0.156592,-0.146373,0.429759,0.523015,-0.041701 0.0174427,0.0199779,0.0622573,0.0279765,0.0257359
+320 chr19_45397229,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 5.18566e-06 118.784 92400.7 5 5.28217e-06 0.0892056 0.0751258,0.104579 -0.0416816,0.155394,-0.140548,0.435225,0.555428 0.0420512,0.0174365,0.0196966,0.0621699,0.0417247
+321 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45422160 5.17947e-06 118.784 92511 5 5.27587e-06 0.0896261 0.0761234,0.104334 0.416364,0.160948,-0.127342,0.517599,-0.0823556 0.0624841,0.017559,0.0204229,0.0279923,0.030751
+322 chr19_45406538,chr19_45408628,chr19_45411941,chr19_45412040,chr19_45412079 5.17229e-06 118.783 92639.6 5 5.26855e-06 0.0894122 0.0752397,0.103663 0.155633,-0.0835381,-0.140875,0.441662,0.538191 0.0174337,0.0562445,0.019693,0.0623233,0.0294436
+323 chr19_45406538,chr19_45408077,chr19_45411941,chr19_45412040,chr19_45412079 5.13403e-06 118.78 93329.8 5 5.22958e-06 0.0895525 0.0753588,0.104512 0.157035,0.0738997,-0.137354,0.368292,0.526923 0.0174565,0.0478464,0.0198298,0.0757799,0.0280087
+324 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416741 5.12712e-06 118.78 93455.6 5 5.22254e-06 0.0895007 0.0771421,0.103934 0.156588,0.447418,-0.156546,0.509025,0.0250362 0.0174536,0.0626966,0.0242913,0.0312987,0.0227274
+325 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431266 5.09795e-06 118.777 93990.4 5 5.19283e-06 0.0895408 0.0757244,0.104656 0.156758,0.446684,-0.141197,0.523802,0.0364205 0.0174537,0.0625981,0.0196924,0.0279665,0.0285523
+326 chr19_45381917,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 5.02247e-06 118.771 95403 5 5.11594e-06 0.0895672 0.0761089,0.104745 -0.0400799,0.155898,0.436339,-0.146341,0.518664 0.0312806,0.0174348,0.0626087,0.0201442,0.0283293
+327 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45423078 4.96962e-06 118.766 96417.6 5 5.06211e-06 0.0895334 0.0767936,0.104756 0.156402,-0.140464,0.436246,0.525218,0.13742 0.0174396,0.0196954,0.0621734,0.0279661,0.0858029
+328 chr19_45405740,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.9499e-06 118.764 96801.6 5 5.04203e-06 0.0895323 0.076176,0.104366 0.18602,0.155487,-0.139172,0.437638,0.526288 0.116364,0.0174344,0.019724,0.0621894,0.0279854
+329 chr19_45385994,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.90535e-06 118.76 97680.8 5 4.99665e-06 0.0895446 0.0752636,0.104591 0.146057,0.155731,0.44178,-0.139489,0.524646 0.115747,0.017434,0.0624726,0.0197232,0.0279619
+330 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431542 4.87783e-06 118.758 98231.8 5 4.96861e-06 0.089775 0.075976,0.104693 0.155611,-0.140859,0.427608,0.49106,0.078053 0.0174353,0.019695,0.0623403,0.0344723,0.0469145
+331 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435494 4.84399e-06 118.755 98918.2 5 4.93414e-06 0.0895449 0.0760971,0.105375 0.156142,0.442168,-0.141527,0.522694,0.128165 0.0174376,0.0624741,0.0196975,0.027998,0.102034
+332 chr19_45390333,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.84307e-06 118.755 98936.9 5 4.93321e-06 0.0895848 0.0761122,0.104506 0.024811,0.155579,0.42551,-0.152249,0.508713 0.0206086,0.017435,0.0639018,0.0218443,0.0308981
+333 chr19_45382092,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.843e-06 118.755 98938.4 5 4.93313e-06 0.0895024 0.0754001,0.1035 -0.0648781,0.156688,0.43852,-0.143115,0.526603 0.0537351,0.0174533,0.0625249,0.019776,0.0280164
+334 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435776 4.84204e-06 118.755 98958.1 5 4.93215e-06 0.0895179 0.0762013,0.104427 0.156029,-0.138408,0.439483,0.52681,0.127281 0.0174353,0.0197581,0.062231,0.0280017,0.0806858
+335 chr19_45393144,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 4.83383e-06 118.754 99126 5 4.92379e-06 0.0895765 0.0757458,0.103722 -0.189037,0.413654,0.15609,-0.145289,0.517561 0.0713261,0.062498,0.0174359,0.0197327,0.0279941
+336 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45429543 4.8252e-06 118.753 99303.5 5 4.915e-06 0.0895411 0.0753986,0.103479 0.157351,0.437017,-0.145124,0.520101,-0.0510498 0.0174852,0.062583,0.0199835,0.0281847,0.0411601
+337 chr19_45398014,chr19_45403192,chr19_45406538,chr19_45411941,chr19_45412079 4.82025e-06 118.753 99405.4 5 4.90996e-06 0.08958 0.0762991,0.104651 0.416384,-0.189605,0.155917,-0.139335,0.520205 0.0624839,0.0715054,0.017435,0.019713,0.0279536
+338 chr19_45402860,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.81422e-06 118.752 99529.8 5 4.90382e-06 0.0895405 0.0750251,0.104194 -0.137635,0.155513,0.440489,-0.141801,0.5236 0.11019,0.0174346,0.0624792,0.0197044,0.0279706
+339 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421577 4.79836e-06 118.751 99858.9 5 4.88766e-06 0.0895099 0.0752704,0.104672 0.155911,0.441111,-0.145091,0.524181,0.12137 0.0174349,0.062474,0.0199937,0.0279628,0.100461
+340 chr19_45406538,chr19_45408659,chr19_45411941,chr19_45412040,chr19_45412079 4.78908e-06 118.75 100052 5 4.87821e-06 0.089548 0.0765886,0.104388 0.155144,-0.172247,-0.142058,0.434635,0.522577 0.0174374,0.107735,0.0197062,0.0621708,0.0279875
+341 chr19_45400725,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.78709e-06 118.75 100094 5 4.87618e-06 0.0895209 0.0757228,0.103623 -0.0468799,0.15943,0.441079,-0.13124,0.524418 0.0399156,0.0177224,0.0624756,0.0213445,0.0279623
+342 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414399 4.78025e-06 118.749 100237 5 4.86922e-06 0.0893241 0.0745325,0.103371 0.156876,0.442231,-0.136704,0.541785,-0.0194521 0.0175208,0.0624799,0.0206287,0.037795,0.02889
+343 chr19_45403216,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.77689e-06 118.749 100308 5 4.86579e-06 0.0895201 0.0742067,0.103647 -0.0469762,0.15942,0.441103,-0.13135,0.524451 0.0400324,0.0177214,0.0624754,0.0213112,0.0279623
+344 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433675 4.76408e-06 118.748 100577 5 4.85274e-06 0.0895399 0.0752681,0.105138 0.156136,0.442196,-0.141472,0.522737,0.125872 0.0174377,0.0624745,0.0196966,0.0279971,0.101271
+345 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45439261 4.76175e-06 118.747 100627 5 4.85037e-06 0.0895596 0.0760201,0.104164 0.155735,-0.141805,0.431063,0.521297,0.0600389 0.0174342,0.0197012,0.0622219,0.0280324,0.0374097
+346 chr19_45381420,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.75433e-06 118.747 100784 5 4.84282e-06 0.0895399 0.0755871,0.10358 0.0744504,0.15609,0.444788,-0.138957,0.523538 0.0600312,0.017437,0.0625252,0.019754,0.0279722
+347 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439498 4.67966e-06 118.74 102392 5 4.76675e-06 0.0895454 0.0753366,0.103431 0.155288,0.440013,-0.14179,0.522294,-0.106806 0.0174373,0.0624865,0.0197044,0.0280178,0.0861999
+348 chr19_45388582,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.66741e-06 118.739 102661 5 4.75427e-06 0.0895337 0.0757678,0.104709 -0.0671609,0.15494,0.444101,-0.137304,0.525214 0.054996,0.0174451,0.0625063,0.0199113,0.0279683
+349 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421972 4.66499e-06 118.739 102714 5 4.75181e-06 0.0895287 0.0756942,0.105391 0.157484,0.434501,-0.148213,0.51733,-0.0311166 0.0174992,0.0627599,0.0206286,0.0286024,0.0261424
+350 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431795 4.65362e-06 118.737 102965 5 4.74023e-06 0.089515 0.0761487,0.103582 0.156724,0.446368,-0.14117,0.523754,0.0344261 0.017455,0.0625974,0.0196926,0.0279684,0.0285847
+351 chr19_45384063,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.63151e-06 118.735 103456 5 4.71771e-06 0.0894879 0.074917,0.103608 -0.0961134,0.159572,-0.1425,0.433698,0.524527 0.0627144,0.0176172,0.0197202,0.0621771,0.0279625
+352 chr19_45399001,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.59833e-06 118.732 104203 5 4.68391e-06 0.0894985 0.0756237,0.104313 0.105865,0.155952,0.441703,-0.14402,0.524751 0.0899248,0.0174355,0.062473,0.0198683,0.0279627
+353 chr19_45400775,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.57706e-06 118.73 104687 5 4.66224e-06 0.0893909 0.074976,0.104116 0.0263116,0.157358,0.442071,-0.153071,0.52408 0.0282893,0.0175255,0.0624765,0.0236232,0.0279658
+354 chr19_45387034,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.57475e-06 118.73 104740 5 4.65989e-06 0.0895692 0.0763532,0.10512 0.0239531,0.155213,0.432159,-0.150454,0.51026 0.020338,0.0174396,0.0629933,0.0213058,0.0304745
+355 chr19_45389596,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.57114e-06 118.73 104822 5 4.65622e-06 0.0893859 0.0761021,0.104942 -0.0243014,0.157564,0.439509,-0.141631,0.535929 0.0251361,0.0175402,0.0625108,0.0197058,0.0303468
+356 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433123 4.5605e-06 118.729 105067 5 4.64538e-06 0.0894958 0.075588,0.104096 0.156841,-0.140366,0.441388,0.525388,0.0408665 0.0174503,0.019697,0.0623037,0.027969,0.0265962
+357 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45434366 4.55465e-06 118.728 105202 5 4.63942e-06 0.0895077 0.0754008,0.104311 0.156716,0.446211,-0.141262,0.523839,0.0335114 0.0174554,0.0625932,0.0196938,0.0279672,0.0282631
+358 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426789 4.55386e-06 118.728 105220 5 4.63861e-06 0.0895128 0.0755948,0.103044 0.156159,0.442528,-0.139548,0.525268,0.112151 0.0174385,0.0624778,0.0197244,0.0279696,0.0940967
+359 chr19_45387057,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.55385e-06 118.728 105221 5 4.6386e-06 0.0895675 0.0764568,0.104585 0.0238678,0.155214,0.432191,-0.150423,0.510312 0.020338,0.0174396,0.0629935,0.0213068,0.030474
+360 chr19_45406538,chr19_45408475,chr19_45412040,chr19_45412079,chr19_45427780 4.51756e-06 118.725 106066 5 4.60164e-06 0.089341 0.0768475,0.104603 0.148418,-0.124948,0.472186,0.546193,0.490851 0.0174715,0.0248096,0.06186,0.027646,0.097158
+361 chr19_45381443,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.50372e-06 118.723 106392 5 4.58754e-06 0.0895152 0.0764998,0.103364 0.124872,0.156059,0.442677,-0.141077,0.523897 0.104969,0.0174369,0.0624796,0.019692,0.0279663
+362 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45437608 4.48009e-06 118.721 106953 5 4.56347e-06 0.0894959 0.0755507,0.103417 0.156005,-0.138444,0.439345,0.526707,0.12351 0.0174354,0.0197606,0.0622317,0.028001,0.0807735
+363 chr19_45396276,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.47829e-06 118.721 106996 5 4.56163e-06 0.089499 0.075552,0.10439 -0.133464,0.159626,-0.142975,0.43204,0.521946 0.0873498,0.0176237,0.0197392,0.0622035,0.0280115
+364 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45415640 4.47372e-06 118.72 107105 5 4.55698e-06 0.089303 0.0755074,0.103366 0.15572,0.442306,-0.131228,0.536745,-0.0138752 0.0174355,0.0624924,0.028026,0.037685,0.028871
+365 chr19_45406538,chr19_45408475,chr19_45410273,chr19_45411941,chr19_45412079 4.47204e-06 118.72 107145 5 4.55527e-06 0.0895156 0.0764198,0.104194 0.15373,-0.0327003,0.442656,-0.124914,0.52438 0.0175384,0.0315199,0.0624852,0.0250364,0.0279637
+366 chr19_45385190,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.46017e-06 118.719 107431 5 4.54318e-06 0.089502 0.0756571,0.104328 0.08998,0.155539,0.44372,-0.139242,0.526171 0.0769982,0.0174347,0.0624993,0.0197429,0.0279992
+367 chr19_45396318,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 4.39432e-06 118.713 109040 5 4.4761e-06 0.0895011 0.0747207,0.105394 0.138039,0.418594,0.157345,-0.136672,0.525894 0.053284,0.0624856,0.0174509,0.0197916,0.0279894
+368 chr19_45396144,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 4.39384e-06 118.713 109052 5 4.47561e-06 0.0894823 0.0741881,0.10481 0.0691906,0.423982,0.157189,-0.146285,0.522015 0.0268017,0.0625373,0.0174484,0.0197648,0.0279466
+369 chr19_45399896,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.39143e-06 118.712 109112 5 4.47316e-06 0.0894947 0.0755203,0.104061 -0.043764,0.15916,0.44108,-0.131886,0.524411 0.0398058,0.0177176,0.0624767,0.0213349,0.0279627
+370 chr19_45406538,chr19_45408549,chr19_45410273,chr19_45411941,chr19_45412079 4.38892e-06 118.712 109175 5 4.4706e-06 0.0895202 0.0750972,0.104553 0.155249,-0.131355,0.440358,-0.142063,0.522818 0.0174384,0.111265,0.0624828,0.0197161,0.0279978
+371 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45424989 4.32304e-06 118.705 110838 5 4.4035e-06 0.0895013 0.076331,0.104427 0.156094,0.443184,-0.141814,0.523532,0.0862717 0.0174378,0.0624885,0.0197074,0.0279744,0.0748432
+372 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45424351 4.31572e-06 118.705 111026 5 4.39604e-06 0.0894486 0.0746913,0.10499 0.156796,-0.144653,0.439428,0.526533,0.055643 0.0174505,0.0198561,0.0622396,0.0279981,0.0378802
+373 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45413760,chr19_45427780 4.2858e-06 118.702 111801 5 4.36556e-06 0.0893163 0.074887,0.104814 0.149463,0.476039,0.546987,-0.121593,0.490129 0.0174601,0.0622014,0.0276356,0.0246935,0.0971695
+374 chr19_45399344,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.27891e-06 118.701 111982 5 4.35854e-06 0.0894869 0.0754268,0.103726 -0.0428154,0.159076,0.441081,-0.132106,0.524408 0.0397886,0.017716,0.062477,0.0213248,0.0279629
+375 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45430882 4.27722e-06 118.701 112026 5 4.35683e-06 0.0895157 0.0765405,0.104655 0.155193,0.439276,-0.141789,0.522836,-0.104429 0.0174398,0.0625065,0.0197064,0.0279983,0.0898522
+376 chr19_45384332,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 4.25899e-06 118.699 112505 5 4.33826e-06 0.0895013 0.0758495,0.103468 -0.0281862,0.155886,-0.149665,0.431166,0.517699 0.0188349,0.0174351,0.0205423,0.0622282,0.0283273
+377 chr19_45406538,chr19_45408564,chr19_45411941,chr19_45412040,chr19_45412079 4.25579e-06 118.699 112590 5 4.33499e-06 0.0896124 0.075969,0.105544 0.153921,-0.0300042,-0.132785,0.4257,0.516542 0.0174713,0.0190967,0.0203672,0.0624625,0.0284164
+378 chr19_45384843,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.19697e-06 118.693 114168 5 4.27507e-06 0.0895043 0.0751806,0.104848 -0.116497,0.155153,0.439726,-0.14154,0.52362 0.102154,0.0174409,0.0624961,0.0196997,0.0279726
+379 chr19_45383115,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.19563e-06 118.692 114204 5 4.27371e-06 0.0894758 0.0755889,0.102908 -0.028446,0.155874,0.437332,-0.141758,0.526457 0.0257327,0.0174351,0.0625939,0.0197068,0.028019
+380 chr19_45382156,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.19512e-06 118.692 114218 5 4.27319e-06 0.0895077 0.0753633,0.103248 0.129105,0.155654,0.440652,-0.140176,0.523459 0.112884,0.0174345,0.0624798,0.0197021,0.0279767
+381 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45438729 4.19043e-06 118.692 114346 5 4.26842e-06 0.0894796 0.0756314,0.10519 0.155674,0.441646,-0.140824,0.526079,0.0755192 0.0174344,0.0624739,0.0196918,0.027999,0.0679472
+382 chr19_45397952,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.16456e-06 118.689 115056 5 4.24207e-06 0.0894788 0.0758258,0.103965 -0.0418296,0.158976,0.44109,-0.132371,0.524412 0.0397722,0.0177121,0.0624773,0.0213011,0.027963
+383 chr19_45406538,chr19_45411941,chr19_45412079,chr19_45425212,chr19_45435661 4.16203e-06 118.689 115126 5 4.23949e-06 0.0894096 0.0745992,0.1038 0.159465,-0.149915,0.52519,0.496862,0.287723 0.0174495,0.019593,0.0279672,0.109482,0.0508576
+384 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45418743 4.13984e-06 118.687 115743 5 4.21688e-06 0.0894769 0.076375,0.103927 0.418654,0.162092,-0.142534,0.519596,-0.170473 0.0624865,0.0176317,0.0196922,0.0279612,0.0665018
+385 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435986 4.07179e-06 118.679 117678 5 4.14757e-06 0.089497 0.0750427,0.103265 0.155249,-0.143187,0.433722,0.52225,-0.0921122 0.017437,0.0197522,0.0621789,0.0280028,0.0617339
+386 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45419665 4.06237e-06 118.678 117950 5 4.13798e-06 0.0894863 0.0755269,0.104243 0.156044,-0.141571,0.437633,0.523171,0.138201 0.0174362,0.0196988,0.0621947,0.0279769,0.0931808
+387 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435764 4.05689e-06 118.678 118110 5 4.13239e-06 0.089472 0.0763744,0.104383 0.156002,0.444331,-0.139332,0.525892,0.0663001 0.0174368,0.0625245,0.0197453,0.0279925,0.0612031
+388 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45428964 4.05537e-06 118.678 118154 5 4.13085e-06 0.089499 0.0760719,0.103525 0.155223,0.439381,-0.141784,0.522907,-0.101247 0.0174397,0.0625067,0.0197077,0.0279983,0.0907963
+389 chr19_45386229,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 4.04655e-06 118.677 118412 5 4.12186e-06 0.0894144 0.0763765,0.102069 -0.0717727,0.15588,0.467404,-0.141491,0.524318 0.0750491,0.0174357,0.0680332,0.0197016,0.0279632
+390 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45415950 4.0457e-06 118.677 118436 5 4.121e-06 0.0894996 0.0755371,0.104529 0.155098,0.43982,-0.142706,0.522132,-0.0722014 0.0174428,0.0624955,0.0197585,0.0280425,0.0648979
+391 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433126 4.01817e-06 118.674 119248 5 4.09295e-06 0.0894485 0.0739654,0.103755 0.156745,-0.13882,0.440241,0.527486,0.0636059 0.0174499,0.0197481,0.0622725,0.0280413,0.0440868
+392 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420145 3.99062e-06 118.671 120071 5 4.06489e-06 0.0894932 0.0754289,0.10449 0.155254,0.439438,-0.141883,0.522926,-0.100918 0.0174392,0.0625061,0.0197121,0.0279985,0.0917754
+393 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436657 3.96393e-06 118.668 120880 5 4.0377e-06 0.0894602 0.0756617,0.103727 0.156537,-0.145956,0.431087,0.52295,-0.0369468 0.0174445,0.0199995,0.0622348,0.0279831,0.0255841
+394 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427141 3.91861e-06 118.663 122278 5 3.99154e-06 0.0894691 0.0768163,0.103204 0.156103,0.442402,-0.139723,0.525146,0.100471 0.0174388,0.0624791,0.0197234,0.0279695,0.0946504
+395 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45413760 3.90239e-06 118.661 122786 5 3.97502e-06 0.08946 0.0758902,0.102765 0.154233,0.442155,-0.127161,0.524545,-0.0275792 0.0175167,0.0624815,0.0252652,0.0279644,0.0316499
+396 chr19_45391454,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.89057e-06 118.66 123159 5 3.96298e-06 0.0893822 0.0753563,0.104108 0.108597,0.419603,0.156522,-0.158247,0.521164 0.0436226,0.0624918,0.0174406,0.0207619,0.0279487
+397 chr19_45383037,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.88168e-06 118.659 123441 5 3.95392e-06 0.0894271 0.0755348,0.10413 0.0675492,0.417041,0.155845,-0.143439,0.527897 0.0267586,0.0624846,0.0174352,0.0197005,0.0280482
+398 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440742 3.87448e-06 118.658 123670 5 3.94659e-06 0.0894733 0.0748322,0.10567 0.155441,0.440446,-0.140555,0.5247,-0.127573 0.0174363,0.0624846,0.0196948,0.0279634,0.120246
+399 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45423321 3.87073e-06 118.657 123790 5 3.94277e-06 0.0894761 0.0760636,0.104969 0.155356,0.439213,-0.142321,0.523521,-0.0949787 0.0174376,0.0625159,0.0197372,0.0279774,0.0894051
+400 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433127 3.86457e-06 118.657 123988 5 3.93649e-06 0.0894564 0.0759705,0.103405 0.156169,0.442877,-0.140941,0.525379,0.0738056 0.0174406,0.0624866,0.019692,0.0279759,0.0710809
+401 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45419920 3.85044e-06 118.655 124442 5 3.9221e-06 0.0894639 0.0751463,0.104203 0.155826,0.442758,-0.139749,0.525288,0.0858841 0.0174351,0.0624844,0.0197231,0.0279732,0.0821773
+402 chr19_45402104,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.83547e-06 118.653 124928 5 3.90685e-06 0.0894354 0.074929,0.104792 -0.124763,0.155164,-0.141676,0.435625,0.527828 0.0883585,0.0174386,0.0197012,0.062173,0.0280645
+403 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45438554 3.81841e-06 118.652 125486 5 3.88948e-06 0.0895007 0.0764352,0.104167 0.154896,0.444092,-0.139574,0.521119,0.0198229 0.0174506,0.0625179,0.019731,0.0281373,0.0184064
+404 chr19_45402516,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.81648e-06 118.651 125550 5 3.88751e-06 0.0894609 0.0761334,0.104198 0.0659856,0.156325,0.440946,-0.139116,0.525481 0.0637846,0.0174453,0.0624784,0.0197678,0.0279793
+405 chr19_45382433,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.81407e-06 118.651 125629 5 3.88506e-06 0.0894723 0.075327,0.103301 -0.0541485,0.155553,-0.138388,0.434204,0.525196 0.0375149,0.017435,0.0197728,0.0621757,0.0279681
+406 chr19_45396973,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.81304e-06 118.651 125663 5 3.884e-06 0.0894522 0.0745602,0.103839 -0.0382408,0.158653,0.441095,-0.133081,0.524388 0.0396149,0.0177027,0.0624785,0.0212982,0.0279635
+407 chr19_45398319,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.8112e-06 118.651 125724 5 3.88213e-06 0.0894504 0.0752611,0.105349 0.104251,0.155687,0.441477,-0.143712,0.524278 0.10245,0.0174347,0.0624752,0.0198843,0.0279635
+408 chr19_45382075,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.81034e-06 118.651 125752 5 3.88126e-06 0.089513 0.0754015,0.103712 0.117409,0.155799,0.442619,-0.140841,0.518188 0.108549,0.0174351,0.0624826,0.0196924,0.0285646
+409 chr19_45398785,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.80855e-06 118.65 125811 5 3.87943e-06 0.0894504 0.0752452,0.103439 -0.0386315,0.158755,0.441173,-0.133073,0.524391 0.040193,0.0177239,0.0624779,0.0213144,0.0279635
+410 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45441453 3.8053e-06 118.65 125919 5 3.87612e-06 0.089466 0.0757629,0.103996 0.156006,0.426387,-0.14115,0.525363,0.0526894 0.0174374,0.0642194,0.0196937,0.027976,0.0514019
+411 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45434881 3.80325e-06 118.65 125987 5 3.87403e-06 0.0893895 0.0764731,0.104618 0.15543,0.442753,-0.140842,0.531928,-0.0762725 0.0174371,0.0624874,0.0196921,0.0291447,0.0844026
+412 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45413946 3.79654e-06 118.649 126209 5 3.86719e-06 0.0894818 0.0747808,0.10454 0.155763,0.443519,-0.139374,0.522781,0.066093 0.0174349,0.0625013,0.0197455,0.0280094,0.0625959
+413 chr19_45386634,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.79549e-06 118.649 126244 5 3.86613e-06 0.0894248 0.0756038,0.104123 -0.0561801,0.155697,0.461148,-0.139712,0.524436 0.0588017,0.0174348,0.0657317,0.0197316,0.0279629
+414 chr19_45384338,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.7735e-06 118.646 126980 5 3.84373e-06 0.0894645 0.0749094,0.10454 -0.0266539,0.155829,-0.149232,0.431401,0.518063 0.0188563,0.0174352,0.020553,0.062229,0.0283291
+415 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45423934 3.77225e-06 118.646 127022 5 3.84246e-06 0.0894523 0.0757586,0.105591 0.154385,0.44169,-0.128076,0.524418,-0.0257169 0.0175044,0.0624785,0.0248702,0.0279644,0.0303417
+416 chr19_45386046,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.75849e-06 118.645 127487 5 3.82844e-06 0.0894027 0.0746078,0.103518 -0.0646097,0.155869,0.463974,-0.141629,0.524094 0.0717629,0.0174359,0.0672203,0.0197087,0.0279664
+417 chr19_45406538,chr19_45410273,chr19_45411677,chr19_45411941,chr19_45412079 3.74794e-06 118.643 127846 5 3.81769e-06 0.0894764 0.0753485,0.103295 0.155098,0.439734,-0.0655547,-0.142767,0.522136 0.0174442,0.0625016,0.0629517,0.0197734,0.0280539
+418 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45430280 3.73444e-06 118.642 128308 5 3.80394e-06 0.0894619 0.0749166,0.104502 0.155294,0.443733,-0.14631,0.519249,-0.0183445 0.0174396,0.062512,0.0204269,0.0284534,0.018406
+419 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435466 3.73207e-06 118.642 128390 5 3.80153e-06 0.0894455 0.0748182,0.103757 0.156165,0.438634,-0.14413,0.523842,-0.0248954 0.0174411,0.0625469,0.0199563,0.0279703,0.0249836
+420 chr19_45406538,chr19_45406935,chr19_45410273,chr19_45411941,chr19_45412079 3.7272e-06 118.641 128557 5 3.79657e-06 0.0894709 0.0751276,0.103803 0.155436,-0.0859921,0.439255,-0.142116,0.522894 0.0174366,0.0831858,0.0625171,0.0197271,0.0280052
+421 chr19_45402861,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.70277e-06 118.638 129406 5 3.77168e-06 0.0894612 0.0758493,0.1031 -0.10956,0.155735,0.440693,-0.141495,0.523755 0.107457,0.0174348,0.0624819,0.0197007,0.027972
+422 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436753 3.69328e-06 118.637 129738 5 3.76202e-06 0.0894445 0.0758275,0.10436 0.1569,-0.141378,0.440413,0.523649,0.0397106 0.0174561,0.0196969,0.0622864,0.0279697,0.0282609
+423 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421453 3.68668e-06 118.636 129970 5 3.75529e-06 0.089451 0.0754479,0.104469 0.155977,0.43986,-0.140043,0.525475,0.100911 0.0174371,0.0624998,0.0197109,0.0279803,0.100583
+424 chr19_45383139,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.68221e-06 118.636 130128 5 3.75074e-06 0.0894392 0.0752651,0.106212 -0.0252702,0.15584,0.437769,-0.14164,0.526238 0.025653,0.0174354,0.0625971,0.0197063,0.0280194
+425 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427727 3.6812e-06 118.636 130164 5 3.74971e-06 0.0894809 0.0749067,0.104652 0.154859,-0.14291,0.432432,0.521282,-0.089182 0.0174445,0.0197429,0.0622013,0.0280519,0.0621378
+426 chr19_45404579,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.68067e-06 118.636 130182 5 3.74917e-06 0.0893261 0.0756052,0.102811 0.0187724,0.156819,0.441386,-0.15011,0.52453 0.0314473,0.0175358,0.0624786,0.0249682,0.0279641
+427 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432135 3.67367e-06 118.635 130430 5 3.74204e-06 0.08945 0.075678,0.103956 0.155641,-0.14282,0.430941,0.524519,-0.0601309 0.0174348,0.0197401,0.0622437,0.0279637,0.0427051
+428 chr19_45398014,chr19_45403048,chr19_45406538,chr19_45411941,chr19_45412079 3.66943e-06 118.634 130581 5 3.73772e-06 0.0894558 0.0749278,0.103142 0.417209,0.165749,0.156247,-0.138149,0.525346 0.0624855,0.0656167,0.017438,0.0197454,0.027981
+429 chr19_45401887,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.66884e-06 118.634 130602 5 3.73712e-06 0.0894467 0.0754891,0.103626 0.105717,0.155835,0.443577,-0.139967,0.525352 0.106256,0.0174354,0.0625065,0.0197147,0.0279765
+430 chr19_45404926,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.6676e-06 118.634 130646 5 3.73586e-06 0.089475 0.0746589,0.104208 -0.135468,0.155022,-0.136862,0.435181,0.524368 0.0950092,0.0174412,0.0199001,0.062173,0.0279639
+431 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45423678 3.65525e-06 118.633 131088 5 3.72328e-06 0.089466 0.0762021,0.103822 0.155284,0.439592,-0.141818,0.523045,-0.0933301 0.0174395,0.0625073,0.0197127,0.0279989,0.091814
+432 chr19_45381306,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.60941e-06 118.627 132753 5 3.67658e-06 0.0894407 0.0750251,0.104539 0.0452158,0.155329,0.438079,-0.141773,0.525669 0.0463465,0.0174389,0.0625806,0.0197123,0.0279893
+433 chr19_45383091,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.58304e-06 118.624 133729 5 3.64973e-06 0.0894312 0.0762739,0.103898 -0.0246969,0.155848,0.437926,-0.141623,0.526207 0.0257937,0.0174356,0.0625942,0.0197065,0.0280208
+434 chr19_45382984,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.55846e-06 118.621 134653 5 3.62468e-06 0.0894024 0.0756256,0.104582 0.0664725,0.417169,0.155816,-0.14336,0.527819 0.0266872,0.0624854,0.0174353,0.0197001,0.0280487
+435 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439605 3.55358e-06 118.62 134838 5 3.61971e-06 0.0894568 0.0755777,0.103901 0.155172,0.443745,-0.142375,0.522057,-0.0289093 0.017443,0.0625134,0.019749,0.0280718,0.0293579
+436 chr19_45402470,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.55277e-06 118.62 134869 5 3.61889e-06 0.08932 0.0763015,0.105357 0.0172124,0.156751,0.441358,-0.149328,0.524519 0.0316303,0.017542,0.0624795,0.0249994,0.0279643
+437 chr19_45401868,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.54137e-06 118.619 135303 5 3.60728e-06 0.0893973 0.0752469,0.103429 -0.0296825,0.157937,0.458728,-0.134795,0.524089 0.0359893,0.0176459,0.0658321,0.0210399,0.0279677
+438 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421100 3.53025e-06 118.617 135730 5 3.59595e-06 0.0894401 0.074105,0.104563 0.156782,0.436535,-0.147013,0.51873,-0.022802 0.0174754,0.0627217,0.0207866,0.0286578,0.0248113
+439 chr19_45404993,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.51825e-06 118.616 136192 5 3.58373e-06 0.0894276 0.0754433,0.104576 0.0655568,0.155996,0.442886,-0.142979,0.524252 0.0697401,0.0174379,0.0624904,0.0198155,0.0279642
+440 chr19_45394439,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.51824e-06 118.616 136193 5 3.58372e-06 0.0894332 0.0750062,0.103429 0.0624103,0.155684,0.443908,-0.139158,0.52584 0.0657798,0.0174349,0.0625226,0.0197782,0.0279996
+441 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427648 3.51723e-06 118.616 136232 5 3.58269e-06 0.089431 0.0754073,0.104744 0.159008,-0.142697,0.431999,0.522213,-0.117271 0.0176033,0.0197369,0.0622152,0.0280125,0.0857703
+442 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418446 3.49325e-06 118.613 137167 5 3.55826e-06 0.0894408 0.0752752,0.103896 0.155701,0.440252,-0.142053,0.523846,-0.0853452 0.017435,0.0624922,0.0197289,0.0279711,0.0892094
+443 chr19_45396899,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.49182e-06 118.613 137223 5 3.55681e-06 0.0893926 0.0758029,0.104117 -0.0225226,0.156222,0.446302,-0.135929,0.529389 0.026494,0.017446,0.0627183,0.0205418,0.0285507
+444 chr19_45397965,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.48179e-06 118.611 137618 5 3.54659e-06 0.089417 0.0749732,0.104777 0.0380072,0.156062,0.444571,-0.137939,0.527183 0.041697,0.0174396,0.06256,0.0199591,0.0281194
+445 chr19_45403119,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.4652e-06 118.609 138277 5 3.52969e-06 0.0893162 0.0761021,0.103488 0.0159255,0.156661,0.441416,-0.148737,0.52454 0.0314512,0.0175385,0.0624793,0.0249893,0.0279646
+446 chr19_45381305,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.45309e-06 118.608 138762 5 3.51735e-06 0.0894282 0.0755909,0.104503 0.0431865,0.15534,0.438237,-0.141688,0.525617 0.0463996,0.0174391,0.0625814,0.0197103,0.0279897
+447 chr19_45394592,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.44883e-06 118.607 138933 5 3.51302e-06 0.0894326 0.0758403,0.10419 0.132606,0.156155,-0.139162,0.436068,0.525211 0.0971695,0.0174384,0.019737,0.0621772,0.0279693
+448 chr19_45402103,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.44741e-06 118.607 138991 5 3.51157e-06 0.0894164 0.0764383,0.103434 -0.0820367,0.155379,0.441288,-0.141718,0.526356 0.0899254,0.0174384,0.062477,0.0197125,0.0280383
+449 chr19_45396434,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.43345e-06 118.605 139556 5 3.49735e-06 0.0894317 0.0759037,0.104564 0.13402,0.156145,-0.139139,0.436036,0.525192 0.0984155,0.0174382,0.0197383,0.062177,0.0279691
+450 chr19_45406538,chr19_45409283,chr19_45410273,chr19_45411941,chr19_45412079 3.42989e-06 118.605 139701 5 3.49372e-06 0.0894354 0.074592,0.10317 0.155701,-0.0837857,0.440268,-0.142043,0.523844 0.017435,0.089358,0.0624927,0.0197298,0.0279716
+451 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45433148 3.42675e-06 118.605 139829 5 3.49052e-06 0.0894293 0.0758932,0.104322 0.421312,0.156149,-0.139238,0.524959,0.209467 0.062507,0.0174375,0.0197189,0.0279749,0.0839113
+452 chr19_45392093,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.41221e-06 118.603 140425 5 3.47571e-06 0.08947 0.0762394,0.10463 0.0719859,0.156025,-0.139256,0.437218,0.520227 0.0517859,0.0174369,0.0197315,0.0621922,0.0281303
+453 chr19_45404805,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.40036e-06 118.601 140914 5 3.46365e-06 0.0894119 0.0751917,0.104042 -0.0319115,0.158232,0.440977,-0.133908,0.524815 0.0385934,0.0177038,0.0624822,0.0214371,0.0279668
+454 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45434751 3.39465e-06 118.6 141151 5 3.45782e-06 0.0894354 0.0758024,0.104062 0.154898,0.441823,-0.139924,0.524987,0.0512583 0.0174561,0.0624767,0.0197207,0.0279685,0.0550903
+455 chr19_45406538,chr19_45407118,chr19_45410273,chr19_45411941,chr19_45412079 3.38997e-06 118.6 141346 5 3.45306e-06 0.0893152 0.0764728,0.104493 0.156639,0.0151477,0.441452,-0.148269,0.524624 0.01754,0.0308389,0.0624791,0.0246995,0.027966
+456 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45430417 3.38853e-06 118.6 141406 5 3.4516e-06 0.0894373 0.0754682,0.104205 0.157717,-0.144915,0.431682,0.519754,-0.0551775 0.0174995,0.019914,0.0622259,0.0281803,0.0407619
+457 chr19_45401159,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.37527e-06 118.598 141962 5 3.43808e-06 0.0894253 0.0766004,0.103449 0.0933962,0.156386,-0.138872,0.434774,0.525469 0.0693076,0.0174428,0.0197536,0.0621741,0.027974
+458 chr19_45386654,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.34506e-06 118.594 143243 5 3.40732e-06 0.0894233 0.0753737,0.104591 0.13223,0.156133,-0.139134,0.436208,0.525239 0.0985186,0.0174382,0.01974,0.0621789,0.02797
+459 chr19_45387713,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.33298e-06 118.593 143763 5 3.39501e-06 0.0894225 0.0755182,0.104686 0.137207,0.156098,-0.139081,0.43673,0.525174 0.102417,0.0174378,0.0197429,0.0621852,0.0279691
+460 chr19_45397343,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.32583e-06 118.592 144072 5 3.38773e-06 0.0893541 0.0751618,0.103316 0.120639,0.417535,0.157171,-0.155089,0.521623 0.0494289,0.0624864,0.0174507,0.0204237,0.0279481
+461 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45424237 3.32494e-06 118.591 144111 5 3.38682e-06 0.0894484 0.0757118,0.104635 0.154929,-0.142821,0.432741,0.521502,-0.0850784 0.0174441,0.0197438,0.0621987,0.028049,0.0624886
+462 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426550 3.30529e-06 118.589 144967 5 3.3668e-06 0.089406 0.0757035,0.104648 0.155688,0.442276,-0.142951,0.524909,0.0887149 0.0174351,0.0624811,0.0198335,0.0279677,0.102387
+463 chr19_45382305,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.29337e-06 118.587 145492 5 3.35466e-06 0.0894308 0.0756973,0.103113 -0.0594431,0.155468,-0.136787,0.435327,0.525395 0.0444416,0.017436,0.0199368,0.0621742,0.0279729
+464 chr19_45406538,chr19_45407437,chr19_45410273,chr19_45411941,chr19_45412079 3.26683e-06 118.584 146674 5 3.32763e-06 0.0893074 0.075093,0.103801 0.156458,0.0125752,0.441425,-0.147124,0.524511 0.0175398,0.0316577,0.0624805,0.0250893,0.0279648
+465 chr19_45389174,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.26194e-06 118.583 146894 5 3.32265e-06 0.0893226 0.0751252,0.103396 -0.0116268,0.156084,0.450743,-0.137007,0.531698 0.0205548,0.0174488,0.0644987,0.020846,0.0306985
+466 chr19_45406538,chr19_45407251,chr19_45411941,chr19_45412040,chr19_45412079 3.24029e-06 118.58 147875 5 3.30059e-06 0.0894139 0.0758942,0.104444 0.156139,0.128758,-0.139232,0.436039,0.525215 0.0174385,0.0976398,0.0197367,0.0621779,0.02797
+467 chr19_45385061,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.23883e-06 118.58 147942 5 3.2991e-06 0.0894251 0.0764,0.105177 -0.149141,0.155785,-0.142459,0.433855,0.522757 0.11219,0.0174354,0.0197285,0.0621814,0.0279939
+468 chr19_45392254,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.23755e-06 118.58 148000 5 3.29781e-06 0.0892971 0.0750138,0.104153 -0.01056,0.155652,0.451332,-0.140577,0.524485 0.067905,0.0174382,0.0876893,0.0197994,0.0279651
+469 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433002 3.22833e-06 118.579 148423 5 3.28841e-06 0.0894272 0.0752632,0.105502 0.156221,-0.143889,0.431985,0.521329,-0.0625746 0.0174399,0.019821,0.0622193,0.0280643,0.0471605
+470 chr19_45386163,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.2274e-06 118.579 148466 5 3.28747e-06 0.0894021 0.0758948,0.10418 0.0460779,0.155622,0.443501,-0.139064,0.52652 0.0546141,0.0174354,0.0625167,0.0198131,0.0280674
+471 chr19_45385102,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.2256e-06 118.578 148549 5 3.28563e-06 0.0894179 0.0759097,0.104117 -0.0232253,0.155973,0.44595,-0.14181,0.522536 0.0267704,0.0174382,0.0626765,0.0197203,0.0280536
+472 chr19_45398633,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.21407e-06 118.577 149082 5 3.27388e-06 0.0893916 0.0752359,0.105289 0.0734202,0.156446,-0.138064,0.43697,0.527499 0.0569328,0.0174451,0.0198195,0.0621904,0.0280629
+473 chr19_45390911,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.21143e-06 118.576 149204 5 3.27119e-06 0.0894562 0.0762042,0.103728 0.0984824,0.155667,0.442376,-0.140944,0.518918 0.109195,0.0174354,0.0624832,0.0196929,0.0286394
+474 chr19_45393473,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 3.20862e-06 118.576 149335 5 3.26833e-06 0.0893531 0.0750243,0.103847 0.068555,0.423596,0.156963,-0.144853,0.527156 0.028111,0.06254,0.0174472,0.0197294,0.0280312
+475 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45422946 3.18889e-06 118.573 150259 5 3.24824e-06 0.089446 0.0758622,0.104486 0.1567,-0.135961,0.430114,0.522507,-0.0284003 0.017452,0.0200467,0.062289,0.0280034,0.0213572
+476 chr19_45381292,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.15687e-06 118.569 151783 5 3.21562e-06 0.0894023 0.0758576,0.103401 0.0390496,0.155381,0.43851,-0.141565,0.525522 0.0469576,0.0174393,0.0625884,0.0197087,0.0279915
+477 chr19_45395064,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.14341e-06 118.567 152433 5 3.20191e-06 0.0894507 0.0757133,0.104709 0.0649154,0.155442,0.441625,-0.140649,0.519392 0.0737151,0.0174378,0.0624776,0.019695,0.028558
+478 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433500 3.13223e-06 118.566 152977 5 3.19052e-06 0.0893889 0.0747941,0.105308 0.158076,0.441152,-0.14076,0.524446,-0.0650756 0.0176899,0.0624798,0.0196936,0.0279637,0.0817479
+479 chr19_45388250,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.13051e-06 118.565 153061 5 3.18878e-06 0.0894159 0.0757605,0.105227 -0.081993,0.155371,0.440578,-0.141443,0.523541 0.0967025,0.0174395,0.0624891,0.019703,0.0279858
+480 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45425427 3.12598e-06 118.565 153283 5 3.18416e-06 0.0894477 0.0767956,0.103169 0.15565,0.442272,-0.141092,0.518872,0.0954532 0.0174355,0.0624823,0.0196941,0.0287026,0.109795
+481 chr19_45383583,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.11624e-06 118.563 153762 5 3.17424e-06 0.0893598 0.0751977,0.104827 -0.0535117,0.155829,0.457971,-0.141477,0.524077 0.0769836,0.0174365,0.0667508,0.0197101,0.02797
+482 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416741 3.11596e-06 118.563 153776 5 3.17395e-06 0.089353 0.0759215,0.105003 0.156582,-0.156588,0.440977,0.508986,0.0250728 0.0174551,0.0242934,0.0624035,0.0313013,0.0227312
+483 chr19_45400484,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.106e-06 118.562 154269 5 3.1638e-06 0.0893904 0.0761112,0.104275 -0.0279651,0.157841,0.440972,-0.135209,0.524291 0.0383254,0.0176827,0.0624844,0.0211874,0.0279654
+484 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431266 3.08439e-06 118.559 155349 5 3.1418e-06 0.089392 0.0757679,0.103959 0.156749,-0.141217,0.440186,0.523785,0.0363647 0.0174552,0.0196962,0.0623014,0.0279696,0.0285553
+485 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45428234 3.05823e-06 118.555 156678 5 3.11515e-06 0.0893597 0.0752805,0.105286 0.155221,0.4582,-0.142492,0.522303,-0.0213976 0.0174503,0.0673389,0.0198392,0.0281583,0.0324222
+486 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414451 3.05249e-06 118.554 156973 5 3.1093e-06 0.0893639 0.0751542,0.103358 0.156086,0.445106,-0.137556,0.528152,-0.0204619 0.017444,0.0626691,0.0202453,0.0284336,0.0287503
+487 chr19_45384031,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.0522e-06 118.554 156988 5 3.109e-06 0.0894047 0.0762452,0.104204 0.0611198,0.156099,0.439678,-0.140997,0.523813 0.075249,0.0174425,0.0625229,0.0196931,0.027976
+488 chr19_45381917,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 3.04755e-06 118.554 157227 5 3.10427e-06 0.0894191 0.075106,0.104144 -0.0400869,0.15589,-0.146368,0.42982,0.51864 0.0312845,0.0174362,0.0201485,0.0623132,0.0283328
+489 chr19_45402342,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.04589e-06 118.553 157313 5 3.10258e-06 0.0893019 0.0767221,0.104247 0.00851782,0.156204,0.441458,-0.144994,0.524538 0.0318944,0.0175377,0.062482,0.024847,0.027966
+490 chr19_45384859,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.03892e-06 118.552 157674 5 3.09548e-06 0.0894456 0.0751833,0.103957 0.0719774,0.155275,0.442551,-0.141001,0.518208 0.0861357,0.0174428,0.0624883,0.0196934,0.0289606
+491 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45414125 3.02331e-06 118.55 158488 5 3.07958e-06 0.0893999 0.0758855,0.103202 0.422107,0.156158,-0.142675,0.522102,0.149978 0.0625186,0.0174381,0.0196943,0.0279485,0.0611947
+492 chr19_45384116,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.01808e-06 118.549 158763 5 3.07425e-06 0.0894583 0.075118,0.104271 -0.0197881,0.154531,0.434819,-0.14143,0.517982 0.023667,0.0174914,0.0630093,0.0197034,0.0290333
+493 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427213 3.0118e-06 118.549 159094 5 3.06785e-06 0.0893874 0.0763018,0.104678 0.156189,0.443092,-0.14305,0.523155,0.0264796 0.0174474,0.0625073,0.019891,0.0280176,0.0345736
+494 chr19_45388208,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 3.00851e-06 118.548 159268 5 3.0645e-06 0.0893941 0.0743596,0.104354 0.0791439,0.155939,0.442047,-0.13997,0.52481 0.100953,0.0174382,0.0624799,0.0197289,0.0279669
+495 chr19_45390333,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.99378e-06 118.546 160052 5 3.04949e-06 0.0894432 0.0755141,0.104932 0.0251458,0.15557,-0.15243,0.418893,0.508476 0.0206094,0.0174364,0.0218494,0.0635947,0.0309021
+496 chr19_45385994,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.98084e-06 118.544 160746 5 3.03632e-06 0.089397 0.0746976,0.1044 0.146213,0.155723,-0.13951,0.435304,0.524627 0.115757,0.0174355,0.019727,0.0621751,0.027965
+497 chr19_45406538,chr19_45410076,chr19_45410273,chr19_45411941,chr19_45412079 2.96825e-06 118.542 161428 5 3.02349e-06 0.0893842 0.0763502,0.104075 0.155989,0.0763221,0.442439,-0.139975,0.525355 0.0174398,0.100913,0.0624871,0.0197312,0.0279876
+498 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440761 2.96487e-06 118.542 161612 5 3.02004e-06 0.0893713 0.0763116,0.103724 0.155488,0.444961,-0.13836,0.526943,-0.0140466 0.0174378,0.0626516,0.0200096,0.0281732,0.0195889
+499 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416478 2.96371e-06 118.542 161675 5 3.01887e-06 0.0893561 0.0750646,0.104237 0.156083,0.445037,-0.13774,0.527954,-0.019311 0.0174451,0.0626849,0.0202482,0.0284368,0.0287815
+500 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45437250 2.96166e-06 118.541 161787 5 3.01678e-06 0.0894503 0.0762281,0.10421 0.155978,0.441793,-0.14069,0.514802,0.0467933 0.01744,0.0624797,0.0196954,0.0307918,0.0619835
+501 chr19_45381682,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.95798e-06 118.541 161989 5 3.01303e-06 0.0893948 0.0751404,0.104727 -0.066139,0.155497,0.442999,-0.138583,0.524714 0.0866095,0.0174374,0.0625042,0.0199266,0.0279656
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+539 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431795 2.81619e-06 118.519 170144 5 2.8686e-06 0.0893662 0.0754859,0.10484 0.156715,-0.14119,0.439871,0.523737,0.0343716 0.0174565,0.0196964,0.0623007,0.0279715,0.0285878
+540 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45424374 2.80198e-06 118.517 171007 5 2.85413e-06 0.0893586 0.0759097,0.104911 0.155858,0.442885,-0.141136,0.525732,0.0363787 0.0174373,0.0625078,0.0196961,0.0280282,0.0552043
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+543 chr19_45386688,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.79064e-06 118.515 171702 5 2.84258e-06 0.0893815 0.0757935,0.103344 -0.0484442,0.155478,0.439957,-0.142199,0.523 0.0695701,0.0174384,0.0625239,0.0197809,0.0280455
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+553 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421877 2.75105e-06 118.509 174173 5 2.80225e-06 0.0893374 0.0752227,0.103363 0.156034,0.444662,-0.138228,0.527377,-0.0159843 0.0174463,0.0627166,0.0202685,0.0284407,0.0286918
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+557 chr19_45398360,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.73243e-06 118.506 175360 5 2.78328e-06 0.0893698 0.075973,0.104689 -0.0454233,0.155668,0.441789,-0.138411,0.524785 0.0705972,0.0174358,0.0624794,0.0200716,0.0279684
+558 chr19_45381443,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.73225e-06 118.506 175372 5 2.7831e-06 0.0893671 0.0752015,0.104043 0.124878,0.156051,-0.141099,0.43619,0.523878 0.104978,0.0174384,0.0196958,0.0621822,0.0279694
+559 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431658 2.72327e-06 118.505 175950 5 2.77395e-06 0.0893778 0.0758505,0.103915 0.156839,0.439305,-0.1413,0.521882,0.0168223 0.0175227,0.0625777,0.0197025,0.0282436,0.0255829
+560 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426813 2.7134e-06 118.503 176590 5 2.7639e-06 0.0893192 0.0757029,0.103616 0.155413,0.441595,-0.138295,0.524443,-0.00614046 0.0174955,0.0624808,0.0237789,0.0279658,0.0309544
+561 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45425460 2.71237e-06 118.503 176657 5 2.76285e-06 0.0893346 0.0758822,0.10354 0.156002,0.444482,-0.138385,0.527236,-0.0154817 0.0174452,0.0627056,0.0202422,0.0284286,0.0288346
+562 chr19_45385190,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.70308e-06 118.502 177264 5 2.75339e-06 0.0893536 0.075994,0.103344 0.0899211,0.155531,-0.139265,0.437229,0.526151 0.0770051,0.0174362,0.0197467,0.062202,0.0280023
+563 chr19_45406538,chr19_45407131,chr19_45410273,chr19_45411941,chr19_45412079 2.69398e-06 118.5 177862 5 2.74412e-06 0.0893604 0.0750189,0.103828 0.155932,0.0321899,0.443681,-0.139568,0.524685 0.0174399,0.0516419,0.0625667,0.0198096,0.0279663
+564 chr19_45398206,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.69303e-06 118.5 177925 5 2.74315e-06 0.0893489 0.0758483,0.10395 -0.0130864,0.155063,0.44126,-0.136326,0.524598 0.0292195,0.0174935,0.062485,0.0222112,0.0279664
+565 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45427353 2.69291e-06 118.5 177934 5 2.74302e-06 0.0894204 0.0747325,0.103527 0.404634,0.158627,-0.155259,0.50657,-0.0604324 0.0627087,0.0174885,0.0204585,0.0286591,0.0249874
+566 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420343 2.69097e-06 118.5 178062 5 2.74105e-06 0.0893117 0.0758467,0.104555 0.156213,0.443687,-0.143016,0.526596,0.00773633 0.0174838,0.0626936,0.0203881,0.0284594,0.0194511
+567 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426637 2.6881e-06 118.499 178252 5 2.73813e-06 0.0893132 0.0759041,0.104079 0.156267,0.443939,-0.142872,0.526631,0.00792192 0.0174893,0.0627262,0.0202483,0.0284335,0.0190615
+568 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45415640 2.68571e-06 118.499 178410 5 2.7357e-06 0.089154 0.0762052,0.104553 0.155711,-0.131556,0.435734,0.53634,-0.0134367 0.017437,0.0280263,0.0621925,0.037686,0.0288721
+569 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436415 2.685e-06 118.499 178457 5 2.73497e-06 0.0893269 0.0754481,0.105253 0.1557,0.452697,-0.141493,0.523449,-0.0174091 0.0174359,0.0669317,0.0197347,0.0280542,0.0377524
+570 chr19_45390044,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.68219e-06 118.498 178644 5 2.73211e-06 0.089325 0.0752823,0.104113 -0.0451534,0.155745,0.441675,-0.140804,0.527883 0.0898053,0.017436,0.0624788,0.0196942,0.0287643
+571 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435813 2.67272e-06 118.497 179277 5 2.72247e-06 0.0893255 0.074473,0.104086 0.155688,0.452511,-0.141462,0.523486,-0.0170463 0.0174359,0.0669966,0.0197323,0.028052,0.03788
+572 chr19_45406538,chr19_45407610,chr19_45410273,chr19_45411941,chr19_45412079 2.67216e-06 118.497 179315 5 2.72189e-06 0.0893179 0.0757819,0.104156 0.155923,-0.0418557,0.442046,-0.140939,0.528097 0.0174429,0.0916672,0.0624858,0.0196934,0.0290517
+573 chr19_45396673,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.67177e-06 118.496 179341 5 2.72149e-06 0.0893064 0.0751579,0.103716 -0.0033216,0.155822,0.4415,-0.139326,0.52464 0.0268789,0.0174676,0.0624868,0.0235624,0.0279976
+574 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45426578 2.66875e-06 118.496 179544 5 2.71842e-06 0.089348 0.0757895,0.104925 0.155803,0.441623,-0.142352,0.524638,0.0549265 0.0174367,0.0624787,0.0198474,0.0279656,0.093747
+575 chr19_45399896,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.66459e-06 118.495 179825 5 2.71418e-06 0.0893467 0.0755291,0.103901 -0.0437721,0.159153,-0.131908,0.434587,0.524391 0.0398091,0.0177191,0.0213386,0.0621792,0.0279658
+576 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440270 2.65975e-06 118.495 180151 5 2.70925e-06 0.0893103 0.0743708,0.10455 0.155485,0.441453,-0.140973,0.528394,-0.0144137 0.0174446,0.0624806,0.0196943,0.0298043,0.0382758
+577 chr19_45381298,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.63824e-06 118.491 181621 5 2.68734e-06 0.0893431 0.0755417,0.103822 0.0243721,0.155852,0.442689,-0.140581,0.525783 0.0430069,0.0174379,0.0625075,0.0197015,0.0280582
+578 chr19_45381340,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.63156e-06 118.49 182081 5 2.68054e-06 0.0893553 0.0760698,0.105404 0.0566419,0.155776,0.442413,-0.140974,0.524278 0.095026,0.0174363,0.0624933,0.0196936,0.0279666
+579 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418251 2.621e-06 118.488 182815 5 2.66978e-06 0.0893622 0.0754138,0.104288 0.155322,0.439698,-0.143171,0.52194,-0.0204606 0.0174479,0.0625683,0.0200843,0.0283125,0.0355152
+580 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45424989 2.61918e-06 118.488 182942 5 2.66792e-06 0.0893528 0.0754383,0.103509 0.156086,-0.141836,0.436687,0.523513,0.0861931 0.0174392,0.0197112,0.062191,0.0279775,0.0748495
+581 chr19_45406538,chr19_45412040,chr19_45412079,chr19_45413760,chr19_45427780 2.61824e-06 118.488 183008 5 2.66697e-06 0.0891703 0.0751318,0.103598 0.149456,0.469746,0.546977,-0.121646,0.490108 0.0174616,0.0618994,0.0276383,0.0246959,0.0971776
+582 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427437 2.60766e-06 118.486 183751 5 2.65619e-06 0.0893554 0.0751119,0.105353 0.155496,0.440646,-0.141711,0.523946,-0.049587 0.0174391,0.062501,0.0197417,0.0279797,0.0848844
+583 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427908 2.60683e-06 118.486 183809 5 2.65535e-06 0.0893553 0.0751719,0.104855 0.155495,0.440652,-0.141711,0.523947,-0.0495376 0.0174391,0.0625008,0.0197418,0.0279796,0.0848844
+584 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416178 2.60556e-06 118.486 183898 5 2.65405e-06 0.0893004 0.0755468,0.10442 0.155621,0.441474,-0.140639,0.524867,-0.000577224 0.0176065,0.0626726,0.0215552,0.0307391,0.0203274
+585 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45430882 2.59846e-06 118.484 184401 5 2.64682e-06 0.089368 0.0763314,0.104865 0.155185,-0.141814,0.432781,0.522814,-0.10454 0.0174412,0.0197103,0.0622091,0.0280014,0.0898602
+586 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418790 2.59712e-06 118.484 184496 5 2.64545e-06 0.0893169 0.0754992,0.103566 0.155677,0.441692,-0.140984,0.524161,0.00052084 0.0174557,0.0625607,0.0201687,0.0322794,0.0214908
+587 chr19_45399344,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.59602e-06 118.484 184575 5 2.64433e-06 0.0893388 0.0755048,0.104648 -0.0428194,0.159069,-0.132129,0.434587,0.524388 0.039792,0.0177175,0.0213285,0.0621795,0.027966
+588 chr19_45404130,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.59519e-06 118.484 184633 5 2.64349e-06 0.0893834 0.0755073,0.10477 0.0327782,0.155762,0.441926,-0.140877,0.51825 0.0659971,0.0174367,0.0624835,0.0196938,0.0306901
+589 chr19_45395714,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 2.58778e-06 118.483 185162 5 2.63594e-06 0.0895025 0.0762404,0.104574 0.0499401,0.383922,0.155397,-0.164711,0.489034 0.0206699,0.0640056,0.0174364,0.0218554,0.0310934
+590 chr19_45381091,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.58419e-06 118.482 185420 5 2.63228e-06 0.0893429 0.075578,0.10452 0.0369745,0.155544,0.442751,-0.139959,0.525557 0.0681071,0.017438,0.0625142,0.0197701,0.0280344
+591 chr19_45406538,chr19_45406798,chr19_45410273,chr19_45411941,chr19_45412079 2.58204e-06 118.482 185574 5 2.6301e-06 0.089354 0.0751098,0.102963 0.15531,-0.0149033,0.438908,-0.143559,0.5217 0.0174516,0.0285999,0.0626966,0.0203454,0.0284734
+592 chr19_45397512,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.57786e-06 118.481 185875 5 2.62583e-06 0.0893189 0.0751588,0.103317 -0.0114148,0.155893,0.443997,-0.138598,0.526889 0.0276401,0.0174426,0.0627436,0.0204658,0.0285617
+593 chr19_45391600,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.57515e-06 118.48 186070 5 2.62308e-06 0.0893187 0.0758606,0.103407 -0.0488414,0.155748,0.441966,-0.140717,0.527308 0.111409,0.0174363,0.0624842,0.0196979,0.0286915
+594 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418961 2.55919e-06 118.478 187230 5 2.60682e-06 0.0893192 0.076436,0.10466 0.155959,0.443982,-0.138755,0.526699,-0.0115279 0.0174477,0.0627411,0.0203679,0.0284731,0.027954
+595 chr19_45405049,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.55812e-06 118.478 187309 5 2.60573e-06 0.0893099 0.0746546,0.10367 -0.0335245,0.155747,0.44161,-0.140869,0.52756 0.0946213,0.0174365,0.0624793,0.0196936,0.029265
+596 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45423636 2.55791e-06 118.478 187324 5 2.60552e-06 0.0893133 0.0762434,0.103543 0.156036,0.446446,-0.142837,0.522565,-0.00751547 0.0174712,0.0644098,0.0206639,0.0286455,0.0243422
+597 chr19_45383115,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.55412e-06 118.477 187602 5 2.60166e-06 0.0893286 0.0756627,0.103928 -0.0285243,0.155866,-0.141785,0.430835,0.526441 0.0257351,0.0174366,0.0197107,0.0622965,0.0280221
+598 chr19_45388241,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.5468e-06 118.476 188141 5 2.5942e-06 0.0893316 0.075025,0.103936 0.00436158,0.155535,0.438238,-0.142312,0.521863 0.0206287,0.0174531,0.0645728,0.0208384,0.0306886
+599 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45438729 2.54647e-06 118.476 188165 5 2.59387e-06 0.089332 0.0758352,0.104032 0.155666,-0.140846,0.435169,0.526062,0.0756221 0.0174359,0.0196956,0.0621764,0.0280021,0.0679529
+600 chr19_45392213,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.5458e-06 118.476 188216 5 2.59318e-06 0.0893473 0.0750634,0.104689 -0.0367103,0.155668,0.441743,-0.138904,0.524724 0.0705262,0.017436,0.06248,0.0200657,0.0279686
+601 chr19_45406538,chr19_45409167,chr19_45410273,chr19_45411941,chr19_45412079 2.54476e-06 118.475 188292 5 2.59212e-06 0.0893202 0.0750404,0.102844 0.155898,-0.012073,0.443727,-0.138839,0.526586 0.0174428,0.0290911,0.0626855,0.0203096,0.028418
+602 chr19_45404000,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.54413e-06 118.475 188339 5 2.59148e-06 0.0893418 0.0753212,0.105132 0.0251224,0.155935,0.443337,-0.13969,0.524816 0.0483165,0.0174421,0.0625672,0.0198314,0.027972
+603 chr19_45406538,chr19_45408836,chr19_45410273,chr19_45411941,chr19_45412079 2.54329e-06 118.475 188401 5 2.59062e-06 0.0893057 0.0746292,0.103554 0.156164,-0.00592879,0.442526,-0.142678,0.525462 0.0175187,0.0216585,0.0625679,0.0207148,0.0281904
+604 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416996 2.54312e-06 118.475 188414 5 2.59045e-06 0.0893099 0.0740874,0.101955 0.155858,0.441934,-0.140934,0.527358,-0.0323208 0.017442,0.0624857,0.0196936,0.0291085,0.0914534
+605 chr19_45382156,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.54211e-06 118.475 188488 5 2.58943e-06 0.0893593 0.0761598,0.104997 0.128999,0.155647,-0.1402,0.434145,0.52344 0.112893,0.017436,0.0197059,0.0621825,0.0279799
+606 chr19_45394969,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.54181e-06 118.475 188511 5 2.58912e-06 0.0893176 0.0755402,0.104292 0.00187935,0.155637,0.440437,-0.141552,0.523305 0.0199053,0.0174475,0.0638342,0.0209674,0.0308486
+607 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45428083 2.53638e-06 118.474 188914 5 2.58359e-06 0.0893064 0.0762224,0.104571 0.15605,0.443143,-0.142312,0.525992,0.0055359 0.0174794,0.0627047,0.0202895,0.028451,0.0193569
+608 chr19_45386029,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.53579e-06 118.474 188959 5 2.58298e-06 0.0893176 0.0751435,0.103983 0.0100597,0.156312,0.44362,-0.140674,0.526488 0.0237592,0.0174964,0.0626569,0.0197008,0.0283595
+609 chr19_45384843,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.53535e-06 118.474 188991 5 2.58253e-06 0.089355 0.0750044,0.103298 -0.11613,0.155147,-0.141564,0.433193,0.523601 0.102164,0.0174424,0.0197035,0.0621999,0.0279758
+610 chr19_45406538,chr19_45408312,chr19_45410273,chr19_45411941,chr19_45412079 2.53086e-06 118.473 189326 5 2.57796e-06 0.0893489 0.0756356,0.104405 0.155483,-0.0481346,0.441395,-0.141674,0.523555 0.0174404,0.0899102,0.0624807,0.0197459,0.0280184
+611 chr19_45397952,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.52597e-06 118.472 189693 5 2.57298e-06 0.0893307 0.0759341,0.103909 -0.0418236,0.158967,-0.132396,0.434593,0.524392 0.0397755,0.0177136,0.0213047,0.0621798,0.0279661
+612 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440274 2.52445e-06 118.472 189807 5 2.57143e-06 0.0893305 0.0755465,0.103818 0.155761,0.441473,-0.141106,0.525679,-0.0299185 0.0174364,0.0624799,0.0196978,0.0280732,0.0617982
+613 chr19_45398264,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.51993e-06 118.471 190148 5 2.56683e-06 0.0893143 0.0747443,0.103642 -0.0100687,0.155869,0.443699,-0.138879,0.52658 0.0277482,0.0174427,0.0627412,0.0204643,0.0285515
+614 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421926 2.51959e-06 118.471 190173 5 2.56648e-06 0.0893297 0.075931,0.104203 0.155885,0.443681,-0.140162,0.525648,0.0188229 0.0174405,0.0626296,0.0197549,0.0280714,0.0395551
+615 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418800 2.51699e-06 118.471 190370 5 2.56383e-06 0.0893086 0.0755243,0.105252 0.155848,0.441915,-0.140931,0.527171,-0.0302278 0.0174421,0.0624859,0.0196937,0.0291088,0.0915276
+616 chr19_45395980,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.50556e-06 118.469 191238 5 2.55219e-06 0.0893451 0.0757998,0.104163 -0.0530298,0.155635,0.441266,-0.141311,0.523851 0.102403,0.0174363,0.0624831,0.0197091,0.0279914
+617 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45431897 2.50448e-06 118.468 191321 5 2.55109e-06 0.0893365 0.0751377,0.104942 0.155591,0.437995,-0.142918,0.523429,-0.00853888 0.0174374,0.0629749,0.0201762,0.028059,0.0185769
+618 chr19_45389198,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.50418e-06 118.468 191343 5 2.55079e-06 0.0893319 0.0748514,0.103788 0.0269357,0.155622,0.442927,-0.139918,0.525604 0.0567531,0.0174366,0.0625406,0.0198027,0.0280642
+619 chr19_45381300,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.48924e-06 118.466 192492 5 2.53557e-06 0.0893473 0.0760713,0.10412 -0.0226602,0.155411,0.441356,-0.140919,0.523757 0.0440493,0.0174447,0.0624816,0.0196935,0.0280003
+620 chr19_45382951,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.47669e-06 118.464 193467 5 2.52278e-06 0.0893265 0.074311,0.103522 -0.0156391,0.15563,0.44103,-0.139907,0.526001 0.0356617,0.0174366,0.0624937,0.0198241,0.0281779
+621 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432244 2.47561e-06 118.463 193552 5 2.52168e-06 0.0893371 0.0756811,0.104239 0.155842,0.441043,-0.141437,0.524148,-0.036152 0.0174387,0.0624907,0.0197244,0.0279733,0.0748903
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+660 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45420493 2.35628e-06 118.442 203353 5 2.40014e-06 0.0893081 0.0757463,0.1045 0.155622,0.440811,-0.141786,0.523613,-0.00361891 0.0174439,0.0627499,0.020696,0.0286606,0.0258456
+661 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440742 2.35285e-06 118.441 203650 5 2.39664e-06 0.0893255 0.0751268,0.104725 0.155433,-0.140579,0.433955,0.52468,-0.127688 0.0174378,0.0196985,0.0621871,0.0279665,0.120257
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+665 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433127 2.34785e-06 118.44 204084 5 2.39154e-06 0.0893087 0.0750494,0.105524 0.156162,-0.140963,0.436406,0.525361,0.0739028 0.0174421,0.0196958,0.0621894,0.027979,0.0710874
+666 chr19_45386229,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.34726e-06 118.44 204136 5 2.39094e-06 0.0892557 0.0745974,0.104185 -0.0685042,0.155864,-0.141483,0.459521,0.524308 0.0750005,0.0174373,0.0197054,0.0676597,0.0279665
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+669 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45438593 2.33832e-06 118.439 204916 5 2.38184e-06 0.0893165 0.0759426,0.103252 0.155758,0.441495,-0.141194,0.524708,0.0388306 0.0174372,0.0624811,0.0197127,0.0279732,0.116388
+670 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45419920 2.33696e-06 118.438 205035 5 2.38045e-06 0.0893159 0.0759001,0.103219 0.155819,-0.13977,0.436276,0.525269,0.0859217 0.0174366,0.0197269,0.0621871,0.0279764,0.0821845
+671 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436816 2.33665e-06 118.438 205062 5 2.38013e-06 0.0893225 0.074964,0.104135 0.155503,0.440995,-0.141562,0.523897,-0.0175138 0.0174449,0.0625058,0.0197851,0.0280162,0.0505613
+672 chr19_45402124,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.33241e-06 118.437 205435 5 2.37581e-06 0.0893084 0.0744395,0.103857 0.000324301,0.155694,0.441616,-0.140901,0.524471 0.084399,0.0174363,0.0624806,0.0196947,0.0294475
+673 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436216 2.33017e-06 118.437 205633 5 2.37353e-06 0.0893094 0.0753558,0.104992 0.15583,0.44339,-0.140443,0.525168,0.00508795 0.0174431,0.0628126,0.0197649,0.0280758,0.0185463
+674 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414140 2.32666e-06 118.436 205942 5 2.36996e-06 0.0893089 0.0757026,0.103474 0.155799,0.442307,-0.141307,0.525039,0.0123946 0.0174403,0.0625313,0.0197484,0.0280387,0.0453344
+675 chr19_45384800,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.32572e-06 118.436 206026 5 2.36901e-06 0.0893178 0.0751707,0.104191 0.0358854,0.155532,0.441038,-0.141032,0.524704 0.108836,0.017443,0.0625045,0.0196975,0.0279731
+676 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414141 2.32566e-06 118.436 206031 5 2.36894e-06 0.0893088 0.0759449,0.10451 0.155798,0.442304,-0.141305,0.525036,0.0123268 0.0174403,0.0625314,0.0197483,0.0280387,0.0453077
+677 chr19_45402516,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.32507e-06 118.436 206083 5 2.36834e-06 0.089314 0.0749767,0.103617 0.0662381,0.15632,-0.139132,0.434484,0.525466 0.0637896,0.0174468,0.0197717,0.0621805,0.0279824
+678 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418562 2.32415e-06 118.436 206165 5 2.36741e-06 0.0893111 0.0756761,0.10433 0.155678,0.441877,-0.141635,0.524585,0.025552 0.0174362,0.0624872,0.0198625,0.0279674,0.0905247
+679 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45425498 2.32335e-06 118.436 206236 5 2.36659e-06 0.0893176 0.0754433,0.103694 0.155848,0.440888,-0.141586,0.523955,-0.0194244 0.017443,0.0625225,0.0198117,0.028015,0.0614903
+680 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45429286 2.32155e-06 118.435 206396 5 2.36475e-06 0.0893171 0.0753343,0.105223 0.155746,0.442208,-0.141245,0.524223,-0.0255866 0.0174369,0.0625075,0.0197221,0.0279766,0.079497
+681 chr19_45406538,chr19_45407226,chr19_45410273,chr19_45411941,chr19_45412079 2.32085e-06 118.435 206458 5 2.36404e-06 0.0893192 0.0749043,0.103702 0.155515,-0.0125693,0.440781,-0.141808,0.523625 0.0174462,0.0414066,0.0625409,0.0199185,0.0281083
+682 chr19_45395318,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.32079e-06 118.435 206463 5 2.36399e-06 0.0893098 0.0757655,0.104167 0.0114166,0.155604,0.44255,-0.140142,0.525262 0.0435513,0.0174395,0.0625819,0.0199064,0.0281222
+683 chr19_45399234,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.31866e-06 118.435 206653 5 2.36181e-06 0.0893107 0.0759064,0.103682 0.025936,0.155661,0.441842,-0.141617,0.524585 0.09433,0.0174366,0.0624858,0.019863,0.0279676
+684 chr19_45380970,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.31805e-06 118.435 206708 5 2.36119e-06 0.0893054 0.075467,0.103864 0.00323375,0.15558,0.442134,-0.140002,0.525122 0.0179569,0.0174477,0.0625474,0.0203194,0.0281894
+685 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45438554 2.31609e-06 118.434 206883 5 2.35919e-06 0.0893526 0.0750301,0.104749 0.154889,-0.139595,0.43761,0.521101,0.0198201 0.0174521,0.0197349,0.0622209,0.0281404,0.0184082
+686 chr19_45396973,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.3125e-06 118.434 207204 5 2.35554e-06 0.089304 0.0743354,0.103549 -0.0382291,0.158644,-0.133106,0.434598,0.524367 0.0396182,0.0177042,0.0213017,0.0621811,0.0279666
+687 chr19_45398319,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.31247e-06 118.434 207206 5 2.35551e-06 0.0893024 0.0759614,0.105427 0.104271,0.155679,-0.143736,0.434985,0.524258 0.102459,0.0174362,0.0198881,0.0621776,0.0279666
+688 chr19_45398785,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.31034e-06 118.433 207398 5 2.35334e-06 0.0893023 0.0757682,0.103899 -0.0386296,0.158747,-0.133097,0.434678,0.524371 0.0401963,0.0177254,0.021318,0.0621804,0.0279666
+689 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440199 2.3103e-06 118.433 207401 5 2.35329e-06 0.0893122 0.0748991,0.104066 0.155822,0.441385,-0.141413,0.524534,0.0267323 0.0174419,0.0624853,0.0197736,0.0279657,0.0934285
+690 chr19_45382075,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.30919e-06 118.433 207501 5 2.35216e-06 0.0893645 0.0758698,0.103994 0.117301,0.155791,-0.140864,0.436121,0.518174 0.108558,0.0174366,0.0196962,0.0621851,0.0285677
+691 chr19_45405552,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.30851e-06 118.433 207562 5 2.35147e-06 0.0893081 0.0758002,0.103682 0.0102576,0.155605,0.442487,-0.140193,0.525225 0.0431651,0.0174402,0.062588,0.0199192,0.0281385
+692 chr19_45405694,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.30664e-06 118.433 207730 5 2.34957e-06 0.0893079 0.0754001,0.104042 0.0101285,0.155606,0.442476,-0.140202,0.525215 0.0431756,0.0174402,0.0625879,0.0199191,0.0281383
+693 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432127 2.3055e-06 118.432 207833 5 2.34841e-06 0.0893083 0.0739394,0.103526 0.155859,0.442537,-0.14002,0.524774,0.00558928 0.0174526,0.0626241,0.0201073,0.0279973,0.0256338
+694 chr19_45398014,chr19_45406538,chr19_45407788,chr19_45411941,chr19_45412079 2.3051e-06 118.432 207869 5 2.348e-06 0.0890659 0.0749822,0.104183 0.397272,0.158245,-0.0434928,-0.161511,0.536459 0.06318,0.0174879,0.0202799,0.0217158,0.0287622
+695 chr19_45396240,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.30486e-06 118.432 207891 5 2.34775e-06 0.0893032 0.0751671,0.10444 -0.0172934,0.155651,0.441759,-0.140856,0.525417 0.112692,0.0174384,0.0624873,0.0196961,0.0286074
+696 chr19_45395218,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.30485e-06 118.432 207891 5 2.34774e-06 0.0893126 0.0761515,0.103686 0.00801109,0.155686,0.441658,-0.140918,0.523737 0.0898088,0.0174365,0.0624826,0.0196948,0.0292555
+697 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432126 2.30293e-06 118.432 208065 5 2.34578e-06 0.0893075 0.0753337,0.103677 0.155866,0.442489,-0.14005,0.524789,0.00522317 0.0174559,0.0626216,0.020116,0.028004,0.0250758
+698 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45413946 2.30174e-06 118.432 208172 5 2.34458e-06 0.0893335 0.0747665,0.103396 0.155756,-0.139397,0.43703,0.522763,0.0660568 0.0174364,0.0197494,0.0622041,0.0280125,0.0626015
+699 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45422587 2.30139e-06 118.432 208204 5 2.34422e-06 0.0893105 0.0753667,0.103508 0.155919,0.442707,-0.140963,0.52461,0.00721418 0.0174557,0.0626093,0.0196949,0.027969,0.0265382
+700 chr19_45393680,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.30073e-06 118.432 208263 5 2.34355e-06 0.0893172 0.0752965,0.103616 -0.0356743,0.155658,0.441453,-0.140254,0.524503 0.12101,0.0174366,0.0624827,0.0198175,0.0279652
+701 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433610 2.29905e-06 118.431 208416 5 2.34184e-06 0.0893104 0.0755042,0.103763 0.155875,0.442143,-0.14042,0.524903,0.0129234 0.0174497,0.0625126,0.0197792,0.0280107,0.0490866
+702 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432089 2.29861e-06 118.431 208456 5 2.34139e-06 0.0893033 0.0751873,0.103163 0.155727,0.442688,-0.140265,0.524803,0.00254268 0.0174379,0.0629785,0.0202475,0.0280643,0.0187425
+703 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436244 2.29801e-06 118.431 208510 5 2.34078e-06 0.0893066 0.0752803,0.10366 0.155794,0.443065,-0.140523,0.525054,0.00421473 0.0174417,0.0628031,0.0197645,0.0280771,0.0185357
+704 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414479 2.29718e-06 118.431 208585 5 2.33994e-06 0.0893141 0.0754555,0.104852 0.15577,0.441839,-0.140643,0.524629,0.0326355 0.0174382,0.0624851,0.0197143,0.0279697,0.112434
+705 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45415713 2.29019e-06 118.43 209223 5 2.33281e-06 0.089315 0.07542,0.104908 0.155555,0.440438,-0.141522,0.523399,-0.00464079 0.0174518,0.0627938,0.0199676,0.0285535,0.024458
+706 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432132 2.2899e-06 118.429 209248 5 2.33252e-06 0.0893065 0.0746868,0.104808 0.155833,0.442403,-0.140148,0.524724,0.0047939 0.0174521,0.0626238,0.0201066,0.0279953,0.0256872
+707 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45439908 2.28863e-06 118.429 209365 5 2.33123e-06 0.0893123 0.0764971,0.103726 0.155683,0.441906,-0.140755,0.524712,0.0226844 0.0174362,0.0624893,0.0197012,0.0279776,0.0824115
+708 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45423934 2.28714e-06 118.429 209501 5 2.32971e-06 0.089304 0.074941,0.10317 0.154379,-0.128112,0.435191,0.524398,-0.0256927 0.0175059,0.0248738,0.0621809,0.0279675,0.0303442
+709 chr19_45404691,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.28433e-06 118.428 209759 5 2.32684e-06 0.0893019 0.074802,0.103581 -0.000513694,0.155699,0.441706,-0.140823,0.524567 0.031591,0.0174393,0.0627035,0.0202351,0.0283819
+710 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433130 2.28226e-06 118.428 209949 5 2.32474e-06 0.0893149 0.0755302,0.104169 0.15567,0.441199,-0.141293,0.524057,-0.0258286 0.0174364,0.0624987,0.0197447,0.0280082,0.0941291
+711 chr19_45384294,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.28097e-06 118.428 210068 5 2.32342e-06 0.0893166 0.0749237,0.104717 0.00638199,0.155652,0.442804,-0.140704,0.523773 0.0239287,0.017437,0.0626408,0.0197082,0.0280917
+712 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432278 2.28025e-06 118.428 210134 5 2.32269e-06 0.0893016 0.0749357,0.104363 0.155709,0.442106,-0.14061,0.52463,0.00115572 0.0174381,0.0629829,0.0202553,0.0280658,0.0187246
+713 chr19_45406538,chr19_45407655,chr19_45410273,chr19_45411941,chr19_45412079 2.2767e-06 118.427 210462 5 2.31907e-06 0.0893042 0.074399,0.104047 0.155637,0.00677921,0.442282,-0.140377,0.525028 0.0174397,0.0413553,0.0626126,0.0199527,0.0281611
+714 chr19_45383079,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.27648e-06 118.427 210482 5 2.31885e-06 0.0893033 0.0750216,0.104881 -0.00472212,0.15576,0.440937,-0.141364,0.524756 0.0315128,0.0174418,0.0626409,0.0199282,0.0280246
+715 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45434881 2.27619e-06 118.427 210509 5 2.31855e-06 0.0892385 0.0744649,0.104784 0.155426,-0.140869,0.436146,0.531797,-0.0751637 0.0174386,0.0196959,0.062188,0.0291472,0.0844072
+716 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45436247 2.27605e-06 118.427 210522 5 2.31841e-06 0.0893048 0.0754024,0.103389 0.155781,0.442814,-0.14059,0.524953,0.00350101 0.0174423,0.0628007,0.0197632,0.0280751,0.0185527
+717 chr19_45406538,chr19_45411677,chr19_45411941,chr19_45412040,chr19_45412079 2.27414e-06 118.426 210699 5 2.31646e-06 0.0893283 0.0750006,0.104437 0.15509,-0.065567,-0.142792,0.433233,0.522114 0.0174457,0.0629574,0.0197773,0.0622044,0.0280571
+718 chr19_45394541,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.27337e-06 118.426 210770 5 2.31568e-06 0.08931 0.0756456,0.103794 0.0239845,0.155535,0.441819,-0.140514,0.524849 0.100565,0.0174489,0.0624864,0.0197618,0.0280075
+719 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45432557 2.27302e-06 118.426 210802 5 2.31533e-06 0.0893014 0.0757285,0.10348 0.155698,0.441733,-0.140834,0.524518,0.000275721 0.0174381,0.0629762,0.020244,0.0280626,0.018724
+720 chr19_45381310,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.2692e-06 118.426 211158 5 2.31143e-06 0.0893096 0.07609,0.103659 0.0275964,0.155657,0.441929,-0.140965,0.524641 0.114163,0.0174369,0.062494,0.0196953,0.0279728
+721 chr19_45389489,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.26844e-06 118.425 211228 5 2.31066e-06 0.0893095 0.0754732,0.104414 0.0227548,0.155549,0.441831,-0.140513,0.524823 0.0991129,0.0174476,0.0624877,0.0197673,0.0280046
+722 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435466 2.26714e-06 118.425 211349 5 2.30934e-06 0.0892978 0.0755601,0.103714 0.156158,-0.144159,0.432134,0.52382,-0.0249288 0.0174426,0.0199604,0.0622499,0.0279734,0.024986
+723 chr19_45406538,chr19_45406935,chr19_45411941,chr19_45412040,chr19_45412079 2.26497e-06 118.425 211552 5 2.30712e-06 0.0893233 0.0765815,0.10461 0.155428,-0.086128,-0.142142,0.432762,0.522871 0.0174381,0.0831931,0.0197309,0.0622197,0.0280084
+724 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421890 2.26306e-06 118.424 211730 5 2.30518e-06 0.0893081 0.0762169,0.103994 0.155829,0.44198,-0.141341,0.524088,0.00735045 0.0174487,0.0625062,0.0198086,0.0280313,0.0358408
+725 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45430280 2.26245e-06 118.424 211788 5 2.30455e-06 0.0893134 0.075683,0.103028 0.155287,-0.146325,0.437236,0.519237,-0.0183196 0.017441,0.0204304,0.0622147,0.0284564,0.0184076
+726 chr19_45381721,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.25099e-06 118.422 212866 5 2.29288e-06 0.0893098 0.0748616,0.103409 -0.010005,0.155673,0.441207,-0.141297,0.524069 0.048902,0.0174365,0.0625123,0.0197875,0.0280384
+727 chr19_45402861,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.24868e-06 118.422 213085 5 2.29053e-06 0.0893134 0.0757029,0.104179 -0.109671,0.155728,-0.141519,0.434204,0.523734 0.107466,0.0174363,0.0197045,0.0621843,0.0279751
+728 chr19_45381999,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.24568e-06 118.421 213369 5 2.28747e-06 0.0893051 0.0753348,0.105638 0.00278709,0.155697,0.441641,-0.140901,0.524335 0.100469,0.0174366,0.0624889,0.0196941,0.0285248
+729 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45435762 2.24355e-06 118.421 213572 5 2.2853e-06 0.0893067 0.0760489,0.105374 0.155856,0.441741,-0.141143,0.524234,-0.0177699 0.0174588,0.0624845,0.0197372,0.0279984,0.0974964
+730 chr19_45381306,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.24282e-06 118.42 213641 5 2.28456e-06 0.0892993 0.0762987,0.102418 0.046297,0.155314,-0.141812,0.431693,0.525681 0.0463428,0.0174402,0.0197158,0.0622725,0.0279925
+731 chr19_45400927,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.24267e-06 118.42 213655 5 2.28441e-06 0.0893095 0.0754473,0.103872 -0.0224587,0.155642,0.441322,-0.140852,0.524433 0.10675,0.017438,0.0624959,0.0196954,0.0279662
+732 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421453 2.24085e-06 118.42 213829 5 2.28255e-06 0.0893035 0.0759984,0.103622 0.15597,-0.140065,0.433372,0.525456,0.1011 0.0174385,0.0197146,0.0622022,0.0279834,0.100592
+733 chr19_45383139,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.24083e-06 118.42 213831 5 2.28254e-06 0.0892919 0.0749923,0.10424 -0.0253487,0.155833,-0.141667,0.431272,0.526222 0.0256554,0.0174369,0.0197101,0.0622997,0.0280225
+734 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45433566 2.23578e-06 118.419 214314 5 2.27739e-06 0.0893077 0.0748631,0.104746 0.15566,0.441356,-0.141203,0.524148,-0.0123113 0.0174374,0.0624987,0.0197709,0.0280332,0.0717199
+735 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416693 2.23189e-06 118.418 214687 5 2.27343e-06 0.0893024 0.0753204,0.103678 0.155722,0.441849,-0.141034,0.52466,0.0038831 0.0174393,0.0625378,0.0197485,0.0280384,0.0444726
+736 chr19_45390272,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.23065e-06 118.418 214806 5 2.27217e-06 0.0893052 0.0755418,0.104143 0.00836391,0.155713,0.441969,-0.140875,0.524565 0.0515379,0.0174366,0.0625189,0.0196947,0.0279699
+737 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416678 2.23053e-06 118.418 214818 5 2.27204e-06 0.0893023 0.0757068,0.104637 0.155721,0.441836,-0.141027,0.524651,0.00366677 0.0174393,0.0625378,0.0197486,0.0280384,0.0444684
+738 chr19_45386634,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.22744e-06 118.417 215116 5 2.26889e-06 0.0892682 0.0754912,0.103764 -0.0542409,0.15569,-0.139773,0.453814,0.524419 0.0587814,0.0174364,0.0197357,0.0653907,0.0279662
+739 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427162 2.22548e-06 118.417 215306 5 2.26689e-06 0.0893028 0.0757939,0.104613 0.155667,0.441492,-0.141008,0.52438,-0.001944 0.0174501,0.0625599,0.0198744,0.028089,0.0492743
+740 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45414139 2.22362e-06 118.417 215486 5 2.265e-06 0.0893023 0.0749993,0.104975 0.155711,0.441754,-0.140984,0.524591,0.00231695 0.0174392,0.062536,0.0197501,0.0280395,0.0440088
+741 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45416009 2.22303e-06 118.417 215543 5 2.26441e-06 0.0893051 0.0755783,0.102717 0.155728,0.441555,-0.141089,0.524316,-0.016053 0.017438,0.062482,0.0197373,0.0279902,0.113868
+742 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45418604 2.22284e-06 118.417 215562 5 2.2642e-06 0.0893021 0.0753014,0.104342 0.155714,0.441882,-0.140805,0.524648,0.0024661 0.017439,0.0626138,0.0197518,0.028079,0.0378789
+743 chr19_45401887,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.22278e-06 118.417 215568 5 2.26414e-06 0.0892982 0.0760292,0.105446 0.105584,0.155827,-0.139991,0.437081,0.525331 0.106266,0.0174368,0.0197185,0.0622091,0.0279796
+744 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427074 2.22129e-06 118.416 215712 5 2.26263e-06 0.089303 0.0753247,0.104494 0.155754,0.441773,-0.141073,0.524319,0.003077 0.0174497,0.0625069,0.0197863,0.0280259,0.0348167
+745 chr19_45404579,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.22038e-06 118.416 215800 5 2.2617e-06 0.0891771 0.0750659,0.103795 0.0185343,0.156797,-0.150018,0.434855,0.524508 0.0314501,0.0175372,0.0249736,0.0621816,0.0279672
+746 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45423678 2.22016e-06 118.416 215822 5 2.26148e-06 0.0893183 0.0749968,0.104557 0.155276,-0.141843,0.433097,0.523023,-0.093439 0.017441,0.0197166,0.0622099,0.028002,0.0918221
+747 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45440145 2.21964e-06 118.416 215872 5 2.26095e-06 0.0893041 0.0746484,0.103342 0.155638,0.441482,-0.141072,0.524277,-0.0043487 0.0174496,0.0625013,0.0198005,0.0280777,0.0526551
+748 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45441283 2.2181e-06 118.416 216022 5 2.25939e-06 0.0893019 0.0753001,0.104188 0.155717,0.441666,-0.140835,0.524567,0.00183544 0.0174488,0.0624987,0.0197935,0.0280695,0.053498
+749 chr19_45382733,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.21615e-06 118.415 216212 5 2.2574e-06 0.0893019 0.0760333,0.104255 0.00198399,0.155692,0.441707,-0.140834,0.52456 0.0582199,0.0174364,0.062539,0.0197987,0.0280564
+750 chr19_45388284,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.21497e-06 118.415 216327 5 2.25619e-06 0.0893032 0.075474,0.10391 -0.00527364,0.155719,0.441437,-0.141059,0.524352 0.0645519,0.017439,0.0625182,0.0197848,0.028011
+751 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45423934,chr19_45427780 2.21368e-06 118.415 216453 5 2.25488e-06 0.0891167 0.0752853,0.104587 0.14995,0.475102,0.547275,-0.115187,0.489402 0.017458,0.0622285,0.0276426,0.0240546,0.0971932
+752 chr19_45383488,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.2135e-06 118.415 216471 5 2.2547e-06 0.0893018 0.0756857,0.103676 0.00072191,0.155697,0.441615,-0.140935,0.524467 0.0569894,0.017438,0.0624809,0.0198383,0.02799
+753 chr19_45397471,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.21306e-06 118.415 216514 5 2.25425e-06 0.0893032 0.0750921,0.103983 -0.0105129,0.155706,0.441745,-0.141033,0.524379 0.109765,0.0174368,0.062496,0.0197394,0.0279859
+754 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45421764 2.21252e-06 118.415 216567 5 2.2537e-06 0.0893035 0.0759246,0.102392 0.155653,0.441398,-0.141038,0.524367,-0.00623385 0.017444,0.0625403,0.0197662,0.0280031,0.0784943
+755 chr19_45381307,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.21219e-06 118.415 216599 5 2.25336e-06 0.0893036 0.0754905,0.10339 0.010049,0.155698,0.441614,-0.141029,0.524396 0.0962649,0.0174363,0.0624807,0.0197302,0.0279773
+756 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45437295 2.20862e-06 118.414 216949 5 2.24973e-06 0.0893027 0.0752207,0.104343 0.155684,0.441733,-0.140802,0.524542,0.00706413 0.0174367,0.0624996,0.0197379,0.0279761,0.0906213
+757 chr19_45384179,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.20768e-06 118.414 217042 5 2.24877e-06 0.0893022 0.0747318,0.104493 -0.00158262,0.155713,0.441382,-0.140948,0.524521 0.0263582,0.0174393,0.0625984,0.019707,0.027973
+758 chr19_45384086,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.20666e-06 118.413 217142 5 2.24772e-06 0.0893032 0.0752073,0.104981 0.00921576,0.155723,0.441697,-0.140846,0.52447 0.101471,0.0174393,0.0624876,0.0197043,0.0279656
+759 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45413224 2.2029e-06 118.413 217512 5 2.2439e-06 0.0893027 0.0758875,0.106416 0.155697,0.441528,-0.140958,0.524407,-0.00642352 0.0174364,0.0625015,0.0197194,0.0280006,0.120393
+760 chr19_45384176,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.20277e-06 118.413 217525 5 2.24377e-06 0.0893019 0.075272,0.104555 -0.000140778,0.155696,0.441592,-0.140908,0.524485 0.0263833,0.0174391,0.062598,0.019707,0.0279733
+761 chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079,chr19_45427062 2.20179e-06 118.412 217622 5 2.24277e-06 0.0893023 0.0767574,0.104295 0.155695,0.441597,-0.140955,0.524443,-0.00397597 0.0174363,0.0624817,0.0197253,0.0279788,0.0876138
+762 chr19_45382755,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.19962e-06 118.412 217837 5 2.24056e-06 0.0893022 0.0753624,0.103772 0.00372591,0.155704,0.441639,-0.140916,0.524441 0.116781,0.0174395,0.0624862,0.0196973,0.0279948
+763 chr19_45385990,chr19_45406538,chr19_45410273,chr19_45411941,chr19_45412079 2.19824e-06 118.412 217974 5 2.23915e-06 0.0893019 0.0749135,0.103455 -0.000649817,0.155692,0.441613,-0.140908,0.524477 0.116503,0.0174384,0.0624808,0.019708,0.0279775
+764 chr19_45386046,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.19302e-06 118.411 218492 5 2.23384e-06 0.0892453 0.0761065,0.103238 -0.0617203,0.155854,-0.141616,0.456292,0.524093 0.0717232,0.0174375,0.0197124,0.0668576,0.0279696
+765 chr19_45383091,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.18026e-06 118.408 219772 5 2.22083e-06 0.0892839 0.0751602,0.104038 -0.0247751,0.155841,-0.14165,0.431429,0.526192 0.0257961,0.0174371,0.0197104,0.0622968,0.0280238
+766 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421100 2.14861e-06 118.402 223009 5 2.1886e-06 0.089293 0.0753276,0.10506 0.156778,-0.147061,0.430025,0.518687,-0.0228887 0.0174768,0.0207913,0.0624251,0.0286614,0.0248142
+767 chr19_45402470,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.14463e-06 118.401 223423 5 2.18454e-06 0.0891712 0.0759108,0.103912 0.0169836,0.156729,-0.149241,0.434833,0.524497 0.0316332,0.0175434,0.0250048,0.0621826,0.0279674
+768 chr19_45381305,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.14346e-06 118.401 223544 5 2.18335e-06 0.0892865 0.075944,0.103031 0.0442716,0.155324,-0.141726,0.431842,0.525629 0.046396,0.0174405,0.0197138,0.0622732,0.0279928
+769 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45439605 2.14205e-06 118.401 223692 5 2.18191e-06 0.0893068 0.0748141,0.10413 0.155168,-0.142389,0.437194,0.522052,-0.0287205 0.0174445,0.0197528,0.0622146,0.0280749,0.0293598
+770 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45420086 2.13735e-06 118.4 224183 5 2.17713e-06 0.0893217 0.0754449,0.104737 0.420315,0.162461,-0.120638,0.520827,-0.086511 0.0625072,0.0177122,0.0217694,0.0279547,0.0378103
+771 chr19_45404993,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.13236e-06 118.399 224708 5 2.17204e-06 0.0892792 0.0762912,0.103728 0.0654921,0.155988,-0.143,0.436391,0.524233 0.0697461,0.0174393,0.0198193,0.062193,0.0279673
+772 chr19_45394439,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.13194e-06 118.398 224752 5 2.17162e-06 0.0892848 0.0761986,0.103954 0.0623363,0.155677,-0.139182,0.437415,0.52582 0.0657859,0.0174364,0.0197821,0.0622254,0.0280028
+773 chr19_45398014,chr19_45405062,chr19_45406538,chr19_45411941,chr19_45412079 2.12544e-06 118.397 225440 5 2.16499e-06 0.0889234 0.0751418,0.104507 0.429345,-0.048323,0.156141,-0.175621,0.530874 0.0627911,0.0245258,0.0174407,0.026084,0.0283202
+774 chr19_45388588,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 2.12076e-06 118.396 225937 5 2.16023e-06 0.0893979 0.0760809,0.104126 0.0458682,0.386603,0.155214,-0.161828,0.498573 0.0197383,0.0638769,0.0174368,0.0214924,0.0297019
+775 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418446 2.12002e-06 118.396 226016 5 2.15948e-06 0.0892928 0.0754784,0.104955 0.155693,-0.142077,0.433756,0.523825,-0.0853822 0.0174364,0.0197327,0.0621948,0.0279742,0.0892172
+776 chr19_45396899,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.1169e-06 118.395 226349 5 2.1563e-06 0.0892443 0.0757087,0.104184 -0.0225037,0.156214,-0.135953,0.439823,0.529367 0.026498,0.0174475,0.0205466,0.0624239,0.0285544
+777 chr19_45397965,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.11124e-06 118.394 226956 5 2.15053e-06 0.0892688 0.0746435,0.104379 0.0379877,0.156055,-0.137962,0.438088,0.527163 0.0417015,0.0174411,0.0199632,0.0622635,0.0281226
+778 chr19_45403119,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.09191e-06 118.39 229053 5 2.13084e-06 0.0891674 0.076358,0.10287 0.0156871,0.156639,-0.148645,0.434892,0.524518 0.031454,0.01754,0.0249947,0.0621823,0.0279677
+779 chr19_45402103,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.08854e-06 118.39 229423 5 2.12741e-06 0.089268 0.0756576,0.104097 -0.081913,0.155372,-0.14174,0.434783,0.526333 0.0899331,0.0174398,0.0197163,0.0621795,0.0280414
+780 chr19_45406538,chr19_45409283,chr19_45411941,chr19_45412040,chr19_45412079 2.08157e-06 118.388 230191 5 2.12031e-06 0.0892874 0.0761942,0.102938 0.155694,-0.0838228,-0.142067,0.433772,0.523824 0.0174365,0.0893658,0.0197337,0.0621953,0.0279747
+781 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45425066,chr19_45425212 2.07113e-06 118.386 231351 5 2.10968e-06 0.088846 0.0756245,0.103268 0.169164,0.491033,0.555477,-0.179049,0.531093 0.017686,0.0621088,0.0275572,0.0351523,0.10934
+782 chr19_45404805,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.06944e-06 118.386 231540 5 2.10795e-06 0.0892648 0.0758649,0.103552 -0.0320584,0.158236,-0.133898,0.434506,0.524797 0.0385964,0.0177053,0.0214411,0.0621843,0.0279699
+783 chr19_45401868,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 2.06846e-06 118.385 231650 5 2.10695e-06 0.0892392 0.0752667,0.104938 -0.0280845,0.157808,-0.135147,0.451098,0.52409 0.035959,0.017647,0.0210413,0.065458,0.027971
+784 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45434751 2.05928e-06 118.383 232683 5 2.0976e-06 0.0892873 0.0757874,0.103834 0.154891,-0.139947,0.435331,0.524967,0.051256 0.0174575,0.0197245,0.0621792,0.0279716,0.0550949
+785 chr19_45406538,chr19_45407118,chr19_45411941,chr19_45412040,chr19_45412079 2.04706e-06 118.381 234071 5 2.08516e-06 0.0891664 0.0744454,0.103309 0.156617,0.0149209,-0.148183,0.434929,0.524601 0.0175415,0.0308416,0.0247048,0.062182,0.0279691
+786 chr19_45397466,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 2.0166e-06 118.374 237607 5 2.05414e-06 0.0892787 0.074804,0.103745 0.264972,0.421054,0.155281,-0.139393,0.524795 0.11616,0.0625137,0.0174361,0.0197223,0.0279794
+787 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426550 2.00322e-06 118.371 239194 5 2.04051e-06 0.0892576 0.0751303,0.105016 0.15568,-0.142971,0.435778,0.524888,0.0886084 0.0174366,0.0198372,0.0621835,0.0279708,0.102396
+788 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45421650 1.98929e-06 118.368 240869 5 2.02631e-06 0.0893094 0.0743944,0.103623 0.41819,0.161363,-0.126096,0.520471,-0.0795785 0.0624945,0.0176365,0.0208775,0.0279578,0.0349852
+789 chr19_45406538,chr19_45407437,chr19_45411941,chr19_45412040,chr19_45412079 1.97391e-06 118.365 242746 5 2.01064e-06 0.0891588 0.0756448,0.103713 0.156436,0.012335,-0.14703,0.434908,0.52449 0.0175413,0.0316605,0.0250948,0.0621836,0.0279679
+790 chr19_45386163,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.95786e-06 118.361 244736 5 1.9943e-06 0.089254 0.0762312,0.102565 0.0460764,0.155614,-0.139085,0.437018,0.526501 0.0546194,0.0174369,0.019817,0.0622198,0.0280706
+791 chr19_45381292,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.95561e-06 118.361 245017 5 1.99201e-06 0.0892601 0.0750894,0.102954 0.0401679,0.155365,-0.141603,0.432095,0.525536 0.0469536,0.0174407,0.0197122,0.0622798,0.0279947
+792 chr19_45401587,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45425212 1.94931e-06 118.36 245809 5 1.98559e-06 0.0892104 0.0746994,0.1045 0.310015,0.158201,-0.139734,0.528531,0.495834 0.0560782,0.0174452,0.0197276,0.0280063,0.109493
+793 chr19_45389174,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.94786e-06 118.359 245992 5 1.98411e-06 0.0891723 0.0754919,0.10389 -0.0111794,0.156062,-0.137176,0.443841,0.531404 0.0205538,0.0174504,0.0208507,0.0641832,0.0307022
+794 chr19_45390911,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.94688e-06 118.359 246116 5 1.98311e-06 0.0893079 0.0754694,0.103475 0.098389,0.155659,-0.140966,0.43588,0.518904 0.109205,0.0174369,0.0196967,0.0621856,0.0286425
+795 chr19_45385102,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.93547e-06 118.356 247567 5 1.97149e-06 0.0892666 0.0745265,0.103501 -0.0228929,0.155961,-0.141821,0.439323,0.522544 0.0267707,0.0174397,0.0197241,0.0623734,0.0280568
+796 chr19_45395064,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.90913e-06 118.351 250982 5 1.94466e-06 0.0893029 0.0757438,0.103996 0.0650108,0.155434,-0.140671,0.435142,0.519365 0.0737212,0.0174393,0.0196988,0.0621801,0.0285611
+797 chr19_45392254,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.90871e-06 118.35 251038 5 1.94423e-06 0.0891507 0.0758535,0.103769 -0.00310861,0.155674,-0.140824,0.438054,0.524466 0.0675298,0.0174398,0.0198026,0.0867822,0.0279683
+798 chr19_45381682,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.90189e-06 118.349 251938 5 1.93729e-06 0.0892638 0.0749592,0.10373 -0.0723229,0.155473,-0.138369,0.437169,0.52473 0.0866469,0.0174387,0.019934,0.0622282,0.0279687
+799 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433500 1.90089e-06 118.349 252070 5 1.93627e-06 0.0892408 0.0759921,0.105381 0.15807,-0.140783,0.43466,0.524426,-0.0651128 0.0176914,0.0196973,0.0621822,0.0279668,0.0817547
+800 chr19_45398014,chr19_45405931,chr19_45406538,chr19_45411941,chr19_45412079 1.89923e-06 118.348 252291 5 1.93458e-06 0.0892831 0.0756317,0.10475 0.324238,0.103521,0.157399,-0.136574,0.526467 0.0753566,0.046812,0.0174619,0.0198382,0.0280288
+801 chr19_45388250,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.89838e-06 118.348 252404 5 1.93371e-06 0.0892677 0.0759975,0.103497 -0.0819468,0.155364,-0.141467,0.434077,0.523522 0.0967111,0.0174409,0.0197068,0.0621919,0.0279889
+802 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425427 1.89526e-06 118.347 252820 5 1.93053e-06 0.0892994 0.0754636,0.105204 0.155643,-0.141114,0.435776,0.518857,0.0953668 0.017437,0.0196979,0.0621848,0.0287058,0.109804
+803 chr19_45400484,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.88928e-06 118.346 253620 5 1.92444e-06 0.0892431 0.0751395,0.104787 -0.0281032,0.157845,-0.135203,0.434496,0.52427 0.0383283,0.0176842,0.0211913,0.0621865,0.0279685
+804 chr19_45384031,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.85889e-06 118.339 257766 5 1.89348e-06 0.0892577 0.0756129,0.103209 0.0614731,0.156093,-0.14102,0.433199,0.523789 0.0752545,0.0174439,0.0196969,0.0622244,0.0279791
+805 chr19_45396219,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.85878e-06 118.339 257781 5 1.89337e-06 0.0893906 0.0750148,0.104119 0.0464658,0.386886,0.155506,-0.162601,0.492441 0.0204406,0.0639155,0.0174374,0.0217135,0.0308172
+806 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414451 1.85132e-06 118.337 258820 5 1.88578e-06 0.0892158 0.0760544,0.104108 0.156078,-0.137579,0.438626,0.528132,-0.0204514 0.0174455,0.0202499,0.0623742,0.0284372,0.0287543
+807 chr19_45384116,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.85099e-06 118.337 258867 5 1.88544e-06 0.0893145 0.0738373,0.104463 -0.0201258,0.154504,-0.141463,0.428283,0.51785 0.0236666,0.0174929,0.0197072,0.062703,0.0290363
+808 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45428234 1.84869e-06 118.337 259188 5 1.8831e-06 0.0892103 0.074466,0.104343 0.155218,-0.142491,0.451592,0.52231,-0.0211978 0.0174518,0.0198416,0.0670974,0.0281607,0.0324632
+809 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45435661 1.84318e-06 118.335 259963 5 1.87748e-06 0.089279 0.0756572,0.10329 0.3265,0.156716,-0.143892,0.523161,0.134363 0.0748344,0.0174482,0.0197157,0.0279579,0.0608795
+810 chr19_45402342,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.84267e-06 118.335 260035 5 1.87697e-06 0.0891536 0.07508,0.104363 0.00828853,0.156183,-0.144909,0.434951,0.524516 0.0318973,0.0175392,0.0248524,0.0621851,0.0279691
+811 chr19_45384859,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.84209e-06 118.335 260116 5 1.87638e-06 0.0892971 0.0753483,0.104282 0.0718806,0.155268,-0.141023,0.436053,0.518197 0.0861429,0.0174443,0.0196972,0.0621907,0.0289637
+812 chr19_45383583,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.83758e-06 118.334 260755 5 1.87178e-06 0.0892051 0.075993,0.103847 -0.050616,0.155814,-0.141467,0.450458,0.524081 0.0769468,0.0174381,0.0197138,0.0663956,0.0279732
+813 chr19_45383562,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.82942e-06 118.332 261918 5 1.86347e-06 0.0892282 0.0762754,0.103877 0.0987176,0.420433,0.155345,-0.145477,0.523815 0.0443592,0.0625087,0.0174363,0.0197602,0.0279641
+814 chr19_45388208,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.8264e-06 118.331 262351 5 1.86039e-06 0.0892462 0.0755431,0.104609 0.0792299,0.155932,-0.139991,0.435563,0.524791 0.100961,0.0174397,0.0197327,0.0621825,0.0279701
+815 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427213 1.8192e-06 118.33 263389 5 1.85306e-06 0.0892381 0.0756504,0.105179 0.156178,-0.143058,0.436559,0.523144,0.0262907 0.0174489,0.0198949,0.0622088,0.0280207,0.034576
+816 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45421204 1.80389e-06 118.326 265625 5 1.83746e-06 0.0892668 0.075684,0.103982 0.42074,0.162199,-0.123496,0.521074,-0.0856525 0.0625141,0.0177094,0.0213657,0.0279539,0.0386375
+817 chr19_45401952,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.80368e-06 118.326 265656 5 1.83725e-06 0.0892383 0.0745596,0.105514 0.0324265,0.156242,-0.139425,0.419634,0.525363 0.046111,0.0174551,0.0198127,0.0659759,0.0279967
+818 chr19_45406538,chr19_45410076,chr19_45411941,chr19_45412040,chr19_45412079 1.8014e-06 118.325 265993 5 1.83492e-06 0.0892362 0.074912,0.10426 0.155982,0.0763701,-0.139997,0.435955,0.525336 0.0174412,0.100921,0.019735,0.0621898,0.0279908
+819 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45437250 1.79737e-06 118.324 266589 5 1.83082e-06 0.0893023 0.0757208,0.103572 0.155971,-0.140712,0.435304,0.514777,0.0468192 0.0174415,0.0196992,0.0621822,0.030795,0.0619887
+820 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416478 1.79705e-06 118.324 266636 5 1.83049e-06 0.0892079 0.0755203,0.104632 0.156075,-0.137764,0.438554,0.527932,-0.0192903 0.0174466,0.0202527,0.06239,0.0284404,0.0287856
+821 chr19_45401918,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.79188e-06 118.323 267406 5 1.82523e-06 0.0892312 0.0743191,0.103369 0.0372373,0.156317,-0.139238,0.43535,0.52576 0.050401,0.0174578,0.019829,0.0621809,0.028022
+822 chr19_45386040,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.78999e-06 118.323 267688 5 1.8233e-06 0.0892532 0.0754733,0.104152 -0.0526475,0.155001,-0.13901,0.434584,0.524446 0.0680427,0.0174596,0.0198523,0.0621842,0.0279671
+823 chr19_45395266,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.78298e-06 118.321 268740 5 1.81617e-06 0.0892021 0.0759594,0.102998 -0.0175454,0.156017,-0.137392,0.438739,0.528125 0.0273717,0.0174446,0.0204519,0.062435,0.0285425
+824 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45417632 1.77895e-06 118.32 269349 5 1.81206e-06 0.08916 0.07514,0.103455 0.156008,-0.139551,0.433991,0.530246,-0.00771124 0.0174597,0.0200541,0.0622608,0.0321738,0.0214565
+825 chr19_45406538,chr19_45410273,chr19_45412079,chr19_45420086,chr19_45425212 1.77378e-06 118.319 270134 5 1.80679e-06 0.0888165 0.0748619,0.103098 0.169404,0.489273,0.553844,-0.173328,0.529269 0.0176972,0.0621199,0.027589,0.0342049,0.109347
+826 chr19_45380937,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.77362e-06 118.319 270159 5 1.80663e-06 0.0892462 0.0749776,0.10301 -0.0429588,0.155408,-0.142338,0.43328,0.523108 0.0566521,0.0174409,0.0197846,0.0622279,0.0280243
+827 chr19_45406538,chr19_45406613,chr19_45411941,chr19_45412040,chr19_45412079 1.77151e-06 118.318 270480 5 1.80448e-06 0.0892003 0.0757072,0.103919 0.155466,-0.072102,-0.140897,0.435579,0.528294 0.0174402,0.110289,0.0196967,0.062184,0.0285715
+828 chr19_45381748,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.76587e-06 118.317 271344 5 1.79874e-06 0.0892417 0.0755505,0.104484 -0.0430112,0.155563,-0.140681,0.434461,0.5243 0.057197,0.0174377,0.0196995,0.0621865,0.027968
+829 chr19_45385153,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.76262e-06 118.316 271844 5 1.79543e-06 0.0892802 0.0751409,0.104359 0.066702,0.155299,-0.141016,0.435985,0.518634 0.0861608,0.0174444,0.0196974,0.0621911,0.0289696
+830 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440761 1.76013e-06 118.315 272230 5 1.79288e-06 0.0892176 0.0753637,0.10367 0.155488,-0.138489,0.438178,0.526821,-0.0134847 0.0174394,0.0200122,0.0623384,0.0281758,0.0195859
+831 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45429099 1.75106e-06 118.313 273640 5 1.78364e-06 0.0892029 0.075539,0.105078 0.15504,-0.13485,0.43528,0.525703,-0.0148955 0.0174866,0.0232243,0.0621833,0.0280817,0.0300532
+832 chr19_45384529,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.74714e-06 118.312 274253 5 1.77966e-06 0.0892229 0.07477,0.105549 0.0389362,0.15562,-0.139413,0.436662,0.526085 0.0553488,0.0174372,0.0198141,0.0622189,0.028062
+833 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432506 1.74435e-06 118.311 274691 5 1.77682e-06 0.0892351 0.0754277,0.103425 0.155991,-0.139825,0.434304,0.524689,-0.0477796 0.017442,0.019755,0.0621906,0.0279689,0.0656391
+834 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45441419 1.73884e-06 118.31 275562 5 1.7712e-06 0.0892379 0.0757641,0.104655 0.155291,-0.139599,0.436304,0.524537,-0.0427225 0.0174455,0.0197811,0.0622017,0.0279674,0.0586102
+835 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45441433 1.73817e-06 118.31 275669 5 1.77052e-06 0.0892378 0.0751259,0.105212 0.155291,-0.1396,0.436303,0.524537,-0.0426915 0.0174455,0.0197811,0.0622017,0.0279674,0.0586102
+836 chr19_45397307,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.7371e-06 118.31 275837 5 1.76943e-06 0.0892127 0.0755163,0.104337 -0.0233394,0.157477,-0.136107,0.434797,0.524447 0.0393747,0.0176976,0.0213144,0.062184,0.0279677
+837 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425171 1.73447e-06 118.309 276257 5 1.76675e-06 0.0892241 0.0752933,0.105189 0.15612,-0.142879,0.436461,0.523287,0.0239733 0.0174481,0.0198951,0.0622103,0.0280182,0.0344318
+838 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427931 1.72047e-06 118.305 278504 5 1.75249e-06 0.0891724 0.0750236,0.104015 0.156364,-0.143067,0.438008,0.527415,0.00959221 0.0174879,0.0201439,0.0624407,0.0285669,0.0190087
+839 chr19_45390569,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.71875e-06 118.305 278783 5 1.75074e-06 0.0892157 0.0749987,0.103837 0.0699733,0.15567,-0.142488,0.43564,0.524809 0.103584,0.017437,0.0198316,0.0621853,0.0279719
+840 chr19_45390570,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.71875e-06 118.305 278783 5 1.75074e-06 0.0892157 0.0763648,0.104366 0.0699733,0.15567,-0.142488,0.43564,0.524809 0.103584,0.017437,0.0198316,0.0621853,0.0279719
+841 chr19_45397603,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.71849e-06 118.305 278825 5 1.75047e-06 0.0892155 0.0757679,0.104805 0.0697361,0.15567,-0.142504,0.435644,0.524806 0.103321,0.017437,0.0198347,0.0621854,0.0279718
+842 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425175 1.71501e-06 118.304 279391 5 1.74693e-06 0.0891961 0.0749862,0.104225 0.156035,-0.138099,0.438364,0.527582,-0.0173397 0.0174467,0.0202475,0.0624127,0.0284412,0.0288066
+843 chr19_45398014,chr19_45406538,chr19_45412079,chr19_45415640,chr19_45427780 1.71233e-06 118.303 279828 5 1.7442e-06 0.0881003 0.0739081,0.103052 0.451563,0.155946,0.631266,-0.102833,0.477261 0.062232,0.0174368,0.0307654,0.0203376,0.0973508
+844 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440529 1.6995e-06 118.3 281941 5 1.73113e-06 0.0892324 0.0747606,0.104391 0.15538,-0.143539,0.437692,0.520783,0.0121753 0.0174432,0.0200796,0.0622993,0.0285025,0.018152
+845 chr19_45386688,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.69453e-06 118.299 282768 5 1.72606e-06 0.0892336 0.0755422,0.103961 -0.0485327,0.15547,-0.142225,0.433461,0.522976 0.0695764,0.0174399,0.0197848,0.0622266,0.0280486
+846 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45424374 1.69424e-06 118.299 282816 5 1.72577e-06 0.0892096 0.0755547,0.103445 0.155849,-0.141157,0.436363,0.525703,0.0361213 0.0174388,0.0197,0.0622095,0.0280313,0.0552084
+847 chr19_45399456,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.6907e-06 118.298 283409 5 1.72216e-06 0.0892157 0.0759596,0.104318 0.0737486,0.155441,-0.14019,0.436423,0.525711 0.110933,0.017441,0.0197281,0.0622115,0.0280302
+848 chr19_45406538,chr19_45412040,chr19_45412079,chr19_45412955,chr19_45427780 1.6773e-06 118.294 285673 5 1.70851e-06 0.0888457 0.0750211,0.103521 0.167616,0.475313,0.552101,-0.16398,0.504558 0.0176542,0.0618444,0.0275395,0.0342793,0.0970931
+849 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421237 1.66896e-06 118.292 287101 5 1.70002e-06 0.0892274 0.0749073,0.104235 0.155409,-0.141229,0.43562,0.523606,-0.0772452 0.017442,0.019702,0.0621853,0.027996,0.114038
+850 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45429941 1.66866e-06 118.292 287152 5 1.69971e-06 0.0892295 0.0753483,0.104308 0.15568,-0.141684,0.434658,0.522986,-0.0427907 0.0174372,0.0197285,0.0621848,0.0280521,0.063047
+851 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421877 1.66806e-06 118.292 287255 5 1.69911e-06 0.0891891 0.0760027,0.103606 0.156026,-0.138253,0.438175,0.527354,-0.0159572 0.0174478,0.0202731,0.0624219,0.0284444,0.028696
+852 chr19_45398014,chr19_45406538,chr19_45409113,chr19_45411941,chr19_45412079 1.66421e-06 118.291 287919 5 1.69518e-06 0.0892132 0.0745545,0.104139 0.41989,0.15711,0.113088,-0.136944,0.525678 0.0625051,0.0174553,0.0516442,0.0198207,0.0280049
+853 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414887 1.66382e-06 118.291 287986 5 1.69479e-06 0.089198 0.0753521,0.103845 0.155734,-0.138237,0.43778,0.52678,0.0187496 0.0174374,0.0202263,0.0623468,0.0282463,0.0320686
+854 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426105 1.65889e-06 118.289 288843 5 1.68976e-06 0.0891659 0.0764217,0.104172 0.156261,-0.143179,0.437356,0.526742,0.0083308 0.0174876,0.020375,0.0623962,0.0284612,0.0193924
+855 chr19_45393247,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.65763e-06 118.289 289062 5 1.68848e-06 0.0891598 0.0755239,0.104239 0.10308,0.418037,0.156735,-0.152866,0.522059 0.0479581,0.0624947,0.0174487,0.0203486,0.0279513
+856 chr19_45398360,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.65735e-06 118.289 289111 5 1.6882e-06 0.0892217 0.0758538,0.104096 -0.0454005,0.155661,-0.138436,0.435296,0.524765 0.0706031,0.0174373,0.0200754,0.0621818,0.0279715
+857 chr19_45398014,chr19_45402262,chr19_45406538,chr19_45411941,chr19_45412079 1.65049e-06 118.287 290312 5 1.68121e-06 0.0886941 0.0746965,0.103747 0.417098,-0.0661566,0.154698,-0.141248,0.572945 0.062493,0.0407638,0.0174409,0.0196965,0.0419636
+858 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431658 1.64702e-06 118.286 290924 5 1.67768e-06 0.0892286 0.0753835,0.103152 0.156822,-0.141323,0.432789,0.52188,0.016697 0.0175241,0.0197064,0.0622843,0.0282471,0.0255869
+859 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426813 1.64616e-06 118.286 291076 5 1.6768e-06 0.0891711 0.0763379,0.104592 0.155406,-0.138321,0.435102,0.524423,-0.00613314 0.017497,0.0237826,0.0621833,0.0279689,0.030957
+860 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425460 1.64492e-06 118.286 291296 5 1.67553e-06 0.0891864 0.0754624,0.104937 0.155995,-0.138409,0.437997,0.527214,-0.0154624 0.0174467,0.0202468,0.062411,0.0284322,0.0288388
+861 chr19_45398206,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.63657e-06 118.284 292783 5 1.66702e-06 0.0892015 0.0744304,0.104424 -0.0132101,0.15505,-0.136305,0.434779,0.524579 0.0292218,0.017495,0.0222153,0.0621871,0.0279695
+862 chr19_45406538,chr19_45407131,chr19_45411941,chr19_45412040,chr19_45412079 1.63212e-06 118.282 293581 5 1.66249e-06 0.0892119 0.0744792,0.103193 0.155924,0.0320782,-0.139595,0.43718,0.524665 0.0174414,0.0516468,0.0198136,0.0622695,0.0279694
+863 chr19_45390044,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.62641e-06 118.281 294611 5 1.65668e-06 0.0891768 0.0746405,0.103405 -0.0450835,0.155737,-0.140827,0.435179,0.527857 0.0898128,0.0174375,0.019698,0.0621812,0.0287675
+864 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45420343 1.62617e-06 118.281 294655 5 1.65643e-06 0.0891631 0.0751102,0.103888 0.156196,-0.143,0.437141,0.526537,0.00759284 0.0174853,0.0203916,0.062394,0.0284627,0.0194524
+865 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426637 1.6246e-06 118.28 294940 5 1.65483e-06 0.0891646 0.0758691,0.102844 0.156249,-0.142862,0.437393,0.526576,0.00778834 0.0174908,0.0202517,0.0624269,0.0284368,0.019063
+866 chr19_45396673,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.62116e-06 118.28 295566 5 1.65133e-06 0.0891584 0.075958,0.104154 -0.00337333,0.155816,-0.139324,0.435007,0.524623 0.0268811,0.0174691,0.0235662,0.0621891,0.0280007
+867 chr19_45406538,chr19_45407610,chr19_45411941,chr19_45412040,chr19_45412079 1.61928e-06 118.279 295908 5 1.64942e-06 0.0891696 0.0749841,0.104143 0.155915,-0.0416777,-0.140962,0.435546,0.528062 0.0174444,0.0916747,0.0196972,0.0621881,0.0290549
+868 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426578 1.61911e-06 118.279 295939 5 1.64925e-06 0.0891999 0.0746012,0.103635 0.155795,-0.142375,0.435131,0.524618,0.0549324 0.0174382,0.0198511,0.0621812,0.0279688,0.0937548
+869 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440270 1.61151e-06 118.277 297335 5 1.6415e-06 0.0891621 0.0758849,0.104162 0.155479,-0.140996,0.434952,0.528348,-0.0143173 0.017446,0.0196981,0.0621832,0.0298072,0.0382791
+870 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436415 1.6073e-06 118.276 298113 5 1.63722e-06 0.0891756 0.074542,0.10396 0.155692,-0.141479,0.4455,0.523492,-0.0163836 0.0174374,0.0197381,0.0666121,0.0280569,0.0377551
+871 chr19_45381298,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.60107e-06 118.274 299274 5 1.63087e-06 0.089195 0.0752325,0.103715 0.0243948,0.155844,-0.140603,0.436205,0.525765 0.043011,0.0174394,0.0197053,0.0622105,0.0280614
+872 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418251 1.60068e-06 118.274 299348 5 1.63047e-06 0.0892163 0.0759813,0.104087 0.155307,-0.14324,0.433218,0.521868,-0.0208794 0.0174493,0.0200877,0.0622664,0.0283154,0.035516
+873 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45435813 1.60032e-06 118.274 299414 5 1.63011e-06 0.0891742 0.0743745,0.102981 0.155681,-0.141448,0.445307,0.523529,-0.016011 0.0174375,0.0197357,0.0666768,0.0280548,0.0378827
+874 chr19_45381340,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.59593e-06 118.273 300238 5 1.62563e-06 0.089207 0.0743312,0.104962 0.0565858,0.155768,-0.140997,0.435921,0.524258 0.0950343,0.0174378,0.0196974,0.0621958,0.0279697
+875 chr19_45388879,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.58644e-06 118.27 302033 5 1.61597e-06 0.0891475 0.0755355,0.103812 0.101839,0.418052,0.156726,-0.152753,0.522067 0.047822,0.0624952,0.0174489,0.0203473,0.0279515
+876 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427437 1.58278e-06 118.269 302732 5 1.61224e-06 0.0892074 0.0764057,0.103337 0.155488,-0.141736,0.434151,0.523925,-0.0496563 0.0174406,0.0197456,0.0622036,0.0279828,0.0848918
+877 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45427908 1.58226e-06 118.269 302831 5 1.61171e-06 0.0892073 0.0757153,0.10327 0.155487,-0.141736,0.434158,0.523926,-0.0496054 0.0174406,0.0197456,0.0622034,0.0279827,0.0848918
+878 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416178 1.5812e-06 118.269 303034 5 1.61063e-06 0.0891522 0.0751881,0.103504 0.155607,-0.140639,0.434968,0.524882,-0.000632159 0.0176081,0.0215581,0.062376,0.0307418,0.0203297
+879 chr19_45406538,chr19_45406798,chr19_45411941,chr19_45412040,chr19_45412079 1.57634e-06 118.267 303969 5 1.60567e-06 0.0892081 0.0766044,0.103792 0.155294,-0.0152642,-0.143646,0.432396,0.521612 0.017453,0.0285999,0.0203489,0.0623929,0.0284763
+880 chr19_45404130,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.5743e-06 118.267 304362 5 1.6036e-06 0.0892352 0.0769899,0.103448 0.0327525,0.155755,-0.140899,0.435433,0.518236 0.0660026,0.0174382,0.0196976,0.062186,0.0306933
+881 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418790 1.57428e-06 118.267 304366 5 1.60358e-06 0.0891694 0.0748414,0.103853 0.155664,-0.141038,0.435224,0.524025,0.000675331 0.0174572,0.0201716,0.0622677,0.0322818,0.0214943
+882 chr19_45381091,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.56729e-06 118.265 305724 5 1.59646e-06 0.0891947 0.0751899,0.103176 0.0369384,0.155536,-0.139983,0.436261,0.525537 0.0681134,0.0174395,0.019774,0.062217,0.0280376
+883 chr19_45397512,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.56331e-06 118.264 306503 5 1.5924e-06 0.0891707 0.0748143,0.10287 -0.0113883,0.155885,-0.138624,0.437508,0.526865 0.0276444,0.0174441,0.0204706,0.0624493,0.0285654
+884 chr19_45391600,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.56103e-06 118.263 306950 5 1.59008e-06 0.0891705 0.0743732,0.103412 -0.0486763,0.15574,-0.140741,0.435468,0.527278 0.111418,0.0174377,0.0197017,0.0621866,0.0286946
+885 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45423636 1.5519e-06 118.261 308756 5 1.58078e-06 0.0891651 0.073906,0.104574 0.156027,-0.142848,0.439948,0.522557,-0.00747769 0.0174728,0.0206658,0.0641391,0.0286479,0.0243579
+886 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418961 1.55179e-06 118.261 308778 5 1.58067e-06 0.089171 0.0745146,0.102601 0.155951,-0.138783,0.43749,0.526673,-0.0114936 0.0174492,0.0203727,0.0624465,0.0284768,0.0279582
+887 chr19_45405049,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.55157e-06 118.26 308823 5 1.58044e-06 0.0891617 0.0746957,0.104307 -0.0334786,0.155739,-0.140892,0.435116,0.527536 0.0946292,0.017438,0.0196974,0.0621818,0.0292682
+888 chr19_45398014,chr19_45402477,chr19_45406538,chr19_45411941,chr19_45412079 1.55074e-06 118.26 308987 5 1.5796e-06 0.089241 0.0750879,0.103768 0.391483,-0.0464295,0.156914,-0.16721,0.497465 0.0636604,0.0217689,0.0174533,0.0230126,0.0302128
+889 chr19_45388241,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.54788e-06 118.259 309558 5 1.57669e-06 0.089186 0.0748846,0.104999 0.00477304,0.155512,-0.142472,0.431441,0.521591 0.0206291,0.0174545,0.0208434,0.0642612,0.0306928
+890 chr19_45392213,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.54425e-06 118.258 310286 5 1.57299e-06 0.0891991 0.0755326,0.104077 -0.0366888,0.155661,-0.138929,0.43525,0.524704 0.0705321,0.0174375,0.0200695,0.0621824,0.0279717
+891 chr19_45406538,chr19_45409167,chr19_45411941,chr19_45412040,chr19_45412079 1.54288e-06 118.258 310560 5 1.5716e-06 0.0891719 0.0751317,0.102562 0.15589,-0.0120318,-0.138868,0.437233,0.52656 0.0174443,0.0290949,0.0203141,0.0623901,0.0284216
+892 chr19_45404000,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.54184e-06 118.258 310770 5 1.57054e-06 0.0891934 0.0749698,0.103551 0.0250268,0.155927,-0.139717,0.436838,0.524796 0.0483212,0.0174435,0.0198354,0.0622701,0.0279752
+893 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45416996 1.54147e-06 118.258 310845 5 1.57016e-06 0.0891617 0.0751736,0.103639 0.15585,-0.140957,0.435435,0.527323,-0.032149 0.0174435,0.0196974,0.0621881,0.0291117,0.0914609
+894 chr19_45394969,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.54112e-06 118.258 310916 5 1.5698e-06 0.0891712 0.0755247,0.104663 0.00226191,0.155617,-0.141713,0.433704,0.523035 0.0199042,0.017449,0.0209717,0.0635215,0.0308518
+895 chr19_45406538,chr19_45408836,chr19_45411941,chr19_45412040,chr19_45412079 1.53928e-06 118.257 311288 5 1.56792e-06 0.0891574 0.074809,0.103969 0.156146,-0.00579551,-0.142662,0.436003,0.52542 0.0175202,0.0216599,0.0207184,0.0622688,0.0281936
+896 chr19_45406538,chr19_45408312,chr19_45411941,chr19_45412040,chr19_45412079 1.53481e-06 118.256 312193 5 1.56338e-06 0.0892006 0.0752385,0.104817 0.155476,-0.0480673,-0.141696,0.434898,0.523536 0.0174419,0.0899178,0.0197497,0.0621832,0.0280216
+897 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45428083 1.5335e-06 118.255 312461 5 1.56204e-06 0.089158 0.0754618,0.102614 0.156032,-0.142294,0.436593,0.525928,0.00537371 0.0174809,0.020293,0.0624044,0.0284543,0.019358
+898 chr19_45386029,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.53257e-06 118.255 312650 5 1.56109e-06 0.0891688 0.0751541,0.103133 0.00988321,0.156294,-0.140701,0.437075,0.526434 0.0237606,0.0174979,0.0197046,0.0623571,0.0283628
+899 chr19_45398264,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.52765e-06 118.254 313656 5 1.55608e-06 0.089166 0.0757548,0.104383 -0.0100164,0.155861,-0.138911,0.4372,0.52655 0.0277522,0.0174442,0.020469,0.0624461,0.0285552
+900 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421926 1.52666e-06 118.253 313860 5 1.55508e-06 0.0891813 0.0758144,0.102699 0.155877,-0.140188,0.437178,0.525623,0.0187264 0.017442,0.0197588,0.0623329,0.0280747,0.0395594
+901 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418800 1.5257e-06 118.253 314058 5 1.5541e-06 0.0891604 0.0756334,0.102674 0.15584,-0.140954,0.435416,0.527136,-0.0300554 0.0174436,0.0196975,0.0621883,0.029112,0.091535
+902 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431897 1.52567e-06 118.253 314065 5 1.55406e-06 0.0891896 0.0751974,0.104316 0.155582,-0.142981,0.431451,0.523388,-0.00870275 0.0174389,0.0201805,0.062675,0.0280622,0.0185784
+903 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440274 1.51968e-06 118.251 315302 5 1.54796e-06 0.0891806 0.0764974,0.104548 0.155751,-0.141125,0.434922,0.52562,-0.029003 0.0174379,0.0197017,0.0621832,0.0280761,0.0618042
+904 chr19_45395980,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.51954e-06 118.251 315332 5 1.54782e-06 0.0891969 0.0747166,0.104493 -0.0529597,0.155627,-0.141333,0.434769,0.523831 0.102412,0.0174378,0.0197129,0.0621856,0.0279945
+905 chr19_45389198,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.51846e-06 118.251 315556 5 1.54672e-06 0.0891836 0.0751196,0.104479 0.0268778,0.155615,-0.139942,0.436434,0.525582 0.0567586,0.0174381,0.0198067,0.0622436,0.0280674
+906 chr19_45388581,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.51725e-06 118.251 315806 5 1.54549e-06 0.0892293 0.0753824,0.104501 -0.113873,0.418921,0.153851,-0.135014,0.524078 0.0524278,0.0624998,0.0174505,0.0199443,0.0279701
+907 chr19_45398014,chr19_45403412,chr19_45406538,chr19_45411941,chr19_45412079 1.51381e-06 118.25 316526 5 1.54198e-06 0.0888597 0.076319,0.102936 0.428543,-0.0438166,0.155561,-0.171713,0.530864 0.062799,0.0239907,0.0174375,0.0255737,0.0283808
+908 chr19_45383830,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.51158e-06 118.249 316991 5 1.53971e-06 0.0891598 0.0739747,0.103562 0.0636715,0.413345,0.155849,-0.14559,0.525727 0.0298752,0.0625217,0.0174385,0.0197715,0.0280096
+909 chr19_45398014,chr19_45404431,chr19_45406538,chr19_45411941,chr19_45412079 1.51139e-06 118.249 317031 5 1.53952e-06 0.088859 0.0755022,0.103508 0.428532,-0.0438096,0.155566,-0.171714,0.53087 0.0627988,0.0240013,0.0174375,0.0255805,0.0283819
+910 chr19_45381300,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.51083e-06 118.249 317149 5 1.53895e-06 0.0891993 0.0759899,0.103099 -0.0226969,0.155403,-0.140943,0.434865,0.523736 0.044053,0.0174461,0.0196973,0.062184,0.0280034
+911 chr19_45386855,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.5051e-06 118.247 318356 5 1.53311e-06 0.0891691 0.0747597,0.104124 -0.00888345,0.155442,-0.138698,0.438757,0.5252 0.0271677,0.0174537,0.0208451,0.0631706,0.0280593
+912 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45426458 1.50467e-06 118.247 318447 5 1.53268e-06 0.0892213 0.0755732,0.102601 0.417592,0.15486,-0.144117,0.519658,-0.259247 0.0624952,0.0174384,0.0197215,0.0279706,0.119557
+913 chr19_45382951,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.50269e-06 118.247 318868 5 1.53065e-06 0.0891784 0.0756942,0.103857 -0.0156457,0.155622,-0.13993,0.434535,0.525982 0.0356648,0.0174381,0.0198278,0.0621964,0.0281809
+914 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432244 1.50169e-06 118.246 319079 5 1.52964e-06 0.0891889 0.0750908,0.103381 0.155834,-0.14146,0.434546,0.524128,-0.03613 0.0174401,0.0197282,0.0621934,0.0279765,0.0748969
+915 chr19_45382859,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.49984e-06 118.246 319473 5 1.52775e-06 0.0891879 0.0747097,0.104791 -0.0482025,0.155981,-0.141349,0.435217,0.524052 0.100842,0.0174483,0.019717,0.0621823,0.0279811
+916 chr19_45398716,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.49949e-06 118.246 319548 5 1.5274e-06 0.0891631 0.0750191,0.104427 -0.00920859,0.155903,-0.13921,0.436974,0.526257 0.0283251,0.0174502,0.020388,0.0624405,0.0285031
+917 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432480 1.49918e-06 118.246 319613 5 1.52708e-06 0.0891929 0.0747338,0.103847 0.155683,-0.141017,0.435297,0.523894,-0.0434503 0.0174374,0.0196981,0.0621831,0.0279918,0.0883699
+918 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421744 1.49903e-06 118.245 319645 5 1.52693e-06 0.0891563 0.075237,0.104243 0.15552,-0.13987,0.432681,0.525489,0.00386368 0.0174686,0.0207688,0.0640092,0.02869,0.0240277
+919 chr19_45404949,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.48929e-06 118.243 321737 5 1.517e-06 0.0891767 0.0747458,0.103412 0.0401448,0.155634,-0.142079,0.435453,0.5246 0.0946248,0.0174379,0.019883,0.062187,0.0279698
+920 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45417638 1.48295e-06 118.241 323112 5 1.51055e-06 0.0891743 0.0748493,0.103969 0.15579,-0.140762,0.436017,0.525513,-0.0375154 0.0174392,0.0197013,0.0622189,0.0280802,0.0898056
+921 chr19_45403511,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.47731e-06 118.239 324345 5 1.50481e-06 0.0891747 0.0751596,0.103929 0.0386883,0.155643,-0.14203,0.435434,0.524585 0.0952179,0.0174378,0.0198829,0.062187,0.0279696
+922 chr19_45382954,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.47643e-06 118.239 324539 5 1.50391e-06 0.0891738 0.0755592,0.104447 -0.0142333,0.155628,-0.140019,0.434588,0.525845 0.0356469,0.0174381,0.019828,0.0621965,0.0281809
+923 chr19_45406538,chr19_45409235,chr19_45411941,chr19_45412040,chr19_45412079 1.47487e-06 118.238 324882 5 1.50232e-06 0.0891742 0.0755438,0.103863 0.155636,0.0380131,-0.142021,0.435434,0.524593 0.0174379,0.0945419,0.0198838,0.0621871,0.0279699
+924 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421163 1.47474e-06 118.238 324910 5 1.50219e-06 0.0891834 0.0757,0.104405 0.155899,-0.141882,0.434073,0.52373,-0.026872 0.0174444,0.019819,0.0622289,0.0280185,0.0616929
+925 chr19_45390743,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.47425e-06 118.238 325018 5 1.50169e-06 0.0891876 0.0753674,0.104019 -0.0422945,0.155496,-0.139857,0.434787,0.524547 0.0949412,0.0174428,0.0198437,0.0621868,0.0279687
+926 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45413070 1.47235e-06 118.238 325438 5 1.49975e-06 0.0891653 0.0747564,0.104034 0.15549,-0.141287,0.43489,0.526052,-0.0226673 0.0174464,0.0197238,0.0621857,0.0283281,0.064427
+927 chr19_45382717,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.46976e-06 118.237 326012 5 1.49711e-06 0.0891949 0.0757098,0.10402 -0.00753723,0.155409,-0.142195,0.430416,0.521838 0.019957,0.0174533,0.0199863,0.0634359,0.0288307
+928 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45426567 1.46755e-06 118.236 326502 5 1.49487e-06 0.0891815 0.0752642,0.103442 0.155624,-0.140912,0.437234,0.524149,-0.0337908 0.0174381,0.0196974,0.0623774,0.0279775,0.0788317
+929 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414109 1.4642e-06 118.235 327249 5 1.49145e-06 0.0891862 0.0758013,0.102985 0.155424,-0.142526,0.433081,0.522451,-0.0105933 0.0174509,0.0201283,0.0624072,0.0284495,0.0273278
+930 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45431636 1.46145e-06 118.234 327864 5 1.48865e-06 0.0891751 0.0749692,0.103891 0.155566,-0.142573,0.432157,0.523578,-0.00655955 0.0174409,0.0202419,0.0627472,0.0280802,0.0185506
+931 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45437717 1.45839e-06 118.234 328552 5 1.48554e-06 0.0891835 0.0747637,0.1028 0.155467,-0.141719,0.434365,0.523773,-0.0213986 0.0174453,0.0197867,0.0622083,0.0280159,0.0507293
+932 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45429177 1.45766e-06 118.233 328717 5 1.48479e-06 0.0891778 0.0754797,0.103768 0.156026,-0.141353,0.436018,0.523995,0.0134394 0.0174574,0.0197251,0.0622214,0.0279915,0.0330083
+933 chr19_45406538,chr19_45407243,chr19_45411941,chr19_45412040,chr19_45412079 1.45608e-06 118.233 329075 5 1.48318e-06 0.0891709 0.0752592,0.104437 0.155638,0.0353228,-0.141938,0.435409,0.524579 0.017438,0.0951386,0.019884,0.0621872,0.0279698
+934 chr19_45391751,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.45522e-06 118.233 329269 5 1.4823e-06 0.0891808 0.0742287,0.10478 -0.0470637,0.155683,-0.141324,0.434454,0.524218 0.112754,0.0174375,0.0197203,0.0622028,0.0279742
+935 chr19_45383216,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.45404e-06 118.232 329537 5 1.4811e-06 0.0891764 0.0749783,0.103267 0.04586,0.155689,-0.140823,0.435372,0.524844 0.113636,0.0174375,0.0196991,0.0621854,0.027984
+936 chr19_45392422,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.44941e-06 118.231 330590 5 1.47638e-06 0.0891692 0.0746866,0.104583 0.0152971,0.155568,-0.139917,0.436377,0.525482 0.0435144,0.0174408,0.0199056,0.0622847,0.0281176
+937 chr19_45389346,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.44561e-06 118.23 331457 5 1.47252e-06 0.0891931 0.0738787,0.104197 0.0358514,0.155833,-0.140861,0.435222,0.521825 0.0932981,0.0174419,0.0196983,0.0621834,0.0288068
+938 chr19_45383432,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.44128e-06 118.228 332454 5 1.4681e-06 0.0891796 0.0757349,0.10397 -0.044194,0.155623,-0.140081,0.434856,0.52452 0.113027,0.0174383,0.0198164,0.0621862,0.0279685
+939 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45415935 1.44052e-06 118.228 332628 5 1.46733e-06 0.0891877 0.0751567,0.104859 0.155397,-0.141979,0.432861,0.522375,-0.00885857 0.0174555,0.0199061,0.0624885,0.0285434,0.0240583
+940 chr19_45397668,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.43886e-06 118.228 333012 5 1.46564e-06 0.0891584 0.0752873,0.103171 -0.0292207,0.155615,-0.14086,0.435347,0.526015 0.111913,0.0174397,0.0196991,0.0621884,0.0285856
+941 chr19_45387210,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.4318e-06 118.226 334655 5 1.45845e-06 0.0891681 0.0750459,0.105501 0.0170867,0.15567,-0.140175,0.435847,0.525275 0.0509195,0.0174376,0.0198243,0.0622201,0.0280724
+942 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45420493 1.43092e-06 118.225 334860 5 1.45755e-06 0.0891606 0.0747371,0.105339 0.155609,-0.141871,0.43427,0.523532,-0.00387146 0.0174454,0.0206999,0.0624484,0.0286638,0.0258467
+943 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45433892 1.42698e-06 118.224 335784 5 1.45354e-06 0.089191 0.0759349,0.104668 0.41448,0.157769,-0.143452,0.519979,-0.183934 0.0625092,0.0174742,0.0197082,0.0279662,0.0861474
+944 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414749 1.42624e-06 118.224 335959 5 1.45278e-06 0.0891732 0.0766681,0.104627 0.155854,-0.141695,0.434267,0.523872,-0.0215475 0.0174442,0.0198188,0.0622303,0.0280188,0.0614157
+945 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45421885 1.42523e-06 118.224 336197 5 1.45175e-06 0.089173 0.0756856,0.104304 0.155853,-0.14169,0.434279,0.523874,-0.0214295 0.0174441,0.0198184,0.0622295,0.028019,0.0614143
+946 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45422637 1.42393e-06 118.223 336504 5 1.45043e-06 0.0891727 0.0749479,0.104597 0.155852,-0.141683,0.434287,0.523881,-0.0212669 0.0174442,0.019818,0.0622292,0.0280186,0.0614128
+947 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414413 1.42131e-06 118.222 337124 5 1.44776e-06 0.0891724 0.0746811,0.103168 0.155862,-0.141654,0.434326,0.523858,-0.0214715 0.0174452,0.0198119,0.062226,0.0280239,0.0627476
+948 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436816 1.42048e-06 118.222 337322 5 1.44692e-06 0.089175 0.0747173,0.103311 0.155492,-0.141596,0.434507,0.523867,-0.0178108 0.0174464,0.0197888,0.0622071,0.0280193,0.0505647
+949 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45438593 1.41833e-06 118.221 337833 5 1.44473e-06 0.0891684 0.0747702,0.103926 0.15575,-0.141217,0.435,0.524687,0.0387632 0.0174386,0.0197165,0.0621836,0.0279763,0.116398
+950 chr19_45402124,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.41497e-06 118.22 338636 5 1.4413e-06 0.0891603 0.0743112,0.103884 0.000371568,0.155687,-0.140924,0.435124,0.524446 0.084406,0.0174378,0.0196984,0.0621831,0.0294507
+951 chr19_45384800,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.41299e-06 118.22 339110 5 1.43928e-06 0.0891701 0.0757307,0.103183 0.0363341,0.155523,-0.141057,0.434548,0.524687 0.108844,0.0174445,0.0197013,0.0622063,0.0279762
+952 chr19_45380970,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.41082e-06 118.219 339631 5 1.43708e-06 0.0891581 0.0749219,0.104326 0.00350308,0.155563,-0.139949,0.435714,0.525156 0.0179606,0.0174492,0.020325,0.0622572,0.0281932
+953 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414140 1.4108e-06 118.219 339635 5 1.43706e-06 0.0891607 0.0753915,0.102785 0.155791,-0.141327,0.43581,0.525016,0.0123193 0.0174418,0.0197521,0.062234,0.0280419,0.0453385
+954 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414141 1.4102e-06 118.219 339781 5 1.43644e-06 0.0891606 0.0757519,0.103838 0.15579,-0.141325,0.435807,0.525013,0.0122514 0.0174418,0.019752,0.062234,0.0280419,0.0453118
+955 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45418562 1.40941e-06 118.219 339972 5 1.43564e-06 0.0891629 0.0744496,0.103604 0.155671,-0.141654,0.43538,0.524565,0.025424 0.0174377,0.0198662,0.0621896,0.0279706,0.0905322
+956 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45425498 1.40922e-06 118.219 340018 5 1.43544e-06 0.0891695 0.0759415,0.103928 0.15584,-0.141608,0.434391,0.523936,-0.0193756 0.0174445,0.0198157,0.0622259,0.0280182,0.0614965
+957 chr19_45406538,chr19_45407226,chr19_45411941,chr19_45412040,chr19_45412079 1.4087e-06 118.218 340142 5 1.43492e-06 0.0891713 0.0754651,0.103175 0.155507,-0.0126345,-0.141836,0.434284,0.5236 0.0174476,0.0414105,0.0199225,0.0622436,0.0281115
+958 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436216 1.40804e-06 118.218 340302 5 1.43425e-06 0.0891605 0.0766037,0.105144 0.155816,-0.140487,0.436795,0.525117,0.00485452 0.0174446,0.0197687,0.0625087,0.0280789,0.0185464
+959 chr19_45395318,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.40761e-06 118.218 340405 5 1.43381e-06 0.0891616 0.0759172,0.103641 0.0113722,0.155597,-0.140168,0.436056,0.525239 0.043556,0.017441,0.0199105,0.0622854,0.0281255
+960 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45429286 1.40654e-06 118.218 340666 5 1.43271e-06 0.0891686 0.0751836,0.103444 0.155738,-0.141264,0.4357,0.524207,-0.0252622 0.0174384,0.0197259,0.0622094,0.0279797,0.079503
+961 chr19_45399234,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.40618e-06 118.218 340751 5 1.43235e-06 0.0891625 0.0751309,0.104764 0.0258225,0.155654,-0.141637,0.435347,0.524565 0.0943378,0.017438,0.0198667,0.0621882,0.0279707
+962 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45440199 1.40229e-06 118.217 341698 5 1.42839e-06 0.0891642 0.0760625,0.104447 0.155815,-0.141439,0.434894,0.524515,0.0268737 0.0174434,0.0197774,0.0621877,0.0279688,0.0934361
+963 chr19_45405552,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.40018e-06 118.216 342212 5 1.42624e-06 0.0891599 0.0746571,0.103306 0.0102109,0.155598,-0.140218,0.435993,0.525202 0.0431698,0.0174417,0.0199233,0.0622915,0.0281418
+964 chr19_45405694,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.39905e-06 118.216 342488 5 1.42509e-06 0.0891597 0.0742575,0.10286 0.0100818,0.155599,-0.140228,0.435981,0.525192 0.0431803,0.0174417,0.0199232,0.0622914,0.0281416
+965 chr19_45395218,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.39834e-06 118.215 342662 5 1.42437e-06 0.0891645 0.0754162,0.10385 0.0079958,0.155678,-0.140941,0.435165,0.523719 0.0898163,0.017438,0.0196986,0.0621851,0.0292586
+966 chr19_45396240,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.39782e-06 118.215 342789 5 1.42384e-06 0.089155 0.0752327,0.104109 -0.0171153,0.155644,-0.140879,0.435264,0.525388 0.112702,0.0174399,0.0196999,0.0621897,0.0286106
+967 chr19_45393680,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.39619e-06 118.215 343189 5 1.42218e-06 0.0891691 0.0750262,0.103346 -0.0357819,0.15565,-0.140275,0.434962,0.524483 0.12102,0.0174381,0.0198213,0.0621851,0.0279683
+968 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432127 1.39604e-06 118.215 343228 5 1.42202e-06 0.0891597 0.0759343,0.103494 0.155845,-0.140077,0.435998,0.524743,0.00537419 0.0174541,0.0201111,0.0623237,0.0280004,0.025635
+969 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432126 1.39474e-06 118.214 343548 5 1.42069e-06 0.0891589 0.0757635,0.104348 0.155852,-0.140105,0.435954,0.524758,0.00502586 0.0174574,0.0201198,0.0623216,0.0280071,0.0250771
+970 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45422587 1.39462e-06 118.214 343576 5 1.42058e-06 0.0891622 0.0755207,0.104374 0.155908,-0.140985,0.436196,0.524588,0.00711205 0.0174572,0.0196987,0.0623115,0.0279721,0.0265405
+971 chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079,chr19_45428459 1.39344e-06 118.214 343867 5 1.41937e-06 0.089212 0.0752443,0.104751 0.404917,0.158891,-0.154119,0.508472,-0.0586762 0.0627675,0.0175175,0.0205245,0.0286577,0.0276208
+972 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45414479 1.39296e-06 118.214 343987 5 1.41888e-06 0.0891658 0.075207,0.105926 0.155762,-0.140668,0.435342,0.524609,0.0324441 0.0174396,0.0197181,0.0621875,0.0279728,0.112443
+973 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432089 1.39266e-06 118.214 344060 5 1.41858e-06 0.0891549 0.0751727,0.105349 0.155717,-0.14033,0.43612,0.524762,0.002374 0.0174394,0.0202518,0.0626784,0.0280676,0.018744
+974 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433610 1.39195e-06 118.213 344234 5 1.41786e-06 0.0891618 0.0751551,0.103615 0.155863,-0.140456,0.435623,0.524872,0.0125744 0.0174512,0.0197829,0.0622136,0.0280138,0.0490897
+975 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45415713 1.39155e-06 118.213 344334 5 1.41745e-06 0.0891678 0.0752142,0.10287 0.155539,-0.141583,0.433888,0.523314,-0.00491875 0.0174533,0.0199714,0.0624908,0.0285567,0.0244585
+976 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45436244 1.3894e-06 118.213 344868 5 1.41525e-06 0.0891579 0.0758796,0.10406 0.155781,-0.140567,0.436474,0.525002,0.00397794 0.0174432,0.0197683,0.062499,0.0280802,0.0185358
+977 chr19_45383240,chr19_45398014,chr19_45406538,chr19_45411941,chr19_45412079 1.38895e-06 118.212 344978 5 1.4148e-06 0.0891582 0.0758609,0.104671 0.13793,0.416003,0.156358,-0.14639,0.52183 0.0654719,0.0624985,0.0174438,0.0198114,0.027952
+978 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45439908 1.38858e-06 118.212 345070 5 1.41443e-06 0.0891642 0.0752534,0.103284 0.155676,-0.140778,0.435415,0.524693,0.022703 0.0174377,0.019705,0.062192,0.0279807,0.0824187
+979 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432132 1.38696e-06 118.212 345473 5 1.41278e-06 0.0891579 0.0746412,0.103477 0.155819,-0.140205,0.435866,0.524693,0.004577 0.0174536,0.0201104,0.0623234,0.0279984,0.0256884
+980 chr19_45404691,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.38576e-06 118.211 345772 5 1.41155e-06 0.0891539 0.0748323,0.103381 -0.000436748,0.155691,-0.140858,0.435201,0.524535 0.031595,0.0174408,0.0202395,0.0624076,0.0283854
+981 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45433130 1.38473e-06 118.211 346030 5 1.4105e-06 0.0891668 0.0752038,0.104027 0.155662,-0.141316,0.434704,0.524036,-0.0258527 0.0174379,0.0197485,0.0622014,0.0280114,0.0941375
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+983 chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079,chr19_45432278 1.38247e-06 118.21 346596 5 1.4082e-06 0.0891534 0.075772,0.102902 0.1557,-0.140677,0.435539,0.524588,0.000983982 0.0174396,0.0202596,0.0626827,0.0280691,0.018726
+984 chr19_45383079,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.38229e-06 118.21 346642 5 1.40801e-06 0.0891553 0.0750879,0.102894 -0.0048594,0.155755,-0.1414,0.434428,0.524744 0.0315157,0.0174433,0.0199322,0.062343,0.0280276
+985 chr19_45406538,chr19_45407655,chr19_45411941,chr19_45412040,chr19_45412079 1.38056e-06 118.21 347075 5 1.40625e-06 0.089156 0.0753035,0.103729 0.15563,0.00666689,-0.140409,0.435778,0.524999 0.0174411,0.0413594,0.0199568,0.0623155,0.0281643
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+1000 chr19_45390272,chr19_45406538,chr19_45411941,chr19_45412040,chr19_45412079 1.35305e-06 118.201 354132 5 1.37823e-06 0.089157 0.0752682,0.103551 0.00830437,0.155705,-0.140898,0.435474,0.524544 0.0515425,0.0174381,0.0196985,0.0622216,0.027973
diff --git a/finemap-ivis/data/finemap.ld b/finemap-ivis/data/finemap.ld
new file mode 100644
index 0000000..8aae9c0
--- /dev/null
+++ b/finemap-ivis/data/finemap.ld
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diff --git a/finemap-ivis/data/finemap.snp b/finemap-ivis/data/finemap.snp
new file mode 100644
index 0000000..ee22919
--- /dev/null
+++ b/finemap-ivis/data/finemap.snp
@@ -0,0 +1,473 @@
+index rsid chromosome position allele1 allele2 maf beta se z prob log10bf mean sd mean_incl sd_incl
+265 chr19_45412079 19 45412079 C T 0.11233 0.564139 0.0282044 20.0018 1 12.4747 0.167031 0.0591555 0.167031 0.0591555
+228 chr19_45406538 19 45406538 C CG 0.444518 0.144249 0.0183394 7.8655 1 12.4747 0.167031 0.0591555 0.167031 0.0591555
+263 chr19_45411941 19 45411941 T C 0.283564 -0.225057 0.0203146 -11.0786 0.999868 6.35529 0.16701 0.0591849 0.167032 0.0591578
+371 chr19_45427780 19 45427780 G A 0.008022 0.511617 0.107121 4.77605 0.803744 3.08701 0.135702 0.0841494 0.168837 0.056707
+259 chr19_45410273 19 45410273 G A 0.019869 0.454489 0.0665824 6.82597 0.597053 2.64548 0.0934085 0.079188 0.156449 0.0253007
+264 chr19_45412040 19 45412040 C T 0.020072 0.448 0.0662931 6.75787 0.360552 2.22588 0.0563983 0.0766414 0.156422 0.0254068
+349 chr19_45425212 19 45425212 G C 0.006321 0.456002 0.122284 3.72903 0.147324 1.7122 0.0246615 0.0628837 0.167396 0.0542952
+155 chr19_45398014 19 45398014 T C 0.019845 0.450662 0.0675677 6.66978 0.0399746 1.09422 0.0168408 0.0836824 0.421287 0.0692222
+454 chr19_45439163 19 45439163 C G 0.141915 0.137713 0.0264669 5.20321 0.0138109 0.620976 0.00229623 0.0203098 0.166263 0.0510425
+261 chr19_45410548 19 45410548 A G 0.021976 0.382696 0.063406 6.03564 0.00222914 -0.176178 0.000351727 0.00748671 0.157786 0.0174273
+382 chr19_45429708 19 45429708 G C 0.144069 -0.160056 0.0268676 -5.95721 0.00135184 -0.393772 0.000223537 0.00635403 0.165357 0.0505934
+127 chr19_45395330 19 45395330 A G 0.104495 -0.117836 0.0297572 -3.9599 0.00130185 -0.41016 -0.000130044 0.00375382 -0.0998922 0.0293002
+130 chr19_45395816 19 45395816 G A 0.078438 -0.130039 0.0338792 -3.83831 0.000963648 -0.540948 -0.000105053 0.00353241 -0.109016 0.0327966
+230 chr19_45406673 19 45406673 G A 0.421215 -0.104497 0.0186049 -5.61666 0.000919322 -0.561418 0.00015628 0.00539999 0.169995 0.0533589
+299 chr19_45418086 19 45418086 G A 0.014149 -0.324921 0.0793012 -4.0973 0.000723381 -0.665604 0.000119175 0.00462724 0.164748 0.0497678
+436 chr19_45436010 19 45436010 G A 0.020064 0.117365 0.0686329 1.71005 0.000697226 -0.681609 0.000116282 0.00462893 0.166777 0.0541897
+185 chr19_45401666 19 45401666 A G 0.168925 -0.102378 0.0242721 -4.21794 0.000637012 -0.720861 -3.84408e-05 0.00282304 -0.0603455 0.094189
+257 chr19_45409595 19 45409595 G T 0.048525 -0.0697312 0.0428967 -1.62556 0.000630307 -0.725459 0.000105277 0.00439158 0.167025 0.0521321
+451 chr19_45438575 19 45438575 C A 0.040612 0.191084 0.0468595 4.07781 0.000451743 -0.870198 7.50688e-05 0.00369428 0.166176 0.0510804
+66 chr19_45385759 19 45385759 G C 0.184774 -0.046706 0.0237683 -1.96506 0.000432776 -0.888834 -3.06226e-05 0.00154886 -0.0707585 0.0232085
+238 chr19_45407326 19 45407326 G A 0.050702 -0.0613316 0.0420064 -1.46005 0.000351898 -0.978716 5.86857e-05 0.00327894 0.166769 0.0524456
+131 chr19_45395909 19 45395909 C G 0.105517 -0.00143362 0.029601 -0.0484316 0.0003509 -0.97995 2.93974e-05 0.00165674 0.0837772 0.0283894
+239 chr19_45407409 19 45407409 C T 0.048993 -0.0590159 0.0426411 -1.38401 0.000341636 -0.991574 5.6929e-05 0.00322839 0.166636 0.052436
+311 chr19_45420079 19 45420079 A AC 0.024848 0.380784 0.0634524 6.0011 0.000278706 -1.08002 4.57619e-05 0.00281939 0.164194 0.0396078
+221 chr19_45405181 19 45405181 T C 0.050741 -0.065114 0.0419447 -1.55238 0.000270825 -1.09248 3.51675e-05 0.00242757 0.129853 0.0700183
+163 chr19_45398462 19 45398462 A AAGTG 0.050882 -0.0686606 0.0418917 -1.639 0.000200447 -1.2232 2.50776e-05 0.00203395 0.125108 0.0706377
+203 chr19_45403182 19 45403182 G A 0.051139 -0.0663035 0.0417993 -1.58624 0.000194104 -1.23717 2.4229e-05 0.00199908 0.124825 0.0707839
+164 chr19_45398465 19 45398465 C G 0.050897 -0.0691148 0.0418835 -1.65017 0.000193603 -1.23829 2.41229e-05 0.00199333 0.1246 0.0707192
+222 chr19_45405521 19 45405521 G C 0.282172 -0.0939453 0.0202653 -4.63577 0.000192949 -1.23976 -8.01287e-06 0.0014428 -0.0415284 0.095207
+156 chr19_45398201 19 45398201 C T 0.052271 -0.0590902 0.0413406 -1.42935 0.000184996 -1.25804 2.26794e-05 0.00192529 0.122594 0.0707845
+173 chr19_45399526 19 45399526 C T 0.052763 -0.0613441 0.0411247 -1.49166 0.000178554 -1.27344 2.17219e-05 0.00188106 0.121654 0.0708497
+345 chr19_45425066 19 45425066 G A 0.067512 -0.20035 0.0373259 -5.36759 0.000165001 -1.30773 2.8544e-05 0.00231482 0.172993 0.0505316
+117 chr19_45393742 19 45393742 G A 0.106111 -0.00795014 0.0294988 -0.269507 0.000157247 -1.32864 1.19116e-05 0.00101404 0.0757511 0.0283185
+204 chr19_45403192 19 45403192 G A 0.014929 -0.269696 0.0773566 -3.4864 0.00014817 -1.35446 -2.5777e-05 0.00264031 -0.173969 0.129569
+305 chr19_45418749 19 45418749 A T 0.017801 -0.133116 0.0772986 -1.72211 0.000147771 -1.35563 2.53921e-05 0.00218167 0.171834 0.0518401
+331 chr19_45422160 19 45422160 C G 0.097136 -0.175665 0.0312049 -5.62941 0.00012629 -1.42386 2.16843e-05 0.00202574 0.171703 0.0549139
+113 chr19_45393144 19 45393144 C G 0.015054 -0.229134 0.0768183 -2.9828 0.000121138 -1.44195 -2.2752e-05 0.00221101 -0.187819 0.0712998
+133 chr19_45396144 19 45396144 C T 0.119916 0.0245035 0.0280753 0.872777 0.000120118 -1.44562 8.35768e-06 0.000817104 0.0695787 0.0267905
+304 chr19_45418743 19 45418743 A T 0.01766 -0.131545 0.0776494 -1.69409 0.000115972 -1.46088 1.99075e-05 0.00193124 0.171658 0.0519355
+105 chr19_45391454 19 45391454 C T 0.046129 -0.0602412 0.0435127 -1.38445 0.000110281 -1.48274 1.21149e-05 0.00124115 0.109855 0.043607
+137 chr19_45396318 19 45396318 C T 0.027509 0.121396 0.0568466 2.1355 0.000108655 -1.48919 1.48657e-05 0.00153032 0.136816 0.0532619
+201 chr19_45403048 19 45403048 C T 0.017848 0.171443 0.0799637 2.14402 0.00010598 -1.50002 1.87375e-05 0.00199665 0.176803 0.0797552
+35 chr19_45383037 19 45383037 A T 0.120064 0.00897073 0.0283079 0.316899 0.000101434 -1.51906 6.8456e-06 0.00073111 0.0674882 0.026748
+414 chr19_45433148 19 45433148 A G 0.010808 0.171768 0.0945402 1.81688 9.69067e-05 -1.53889 1.60749e-05 0.00171459 0.16588 0.0531317
+115 chr19_45393473 19 45393473 C T 0.107921 -0.013003 0.0292171 -0.445048 9.45221e-05 -1.54971 6.59752e-06 0.000731516 0.0697987 0.0281047
+34 chr19_45382984 19 45382984 T G 0.120798 0.0083474 0.0282143 0.295857 9.30324e-05 -1.55661 6.17888e-06 0.000690324 0.0664165 0.0266765
+274 chr19_45414125 19 45414125 G T 0.020462 0.129953 0.0742287 1.75071 8.7309e-05 -1.58419 1.44695e-05 0.00162513 0.165727 0.0527865
+146 chr19_45397343 19 45397343 G T 0.034306 -0.0496312 0.0510238 -0.972705 8.42588e-05 -1.59963 1.0106e-05 0.00119068 0.11994 0.0494091
+402 chr19_45432504 19 45432504 A T 0.00924 0.28304 0.113182 2.50076 7.5379e-05 -1.648 1.25572e-05 0.00152062 0.166588 0.0540939
+312 chr19_45420086 19 45420086 A AC 0.071921 -0.210008 0.0375799 -5.58832 7.45003e-05 -1.6531 1.28046e-05 0.00153886 0.171873 0.0474141
+284 chr19_45415640 19 45415640 G A 0.358947 0.0735926 0.0190978 3.85347 7.12085e-05 -1.67272 1.16288e-05 0.00143961 0.163307 0.0493661
+128 chr19_45395619 19 45395619 A G 0.162042 0.0132482 0.0247992 0.534218 7.02227e-05 -1.67878 3.94131e-06 0.000510121 0.0561259 0.0235754
+367 chr19_45427353 19 45427353 C G 0.159669 -0.0858686 0.0252563 -3.39988 6.97719e-05 -1.68157 1.17531e-05 0.00147207 0.16845 0.0518169
+428 chr19_45435661 19 45435661 C T 0.029991 0.255443 0.0574994 4.44253 6.05071e-05 -1.74345 9.92043e-06 0.00130573 0.163955 0.0360245
+220 chr19_45405062 19 45405062 A G 0.386366 0.163338 0.0187835 8.69585 5.90211e-05 -1.75425 -1.87079e-06 0.000755327 -0.0316969 0.0930683
+129 chr19_45395714 19 45395714 T C 0.465115 0.123126 0.0182441 6.74883 5.60458e-05 -1.77672 2.70806e-06 0.000393423 0.0483187 0.0206674
+147 chr19_45397466 19 45397466 A G 0.005611 0.220483 0.126132 1.74804 5.43093e-05 -1.79039 1.44563e-05 0.0021401 0.266185 0.116111
+244 chr19_45407788 19 45407788 G A 0.429785 0.120407 0.0184081 6.54099 5.33192e-05 -1.79838 9.00198e-06 0.00129238 0.168832 0.0531314
+323 chr19_45421650 19 45421650 G A 0.076994 -0.173148 0.0347439 -4.98354 5.0796e-05 -1.81943 8.72314e-06 0.00128321 0.171729 0.0541052
+319 chr19_45421204 19 45421204 C T 0.065772 -0.187834 0.0372343 -5.04465 5.04098e-05 -1.82275 8.68306e-06 0.00127809 0.17225 0.0523107
+45 chr19_45383562 19 45383562 G A 0.040073 0.0215507 0.0477146 0.451658 4.96715e-05 -1.82916 4.93489e-06 0.000766762 0.0993507 0.0443416
+314 chr19_45420225 19 45420225 T A 0.01354 -0.182542 0.0965199 -1.89124 4.95698e-05 -1.83005 8.41918e-06 0.00127217 0.169845 0.0616699
+90 chr19_45388588 19 45388588 A ATT 0.467539 0.095058 0.0184119 5.16285 4.70439e-05 -1.85276 2.09177e-06 0.000333648 0.0444642 0.0197321
+114 chr19_45393247 19 45393247 T A 0.036257 -0.059438 0.0498015 -1.1935 4.23883e-05 -1.89802 4.34567e-06 0.000736826 0.10252 0.0479384
+253 chr19_45409113 19 45409113 C T 0.029444 0.0907964 0.0544876 1.66637 4.22389e-05 -1.89956 7.08731e-06 0.00114808 0.167791 0.055255
+206 chr19_45403412 19 45403412 C T 0.396425 0.162954 0.0187068 8.71094 4.18444e-05 -1.90363 -1.14181e-06 0.000621818 -0.0272869 0.0921727
+211 chr19_45404431 19 45404431 T C 0.396103 0.16312 0.0187114 8.71769 4.17894e-05 -1.9042 -1.1404e-06 0.000621341 -0.0272891 0.0921612
+193 chr19_45402262 19 45402262 T G 0.117411 0.384182 0.0284838 13.4877 4.16062e-05 -1.90611 -1.91686e-06 0.000727872 -0.0460714 0.10301
+91 chr19_45388879 19 45388879 C CT 0.036468 -0.0602132 0.0495999 -1.21398 4.0631e-05 -1.91641 4.11633e-06 0.00071404 0.10131 0.0478023
+47 chr19_45383830 19 45383830 C T 0.094116 -0.00398167 0.0318801 -0.124895 4.03642e-05 -1.91927 2.57905e-06 0.000448077 0.0638946 0.0298612
+134 chr19_45396219 19 45396219 C T 0.460476 0.117562 0.0182993 6.42442 3.93436e-05 -1.93039 1.75311e-06 0.000307523 0.0445589 0.0204521
+354 chr19_45426458 19 45426458 G A 0.005291 -0.301308 0.130866 -2.30242 3.85524e-05 -1.93922 6.37874e-06 0.00108261 0.165456 0.0550126
+88 chr19_45388581 19 45388581 T A 0.028828 -0.204682 0.0608 -3.36648 3.83896e-05 -1.94106 -4.33798e-06 0.00077175 -0.112999 0.052405
+208 chr19_45403858 19 45403858 A G 0.396576 0.163283 0.0187052 8.72928 3.80779e-05 -1.9446 -9.98765e-07 0.000590188 -0.0262295 0.0919764
+270 chr19_45413576 19 45413576 C T 0.172366 0.352509 0.0238435 14.7843 3.75416e-05 -1.95076 5.88979e-06 0.000967168 0.156887 0.0174406
+420 chr19_45433892 19 45433892 C T 0.01027 -0.174867 0.0926581 -1.88723 3.69504e-05 -1.95765 6.2082e-06 0.0010722 0.168015 0.0537063
+196 chr19_45402477 19 45402477 C T 0.36778 -0.0675922 0.0189467 -3.56749 3.53132e-05 -1.97733 -9.10863e-07 0.00057287 -0.0257939 0.0928876
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+116 chr19_45393680 19 45393680 A C 0.005213 -0.210711 0.131755 -1.59926 3.69693e-06 -2.95744 -1.32035e-07 0.0002426 -0.0357149 0.121014
+332 chr19_45422587 19 45422587 A G 0.122007 -0.0279355 0.0279832 -0.998295 3.69601e-06 -2.95755 5.76262e-07 0.000301618 0.155915 0.0174562
+394 chr19_45432089 19 45432089 T C 0.351519 0.00328421 0.0191375 0.171611 3.69127e-06 -2.95811 5.74816e-07 0.000301055 0.155723 0.0174385
+417 chr19_45433610 19 45433610 C G 0.032543 -0.0190073 0.0540284 -0.351803 3.691e-06 -2.95814 5.75319e-07 0.000301329 0.155871 0.0174503
+281 chr19_45414479 19 45414479 G C 0.005978 0.0289651 0.124753 0.23218 3.69014e-06 -2.95824 5.74803e-07 0.000301094 0.155767 0.0174387
+438 chr19_45436244 19 45436244 T C 0.335018 -0.0298414 0.0202518 -1.47352 3.68741e-06 -2.95856 5.74457e-07 0.000301024 0.155789 0.0174422
+285 chr19_45415713 19 45415713 G A 0.158881 -0.0825265 0.0251237 -3.28481 3.68174e-06 -2.95923 5.72691e-07 0.000300338 0.155549 0.0174524
+458 chr19_45439908 19 45439908 C T 0.011175 -0.0124195 0.109821 -0.113088 3.67722e-06 -2.95976 5.7247e-07 0.0003004 0.15568 0.0174368
+397 chr19_45432132 19 45432132 AAAAC A 0.13788 0.0213601 0.0280086 0.762625 3.67687e-06 -2.95981 5.72957e-07 0.000300669 0.155828 0.0174527
+213 chr19_45404691 19 45404691 A G 0.0886476 0.0475182 0.0333882 1.4232 3.67009e-06 -2.96061 -1.77868e-09 6.05305e-05 -0.000484641 0.0315925
+411 chr19_45433130 19 45433130 AGAAAGAAAGAAAGAAG A 0.008592 -0.0796359 0.105504 -0.754814 3.667e-06 -2.96097 5.7083e-07 0.000299956 0.155667 0.017437
+55 chr19_45384294 19 45384294 G C 0.158194 0.0544256 0.0255376 2.13119 3.66428e-06 -2.9613 2.33278e-08 4.74005e-05 0.00636628 0.0239298
+400 chr19_45432278 19 45432278 T C 0.353669 0.0019353 0.0190759 0.101453 3.66272e-06 -2.96148 5.70306e-07 0.000299856 0.155706 0.0174387
+37 chr19_45383079 19 45383079 T C 0.085578 0.0835294 0.0328261 2.54461 3.65877e-06 -2.96195 -1.74669e-08 6.09674e-05 -0.00477399 0.031514
+242 chr19_45407655 19 45407655 C T 0.04769 -0.02722 0.04309 -0.631702 3.65726e-06 -2.96213 5.69196e-07 0.000299497 0.155634 0.0174402
+120 chr19_45394541 19 45394541 G C 0.007539 0.0294777 0.104484 0.282126 3.65289e-06 -2.96265 8.7725e-08 0.000197617 0.0240152 0.100569
+439 chr19_45436247 19 45436247 T C 0.333641 -0.0305609 0.0202334 -1.51042 3.65288e-06 -2.96265 5.69031e-07 0.000299587 0.155776 0.0174428
+404 chr19_45432557 19 45432557 G C 0.352722 0.00123727 0.0190699 0.064881 3.65172e-06 -2.96279 5.6855e-07 0.000299383 0.155694 0.0174386
+11 chr19_45381310 19 45381310 T C 0.005791 -0.0575685 0.128802 -0.446954 3.64553e-06 -2.96352 1.00442e-07 0.00022424 0.0275521 0.114167
+96 chr19_45389489 19 45389489 G A 0.007765 0.0333662 0.103047 0.323794 3.64493e-06 -2.96359 8.30402e-08 0.000194164 0.0227824 0.0991162
+328 chr19_45421890 19 45421890 TC T 0.063345 -0.00570736 0.0378082 -0.150956 3.63427e-06 -2.96487 5.66309e-07 0.000298917 0.155825 0.0174492
+16 chr19_45381721 19 45381721 T C 0.032816 -0.0401324 0.0509578 -0.787561 3.61737e-06 -2.96689 -3.63702e-08 9.49569e-05 -0.0100543 0.0489035
+19 chr19_45381999 19 45381999 C T 0.007804 0.391731 0.102729 3.81326 3.60817e-06 -2.968 9.89148e-09 0.00019092 0.00274141 0.100472
+429 chr19_45435762 19 45435762 T G 0.008015 0.00886315 0.105263 0.0842003 3.60395e-06 -2.96851 5.61682e-07 0.00029772 0.155852 0.0174594
+180 chr19_45400927 19 45400927 A G 0.006626 -0.104786 0.13554 -0.7731 3.60356e-06 -2.96855 -8.10706e-08 0.000207102 -0.0224974 0.106754
+416 chr19_45433566 19 45433566 C T 0.014952 -0.0634777 0.079686 -0.796598 3.59452e-06 -2.96964 5.59509e-07 0.000296957 0.155656 0.017438
+293 chr19_45416693 19 45416693 G A 0.040097 -0.122896 0.0472168 -2.60281 3.58566e-06 -2.97072 5.58356e-07 0.000296711 0.155719 0.0174398
+99 chr19_45390272 19 45390272 C A 0.029101 -0.0254127 0.0651176 -0.390259 3.5837e-06 -2.97095 2.98932e-08 9.88375e-05 0.00834143 0.0515396
+292 chr19_45416678 19 45416678 A G 0.040105 -0.123108 0.0472118 -2.60757 2.23053e-06 -3.17688 3.4734e-07 0.000234022 0.155721 0.0174393
+365 chr19_45427162 19 45427162 G A 0.032738 -0.0533913 0.0525999 -1.01505 2.22548e-06 -3.17786 3.46433e-07 0.000233678 0.155667 0.0174501
+275 chr19_45414139 19 45414139 T TTTG 0.040994 -0.123075 0.0464112 -2.65185 2.22362e-06 -3.17822 3.46242e-07 0.000233645 0.155711 0.0174392
+288 chr19_45416009 19 45416009 G A 0.005845 -0.0192501 0.12238 -0.157298 2.22303e-06 -3.17834 3.4619e-07 0.00023364 0.155728 0.017438
+303 chr19_45418604 19 45418604 G A 0.056165 -0.0577211 0.0403488 -1.43055 2.22284e-06 -3.17838 3.46127e-07 0.000233608 0.155714 0.017439
+363 chr19_45427074 19 45427074 A AT 0.067255 -0.00799845 0.0382586 -0.209063 2.22129e-06 -3.17868 3.45975e-07 0.000233588 0.155754 0.0174497
+460 chr19_45440145 19 45440145 CTG C 0.028305 -0.0673334 0.058476 -1.15147 2.21964e-06 -3.179 3.45461e-07 0.000233329 0.155638 0.0174496
+469 chr19_45441283 19 45441283 G T 0.027361 -0.0586061 0.0590085 -0.993181 2.2181e-06 -3.1793 3.45396e-07 0.000233365 0.155717 0.0174488
+28 chr19_45382733 19 45382733 C CG 0.022975 -0.0404094 0.0607104 -0.665608 2.21615e-06 -3.17968 4.39683e-09 8.67208e-05 0.00198399 0.0582199
+87 chr19_45388284 19 45388284 G A 0.018534 -0.015374 0.0681309 -0.225654 2.21497e-06 -3.17992 -1.16809e-08 9.63911e-05 -0.00527364 0.0645519
+44 chr19_45383488 19 45383488 G A 0.024005 -0.0385001 0.0600582 -0.641046 2.2135e-06 -3.1802 1.59795e-09 8.47946e-05 0.00072191 0.0569894
+148 chr19_45397471 19 45397471 G C 0.006298 0.0148455 0.121039 0.12265 2.21306e-06 -3.18029 -2.32658e-08 0.000164038 -0.0105129 0.109765
+325 chr19_45421764 19 45421764 A G 0.012447 -0.056421 0.084509 -0.667633 2.21252e-06 -3.1804 3.44385e-07 0.000232976 0.155653 0.017444
+10 chr19_45381307 19 45381307 T C 0.008186 -0.0024281 0.113743 -0.0213473 2.21219e-06 -3.18046 2.22303e-08 0.000143957 0.010049 0.0962649
+447 chr19_45437295 19 45437295 T C 0.009256 0.0167136 0.0999853 0.167161 2.20862e-06 -3.18116 3.43848e-07 0.000232815 0.155684 0.0174367
+54 chr19_45384179 19 45384179 G A 0.123849 0.0151884 0.028079 0.540916 2.20768e-06 -3.18135 -3.49391e-09 3.92343e-05 -0.00158262 0.0263582
+50 chr19_45384086 19 45384086 A G 0.007344 0.0360947 0.120496 0.299552 2.20666e-06 -3.18155 2.0336e-08 0.000151354 0.00921576 0.101471
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+53 chr19_45384176 19 45384176 C T 0.123576 0.016837 0.0280722 0.599775 2.20277e-06 -3.18231 -3.10102e-10 3.91579e-05 -0.000140778 0.0263833
+362 chr19_45427062 19 45427062 C A 0.009895 -0.00533337 0.113427 -0.0470204 2.20179e-06 -3.18251 3.42808e-07 0.000232471 0.155695 0.0174363
+29 chr19_45382755 19 45382755 A C 0.005541 0.0700451 0.144692 0.484099 2.19962e-06 -3.18294 8.19558e-09 0.000173288 0.00372591 0.116781
+67 chr19_45385990 19 45385990 TC T 0.005564 -0.0431248 0.153275 -0.281355 2.19824e-06 -3.18321 -1.42845e-09 0.000172736 -0.000649817 0.116503
+348 chr19_45425178 19 45425178 G A 0.106696 0.495981 0.0292646 16.9481 0 -inf 0 0 0 0
+360 chr19_45426792 19 45426792 G A 0.112775 0.465887 0.0280575 16.6047 0 -inf 0 0 0 0
+269 chr19_45413233 19 45413233 G T 0.10661 0.51982 0.0290533 17.8919 0 -inf 0 0 0 0
+178 chr19_45400747 19 45400747 T A 0.119307 0.460468 0.0278646 16.5252 0 -inf 0 0 0 0
diff --git a/finemap-ivis/data/finemap.z b/finemap-ivis/data/finemap.z
new file mode 100644
index 0000000..f55b6a5
--- /dev/null
+++ b/finemap-ivis/data/finemap.z
@@ -0,0 +1,473 @@
+rsid chromosome position allele1 allele2 maf beta se
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diff --git a/flashfm-ivis/app.R b/flashfm-ivis/app.R
new file mode 100644
index 0000000..61482c4
--- /dev/null
+++ b/flashfm-ivis/app.R
@@ -0,0 +1,4810 @@
+# flashfm-ivis version_2022_03_10
+
+
+####----Libraries----
+#if (!require(devtools)) install.packages("devtools")
+#devtools::install_github("gaospecial/ggVennDiagram")
+library("ggVennDiagram")
+library(dplyr)
+library(ggplot2)
+library(shiny)
+library(shinydashboard)
+library(plotly)
+library(DT)
+library(gaston)
+#library(RColorBrewer)
+#display.brewer.all(type = 'qual')
+library(randomcoloR)
+library(igraph)
+library(networkD3)
+library(stringr)
+library(shinybusy)
+# remotes::install_github("rstudio/webshot2")
+# library(webshot2)
+
+
+
+####---- Users' input files and guideline----
+#1. Finemapwithflashfm:Need
+#---> .z files: GWAS
+#use trait as file name, one z file for each trait to get info for Regional association plots
+#---> .ld file: LD matrix
+#orginal fld="finemap.ld", it will be r^2 in the internal wrapper function
+#or check whether there is negative value in the matrix, if not, then already r^2 is done
+#---> .RData files: mpp.pp and snpGroups
+#output from PPsummarise and makeSNPgroup2 – these are the mpp.pp and snpGroups objects
+#suggest that the user saves the mpp.pp and snpGroups objects to a single RData file,
+#but should give an example of the command to do this: save(mpp.pp,snpGroups,file="anyfilename.RData")
+#Processing of the files is in a wrapper function at the end of the finemap-example.R file.
+#2. FLASHFMwithJAM:Need
+#---> flashfmwithjam.RData (this is just the output from FLASHFMwithJAM)
+#---> snpinfo.RData (this will have to be given as an example and the user has to construct it
+#from their data. We should fix the first 4 names and allow the names for the pvalue columns to
+#be flexible, but restrict that pvalue columns have to be given in same order as the betas
+#submitted to flashfm. There could be 2-6 columns for p-values.
+
+
+####----pre-loaded data as examples----
+#users will overwrite it if they upload new files
+#load("data/R_data.RData")
+
+
+####----wrapper functions----
+#1. Finemapwithflashfm:Need
+# below fz is a vector of z files for each trait; fld is ld file;
+# e.g. fz <- c("fname1.z","fname2.z","fname3.z","fname4.z")
+# mpp.pp,snpGroups are from the RData files I sent awhile ago - in current use for tool
+# fz <- numeric(4); for(i in 1:4) fz[i] <- paste0("/net/bliss-04-nfs/scratch/jennifer/flashfm/scripts/Example/Uganda-APOE-LDL/finemap",i,".z")
+format.flashfm.finemap <- function(fz, fld, mpp.pp, snpGroups) {
+ M <- length(fz)
+ GWAS_0 <- vector("list",M)
+ GWAS_1 <- vector("list",M)
+ GWAS <- vector("list",M)
+ for(i in 1:M) {
+ GWAS_0[[i]] <- read.table(fz[i], header = TRUE, as.is = TRUE, sep = " ")
+ GWAS_1[[i]]$rs <- GWAS_0[[i]]$rsid
+ GWAS_1[[i]]$chr <- GWAS_0[[i]]$chromosome
+ GWAS_1[[i]]$ps <- GWAS_0[[i]]$position
+ GWAS_1[[i]]$allele1 <- GWAS_0[[i]]$allele1
+ GWAS_1[[i]]$allele0 <- GWAS_0[[i]]$allele2
+ GWAS_1[[i]]$beta <- GWAS_0[[i]]$beta
+ GWAS_1[[i]]$se <- GWAS_0[[i]]$se
+ #GWAS[[i]]$p <- 2*(1-pnorm(abs(GWAS[[i]]$beta/GWAS[[i]]$se)))
+ GWAS_1[[i]]$pval <- 2*(pnorm(abs(GWAS_1[[i]]$beta/GWAS_1[[i]]$se),lower.tail = F)) #check!!!
+ GWAS_1[[i]]$af <- 1-GWAS_0[[i]]$maf
+ GWAS_1[[i]]$no <- GWAS_0[[i]]$se*0
+ GWAS[[i]] = as.data.frame(GWAS_1[[i]])
+ }
+ r <- read.table(fld, header = FALSE, as.is = TRUE, sep = " ")
+ r2 <- r^2
+ colnames(r2) <- GWAS_0[[1]]$rsid
+ rownames(r2) <- GWAS_0[[1]]$rsid
+
+ return(list(GWAS=GWAS,
+ LD=r2,
+ MPP=mpp.pp$MPP,
+ MPPg=mpp.pp$MPPg,
+ PP=mpp.pp$PP,
+ PPg=mpp.pp$PPg,
+ snpGroups=snpGroups))
+}
+#1. Finemapwithflashfm:Need
+#inputs_used = format.flashfm.finemap(fz=fz, fld=fld, mpp.pp=mpp.pp, snpGroups = snpGroups)
+#2. FLASHFMwithJAM:Need
+#inputs_used = format.flashfm.finemap(fz=fz, fld=fld, mpp.pp=fm$mpp.pp, snpGroups = fm$snpGroups)
+#3. Pre-loaded data (need match the variable names)
+#inputs_used = format.flashfm.finemap(...)
+######
+
+
+## snp group network: ppg_network_links and nodes function----
+ppg_network_links_nodes <- function(mpp.pp) {
+ M = length(mpp.pp$PPg)
+ ppg_network_links_fm_0 = c()
+ ppg_network_links_flashfm_0 = c()
+ ppg_network_nodes_0 = c()
+ for (m in 1:M){
+ ppg_rownames = rownames(mpp.pp$PPg[[m]])
+ ppg_rownames = str_split(ppg_rownames, "%", simplify = TRUE)
+ ppg_rownames[ppg_rownames==""] <- 'NA'
+ #fm----
+ ppg_rownames_link_fm = c()
+ for (i in 1:dim(ppg_rownames)[1]){
+ if (length(ppg_rownames[i,][ppg_rownames[i,] != 'NA'])==1){
+ ppg_rownames[i,2] = ppg_rownames[i,1]
+ }
+ tmp = combn(ppg_rownames[i,][ppg_rownames[i,] != 'NA'], 2)
+ tmp = as.data.frame(t(tmp))
+ tmp$level = i
+ tmp$value = mpp.pp$PPg[[m]][i,1]
+ tmp$trait_linecolor = names(mpp.pp$PPg)[m]
+ # if(m==1){tmp$color = "chocolate"}
+ # if(m==2){tmp$color = "pink"}
+ # if(m==3){tmp$color = "lightgreen"}
+ # if(m==4){tmp$color = "dodgerblue"}
+ # if(m==5){tmp$color = "darkred"}
+ # if(m==6){tmp$color = "gold"}
+ if(m==1){tmp$color = "darkorange"}
+ if(m==2){tmp$color = "forestgreen"}
+ if(m==3){tmp$color = "lightgreen"}
+ if(m==4){tmp$color = "pink"}
+ if(m==5){tmp$color = "darkred"} ##CHECK COLOR WHEN RUNNING 6 TRAITS
+ if(m==6){tmp$color = "gold"} ##CHECK COLOR WHEN RUNNING 6 TRAITS
+ ppg_rownames_link_fm = rbind(ppg_rownames_link_fm, tmp)
+ }
+ colnames(ppg_rownames_link_fm)[1:2] = c('source', 'target')
+ ppg_network_links_fm_0 = rbind(ppg_network_links_fm_0, ppg_rownames_link_fm)
+ #flashfm----
+ ppg_rownames_link_flashfm = c()
+ for (i in 1:dim(ppg_rownames)[1]){
+ if (length(ppg_rownames[i,][ppg_rownames[i,] != 'NA'])==1){
+ ppg_rownames[i,2] = ppg_rownames[i,1]
+ }
+ tmp = combn(ppg_rownames[i,][ppg_rownames[i,] != 'NA'], 2)
+ tmp = as.data.frame(t(tmp))
+ tmp$level = i
+ tmp$value = mpp.pp$PPg[[m]][i,2]
+ tmp$trait_linecolor = names(mpp.pp$PPg)[m]
+ # if(m==1){tmp$color = "chocolate"}
+ # if(m==2){tmp$color = "pink"}
+ # if(m==3){tmp$color = "lightgreen"}
+ # if(m==4){tmp$color = "dodgerblue"}
+ # if(m==5){tmp$color = "darkred"}
+ # if(m==6){tmp$color = "gold"}
+ if(m==1){tmp$color = "darkorange"}
+ if(m==2){tmp$color = "forestgreen"}
+ if(m==3){tmp$color = "lightgreen"}
+ if(m==4){tmp$color = "pink"}
+ if(m==5){tmp$color = "darkred"} ##CHECK COLOR WHEN RUNNING 6 TRAITS
+ if(m==6){tmp$color = "gold"} ##CHECK COLOR WHEN RUNNING 6 TRAITS
+ ppg_rownames_link_flashfm = rbind(ppg_rownames_link_flashfm, tmp)
+ }
+ colnames(ppg_rownames_link_flashfm)[1:2] = c('source', 'target')
+ ppg_network_links_flashfm_0 = rbind(ppg_network_links_flashfm_0, ppg_rownames_link_flashfm)
+ #nodes
+ assign(paste0("ppg_rownames_",m),ppg_rownames)
+ ppg_network_nodes_0 = c(ppg_network_nodes_0, ppg_rownames)
+ }
+ ppg_network_nodes_1 = unique(ppg_network_nodes_0)
+ ppg_network_nodes_1 = ppg_network_nodes_1[!ppg_network_nodes_1=='NA']
+ ppg_network_nodes_1 = as.data.frame(ppg_network_nodes_1)
+ colnames(ppg_network_nodes_1) <- 'name'
+ ppg_network_nodes_1$id = c(0:(length(ppg_network_nodes_1[,1])-1))
+ ppg_network_nodes_3 = c()
+ for (m in 1:M){
+ ppg_network_nodes_2 = ppg_network_nodes_1
+ ppg_network_nodes_2[, names(mpp.pp$PPg)[m]] = 0
+ for (j in 1:length(ppg_network_nodes_2[,1])){
+ ppg_network_nodes_2[, names(mpp.pp$PPg)[m]][j] = sum(ppg_network_nodes_2$name[j] == get(paste0("ppg_rownames_",m)))
+ }
+ ppg_network_nodes_3 = cbind(ppg_network_nodes_3, ppg_network_nodes_2[, names(mpp.pp$PPg)[m]])
+ }
+ ppg_network_nodes = cbind(ppg_network_nodes_1, ppg_network_nodes_3)
+ colnames(ppg_network_nodes) <- c('name', 'id', names(mpp.pp$PPg))
+ ppg_network_nodes$role_size_total = rowSums(ppg_network_nodes[,3:dim(ppg_network_nodes)[2]])
+ ppg_network_nodes$group_color = 'trait:--'
+ for (i in 1:length(ppg_network_nodes[,1])){
+ for (j in 3:(2+M)){
+ if (ppg_network_nodes[i,j]>0){
+ tmp = colnames(ppg_network_nodes)[j]
+ ppg_network_nodes$group_color[i] = paste(c(ppg_network_nodes$group_color[i],tmp), collapse="+")
+ }
+ }
+ }
+ #consistent with the original tables
+ ppg_network_links_flashfm = ppg_network_links_flashfm_0[,c(3,1,2,4,5,6)]
+ colnames(ppg_network_links_flashfm) = c('level', 'source', 'target',
+ "importance_linesize", "trait_linecolor", "color")
+ ppg_network_links_fm = ppg_network_links_fm_0[,c(3,1,2,4,5,6)]
+ colnames(ppg_network_links_fm) = c('level', 'source', 'target',
+ "importance_linesize", "trait_linecolor", "color")
+ ppg_network_nodes$role_size = ppg_network_nodes$role_size_total
+ ppg_network_nodes2 = ppg_network_nodes
+ # create data: group fm
+ plt_links_group_fm <- data.frame(
+ source=ppg_network_links_fm$source,
+ target=ppg_network_links_fm$target,
+ l_color =ppg_network_links_fm$color,
+ l_size =ppg_network_links_fm$importance_linesize*10
+ )
+ plt_nodes_group_fm <- data.frame(
+ name =ppg_network_nodes2$name,
+ n_color=ppg_network_nodes2$group_color,
+ n_size =ppg_network_nodes2$role_size^3 #updated20220208
+ )
+ for (i in 1:length(plt_links_group_fm$source)){
+ plt_links_group_fm$source_id[i] = ppg_network_nodes2$id[which(plt_links_group_fm$source[i]==ppg_network_nodes2$name)]
+ plt_links_group_fm$target_id[i] = ppg_network_nodes2$id[which(plt_links_group_fm$target[i]==ppg_network_nodes2$name)]
+ }
+ # create data: group flashfm
+ plt_links_group_flashfm <- data.frame(
+ source=ppg_network_links_flashfm$source,
+ target=ppg_network_links_flashfm$target,
+ l_color =ppg_network_links_flashfm$color,
+ l_size =ppg_network_links_flashfm$importance_linesize*10
+ )
+ plt_nodes_group_flashfm <- data.frame(
+ name =ppg_network_nodes2$name,
+ n_color=ppg_network_nodes2$group_color,
+ n_size =ppg_network_nodes2$role_size^3 #updated20220208
+ )
+ for (i in 1:length(plt_links_group_flashfm$source)){
+ plt_links_group_flashfm$source_id[i] = ppg_network_nodes2$id[which(plt_links_group_flashfm$source[i]==ppg_network_nodes2$name)]
+ plt_links_group_flashfm$target_id[i] = ppg_network_nodes2$id[which(plt_links_group_flashfm$target[i]==ppg_network_nodes2$name)]
+ }
+
+ return(list(plt_links_group_fm = plt_links_group_fm,
+ plt_nodes_group_fm = plt_nodes_group_fm,
+ plt_links_group_flashfm = plt_links_group_flashfm,
+ plt_nodes_group_flashfm = plt_nodes_group_flashfm))
+}
+# ##CHECK--IMPORTANT
+# ppg_network_links_nodes = ppg_network_links_nodes(mpp.pp)
+# plt_links_group_fm = ppg_network_links_nodes$plt_links_group_fm
+# plt_nodes_group_fm = ppg_network_links_nodes$plt_nodes_group_fm
+# plt_links_group_flashfm = ppg_network_links_nodes$plt_links_group_flashfm
+# plt_nodes_group_flashfm = ppg_network_links_nodes$plt_nodes_group_flashfm
+ColourScale_group <- 'd3.scaleOrdinal().range(["lightblue", "lightcoral", "lightgoldenrod", "lightgreen",
+ "lightpink", "lightsalmon", "lightyellow", "lightslateblue"]);'
+### ALL DONE: snp group network
+
+
+## snp individual network: pp_network_links and nodes function----
+#Step 1: define credible sets based on a specific significant level (i.e. 0.99)
+credset <- function(modPP,cred=0.99) {
+ tmp <- modPP[order(modPP, decreasing = TRUE)]
+ cpp <- cumsum(tmp)
+ wh <- which(cpp <= cred)
+ if (!length(wh)) wh <- 1
+ wh <- c(wh, max(wh) + 1)
+ keepmodPP <- tmp[wh]
+ mods <- names(keepmodPP)
+ usnps <- unique(unlist(strsplit(mods,"%")))
+ return(usnps)
+}
+
+# M <- length(mpp.pp$PP) # number of traits
+# cs1_selected <- csM_selected <- vector("list",M)
+# for(i in 1:M){
+# tmp <- mpp.pp$PP[[i]][,1] # trait i, single-trait fine-mapping
+# cs1_selected[[i]] <- credset(tmp,0.99)
+# tmp <- mpp.pp$PP[[i]][,2] # trait i, multi-trait fine-mapping
+# csM_selected[[i]] <- credset(tmp,0.99)
+# }
+
+pp_network_links_nodes <- function(mpp.pp, cs1, csM) {
+ fun_check_cs <- function(x,cs){
+ if (x %in% cs){
+ return(x)
+ }else{
+ return("NA")
+ }
+ }
+ #pp_network_links_nodes <- function(mpp.pp, cs1, csM) {
+ M = length(mpp.pp$PP)
+ pp_rownames_cs1_link_all = c()
+ pp_cs1_node_0 = c()
+ pp_rownames_csM_link_all = c()
+ pp_csM_node_0 = c()
+ for (m in 1:M){
+ pp_rownames = rownames(mpp.pp$PP[[m]])
+ pp_rownames = str_split(pp_rownames, "%", simplify = TRUE)
+ pp_rownames[pp_rownames==""] <- 'NA'
+ pp_rownames_cs1 = apply(pp_rownames, 1:2, fun_check_cs, cs = cs1[[m]])
+ #pp_rownames_cs1 = pp_rownames_cs1[rowSums(pp_rownames_cs1 == "NA")1){
+ tmp = combn(pp_rownames_cs1[i,][pp_rownames_cs1[i,] != 'NA'], 2)
+ tmp = as.data.frame(t(tmp))
+ tmp$level = i
+ tmp$value = mpp.pp$PP[[m]][i,1] #check: flashfm will be mpp.pp$PP[[m]][i,2]
+ tmp$trait_linecolor = names(mpp.pp$PP)[m]
+ if(m==1){tmp$color = "lightgreen"}
+ if(m==2){tmp$color = "pink"}
+ if(m==3){tmp$color = "chocolate"}
+ if(m==4){tmp$color = "orchid"}
+ if(m==5){tmp$color = "darkred"}
+ if(m==6){tmp$color = "gold"} #check: our maximum is 6 traits
+ pp_rownames_cs1_link = rbind(pp_rownames_cs1_link, tmp)
+ }
+ }
+ colnames(pp_rownames_cs1_link)[1:2] = c('source', 'target')
+ pp_rownames_cs1_link_all = rbind(pp_rownames_cs1_link_all,pp_rownames_cs1_link)
+ #nodes
+ pp_cs1_node_0 = c(pp_cs1_node_0, cs1[[m]])
+ ##csM
+ pp_rownames_csM_link = c()
+ for (i in 1:dim(pp_rownames_csM)[1]){
+ # if (length(pp_rownames_csM[i,][pp_rownames_csM[i,] != 'NA'])==1){
+ # pp_rownames_csM[i,2] = pp_rownames_csM[i,1]
+ # }
+ if (sum(pp_rownames_csM[i,] != 'NA')>1){
+ tmp = combn(pp_rownames_csM[i,][pp_rownames_csM[i,] != 'NA'], 2)
+ tmp = as.data.frame(t(tmp))
+ tmp$level = i
+ tmp$value = mpp.pp$PP[[m]][i,2] #check: flashfm will be mpp.pp$PP[[m]][i,2]
+ tmp$trait_linecolor = names(mpp.pp$PP)[m]
+ if(m==1){tmp$color = "lightgreen"}
+ if(m==2){tmp$color = "pink"}
+ if(m==3){tmp$color = "chocolate"}
+ if(m==4){tmp$color = "orchid"}
+ if(m==5){tmp$color = "darkred"}
+ if(m==6){tmp$color = "gold"} #check: our maximum is 6 traits
+ pp_rownames_csM_link = rbind(pp_rownames_csM_link, tmp)
+ }
+ }
+ colnames(pp_rownames_csM_link)[1:2] = c('source', 'target')
+ pp_rownames_csM_link_all = rbind(pp_rownames_csM_link_all,pp_rownames_csM_link)
+ #nodes
+ pp_csM_node_0 = c(pp_csM_node_0, csM[[m]])
+ } #checked.
+ ##cs1
+ pp_cs1_link = pp_rownames_cs1_link_all
+ pp_cs1_node = pp_cs1_node_0
+ pp_cs1_node = as.data.frame(unique(pp_cs1_node))
+ colnames(pp_cs1_node) <- 'name'
+ pp_cs1_node$id = c(0:(length(pp_cs1_node[,1])-1))
+
+ pp_cs1_node_sum = c()
+ for (m in 1:M){
+ pp_cs1_node[paste0('role_size_',m)] = 0
+ for (i in 1:length(pp_cs1_node[,1])){
+ pp_cs1_node[ ,paste0('role_size_',m)][i] = sum(pp_cs1_node$name[i] == get(paste0("pp_rownames_cs1_",m)))
+ }
+ pp_cs1_node_sum = cbind(pp_cs1_node_sum, pp_cs1_node[ ,paste0('role_size_',m)])
+ }
+ pp_cs1_node$role_size_all = rowSums(pp_cs1_node_sum)
+ colnames(pp_cs1_node) <- c('name', 'id', names(mpp.pp$PP), 'role_size_total')
+ pp_cs1_node$group_color = 'trait:--'
+ for (i in 1:length(pp_cs1_node[,1])){
+ for (j in 3:(2+length(names(mpp.pp$PP)))){
+ if (pp_cs1_node[i,j]>0){
+ tmp = colnames(pp_cs1_node)[j]
+ pp_cs1_node$group_color[i] = paste(c(pp_cs1_node$group_color[i],tmp), collapse="+")
+ }
+ }
+ }
+ ### ALL DONE
+ # create data:
+ plt_links_snp <- data.frame(
+ source=pp_cs1_link$source,
+ target=pp_cs1_link$target,
+ l_color =pp_cs1_link$color,
+ l_size =pp_cs1_link$value*10
+ )
+ plt_nodes_snp <- data.frame(
+ name =pp_cs1_node$name,
+ n_color=pp_cs1_node$group_color,
+ n_size =pp_cs1_node$role_size_total
+ )
+ for (i in 1:length(plt_links_snp$source)){
+ plt_links_snp$source_id[i] = pp_cs1_node$id[which(plt_links_snp$source[i]==pp_cs1_node$name)]
+ plt_links_snp$target_id[i] = pp_cs1_node$id[which(plt_links_snp$target[i]==pp_cs1_node$name)]
+ }
+ ##csM
+ pp_csM_link = pp_rownames_csM_link_all
+ pp_csM_node = pp_csM_node_0
+ pp_csM_node = as.data.frame(unique(pp_csM_node))
+ colnames(pp_csM_node) <- 'name'
+ pp_csM_node$id = c(0:(length(pp_csM_node[,1])-1))
+ pp_csM_node_sum = c()
+ for (m in 1:M){
+ pp_csM_node[paste0('role_size_',m)] = 0
+ for (i in 1:length(pp_csM_node[,1])){
+ pp_csM_node[ ,paste0('role_size_',m)][i] = sum(pp_csM_node$name[i] == get(paste0("pp_rownames_csM_",m)))
+ }
+ pp_csM_node_sum = cbind(pp_csM_node_sum, pp_csM_node[ ,paste0('role_size_',m)])
+ }
+ pp_csM_node$role_size_all = rowSums(pp_csM_node_sum)
+ colnames(pp_csM_node) <- c('name', 'id', names(mpp.pp$PP), 'role_size_total')
+ pp_csM_node$group_color = 'trait:--'
+ for (i in 1:length(pp_csM_node[,1])){
+ for (j in 3:(2+length(names(mpp.pp$PP)))){
+ if (pp_csM_node[i,j]>0){
+ tmp = colnames(pp_csM_node)[j]
+ pp_csM_node$group_color[i] = paste(c(pp_csM_node$group_color[i],tmp), collapse="+")
+ }
+ }
+ }
+ ### ALL DONE
+ # create data:
+ plt_links_snp_flashfm <- data.frame(
+ source=pp_csM_link$source,
+ target=pp_csM_link$target,
+ l_color =pp_csM_link$color,
+ l_size =pp_csM_link$value*10
+ )
+ plt_nodes_snp_flashfm <- data.frame(
+ name =pp_csM_node$name,
+ n_color=pp_csM_node$group_color,
+ n_size =pp_csM_node$role_size_total
+ )
+
+ for (i in 1:length(plt_links_snp_flashfm$source)){
+ plt_links_snp_flashfm$source_id[i] = pp_csM_node$id[which(plt_links_snp_flashfm$source[i]==pp_csM_node$name)]
+ plt_links_snp_flashfm$target_id[i] = pp_csM_node$id[which(plt_links_snp_flashfm$target[i]==pp_csM_node$name)]
+ }
+ ##IMPORTANT
+ return(list(plt_links_snp = plt_links_snp,
+ plt_nodes_snp = plt_nodes_snp,
+ plt_links_snp_flashfm = plt_links_snp_flashfm,
+ plt_nodes_snp_flashfm = plt_nodes_snp_flashfm))
+}
+## testing the whole function
+# pp_network_links_nodes = pp_network_links_nodes(mpp.pp, cs1_selected, csM_selected)
+# plt_links_snp = pp_network_links_nodes$plt_links_snp
+# plt_nodes_snp = pp_network_links_nodes$plt_nodes_snp
+# plt_links_snp_flashfm = pp_network_links_nodes$plt_links_snp_flashfm
+# plt_nodes_snp_flashfm = pp_network_links_nodes$plt_nodes_snp_flashfm
+#color
+ColourScale_snp <- 'd3.scaleOrdinal().range(["lightblue", "lightcoral", "lightgoldenrod", "lightgreen",
+ "lightpink", "lightsalmon", "lightyellow", "lightslateblue"]);'
+### ALL DONE: snp individual network
+
+
+
+#generate a vector of color based on snpGroups----
+# set.seed(1)
+# n <- length(colnames(snpGroups$group.sizes))
+# col_vector = distinctColorPalette(n)
+# pal <- c("gray", col_vector)
+# pal <- setNames(pal, c("0", colnames(snpGroups$group.sizes)))
+
+
+#generating the full table GWAS_all_final----
+GWAS_all_final_table <- function(GWAS, mpp.pp, cs1, csM, snpGroups){
+ M <- length(names(mpp.pp$MPP))
+ for (m in 1:M){
+ GWAS_0 = GWAS[[m]]
+ #IMPORTANT: the order must be [1]"rs" [2]"chr" [3]"ps" [4]"allele1" [5]"allele0" [6] "maf/af" [7] "no."
+ GWAS_0 = GWAS_0[ , c(1,2,3,4,5,9,10,6:8)]
+ #then: [6] "beta" [7]"se" [8]"pval"
+ colnames(GWAS_0)[8:10] = c(paste0('beta_',m), paste0('se_',m), paste0('pval_',m))
+ colnames(GWAS_0)[6:7] = c('af','no')
+ GWAS_0[,7] = 0 #updated20220208
+ if (m == 1){
+ GWAS_all_2 = GWAS_0
+ }else {
+ GWAS_all_2 = merge(GWAS_all_2, GWAS_0, by = colnames(GWAS_all_2[,1:7]))
+ }
+ # Credible set
+ cs1_0 = as.data.frame(cs1[[m]])
+ csM_0 = as.data.frame(csM[[m]])
+ GWAS_all_2[paste0("cs1_",m)] = 0
+ GWAS_all_2[paste0("csM_",m)] = 0
+ for (i in 1:length(GWAS_all_2[,1])){
+ if (sum(GWAS_all_2$rs[i] == cs1_0)>0){
+ GWAS_all_2[ ,paste0("cs1_",m)][i] = 1
+ }
+ if (sum(GWAS_all_2$rs[i] == csM_0)>0){
+ GWAS_all_2[ ,paste0("csM_",m)][i] = 1
+ }
+ }
+ # MPP
+ temp = as.data.frame(mpp.pp$MPP[[m]])
+ temp$rs = rownames(temp)
+ colnames(temp)[1:2] = c(paste0("MPP_",names(mpp.pp$MPP)[m],"_Single"),
+ paste0("MPP_",names(mpp.pp$MPP)[m],"_Multi"))
+ GWAS_all_2 = merge(GWAS_all_2, temp, by = c("rs"), all.x=TRUE)
+ }
+ # snpGroups
+ GWAS_all_final_2 = GWAS_all_2
+ GWAS_all_final_2$snpGroups_fm = 0
+ GWAS_all_final_2$snpGroups_flashfm = 0
+ GWAS_all_final_2$snpGroups_fm_size = 0
+ GWAS_all_final_2$snpGroups_flashfm_size = 0
+ for (j in 1:length(snpGroups$groups.fm)){
+ for (h in 1:length(GWAS_all_2[,1])){
+ if (sum(GWAS_all_final_2$rs[h] == snpGroups$groups.fm[[j]])>0){
+ GWAS_all_final_2$snpGroups_fm[h] = names(snpGroups$groups.fm)[j]
+ GWAS_all_final_2$snpGroups_fm_size[h] = length(snpGroups$groups.fm[[j]]) ##Check!!!
+ }
+ }
+ }
+ for (j in 1:length(snpGroups$groups.flashfm)){
+ for (h in 1:length(GWAS_all_2[,1])){
+ if (sum(GWAS_all_final_2$rs[h] == snpGroups$groups.flashfm[[j]])>0){
+ GWAS_all_final_2$snpGroups_flashfm[h] = names(snpGroups$groups.flashfm)[j]
+ GWAS_all_final_2$snpGroups_flashfm_size[h] = length(snpGroups$groups.flashfm[[j]]) ##Check!!!
+ }
+ }
+ }
+
+ for (m in 1:M){ #check!!!_updated20220218
+ # MPPg_fm
+ temp_mppg_fm = as.data.frame(mpp.pp$MPPg[[m]][,1])
+ temp_mppg_fm$snpGroups_fm = rownames(temp_mppg_fm)
+ colnames(temp_mppg_fm)[1] = paste0("MPPg_",names(mpp.pp$MPPg)[m],"_Single")
+ GWAS_all_final_2 = merge(GWAS_all_final_2, temp_mppg_fm, all.x=TRUE)
+
+ # MPPg_flashfm
+ temp_mppg_flashfm = as.data.frame(mpp.pp$MPPg[[m]][,2])
+ temp_mppg_flashfm$snpGroups_flashfm = rownames(temp_mppg_flashfm)
+ colnames(temp_mppg_flashfm)[1] = paste0("MPPg_",names(mpp.pp$MPPg)[m],"_Multi")
+ GWAS_all_final_2 = merge(GWAS_all_final_2, temp_mppg_flashfm, all.x=TRUE)
+ }
+
+ # GWAS_all_final_2 %>% count(snpGroups_fm) #check groups of fm
+ # GWAS_all_final_2 %>% count(snpGroups_flashfm) #check groups of flashfm
+ # GWAS_all_final_2$rs[which(GWAS_all_final$snpGroups_fm=='B')] #check individual snps in the group
+ # GWAS_all_final_2$rs[which(GWAS_all_final$snpGroups_flashfm=='E')]
+ #update p-values
+ for (m in 1:M){
+ GWAS_all_final_2[paste0("pval_log_",m)] = -log10(GWAS_all_final_2[,paste0('pval_',m)])
+ GWAS_all_final_2[paste0("cs_trait_",m)] = GWAS_all_final_2[,paste0("cs1_",m)] + GWAS_all_final_2[,paste0("csM_",m)]
+ }
+ GWAS_all_final_2[is.na(GWAS_all_final_2)] <- 0
+ GWAS_all_final_2 = GWAS_all_final_2[ , -which(names(GWAS_all_final_2) %in% c("no"))]
+ return(GWAS_all_final_2)
+ #End of the full table GWAS_all_final
+}
+
+#GWAS_all_final <- GWAS_all_final_table(GWAS, mpp.pp, cs1, csM)
+
+
+####----Max size of input file----
+options(shiny.maxRequestSize = 100*1024^2)
+
+#
+
+
+####----Shiny user interface----
+ui <- dashboardPage(
+
+ #Dashboard header----
+ dashboardHeader(
+ title = "flashfm-ivis R package",
+ # Drop-down menu for messages
+ dropdownMenu(type = "messages", badgeStatus = "success",
+ headerText = 'Contact info:',
+ messageItem("Contact 1: Feng",
+ "feng.zhou[AT]mrc-bsu.cam.ac.uk"
+ ),
+ messageItem("Contact 2: Jenn",
+ "jennifer.asimit[AT]mrc-bsu.cam.ac.uk"
+ )
+ ),
+
+ # Drop-down menu for notifications
+ dropdownMenu(type = "notifications", #badgeStatus = "warning",
+ headerText = 'Update and version info:',
+ notificationItem(icon = icon("users"), status = "info",
+ "Update: this is Version 0.3"
+ ),
+ notificationItem(icon = icon("users"), status = "info",
+ "Update: modified on 08-Mar-2022"
+ )
+ ),
+
+ # Drop-down menu for tasks, with progress bar
+ dropdownMenu(type = "tasks", badgeStatus = "danger",
+ headerText = 'Source code:',
+ taskItem(value = 100, color = "aqua",
+ text = "Source code #1: flashfm",
+ href = 'https://github.com/jennasimit/flashfm'
+ ),
+ taskItem(value = 100, color = "green",
+ text = "Source code #2: flashfm-ivis",
+ href = 'https://github.com/fz-cambridge/flashfm-ivis'
+ ),
+ taskItem(value = 100, color = "yellow",
+ text = "Source code #3: finemap-ivis",
+ href = 'http://shiny.mrc-bsu.cam.ac.uk/apps/finemap-ivis/'
+ )
+ )
+ ),
+
+ #Dashboard sidebar----
+ dashboardSidebar(
+ #Data inputs--
+ radioButtons(inputId = "control_widgets_0",
+ label = h4("Select data source:"),
+ choices = list("Pre-loaded example" = 0,
+ "FINEMAP with flashfm" = 1,
+ "FLASHFM with JAM" = 2,
+ "Single RData file" = 3,
+ "Single csv file" = 4),
+ selected = 0),
+
+ fileInput(inputId = "inFiles",
+ label = h4("Upload external data"),
+ multiple = TRUE, accept = NULL, buttonLabel = "Browse..."),
+
+ #Control widgets--
+ radioButtons(inputId = "control_widgets_1",
+ label = h4("Credible sets:"),
+ choices = list("Show all SNPs (default)" = 3,
+ "Show cs1 and csM" = 2,
+ "Show cs1 or csM" = 1,
+ "Show cs1 but not csM" = 1.1,
+ "Show csM but not cs1" = 1.2,
+ "Show no cs" = 0 ),
+ selected = 3),
+
+ sliderInput(inputId = "control_widgets_2",
+ label = h4("Define MPP value"),
+ min = 0, max = 1, value = c(0, 1)),
+
+ #Dashboard Menu--
+ sidebarMenu(
+ menuItem("Dashboard", icon = icon("dashboard"), startExpanded = TRUE,
+ menuSubItem("Checking input values", tabName = "input_checking",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_0 Input values", tabName = "ch_0",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_1 Single trait", tabName = "ch_1",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_2 Multi trait", tabName = "ch_2",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_3 Comparison (Linked)", tabName = "ch_3",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_4 Comparison (Methods)", tabName = "ch_4",
+ icon = icon("angle-right")),
+ menuSubItem("Ch_5 Comparison (Traits)", tabName = "ch_5",
+ icon = icon("angle-right")),
+ menuSubItem("Data and table details", tabName = "data_detail",
+ icon = icon("angle-right")),
+ menuSubItem("README", tabName = "readme",
+ icon = icon("angle-right")),
+ menuSubItem(text = "Go to finemap-ivis",
+ href = "http://shiny.mrc-bsu.cam.ac.uk/apps/finemap-ivis/",
+ icon = icon("angle-right"))
+ )
+ )
+ ),
+
+ #Dashboard body----
+ dashboardBody(
+ tabItems(
+ #Input_checking----
+ tabItem(tabName = "input_checking",
+ #check all input files
+ h3("Checking input files"),
+ #h3("Check files in the current temporary server"),
+ fluidRow(column(12, verbatimTextOutput("value"))),
+ br(),
+ h4("--- Key files' location in the server ---"),
+ fluidRow(column(12, DT::dataTableOutput("file_locations"))),
+ br(),
+ h4("--- Information about uploading users' external data ---"),
+ br(),
+ h5("Case 1. Preloaded examples: see README"),
+ br(),
+ h5("Case 2. FINEMAP with flashfm: see README"),
+ br(),
+ h5("Case 3. FLASHFM with JAM: see README"),
+ br(),
+ h5("Case 4. Single RData file: see README"),
+ br(),
+ h5("Case 5. Single csv file: see README"),
+ ),
+ #Dashboard_ch_0 GWAS Summary Statistics and Input Values----
+ tabItem(tabName = "ch_0",
+ h2("Dashboard Charts 0: GWAS Summary Statistics and Input Values"),
+ fluidPage(
+ fluidRow(
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ title = "Single-trait fine-mapping (FINEMAP)",
+ # The id lets us use input$tabset1 on the server to find the current tab
+ id = "GWAS_tabset_1", height = "550px",
+ tabPanel("Trait 1",
+ plotlyOutput("manhattan_1a"),
+ radioButtons(inputId = "control_widgets_ch_0_a1",
+ label = h4("Show a dashed line at the maximum value:"),
+ inline=T,
+ choices = list("Yes" = 0,
+ "No" = 1),
+ selected = 0)),
+ tabPanel("Trait 2", plotlyOutput("manhattan_2a"),
+ radioButtons(inputId = "control_widgets_ch_0_a2",
+ label = h4("Show a dashed line at the maximum value:"),
+ inline=T,
+ choices = list("Yes" = 0,
+ "No" = 1),
+ selected = 0)),
+ #tabPanel("Trait 3", plotlyOutput("manhattan_3a")),
+ tabPanel("Trait 3",
+ selectInput("shouldShow_3a", "show plot (if you have 3 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_3a == 'yes'",
+ plotlyOutput("manhattan_3a")
+ )),
+ #tabPanel("Trait 4", plotlyOutput("manhattan_4a")),
+ tabPanel("Trait 4",
+ selectInput("shouldShow_4a", "show plot (if you have 4 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_4a == 'yes'",
+ plotlyOutput("manhattan_4a")
+ )),
+ tabPanel("Trait 5",
+ selectInput("shouldShow_5a", "show plot (if you have 5 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_5a == 'yes'",
+ plotlyOutput("manhattan_5a")
+ )),
+ tabPanel("Trait 6",
+ selectInput("shouldShow_6a", "show plot (if you have 6 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_6a == 'yes'",
+ plotlyOutput("manhattan_6a")
+ )),
+ width=NULL
+ ),
+ tabBox(
+ title = "Multi-trait fine-mapping (flashfm)",
+ # The id lets us use input$tabset1 on the server to find the current tab
+ id = "GWAS_tabset_2", height = "550px",
+ tabPanel("Trait 1",
+ plotlyOutput("manhattan_1b"),
+ radioButtons(inputId = "control_widgets_ch_0_b1",
+ label = h4("Show a dashed line at the maximum value:"),
+ inline=T,
+ choices = list("Yes" = 0,
+ "No" = 1),
+ selected = 0)),
+ tabPanel("Trait 2",
+ plotlyOutput("manhattan_2b"),
+ radioButtons(inputId = "control_widgets_ch_0_b2",
+ label = h4("Show a dashed line at the maximum value:"),
+ inline=T,
+ choices = list("Yes" = 0,
+ "No" = 1),
+ selected = 0)),
+ #tabPanel("Trait 3", plotlyOutput("manhattan_3b")),
+ tabPanel("Trait 3",
+ selectInput("shouldShow_3b", "show plot (if you have 3 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_3b == 'yes'",
+ plotlyOutput("manhattan_3b")
+ )),
+ #tabPanel("Trait 4", plotlyOutput("manhattan_4b")),
+ tabPanel("Trait 4",
+ selectInput("shouldShow_4b", "show plot (if you have 4 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_4b == 'yes'",
+ plotlyOutput("manhattan_4b")
+ )),
+ tabPanel("Trait 5",
+ selectInput("shouldShow_5b", "show plot (if you have 5 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_5b == 'yes'",
+ plotlyOutput("manhattan_5b")
+ )),
+ tabPanel("Trait 6",
+ selectInput("shouldShow_6b", "show plot (if you have 6 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_6b == 'yes'",
+ plotlyOutput("manhattan_6b")
+ )),
+ width=NULL
+ ),
+ ),
+ column(
+ width=12,
+ box(div(style = 'overflow-y: scroll; overflow-x: scroll; ', DT::dataTableOutput('ch0_table0')),
+ title = "snpGroup$group.sizes",
+ solidHeader = TRUE,
+ collapsible = TRUE, width=NULL)
+ ),
+ column(
+ width=12,
+ box(plotlyOutput("Plot_LD1"),
+ title = "LD Matrix",
+ solidHeader = TRUE, collapsible = TRUE,
+ width=NULL),
+ box(plotOutput("Plot_LD2"),
+ downloadLink("downloadPlot", "Download Plot"),
+ title = "LD Plot",
+ solidHeader = TRUE, collapsible = TRUE,
+ width=NULL)
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_1 forceNetwork (Single)----
+ tabItem(tabName = "ch_1",
+ h2("Dashboard Chart 1: Single Trait Fine Mapping"),
+ fluidRow(
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ id = "tabset1", height = "1000px",
+ tabPanel("Network: Group",
+ sliderInput(inputId = "control_widgets_3a_fm",
+ label = h4("Define thresholds for group model PP (PPg)"),
+ #min = 0, max = max(input$tt()$plt_links_group_fm$l_size)/10, value = c(0, max(tt()$plt_links_group_fm$l_size)/10)),
+ min = 0, max = 1, value = c(0, 1)),
+ forceNetworkOutput(outputId = "ch1_network_group_fm", height = "800px"),
+ downloadButton('d_ch_1_s1', 'Download network as html')
+ ),
+ tabPanel("Network: SNPs",
+ sliderInput(inputId = "control_widgets_3b_fm",
+ label = h4("Define thresholds for SNP model PP (PP)"),
+ #min = 0, max = max(tt()$plt_links_snp$l_size)/10, value = c(0.1, max(tt()$plt_links_snp$l_size)/10)),
+ min = 0, max = 1, value = c(0.1, 1)),
+ forceNetworkOutput(outputId = "ch1_network_snp", height = "800px"),
+ downloadButton('d_ch_1_s2', 'Download network as html')
+ ),
+ width=NULL
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_2 forceNetwork (Multi)----
+ tabItem(tabName = "ch_2",
+ h2("Dashboard Chart 2: Multi Trait Fine Mapping"),
+ fluidRow(
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ id = "tabset1", height = "1000px",
+ tabPanel("Network: Group",
+ sliderInput(inputId = "control_widgets_3a_flashfm",
+ label = h4("Define thresholds for group model PP (PPg)"),
+ #min = 0, max = max(plt_links_group_flashfm$l_size)/10, value = c(0, max(plt_links_group_flashfm$l_size)/10)),
+ min = 0, max = 1, value = c(0, 1)),
+ forceNetworkOutput(outputId = "ch1_network_group_flashfm", height = "800px"),
+ downloadButton('d_ch_2_m1', 'Download network as html')
+ ),
+ tabPanel("Network: SNPs",
+ sliderInput(inputId = "control_widgets_3b_flashfm",
+ label = h4("Define thresholds for SNP model PP (PP)"),
+ #min = 0, max = max(plt_links_snp_flashfm$l_size)/10, value = c(0.1, max(plt_links_snp_flashfm$l_size)/10)),
+ min = 0, max = 1, value = c(0.1, 1)),
+ forceNetworkOutput(outputId = "ch1_network_snp_flashfm", height = "800px"),
+ downloadButton('d_ch_2_m2', 'Download network as html')
+ ),
+ width=NULL
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_3 Linked Regional association plots----
+ tabItem(tabName = "ch_3",
+ h2("Dashboard Chart 3: Comparison Regional Association Plots of Different Traits and Methods"),
+ fluidPage(
+ fluidRow(
+ column(
+ width=12,
+ box(div(style = 'overflow-y: scroll; overflow-x: scroll; ', DT::dataTableOutput('ch0_table0_ch3')),
+ title = "snpGroup$group.sizes",
+ solidHeader = TRUE,
+ collapsible = TRUE, width=NULL)
+ ),
+ column(
+ width=12,
+ radioButtons(inputId = "control_widgets_ch_3",
+ inline = T,
+ label = h4("How many traits: (NOTE - left panel is fm, right panel is flashfm, rows show individual traits)"),
+ choices = list("2 traits" = 2,
+ "3 traits" = 3,
+ "4 traits" = 4,
+ "5 traits" = 5,
+ "6 traits" = 6),
+ selected = 2),
+ radioButtons(inputId = "control_widgets_ch_3_2",
+ label = h4("Show a dashed line at the maximum value:"),
+ inline=T,
+ choices = list("Yes" = 0,
+ "No" = 1),
+ selected = 0)
+ ),
+ column(
+ width=12,
+ box(plotlyOutput("manhattan_ch3_linked"),
+ height = 1400, width=NULL),
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_4 Radar chart, Venn diagrams----
+ tabItem(tabName = "ch_4",
+ h2("Dashboard Chart 4: Comparison Results of Different Methods"),
+ fluidRow(
+ column(
+ width=12,
+ radioButtons(inputId = "control_widgets_ch_4",
+ inline = T,
+ label = h4("How many traits:"),
+ choices = list("3 traits" = 3,
+ "4 traits" = 4,
+ "5 traits" = 5,
+ "6 traits" = 6),
+ selected = 3),
+ ),
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ #title = "Result_1 charts",
+ # The id lets us use input$tabset1 on the server to find the current tab
+ id = "tabset1", height = "800px",
+ tabPanel("Radar chart: Credible Sets", plotlyOutput("fig_ch4_tab1_radar")),
+ tabPanel("Venn diagram 1: cs1",
+ plotlyOutput("fig_ch4_tab2_venn_cs1"),
+ #tableOutput("table_ch4_tab2_venn_cs1"),
+ box(div(style = 'overflow-y: scroll; overflow-x: scroll; ',
+ DT::dataTableOutput('table_ch4_tab2_venn_cs1_table')),
+ title = "Table of credible set cs_1",
+ #status = "warning",
+ solidHeader = TRUE,
+ collapsible = TRUE, width=NULL)
+ ),
+ tabPanel("Venn diagram 2: csM",
+ plotlyOutput("fig_ch4_tab2_venn_csM"),
+ #tableOutput("table_ch4_tab2_venn_cs1"),
+ box(div(style = 'overflow-y: scroll; overflow-x: scroll; ',
+ DT::dataTableOutput('table_ch4_tab2_venn_csM_table')),
+ title = "Table of credible set cs_M",
+ #status = "warning",
+ solidHeader = TRUE,
+ collapsible = TRUE, width=NULL)
+ ),
+ width=NULL
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_5 Sankey Diagrams of Different Traits----
+ tabItem(tabName = "ch_5",
+ h2("Dashboard Chart 5: Sankey Diagrams of Different Traits"),
+ fluidRow(
+ column(# width should be between 1 and 12
+ width=12,
+ tabBox(
+ #title = "Result_1 charts",
+ # The id lets us use input$tabset1 on the server to find the current tab
+ id = "tabset1", height = "2200px",
+ tabPanel("All traits", plotlyOutput("ch5_sankey_all", height = "2000px")),
+ tabPanel("Trait_1", plotlyOutput("ch5_sankey_t1", height = "1400px")),
+ tabPanel("Trait_2", plotlyOutput("ch5_sankey_t2", height = "1400px")),
+ #tabPanel("Trait_3", plotlyOutput("ch5_sankey_t3", height = "800px")),
+ tabPanel("Trait_3",
+ selectInput("shouldShow_ch5a", "show plot (if you have 3 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_ch5a == 'yes'",
+ plotlyOutput("ch5_sankey_t3", height = "1500px")
+ )),
+ #tabPanel("Trait_4", plotlyOutput("ch5_sankey_t4", height = "800px")),
+ tabPanel("Trait_4",
+ selectInput("shouldShow_ch5b", "show plot (if you have 4 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_ch5b == 'yes'",
+ plotlyOutput("ch5_sankey_t4", height = "1500px")
+ )),
+ #tabPanel("Trait_5", plotlyOutput("ch5_sankey_t5", height = "800px")),
+ tabPanel("Trait_5",
+ selectInput("shouldShow_ch5c", "show plot (if you have 5 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_ch5c == 'yes'",
+ plotlyOutput("ch5_sankey_t5", height = "1500px")
+ )),
+ #tabPanel("Trait_6", plotlyOutput("ch5_sankey_t6", height = "800px")),
+ tabPanel("Trait_6",
+ selectInput("shouldShow_ch5d", "show plot (if you have 6 traits), otherwise showing error", c("yes", "no"), "no"),
+ conditionalPanel(
+ condition = "input.shouldShow_ch5d == 'yes'",
+ plotlyOutput("ch5_sankey_t6", height = "1500px")
+ )),
+ width=NULL
+ )
+ )
+ )
+ ),
+ #Dashboard_ch_6----
+ tabItem(tabName = "data_detail",
+ h2("Data details"),
+ # h4("---file name and location---"),
+ # tableOutput("web_inputs_1"),
+ h4("---cs1---"),
+ verbatimTextOutput("web_inputs_cs1"),
+ h4("---csM---"),
+ verbatimTextOutput("web_inputs_csM"),
+ h4("---GWAS---"),
+ verbatimTextOutput("web_inputs_GWAS"),
+ h4("---LD---"),
+ verbatimTextOutput("web_inputs_LD"),
+ h4("---mpp.pp---"),
+ verbatimTextOutput("web_inputs_mpp.pp"),
+ h4("---snpGroups---"),
+ verbatimTextOutput("web_inputs_snpGroups"),
+ h4("---GWAS_all_final---"),
+ div(style = 'overflow-x: scroll', DT::dataTableOutput('GWAS_all_final_table_done')),
+ downloadButton("downloadData_3", "Download"),
+ h4("---LD---"),
+ div(style = 'height:500px; overflow-y: scroll; overflow-x: scroll; ', DT::dataTableOutput('LD_data'))
+ ),
+ #Dashboard_ch_7----
+ tabItem(tabName = "readme",
+ h2("README"),
+ br(),
+ h3("About this package:"),
+ br(),
+ h4("GitHub: "),
+ br(),
+ h4("flashfm R package: https://github.com/jennasimit/flashfm"),
+ h4("flashfm-ivis R package: https://github.com/fz-cambridge/flashfm-ivis"),
+ br(),
+ h4("YouTube: "),
+ #br(),
+ #HTML('')
+ fluidRow(
+ fluidRow(
+ column(6,
+ box(
+ width = NULL,
+ title = "flashfm-ivis and finemap-ivis overview",
+ HTML('')
+ )),
+ column(6,
+ box(
+ width = NULL,
+ title = "finemap-ivis user data input",
+ HTML('')
+ )),
+ column(6,
+ box(
+ width = NULL,
+ title = "flashfm-ivis user data input: FINEMAP with Flashfm",
+ HTML('')
+ )),
+ column(6,
+ box(
+ width = NULL,
+ title = "flashfm-ivis user data input: FLASHFM with JAM",
+ HTML('')
+ )),
+ column(6,
+ box(
+ width = NULL,
+ title = "flashfm-ivis user data input: Single RData file",
+ HTML('')
+ )),
+ )
+ )
+
+ )
+ )
+ )
+) #End of user interface
+####----End of user interface----
+
+
+
+
+
+####----Shiny server----
+server <- function(input, output) {
+
+ #dashboard 0 input file information -done-
+ output$value <- renderPrint({
+ if (input$control_widgets_0==0){
+ print("You are using pre-loaded examples")
+ }else{
+ str(input$inFiles)}
+ })
+ #dashboard 0 input file location -done-
+ output$file_locations <- DT::renderDataTable({
+ if (input$control_widgets_0==0){
+ f_config <- f_ld <- f_snp <- f_z <- f_rdata <- f_csv <- "You are using pre-loaded examples, therefore no external file here"
+ }else if(input$control_widgets_0==1){
+ file <- input$inFiles
+ req(file)
+ f_config <- f_ld <- f_snp <- f_z <- f_rdata <- f_csv <- 'You selected data source "Finemap with flashfm",
+ it requires all files with the following extensions: .rdata; .z; .ld.
+ (e.g. mpp.pp and snpGroups in .RData, LD matrix in .ld, finemap.z files)'
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "config"){f_config = file$datapath[i]}
+ if (ext_check == "ld"){f_ld = file$datapath[i]}
+ if (ext_check == "snp"){f_snp = file$datapath[i]}
+ if (ext_check == "z"){f_z = file$datapath[i]}
+ if (ext_check == "RData"){f_rdata = file$datapath[i]}
+ if (ext_check == "csv"){f_csv = file$datapath[i]}
+ }
+ }else if(input$control_widgets_0==2){
+ file <- input$inFiles
+ req(file)
+ f_config <- f_ld <- f_snp <- f_z <- f_rdata <- f_csv <- 'You selected data source "FLASHFMwithJAM",
+ it requires all files with the following extensions: .rdata files only.
+ (e.g. flashfmwithjam.RData and snpinfo.RData)'
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "config"){f_config = file$datapath[i]}
+ if (ext_check == "ld"){f_ld = file$datapath[i]}
+ if (ext_check == "snp"){f_snp = file$datapath[i]}
+ if (ext_check == "z"){f_z = file$datapath[i]}
+ if (ext_check == "RData"){f_rdata = file$datapath[i]}
+ if (ext_check == "csv"){f_csv = file$datapath[i]}
+ }
+ }else if(input$control_widgets_0==3){
+ file <- input$inFiles
+ req(file)
+ f_config <- f_ld <- f_snp <- f_z <- f_rdata <- f_csv <- 'You selected data source "Single RData file",
+ it requires ONE .rdata file only that contains all data.'
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "config"){f_config = file$datapath[i]}
+ if (ext_check == "ld"){f_ld = file$datapath[i]}
+ if (ext_check == "snp"){f_snp = file$datapath[i]}
+ if (ext_check == "z"){f_z = file$datapath[i]}
+ if (ext_check == "RData"){f_rdata = file$datapath[i]}
+ if (ext_check == "csv"){f_csv = file$datapath[i]}
+ }
+ }
+ location = rbind(f_rdata, f_csv, f_config, f_ld, f_snp, f_z)
+ colnames(location) = c('location')
+ DT::datatable(location, options = list(dom = 't'))
+ })
+
+
+ tt <- reactive({
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 1/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ if (input$control_widgets_0 == 0){
+ load("data/R_data_preloaded.RData")
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 2/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ # #snp group network: ppg_network_links and nodes function---
+ # ppg_network_links_nodes = ppg_network_links_nodes(mpp.pp)
+ # plt_links_group_fm = ppg_network_links_nodes$plt_links_group_fm
+ # plt_nodes_group_fm = ppg_network_links_nodes$plt_nodes_group_fm
+ # plt_links_group_flashfm = ppg_network_links_nodes$plt_links_group_flashfm
+ # plt_nodes_group_flashfm = ppg_network_links_nodes$plt_nodes_group_flashfm
+ # # ColourScale_group <- 'd3.scaleOrdinal().range(["lightblue", "lightcoral", "lightgoldenrod", "lightgreen",
+ # # "lightpink", "lightsalmon", "lightyellow", "lightslateblue"]);'
+ # #snp individual network: pp_network_links and nodes function---
+ # M <- length(mpp.pp$PP) # number of traits
+ # cs1_selected <- csM_selected <- vector("list",M)
+ # for(i in 1:M){
+ # tmp <- mpp.pp$PP[[i]][,1] # trait i, single-trait fine-mapping
+ # cs1_selected[[i]] <- credset(tmp,0.99)
+ # tmp <- mpp.pp$PP[[i]][,2] # trait i, multi-trait fine-mapping
+ # csM_selected[[i]] <- credset(tmp,0.99)
+ # }
+ # pp_network_links_nodes = pp_network_links_nodes(mpp.pp, cs1_selected, csM_selected)
+ # plt_links_snp = pp_network_links_nodes$plt_links_snp
+ # plt_nodes_snp = pp_network_links_nodes$plt_nodes_snp
+ # plt_links_snp_flashfm = pp_network_links_nodes$plt_links_snp_flashfm
+ # plt_nodes_snp_flashfm = pp_network_links_nodes$plt_nodes_snp_flashfm
+ # #generate a vector of color based on snpGroups---
+ # set.seed(1)
+ # n <- length(colnames(snpGroups$group.sizes))
+ # col_vector = distinctColorPalette(n)
+ # pal <- c("gray", col_vector)
+ # pal <- setNames(pal, c("0", colnames(snpGroups$group.sizes)))
+ # #generating the full table GWAS_all_final---
+ # GWAS_all_final <- GWAS_all_final_table(GWAS, mpp.pp, cs1, csM, snpGroups)
+ # #outputs
+ # tt = list(plt_links_group_fm = plt_links_group_fm,
+ # plt_nodes_group_fm = plt_nodes_group_fm,
+ # plt_links_group_flashfm = plt_links_group_flashfm,
+ # plt_nodes_group_flashfm = plt_nodes_group_flashfm,
+ # plt_links_snp = plt_links_snp,
+ # plt_nodes_snp = plt_nodes_snp,
+ # plt_links_snp_flashfm = plt_links_snp_flashfm,
+ # plt_nodes_snp_flashfm = plt_nodes_snp_flashfm,
+ # col_vector = col_vector,
+ # pal = pal,
+ # LD = LD,
+ # cs1 = cs1,
+ # csM = csM,
+ # GWAS = GWAS,
+ # mpp.pp = mpp.pp,
+ # snpGroups = snpGroups,
+ # cs1_selected = cs1_selected,
+ # csM_selected = csM_selected,
+ # GWAS_all_final = GWAS_all_final)
+ }else if (input$control_widgets_0 == 1){ ##CHECK!!!--DONE
+ #1. Finemapwithflashfm: four .z + one .ld files and mpp.pp+snpGroups from the .Rdata file
+ file <- input$inFiles
+ req(file)
+ f_z <- numeric(1);
+ j <- 1;
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "ld"){f_ld_1 = file$datapath[i]}
+ if (ext_check == "z"){
+ f_z[j] = file$datapath[i]
+ j=j+1
+ }
+ if (ext_check == "RData" || ext_check == "rdata" ){f_rdata_1 = file$datapath[i]}
+ }
+ load(f_rdata_1) #loading the RData file (i.e. mpp.pp and snpGroups)
+ wrapped_1 = format.flashfm.finemap(fz=f_z, fld=f_ld_1, mpp.pp=mpp.pp, snpGroups = snpGroups)
+ GWAS = wrapped_1$GWAS
+ LD = as.matrix(wrapped_1$LD)
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 2/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ }else if (input$control_widgets_0 == 2){ ##CHECK!!!ONLY NEED UPDATE HERE
+ #2. FLASHFMwithJAM: four .z + one .ld files and mpp.pp+snpGroups from the two different .Rdata files
+ file <- input$inFiles
+ req(file)
+ f_z <- numeric(1);
+ j <- 1;
+ for (i in 1:length(file$datapath)){
+ ext_check <- tools::file_ext(file$datapath[i])
+ if (ext_check == "ld"){f_ld_1 = file$datapath[i]}
+ if (ext_check == "z"){
+ f_z[j] = file$datapath[i]
+ j=j+1
+ }
+ if (ext_check == "RData" || ext_check == "rdata" ){f_rdata_1 = file$datapath[i]}
+ }
+ load(f_rdata_1) #loading the RData file (i.e. mpp.pp and snpGroups)
+ mpp.pp = fm$mpp.pp
+ snpGroups = fm$snpGroups
+ wrapped_1 = format.flashfm.finemap(fz=f_z, fld=f_ld_1, mpp.pp=mpp.pp, snpGroups = snpGroups)
+ GWAS = wrapped_1$GWAS
+ LD = as.matrix(wrapped_1$LD)
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 2/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ }else if (input$control_widgets_0 == 3){ ##CHECK!!!
+ file <- input$inFiles
+ req(file)
+ load(file$datapath)
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 2/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ }
+ #snp group network: ppg_network_links and nodes function---
+ ppg_network_links_nodes = ppg_network_links_nodes(mpp.pp)
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 3/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ plt_links_group_fm = ppg_network_links_nodes$plt_links_group_fm
+ plt_nodes_group_fm = ppg_network_links_nodes$plt_nodes_group_fm
+ plt_links_group_flashfm = ppg_network_links_nodes$plt_links_group_flashfm
+ plt_nodes_group_flashfm = ppg_network_links_nodes$plt_nodes_group_flashfm
+ # ColourScale_group <- 'd3.scaleOrdinal().range(["lightblue", "lightcoral", "lightgoldenrod", "lightgreen",
+ # "lightpink", "lightsalmon", "lightyellow", "lightslateblue"]);'
+ #snp individual network: pp_network_links and nodes function---
+
+ withProgress(message = 'Calculation in progress', value = 0, {
+ M <- length(mpp.pp$PP) # number of traits
+ cs1_selected <- csM_selected <- vector("list",M)
+ for(i in 1:M){
+ tmp <- mpp.pp$PP[[i]][,1] # trait i, single-trait fine-mapping
+ cs1_selected[[i]] <- credset(tmp,0.99)
+ tmp <- mpp.pp$PP[[i]][,2] # trait i, multi-trait fine-mapping
+ csM_selected[[i]] <- credset(tmp,0.99)
+ # Increment the progress bar, and update the detail text.
+ incProgress(1/M, detail = paste("...Step 4/10 done!", i))
+ # Pause for 0.1 seconds to simulate a long computation.
+ # Sys.sleep(1)
+ }
+ })
+ cs1 = cs1_selected
+ csM = csM_selected
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 5/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.1)
+ }
+ })
+ pp_network_links_nodes = pp_network_links_nodes(mpp.pp, cs1_selected, csM_selected)
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 6/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.1)
+ }
+ })
+ plt_links_snp = pp_network_links_nodes$plt_links_snp
+ plt_nodes_snp = pp_network_links_nodes$plt_nodes_snp
+ plt_links_snp_flashfm = pp_network_links_nodes$plt_links_snp_flashfm
+ plt_nodes_snp_flashfm = pp_network_links_nodes$plt_nodes_snp_flashfm
+ #generate a vector of color based on snpGroups---
+ set.seed(1)
+ n <- length(colnames(snpGroups$group.sizes))
+ col_vector = distinctColorPalette(n)
+ pal <- c("gray", col_vector)
+ pal <- setNames(pal, c("0", colnames(snpGroups$group.sizes)))
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 7/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ #generating the full table GWAS_all_final---
+ GWAS_all_final <- GWAS_all_final_table(GWAS, mpp.pp, cs1, csM, snpGroups)
+ #withProgress_1
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 8/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+
+ if (min(LD)<0){ #updated20220208
+ LD = LD^2
+ }
+ #outputs
+ tt = list(plt_links_group_fm = plt_links_group_fm,
+ plt_nodes_group_fm = plt_nodes_group_fm,
+ plt_links_group_flashfm = plt_links_group_flashfm,
+ plt_nodes_group_flashfm = plt_nodes_group_flashfm,
+ plt_links_snp = plt_links_snp,
+ plt_nodes_snp = plt_nodes_snp,
+ plt_links_snp_flashfm = plt_links_snp_flashfm,
+ plt_nodes_snp_flashfm = plt_nodes_snp_flashfm,
+ col_vector = col_vector,
+ pal = pal,
+ LD = LD,
+ cs1 = cs1,
+ csM = csM,
+ GWAS = GWAS,
+ mpp.pp = mpp.pp,
+ snpGroups = snpGroups,
+ cs1_selected = cs1_selected,
+ csM_selected = csM_selected,
+ GWAS_all_final = GWAS_all_final)
+ })
+
+
+
+ #####GWAS table trait 1a single----
+ GWAS_select_t1_a_cs <- reactive({
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 9/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_1==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==1 & GWAS_all_final$cs1_1==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==1 & GWAS_all_final$csM_1==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t1_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t1_a_cs()[GWAS_select_t1_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[1],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t1_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[1],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 1b multi----
+ GWAS_select_t1_b_cs <- reactive({
+ withProgress(message = 'Calculation in progress',
+ detail = '...Step 10/10 done!', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.025)
+ }
+ })
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_1==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==1 & GWAS_all_final$cs1_1==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_1==1 & GWAS_all_final$csM_1==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t1_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t1_b_cs()[GWAS_select_t1_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t1_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 2a single----
+ GWAS_select_t2_a_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_2==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==1 & GWAS_all_final$cs1_2==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==1 & GWAS_all_final$csM_2==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t2_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t2_a_cs()[GWAS_select_t2_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[2],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t2_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[2],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+ #####GWAS table trait 2b multi----
+ GWAS_select_t2_b_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_2==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==1 & GWAS_all_final$cs1_2==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_2==1 & GWAS_all_final$csM_2==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t2_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t2_b_cs()[GWAS_select_t2_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t2_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 3a single----
+ GWAS_select_t3_a_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_3==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==1 & GWAS_all_final$cs1_3==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==1 & GWAS_all_final$csM_3==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t3_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t3_a_cs()[GWAS_select_t3_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[3],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t3_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[3],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+ #####GWAS table trait 3b multi----
+ GWAS_select_t3_b_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_3==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==1 & GWAS_all_final$cs1_3==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_3==1 & GWAS_all_final$csM_3==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t3_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t3_b_cs()[GWAS_select_t3_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t3_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 4a single----
+ GWAS_select_t4_a_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_4==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==1 & GWAS_all_final$cs1_4==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==1 & GWAS_all_final$csM_4==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t4_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t4_a_cs()[GWAS_select_t4_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[4],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t4_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[4],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+ #####GWAS table trait 4b multi----
+ GWAS_select_t4_b_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_4==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==1 & GWAS_all_final$cs1_4==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_4==1 & GWAS_all_final$csM_4==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t4_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t4_b_cs()[GWAS_select_t4_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t4_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 5a single----
+ GWAS_select_t5_a_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_5==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==1 & GWAS_all_final$cs1_5==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==1 & GWAS_all_final$csM_5==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t5_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t5_a_cs()[GWAS_select_t5_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[5],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t5_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[5],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+ #####GWAS table trait 5b multi----
+ GWAS_select_t5_b_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_5==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==1 & GWAS_all_final$cs1_5==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_5==1 & GWAS_all_final$csM_5==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t5_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t5_b_cs()[GWAS_select_t5_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t5_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+ #####GWAS table trait 6a single----
+ GWAS_select_t6_a_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$cs1_6==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==1 & GWAS_all_final$cs1_6==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==1 & GWAS_all_final$csM_6==1, ]
+ }else{
+ GWAS_all_final
+ }
+ })
+ GWAS_select_t6_a <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t6_a_cs()[GWAS_select_t6_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[6],"_Single")]>=input$control_widgets_2[1]
+ & GWAS_select_t6_a_cs()[, paste0("MPP_",names(mpp.pp$MPP)[6],"_Single")]<=input$control_widgets_2[2]+0.001, ]
+ })
+ #####GWAS table trait 6b multi----
+ GWAS_select_t6_b_cs <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ if (input$control_widgets_1 == 0){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==0, ]
+ }else if (input$control_widgets_1 == 1){
+ GWAS_all_final[GWAS_all_final$csM_6==1, ]
+ }else if (input$control_widgets_1 == 2){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==2, ]
+ }else if (input$control_widgets_1 == 1.1){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==1 & GWAS_all_final$cs1_6==1, ]
+ }else if (input$control_widgets_1 == 1.2){
+ GWAS_all_final[GWAS_all_final$cs_trait_6==1 & GWAS_all_final$csM_6==1, ]
+ }else{
+ GWAS_all_final
+ }
+
+ })
+ GWAS_select_t6_b <- reactive({
+ mpp.pp = tt()$mpp.pp
+ GWAS_select_t6_b_cs()[GWAS_select_t6_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi")]>=input$control_widgets_2[1]
+ & GWAS_select_t6_b_cs()[, paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi")]<=input$control_widgets_2[2]+0.001, ]
+ })
+
+
+ #ch0: Regional association_plot_1a----
+ output$manhattan_1a <- renderPlotly({
+ #withProgress_2
+ withProgress(message = 'Calculation in progress',
+ detail = '...done: manhattan_1a', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.05)
+ }
+ })
+
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_1a <- plot_ly(GWAS_select_t1_a(), x = ~ps, y = ~pval_log_1,
+ type = 'scatter', mode = 'markers',
+ #size = ~MPP_lowdlipo_Single, color = ~snpGroups_fm,
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_1,4),
+ #'
-log10(pval):', signif(pval_log_1,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[1],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_1,
+ '
Credible_set_csM: ', csM_1,
+ sep = ""))
+ fig_1a <- fig_1a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t1_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t1_a()$ps),
+ max(GWAS_select_t1_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t1_a()$pval_log_1)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+
+ M_ch0_a1=input$control_widgets_ch_0_a1 ##CHECK
+ if (M_ch0_a1 == 0){
+ fig_1a <- fig_1a %>% layout(shapes = list(vline(GWAS_select_t1_a()$ps[which(GWAS_select_t1_a()$pval_log_1==max(GWAS_select_t1_a()$pval_log_1))]), hline(7.3)))
+ }else{
+ fig_1a <- fig_1a %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_1a <- fig_1a %>% layout(shapes = list(vline(GWAS_select_t1_a()$ps[which(GWAS_select_t1_a()$pval_log_1==max(GWAS_select_t1_a()$pval_log_1))]), hline(7.3)))
+
+ # fig_1a <- fig_1a %>% layout(annotations = list(
+ # list(x = 0.001 , y = -0.05, align = 'left',
+ # text = "Figure trait_1_FINEMAP: Size = MPP, Color = snpGroups",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_1a
+ })
+
+ #ch0: Manhattan_plot_1b----
+ output$manhattan_1b <- renderPlotly({
+ #withProgress_3
+ withProgress(message = 'Calculation in progress',
+ detail = '...done: manhattan_1b', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.05)
+ }
+ })
+
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_1b <- plot_ly(GWAS_select_t1_b(), x = ~ps, y = ~pval_log_1,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_1,4),
+ #'
-log10(pval):', signif(pval_log_1,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[1],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_1,
+ '
Credible_set_csM: ', csM_1,
+ sep = ""))
+
+ fig_1b <- fig_1b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t1_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t1_b()$ps),
+ max(GWAS_select_t1_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t1_b()$pval_log_1)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+
+ M_ch0_b1=input$control_widgets_ch_0_b1 ##CHECK
+ if (M_ch0_b1 == 0){
+ fig_1b <- fig_1b %>% layout(shapes = list(vline(GWAS_select_t1_b()$ps[which(GWAS_select_t1_b()$pval_log_1==max(GWAS_select_t1_b()$pval_log_1))]), hline(7.3)))
+ }else{
+ fig_1b <- fig_1b %>% layout(shapes = list(hline(7.3)))
+ }
+ # fig_1b <- fig_1b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_1_flashfm: \nY-axis = pval_lowdlipo, \nX-axis = ps, \nSize = MPP_lowdlipo_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_1b
+ })
+
+ #ch0: Manhattan_plot_2a----
+ output$manhattan_2a <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_2a <- plot_ly(GWAS_select_t2_a(), x = ~ps, y = ~pval_log_2,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_2,4),
+ #'
-log10(pval):', signif(pval_log_2,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[2],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_2,
+ '
Credible_set_csM: ', csM_2,
+ sep = ""))
+ fig_2a <- fig_2a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t2_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t2_a()$ps),
+ max(GWAS_select_t2_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t2_a()$pval_log_2)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+
+ M_ch0_a2=input$control_widgets_ch_0_a2 ##CHECK
+ if (M_ch0_a2 == 0){
+ fig_2a <- fig_2a %>% layout(shapes = list(vline(GWAS_select_t2_a()$ps[which(GWAS_select_t2_a()$pval_log_2==max(GWAS_select_t2_a()$pval_log_2))]), hline(7.3)))
+ }else{
+ fig_2a <- fig_2a %>% layout(shapes = list(hline(7.3)))
+ }
+ # fig_2a <- fig_2a %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_2_FINEMAP: \nY-axis = pval_cholesterol, \nX-axis = ps, \nSize = MPP_cholesterol_Single, \nColor = snpGroups_fm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_2a
+ })
+
+ #ch0: Manhattan_plot_2b----
+ output$manhattan_2b <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_2b <- plot_ly(GWAS_select_t2_b(), x = ~ps, y = ~pval_log_2,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_2,4),
+ #'
-log10(pval):', signif(pval_log_2,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[2],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_2,
+ '
Credible_set_csM: ', csM_2,
+ sep = ""))
+ fig_2b <- fig_2b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t2_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t2_b()$ps),
+ max(GWAS_select_t2_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t2_b()$pval_log_2)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+
+ M_ch0_b2=input$control_widgets_ch_0_b2 ##CHECK
+ if (M_ch0_b2 == 0){
+ fig_2b <- fig_2b %>% layout(shapes = list(vline(GWAS_select_t2_b()$ps[which(GWAS_select_t2_b()$pval_log_2==max(GWAS_select_t2_b()$pval_log_2))]), hline(7.3)))
+ }else{
+ fig_2b <- fig_2b %>% layout(shapes = list(hline(7.3)))
+ }
+ # fig_2b <- fig_2b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_2_flashfm: \nY-axis = pval_cholesterol, \nX-axis = ps, \nSize = MPP_cholesterol_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_2b
+ })
+
+ #ch0: Manhattan_plot_3a----
+ output$manhattan_3a <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_3a <- plot_ly(GWAS_select_t3_a(), x = ~ps, y = ~pval_log_3,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_3,4),
+ #'
-log10(pval):', signif(pval_log_3,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[3],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_3,
+ '
Credible_set_csM: ', csM_3,
+ sep = ""))
+ fig_3a <- fig_3a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t3_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t3_a()$ps),
+ max(GWAS_select_t3_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t3_a()$pval_log_3)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+
+ #fig_3a <- fig_3a %>% layout(shapes = list(vline(GWAS_select_t3_a()$ps[which(GWAS_select_t3_a()$pval_log_3==max(GWAS_select_t3_a()$pval_log_3))]), hline(7.3)))
+ fig_3a <- fig_3a %>% layout(shapes = list(hline(7.3)))
+ # fig_3a <- fig_3a %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_3_FINEMAP: \nY-axis = pval_triglycerides, \nX-axis = ps, \nSize = MPP_triglycerides_Single, \nColor = snpGroups_fm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_3a
+ })
+
+ #ch0: Manhattan_plot_3b----
+ output$manhattan_3b <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_3b <- plot_ly(GWAS_select_t3_b(), x = ~ps, y = ~pval_log_3,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_3,4),
+ #'
-log10(pval):', signif(pval_log_3,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[3],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_3,
+ '
Credible_set_csM: ', csM_3,
+ sep = ""))
+ fig_3b <- fig_3b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t3_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t3_b()$ps),
+ max(GWAS_select_t3_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t3_b()$pval_log_3)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_3b <- fig_3b %>% layout(shapes = list(vline(GWAS_select_t3_b()$ps[which(GWAS_select_t3_b()$pval_log_3==max(GWAS_select_t3_b()$pval_log_3))]), hline(7.3)))
+ fig_3b <- fig_3b %>% layout(shapes = list(hline(7.3)))
+ # fig_3b <- fig_3b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_3_flashfm: \nY-axis = pval_triglycerides, \nX-axis = ps, \nSize = MPP_triglycerides_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_3b
+ })
+
+ #ch0: Manhattan_plot_4a----
+ output$manhattan_4a <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_4a <- plot_ly(GWAS_select_t4_a(), x = ~ps, y = ~pval_log_4,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_4,4),
+ #'
-log10(pval):', signif(pval_log_4,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[4],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_4,
+ '
Credible_set_csM: ', csM_4,
+ sep = ""))
+ fig_4a <- fig_4a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t4_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t4_a()$ps),
+ max(GWAS_select_t4_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t4_a()$pval_log_4)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_4a <- fig_4a %>% layout(shapes = list(vline(GWAS_select_t4_a()$ps[which(GWAS_select_t4_a()$pval_log_4==max(GWAS_select_t4_a()$pval_log_4))]), hline(7.3)))
+ fig_4a <- fig_4a %>% layout(shapes = list(hline(7.3)))
+ # fig_4a <- fig_4a %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_4_FINEMAP: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Single, \nColor = snpGroups_fm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_4a
+ })
+
+ #ch0: Manhattan_plot_4b----
+ output$manhattan_4b <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_4b <- plot_ly(GWAS_select_t4_b(), x = ~ps, y = ~pval_log_4,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_4,4),
+ #'
-log10(pval):', signif(pval_log_4,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[4],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_4,
+ '
Credible_set_csM: ', csM_4,
+ sep = ""))
+ fig_4b <- fig_4b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t4_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t4_b()$ps),
+ max(GWAS_select_t4_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t4_b()$pval_log_4)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_4b <- fig_4b %>% layout(shapes = list(vline(GWAS_select_t4_b()$ps[which(GWAS_select_t4_b()$pval_log_4==max(GWAS_select_t4_b()$pval_log_4))]), hline(7.3)))
+ fig_4b <- fig_4b %>% layout(shapes = list(hline(7.3)))
+ # fig_4b <- fig_4b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_4_flashfm: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_4b
+ })
+
+ #ch0: Manhattan_plot_5a----
+ output$manhattan_5a <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_5a <- plot_ly(GWAS_select_t5_a(), x = ~ps, y = ~pval_log_5,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_5,4),
+ #'
-log10(pval):', signif(pval_log_5,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[5],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_5,
+ '
Credible_set_csM: ', csM_5,
+ sep = ""))
+ fig_5a <- fig_5a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t5_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t5_a()$ps),
+ max(GWAS_select_t5_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t5_a()$pval_log_5)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_5a <- fig_5a %>% layout(shapes = list(vline(GWAS_select_t5_a()$ps[which(GWAS_select_t5_a()$pval_log_5==max(GWAS_select_t5_a()$pval_log_5))]), hline(7.3)))
+ fig_5a <- fig_5a %>% layout(shapes = list(hline(7.3)))
+ # fig_5a <- fig_5a %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_5_FINEMAP: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Single, \nColor = snpGroups_fm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_5a
+ })
+
+ #ch0: Manhattan_plot_5b----
+ output$manhattan_5b <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_5b <- plot_ly(GWAS_select_t5_b(), x = ~ps, y = ~pval_log_5,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_5,4),
+ #'
-log10(pval):', signif(pval_log_5,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[5],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_5,
+ '
Credible_set_csM: ', csM_5,
+ sep = ""))
+ fig_5b <- fig_5b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t5_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t5_b()$ps),
+ max(GWAS_select_t5_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t5_b()$pval_log_5)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_5b <- fig_5b %>% layout(shapes = list(vline(GWAS_select_t5_b()$ps[which(GWAS_select_t5_b()$pval_log_5==max(GWAS_select_t5_b()$pval_log_5))]), hline(7.3)))
+ fig_5b <- fig_5b %>% layout(shapes = list(hline(7.3)))
+ # fig_5b <- fig_5b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_5_flashfm: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_5b
+ })
+
+
+ #ch0: Manhattan_plot_6a----
+ output$manhattan_6a <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_6a <- plot_ly(GWAS_select_t6_a(), x = ~ps, y = ~pval_log_6,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Single"))),
+ color = ~snpGroups_fm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_6,4),
+ #'
-log10(pval):', signif(pval_log_6,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Single"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[6],"_Single"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_6,
+ '
Credible_set_csM: ', csM_6,
+ sep = ""))
+ fig_6a <- fig_6a %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t6_a()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t6_a()$ps),
+ max(GWAS_select_t6_a()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t6_a()$pval_log_6)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_6a <- fig_6a %>% layout(shapes = list(vline(GWAS_select_t6_a()$ps[which(GWAS_select_t6_a()$pval_log_6==max(GWAS_select_t6_a()$pval_log_6))]), hline(7.3)))
+ fig_6a <- fig_6a %>% layout(shapes = list(hline(7.3)))
+ # fig_6a <- fig_6a %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_6_FINEMAP: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Single, \nColor = snpGroups_fm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_6a
+ })
+
+ #ch0: Manhattan_plot_6b----
+ output$manhattan_6b <- renderPlotly({
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ fig_6b <- plot_ly(GWAS_select_t6_b(), x = ~ps, y = ~pval_log_6,
+ type = 'scatter', mode = 'markers',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ colors = pal,
+ sizes = c(5, 50),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1,
+ '
Allele_0 (or Allele_2): ', allele0,
+ '
af: ', signif(af,4),
+ #'
ps:', ps,
+ '
pval: ', signif(pval_6,4),
+ #'
-log10(pval):', signif(pval_log_6,4),
+ '
MPP: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi"))),4),
+ '
MPPg: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[6],"_Multi"))),4), " (if MPPg=0: negligible)",
+ '
snpGroups_fm: ', snpGroups_fm, ' (',snpGroups_fm_size, ' SNPs)',
+ '
snpGroups_flashfm: ', snpGroups_flashfm, ' (',snpGroups_flashfm_size, ' SNPs)',
+ '
Credible_set_cs1: ', cs1_6,
+ '
Credible_set_csM: ', csM_6,
+ sep = ""))
+ fig_6b <- fig_6b %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ title = paste0('Chromosome_',
+ GWAS_select_t6_b()$chr[1],
+ '_position \n(NOTE: Size = MPP, Color = snpGroups)'),
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_select_t6_b()$ps),
+ max(GWAS_select_t6_b()$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0-10, max(GWAS_select_t6_b()$pval_log_6)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ showlegend = TRUE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ #fig_6b <- fig_6b %>% layout(shapes = list(vline(GWAS_select_t6_b()$ps[which(GWAS_select_t6_b()$pval_log_6==max(GWAS_select_t6_b()$pval_log_6))]), hline(7.3)))
+ fig_6b <- fig_6b %>% layout(shapes = list(hline(7.3)))
+ # fig_6b <- fig_6b %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left',
+ # text = "Figure trait_6_flashfm: \nY-axis = pval_highdlipo, \nX-axis = ps, \nSize = MPP_highdlipo_Multi, \nColor = snpGroups_flashfm",
+ # showarrow = F, xref='paper', yref='paper')) )
+ fig_6b
+ })
+
+ #ch0: LD matrix----
+ # output$Plot_LD <- renderPlot({
+ # LD.plot(LD[1:20,1:20], snp.positions = GWAS[[1]]$ps[1:20])
+ # })
+ output$Plot_LD1 <- renderPlotly({
+ #withProgress_4
+ withProgress(message = 'Calculation in progress',
+ detail = '...done: LD_heatmap', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.05)
+ }
+ })
+
+ LD = tt()$LD
+ LD_selected = LD[ ,GWAS_select_t1_a()$rs]
+ LD_selected = LD_selected[GWAS_select_t1_a()$rs, ]
+ fig_ld <- plot_ly(x = colnames(LD_selected), y = rownames(LD_selected), z = LD_selected, colors = "Greys", type = "heatmap")
+ fig_ld
+ })
+
+ output$Plot_LD2 <- renderPlot({
+ LD = tt()$LD
+ LD_selected = LD[ ,GWAS_select_t1_a()$rs]
+ LD_selected = LD_selected[GWAS_select_t1_a()$rs, ]
+ if (input$control_widgets_1 == 0){
+ hist(LD_selected)
+ }else if (input$control_widgets_1 == 3){
+ hist(LD_selected)
+ }else{
+ LD.plot(LD_selected, snp.positions = GWAS_select_t1_a()$ps)
+ }
+ #withProgress_5
+ withProgress(message = 'Calculation in progress',
+ detail = '...done: LD_plot', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.05)
+ }
+ })
+ })
+
+ output$downloadPlot <- downloadHandler(
+ filename = function(){paste("LD_", Sys.Date(), ".pdf", sep="")},
+ content = function(file){
+ pdf(file)
+ LD = tt()$LD
+ LD_selected = LD[ ,GWAS_select_t1_a()$rs]
+ LD_selected = LD_selected[GWAS_select_t1_a()$rs, ]
+ if (input$control_widgets_1 == 0){
+ hist(LD_selected)
+ }else if (input$control_widgets_1 == 3){
+ hist(LD_selected)
+ }else{
+ LD.plot(LD_selected, snp.positions = GWAS_select_t1_a()$ps)
+ }
+ dev.off()
+ })
+
+
+
+ #ch0: snpGroup$group.sizes table----
+ output$ch0_table0 <- DT::renderDataTable({
+ snpGroups = tt()$snpGroups
+ col_vector = tt()$col_vector
+ #generate a vector of color based on snpGroups
+ #n <- length(colnames(snpGroups$group.sizes))
+ #col_vector = distinctColorPalette(n)
+ DT::datatable(data.frame(rbind(colnames(snpGroups$group.sizes), snpGroups$group.sizes), row.names = c("Color", 'FINEMAP', 'flashfm')),
+ options = list(dom = 't')) %>% formatStyle(
+ colnames(snpGroups$group.sizes),
+ backgroundColor = styleEqual(colnames(snpGroups$group.sizes), col_vector
+ )
+ )
+ })
+
+ output$ch0_table0_ch3 <- DT::renderDataTable({
+ snpGroups = tt()$snpGroups
+ col_vector = tt()$col_vector
+ #generate a vector of color based on snpGroups
+ #n <- length(colnames(snpGroups$group.sizes))
+ #col_vector = distinctColorPalette(n)
+ DT::datatable(data.frame(rbind(colnames(snpGroups$group.sizes), snpGroups$group.sizes), row.names = c("Color", 'FINEMAP', 'flashfm')),
+ options = list(dom = 't')) %>% formatStyle(
+ colnames(snpGroups$group.sizes),
+ backgroundColor = styleEqual(colnames(snpGroups$group.sizes), col_vector
+ )
+ )
+ })
+
+ #ch1: single trait networks (group vs. snp)----
+ plt_links_group_fm_selected <- reactive({
+ plt_links_group_fm = tt()$plt_links_group_fm
+ plt_links_group_fm[plt_links_group_fm$l_size>=input$control_widgets_3a_fm[1]*10 & plt_links_group_fm$l_size<=input$control_widgets_3a_fm[2]*10+0.001, ]
+ })
+ network_ch1_network_group_fm <- reactive({forceNetwork(Links = plt_links_group_fm_selected(),
+ #Nodes = plt_nodes_group_fm,
+ Nodes = tt()$plt_nodes_group_fm,
+ Source = 'source_id',
+ Target = 'target_id',
+ Value = 'l_size',
+ NodeID = 'name',
+ Nodesize = 'n_size',
+ Group = 'n_color',
+ #height = 500,
+ #width = 500,
+ fontSize = 20,
+ fontFamily = "serif",
+ linkDistance = 100,
+ charge = -300, #numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value).
+ linkColour = plt_links_group_fm_selected()$l_color,
+ #radiusCalculation = "d.nodesize+10",
+ #radiusCalculation = " Math.sqrt(d.nodesize)+10",
+ opacity = 0.8,
+ zoom = T,
+ legend = T,
+ bounded = T,
+ #colourScale = JS(ColourScale),
+ opacityNoHover = 1)
+ })
+
+ output$ch1_network_group_fm <- renderForceNetwork({
+ network_ch1_network_group_fm()
+ })
+
+ # output$d_ch_1_s1 <- downloadHandler(
+ # filename = function() {
+ # paste("ch1_network_group_fm_", Sys.Date(), ".html", sep="")
+ # },
+ # content = function(file) {
+ # saveNetwork(network_ch1_network_group_fm(), file)
+ # }
+ # )
+
+ output$d_ch_1_s1 <- downloadHandler(
+ filename = function() {
+ paste("ch1_network_group_fm_", Sys.Date(), ".html", sep="")
+ },
+ content = function(file) {
+ saveNetwork(network_ch1_network_group_fm(), file)
+ #webshot::install_phantomjs(force = TRUE)
+ #webshot(file,"testing.png", vwidth = 1000, vheight = 900)
+ }
+ )
+
+ plt_links_snp_selected <- reactive({
+ plt_links_snp = tt()$plt_links_snp
+ plt_links_snp[plt_links_snp$l_size>=input$control_widgets_3b_fm[1]*10 & plt_links_snp$l_size<=input$control_widgets_3b_fm[2]*10+0.001, ]
+ })
+ network_ch1_network_snp_fm <- reactive({forceNetwork(Links = plt_links_snp_selected(),
+ #Nodes = plt_nodes_snp,
+ Nodes = tt()$plt_nodes_snp,
+ Source = 'source_id',
+ Target = 'target_id',
+ Value = 'l_size',
+ NodeID = 'name',
+ Nodesize = 'n_size',
+ Group = 'n_color',
+ #height = 500,
+ #width = 500,
+ fontSize = 20,
+ fontFamily = "serif",
+ linkDistance = 100,
+ charge = -300, #numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value).
+ linkColour = plt_links_snp_selected()$l_color,
+ #radiusCalculation = "d.nodesize+10",
+ #radiusCalculation = " Math.sqrt(d.nodesize)+10",
+ opacity = 0.8,
+ zoom = T,
+ legend = T,
+ bounded = F,
+ #colourScale = JS(ColourScale),
+ opacityNoHover = 1)
+ })
+
+ output$ch1_network_snp <- renderForceNetwork({
+ network_ch1_network_snp_fm()
+ })
+
+ output$d_ch_1_s2 <- downloadHandler(
+ filename = function() {
+ paste("ch1_network_snp_fm_", Sys.Date(), ".html", sep="")
+ },
+ content = function(file) {
+ saveNetwork(network_ch1_network_snp_fm(), file)
+ }
+ )
+
+ #ch2: multi trait networks (group vs. snp)----
+ plt_links_group_flashfm_selected <- reactive({
+ plt_links_group_flashfm = tt()$plt_links_group_flashfm
+ plt_links_group_flashfm[plt_links_group_flashfm$l_size>=input$control_widgets_3a_flashfm[1]*10 & plt_links_group_flashfm$l_size<=input$control_widgets_3a_flashfm[2]*10+0.001, ]
+ })
+ network_ch1_network_group_flashfm <- reactive({forceNetwork(Links = plt_links_group_flashfm_selected(),
+ #Nodes = plt_nodes_group_flashfm,
+ Nodes = tt()$plt_nodes_group_flashfm,
+ Source = 'source_id',
+ Target = 'target_id',
+ Value = 'l_size',
+ NodeID = 'name',
+ Nodesize = 'n_size',
+ Group = 'n_color',
+ #height = 500,
+ #width = 500,
+ fontSize = 20,
+ fontFamily = "serif",
+ linkDistance = 100,
+ charge = -300, #numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value).
+ linkColour = plt_links_group_flashfm_selected()$l_color,
+ #radiusCalculation = "d.nodesize+10",
+ #radiusCalculation = " Math.sqrt(d.nodesize)+10",
+ opacity = 0.8,
+ zoom = T,
+ legend = T,
+ bounded = T,
+ #colourScale = JS(ColourScale),
+ opacityNoHover = 1)
+ })
+
+ output$ch1_network_group_flashfm <- renderForceNetwork({
+ network_ch1_network_group_flashfm()
+ })
+
+ output$d_ch_2_m1 <- downloadHandler(
+ filename = function() {
+ paste("ch2_network_group_flashfm_", Sys.Date(), ".html", sep="")
+ },
+ content = function(file) {
+ saveNetwork(network_ch1_network_group_flashfm(), file)
+ }
+ )
+
+
+
+ plt_links_snp_selected_flashfm <- reactive({
+ plt_links_snp_flashfm = tt()$plt_links_snp_flashfm
+ plt_links_snp_flashfm[plt_links_snp_flashfm$l_size>=input$control_widgets_3b_flashfm[1]*10 & plt_links_snp_flashfm$l_size<=input$control_widgets_3b_flashfm[2]*10+0.001, ]
+ })
+ network_ch1_network_snp_flashfm <- reactive({forceNetwork(Links = plt_links_snp_selected_flashfm(),
+ #Nodes = plt_nodes_snp,
+ Nodes = tt()$plt_nodes_snp,
+ Source = 'source_id',
+ Target = 'target_id',
+ Value = 'l_size',
+ NodeID = 'name',
+ Nodesize = 'n_size',
+ Group = 'n_color',
+ #height = 500,
+ #width = 500,
+ fontSize = 20,
+ fontFamily = "serif",
+ linkDistance = 100,
+ charge = -300, #numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value).
+ linkColour = plt_links_snp_selected_flashfm()$l_color,
+ #radiusCalculation = "d.nodesize+10",
+ #radiusCalculation = " Math.sqrt(d.nodesize)+10",
+ opacity = 0.8,
+ zoom = T,
+ legend = T,
+ bounded = F,
+ #colourScale = JS(ColourScale),
+ opacityNoHover = 1)
+ })
+
+ output$ch1_network_snp_flashfm <- renderForceNetwork({
+ network_ch1_network_snp_flashfm()
+ })
+
+ output$d_ch_2_m2 <- downloadHandler(
+ filename = function() {
+ paste("ch2_network_snp_flashfm_", Sys.Date(), ".html", sep="")
+ },
+ content = function(file) {
+ saveNetwork(network_ch1_network_snp_flashfm(), file)
+ }
+ )
+
+ #ch3: Regional association plots link selection----
+ output$manhattan_ch3_linked <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+ pal = tt()$pal
+
+ M_ch3_2 = input$control_widgets_ch_3_2
+
+ shared_data = GWAS_all_final %>% highlight_key()
+ baseplot = plot_ly(shared_data, height = 1200)
+
+ ### New plots here----
+ # Figure 1: Regional association plots
+ vline <- function(x = 0, color = "gray") {
+ list(type = "line", y0 = 0, y1 = 1, yref = "paper", x0 = x, x1 = x,
+ line = list(color = color, dash = 'dash'))}
+
+ hline <- function(y = 0, color = "red") {
+ list(type = "line", x0 = 0, x1 = 1, xref = "paper", y0 = y, y1 = y,
+ line = list(color = color))}
+
+ # Figure fig_1a: pval_log_1, size = ~MPP_lowdlipo_Single, color = ~snpGroups_fm
+ fig_1a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_1, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size, ' SNPs)',
+ '
MPP_trait1_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Single"))),4),
+ '
MPPg_trait1_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[1],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_1,4),
+ '
Credible_set_cs1: ', cs1_1,
+ '
Credible_set_csM: ', csM_1,
+ sep = ""))
+
+ fig_1a_ch3 <- fig_1a_ch3 %>% layout(#title = 'Interactive Regional association plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_1)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_1a_ch3 <- fig_1a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_1==max(GWAS_all_final$pval_log_1))]), hline(7.3)))
+ }else{
+ fig_1a_ch3 <- fig_1a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_1a_ch3 <- fig_1a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_1==max(GWAS_all_final$pval_log_1))]), hline(7.3)))
+ #fig_1a_ch3 <- fig_1a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.05, align = 'left', text = "Figure fig_1a: pval_lowdlipo, \n size = ~MPP_lowdlipo_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_1a_ch3
+
+ # Figure fig_1b: pval_log_1, size = ~MPP_lowdlipo_Multi, color = ~snpGroups_flashfm
+ fig_1b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_1, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size,' SNPs)',
+ '
MPP_trait1_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi"))),4),
+ '
MPPg_trait1_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[1],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_1,4),
+ '
Credible_set_cs1: ', cs1_1,
+ '
Credible_set_csM: ', csM_1,
+ sep = ""))
+
+ fig_1b_ch3 <- fig_1b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_1)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_1b_ch3 <- fig_1b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_1==max(GWAS_all_final$pval_log_1))]), hline(7.3)))
+ }else{
+ fig_1b_ch3 <- fig_1b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_1b_ch3 <- fig_1b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_1==max(GWAS_all_final$pval_log_1))]), hline(7.3)))
+ #fig_1b_ch3 <- fig_1b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_1b: pval_lowdlipo, \n size = ~MPP_lowdlipo_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_1b_ch3
+
+ # Figure fig_2a: pval_log_2, size = ~MPP_cholesterol_Single, color = ~snpGroups_fm
+ fig_2a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_2, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size,' SNPs)',
+ '
MPP_trait2_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Single"))),4),
+ '
MPPg_trait2_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[2],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_2,4),
+ '
Credible_set_cs1: ', cs1_2,
+ '
Credible_set_csM: ', csM_2,
+ sep = ""))
+
+ fig_2a_ch3 <- fig_2a_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_2)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_2a_ch3 <- fig_2a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_2==max(GWAS_all_final$pval_log_2))]), hline(7.3)))
+ }else{
+ fig_2a_ch3 <- fig_2a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_2a_ch3 <- fig_2a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_2==max(GWAS_all_final$pval_log_2))]), hline(7.3)))
+ #fig_2a_ch3 <- fig_2a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_2a: pval_holesterol, \n size = ~MPP_holesterol_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_2a_ch3
+
+ # Figure fig_2b: pval_log_2, size = ~MPP_cholesterol_Multi, color = ~snpGroups_flashfm
+ fig_2b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_2, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size,' SNPs)',
+ '
MPP_trait2_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi"))),4),
+ '
MPPg_trait2_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[2],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_2,4),
+ '
Credible_set_cs1: ', cs1_2,
+ '
Credible_set_csM: ', csM_2,
+ sep = ""))
+
+ fig_2b_ch3 <- fig_2b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_2)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_2b_ch3 <- fig_2b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_2==max(GWAS_all_final$pval_log_2))]), hline(7.3)))
+ }else{
+ fig_2b_ch3 <- fig_2b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_2b_ch3 <- fig_2b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_2==max(GWAS_all_final$pval_log_2))]), hline(7.3)))
+ #fig_2b_ch3 <- fig_2b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_2b: pval_holesterol, \n size = ~MPP_holesterol_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_2b_ch3
+
+ # Figure fig_3a: pval_log_3, size = ~MPP_triglycerides_Single, color = ~snpGroups_fm
+ fig_3a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_3, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size,' SNPs)',
+ '
MPP_trait3_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Single"))),4),
+ '
MPPg_trait3_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[3],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_3,4),
+ '
Credible_set_cs1: ', cs1_3,
+ '
Credible_set_csM: ', csM_3,
+ sep = ""))
+
+ fig_3a_ch3 <- fig_3a_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_3)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_3a_ch3 <- fig_3a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_3==max(GWAS_all_final$pval_log_3))]), hline(7.3)))
+ }else{
+ fig_3a_ch3 <- fig_3a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_3a_ch3 <- fig_3a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_3==max(GWAS_all_final$pval_log_3))]), hline(7.3)))
+ #fig_3a_ch3 <- fig_3a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_3a: pval_triglycerides, \n size = ~MPP_triglycerides_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_3a_ch3
+
+ # Figure fig_3b: pval_log_3, size = ~MPP_triglycerides_Multi, color = ~snpGroups_flashfm
+ fig_3b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_3, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size,' SNPs)',
+ '
MPP_trait3_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi"))),4),
+ '
MPPg_trait3_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[3],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_3,4),
+ '
Credible_set_cs1: ', cs1_3,
+ '
Credible_set_csM: ', csM_3,
+ sep = ""))
+
+ fig_3b_ch3 <- fig_3b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_3)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_3b_ch3 <- fig_3b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_3==max(GWAS_all_final$pval_log_3))]), hline(7.3)))
+ }else{
+ fig_3b_ch3 <- fig_3b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_3b_ch3 <- fig_3b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_3==max(GWAS_all_final$pval_log_3))]), hline(7.3)))
+ #fig_3b_ch3 <- fig_3b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_3b: pval_triglycerides, \n size = ~MPP_triglycerides_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_3b_ch3
+
+ # Figure fig_4a: pval_log_4, size = ~MPP_highdlipo_Single, color = ~snpGroups_fm
+ fig_4a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_4, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size,' SNPs)',
+ '
MPP_trait4_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Single"))),4),
+ '
MPPg_trait4_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[4],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_4,4),
+ '
Credible_set_cs1: ', cs1_4,
+ '
Credible_set_csM: ', csM_4,
+ sep = ""))
+
+ fig_4a_ch3 <- fig_4a_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_4)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_4a_ch3 <- fig_4a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_4==max(GWAS_all_final$pval_log_4))]), hline(7.3)))
+ }else{
+ fig_4a_ch3 <- fig_4a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_4a_ch3 <- fig_4a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_4==max(GWAS_all_final$pval_log_4))]), hline(7.3)))
+ #fig_4a_ch3 <- fig_4a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_4a: pval_highdlipo, \n size = ~MPP_highdlipo_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_4a_ch3
+
+ # Figure fig_4b: pval_log_4, size = ~MPP_highdlipo_Multi, color = ~snpGroups_flashfm
+ fig_4b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_4, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size,' SNPs)',
+ '
MPP_trait4_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi"))),4),
+ '
MPPg_trait4_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[4],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_4,4),
+ '
Credible_set_cs1: ', cs1_4,
+ '
Credible_set_csM: ', csM_4,
+ sep = ""))
+
+ fig_4b_ch3 <- fig_4b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_4)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_4b_ch3 <- fig_4b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_4==max(GWAS_all_final$pval_log_4))]), hline(7.3)))
+ }else{
+ fig_4b_ch3 <- fig_4b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_4b_ch3 <- fig_4b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_4==max(GWAS_all_final$pval_log_4))]), hline(7.3)))
+ #fig_4b_ch3 <- fig_4b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_4b: pval_highdlipo, \n size = ~MPP_highdlipo_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_4b_ch3
+
+ # Figure fig_5a: pval_log_4, size = ~MPP_highdlipo_Single, color = ~snpGroups_fm
+ fig_5a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_5, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size, ' SNPs)',
+ '
MPP_trait5_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Single"))),4),
+ '
MPPg_trait5_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[5],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_5,4),
+ '
Credible_set_cs1: ', cs1_5,
+ '
Credible_set_csM: ', csM_5,
+ sep = ""))
+
+ fig_5a_ch3 <- fig_5a_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_5)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_5a_ch3 <- fig_5a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_5==max(GWAS_all_final$pval_log_5))]), hline(7.3)))
+ }else{
+ fig_5a_ch3 <- fig_5a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_5a_ch3 <- fig_5a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_5==max(GWAS_all_final$pval_log_5))]), hline(7.3)))
+ #fig_5a_ch3 <- fig_5a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_5a: pval_highdlipo, \n size = ~MPP_highdlipo_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_5a_ch3
+
+ # Figure fig_5b: pval_log_4, size = ~MPP_highdlipo_Multi, color = ~snpGroups_flashfm
+ fig_5b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_5, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size,' SNPs)',
+ '
MPP_trait5_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi"))),4),
+ '
MPPg_trait5_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[5],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_5,4),
+ '
Credible_set_cs1: ', cs1_5,
+ '
Credible_set_csM: ', csM_5,
+ sep = ""))
+
+ fig_5b_ch3 <- fig_5b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_5)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_5b_ch3 <- fig_5b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_5==max(GWAS_all_final$pval_log_5))]), hline(7.3)))
+ }else{
+ fig_5b_ch3 <- fig_5b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_5b_ch3 <- fig_5b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_5==max(GWAS_all_final$pval_log_5))]), hline(7.3)))
+ #fig_5b_ch3 <- fig_5b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_5b: pval_highdlipo, \n size = ~MPP_highdlipo_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_5b_ch3
+
+ # Figure fig_6a: pval_log_4, size = ~MPP_highdlipo_Single, color = ~snpGroups_fm
+ fig_6a_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_6, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Single"))),
+ color = ~snpGroups_fm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_fm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_fm: ', snpGroups_fm, ' (', snpGroups_fm_size, ' SNPs)',
+ '
MPP_trait6_Single: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Single"))),4),
+ '
MPPg_trait6_Single: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[6],"_Single"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_6,4),
+ '
Credible_set_cs1: ', cs1_6,
+ '
Credible_set_csM: ', csM_6,
+ sep = ""))
+
+ fig_6a_ch3 <- fig_6a_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_6)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_6a_ch3 <- fig_6a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_6==max(GWAS_all_final$pval_log_6))]), hline(7.3)))
+ }else{
+ fig_6a_ch3 <- fig_6a_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_6a_ch3 <- fig_6a_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_6==max(GWAS_all_final$pval_log_6))]), hline(7.3)))
+ #fig_6a_ch3 <- fig_6a_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_6a: pval_highdlipo, \n size = ~MPP_highdlipo_Single, \n color = ~snpGroups_fm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_6a_ch3
+
+ # Figure fig_6b: pval_log_4, size = ~MPP_highdlipo_Multi, color = ~snpGroups_flashfm
+ fig_6b_ch3 = baseplot %>%
+ add_markers(x = ~ps, y = ~pval_log_6, type = 'scatter',
+ size = ~eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi"))),
+ color = ~snpGroups_flashfm,
+ #colors = pal,
+ colors = tt()$pal,
+ legendgroup = ~snpGroups_flashfm,
+ sizes = c(5, 30),
+ fill = ~'',
+ marker = list(opacity = 1, sizemode = 'diameter'),
+ hoverinfo = 'text',
+ text = ~paste('SNP_ID: ', rs,
+ '
SNP_position: ', ps,
+ '
Allele_1: ', allele1, ', Allele_0: ', allele0,
+ #'
Allele 0:', allele0,
+ '
Group_flashfm: ', snpGroups_flashfm, ' (', snpGroups_flashfm_size, ' SNPs)',
+ '
MPP_trait6_Multi: ', signif(eval(parse(text = paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi"))),4),
+ '
MPPg_trait6_Multi: ', signif(eval(parse(text = paste0("MPPg_",names(mpp.pp$MPPg)[6],"_Multi"))),4),
+ '
(If MPPg=0: it is negligible)',
+ '
af: ', signif(af,4),
+ '
pval: ', signif(pval_6,4),
+ '
Credible_set_cs1: ', cs1_6,
+ '
Credible_set_csM: ', csM_6,
+ sep = ""))
+
+ fig_6b_ch3 <- fig_6b_ch3 %>% layout(title = 'Regional Association Plots',
+ xaxis = list(#title = 'Chromosome 19 position',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(min(GWAS_all_final$ps), max(GWAS_all_final$ps)),
+ #type = 'log',
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwidth = 2,
+ showgrid = FALSE),
+ yaxis = list(title = '-log10(p)',
+ gridcolor = 'rgb(255, 255, 255)',
+ range = c(0, max(GWAS_all_final$pval_log_6)+20),
+ zerolinewidth = 1,
+ ticklen = 5,
+ gridwith = 2,
+ showgrid = FALSE),
+ #showlegend = FALSE,
+ paper_bgcolor = 'rgb(243, 243, 243)',
+ plot_bgcolor = 'rgb(243, 243, 243)')
+ if (M_ch3_2 == 0){
+ fig_6b_ch3 <- fig_6b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_6==max(GWAS_all_final$pval_log_6))]), hline(7.3)))
+ }else{
+ fig_6b_ch3 <- fig_6b_ch3 %>% layout(shapes = list(hline(7.3)))
+ }
+ #fig_6b_ch3 <- fig_6b_ch3 %>% layout(shapes = list(vline(GWAS_all_final$ps[which(GWAS_all_final$pval_log_6==max(GWAS_all_final$pval_log_6))]), hline(7.3)))
+ #fig_6b_ch3 <- fig_6b_ch3 %>% layout(annotations = list(
+ # list(x = 0.05 , y = 0.95, align = 'left', text = "Figure fig_6b: pval_highdlipo, \n size = ~MPP_highdlipo_Multi, \n color = ~snpGroups_flashfm", showarrow = F, xref='paper', yref='paper')) )
+
+ #fig_6b_ch3
+
+ #selection
+ M_ch3=input$control_widgets_ch_3 ##CHECK
+
+ if (M_ch3 == 2){
+ fig_all_ch3_total <- subplot(fig_1a_ch3,
+ style(fig_1b_ch3, showlegend = F),
+ style(fig_2a_ch3, showlegend = F),
+ style(fig_2b_ch3, showlegend = F),
+ nrows = 2) %>%
+ layout(dragmode = "select",
+ plot_bgcolor='#e5ecf6',
+ xaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff'),
+ yaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff')) %>%
+ layout(autosize = T
+ #width = 1000,
+ #height = 1000
+ ) %>%
+ # highlight(on = "plotly_click")
+ highlight(on = "plotly_selected",
+ off = "plotly_deselect")
+ }else if (M_ch3 == 3){
+ fig_all_ch3_total <- subplot(fig_1a_ch3,
+ style(fig_1b_ch3, showlegend = F),
+ style(fig_2a_ch3, showlegend = F),
+ style(fig_2b_ch3, showlegend = F),
+ style(fig_3a_ch3, showlegend = F),
+ style(fig_3b_ch3, showlegend = F),
+ nrows = 3) %>%
+ layout(dragmode = "select",
+ plot_bgcolor='#e5ecf6',
+ xaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff'),
+ yaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff')) %>%
+ layout(autosize = T
+ #width = 1000,
+ #height = 1000
+ )%>%
+ # highlight(on = "plotly_click")
+ highlight(on = "plotly_selected",
+ off = "plotly_deselect")
+ } else if (M_ch3 == 4){
+ fig_all_ch3_total <- subplot(fig_1a_ch3,
+ style(fig_1b_ch3, showlegend = F),
+ style(fig_2a_ch3, showlegend = F),
+ style(fig_2b_ch3, showlegend = F),
+ style(fig_3a_ch3, showlegend = F),
+ style(fig_3b_ch3, showlegend = F),
+ style(fig_4a_ch3, showlegend = F),
+ style(fig_4b_ch3, showlegend = F),
+ nrows = 4) %>%
+ layout(dragmode = "select",
+ plot_bgcolor='#e5ecf6',
+ xaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff'),
+ yaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff')) %>%
+ layout(autosize = T
+ #width = 1000,
+ #height = 1000
+ ) %>%
+ # highlight(on = "plotly_click")
+ highlight(on = "plotly_selected",
+ off = "plotly_deselect")
+ }else if (M_ch3 == 5){
+ fig_all_ch3_total <- subplot(fig_1a_ch3,
+ style(fig_1b_ch3, showlegend = F),
+ style(fig_2a_ch3, showlegend = F),
+ style(fig_2b_ch3, showlegend = F),
+ style(fig_3a_ch3, showlegend = F),
+ style(fig_3b_ch3, showlegend = F),
+ style(fig_4a_ch3, showlegend = F),
+ style(fig_4b_ch3, showlegend = F),
+ style(fig_5a_ch3, showlegend = F),
+ style(fig_5b_ch3, showlegend = F),
+ nrows = 5) %>%
+ layout(dragmode = "select",
+ plot_bgcolor='#e5ecf6',
+ xaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff'),
+ yaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff')) %>%
+ layout(autosize = T
+ #width = 1000,
+ #height = 1000
+ ) %>%
+ # highlight(on = "plotly_click")
+ highlight(on = "plotly_selected",
+ off = "plotly_deselect")
+ }
+ else if (M_ch3 == 6){
+ fig_all_ch3_total <- subplot(fig_1a_ch3,
+ style(fig_1b_ch3, showlegend = F),
+ style(fig_2a_ch3, showlegend = F),
+ style(fig_2b_ch3, showlegend = F),
+ style(fig_3a_ch3, showlegend = F),
+ style(fig_3b_ch3, showlegend = F),
+ style(fig_4a_ch3, showlegend = F),
+ style(fig_4b_ch3, showlegend = F),
+ style(fig_5a_ch3, showlegend = F),
+ style(fig_5b_ch3, showlegend = F),
+ style(fig_6a_ch3, showlegend = F),
+ style(fig_6b_ch3, showlegend = F),
+ nrows = 6) %>%
+ layout(dragmode = "select",
+ plot_bgcolor='#e5ecf6',
+ xaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff'),
+ yaxis = list(zerolinecolor = '#ffff',
+ zerolinewidth = 2,
+ gridcolor = 'ffff')) %>%
+ layout(autosize = T
+ #width = 1000,
+ #height = 1000
+ ) %>%
+ # highlight(on = "plotly_click")
+ highlight(on = "plotly_selected",
+ off = "plotly_deselect")
+ }
+
+ #withProgress_5
+ withProgress(message = 'Calculation in progress',
+ detail = '...done: Linked_Manhattan', value = 0, {
+ for (i in 1:10) {
+ incProgress(1/10)
+ Sys.sleep(0.05)
+ }
+ })
+
+ fig_all_ch3_total
+ })
+
+ #ch4: ----
+ #Radar chart----
+ output$fig_ch4_tab1_radar <- renderPlotly({
+
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ fig_ch4_radar <- plot_ly(type = 'scatterpolar',
+ fill = 'toself',
+ mode = 'markers')
+
+ #selection
+ M_ch4=input$control_widgets_ch_4 ##CHECK
+
+ if (M_ch4 == 3){
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$cs1_1),
+ sum(GWAS_all_final$cs1_2),
+ sum(GWAS_all_final$cs1_3),
+ sum(GWAS_all_final$cs1_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_1')
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$csM_1),
+ sum(GWAS_all_final$csM_2),
+ sum(GWAS_all_final$csM_3),
+ sum(GWAS_all_final$csM_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_M')
+ }else if (M_ch4 == 4){
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$cs1_1),
+ sum(GWAS_all_final$cs1_2),
+ sum(GWAS_all_final$cs1_3),
+ sum(GWAS_all_final$cs1_4),
+ sum(GWAS_all_final$cs1_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_1')
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$csM_1),
+ sum(GWAS_all_final$csM_2),
+ sum(GWAS_all_final$csM_3),
+ sum(GWAS_all_final$csM_4),
+ sum(GWAS_all_final$csM_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_M')
+ }else if (M_ch4 == 5){
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$cs1_1),
+ sum(GWAS_all_final$cs1_2),
+ sum(GWAS_all_final$cs1_3),
+ sum(GWAS_all_final$cs1_4),
+ sum(GWAS_all_final$cs1_5),
+ sum(GWAS_all_final$cs1_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_5_',names(mpp.pp$MPP)[5]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_1')
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$csM_1),
+ sum(GWAS_all_final$csM_2),
+ sum(GWAS_all_final$csM_3),
+ sum(GWAS_all_final$csM_4),
+ sum(GWAS_all_final$csM_5),
+ sum(GWAS_all_final$csM_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_5_',names(mpp.pp$MPP)[5]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_M')
+ }else if (M_ch4 == 6){
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$cs1_1),
+ sum(GWAS_all_final$cs1_2),
+ sum(GWAS_all_final$cs1_3),
+ sum(GWAS_all_final$cs1_4),
+ sum(GWAS_all_final$cs1_5),
+ sum(GWAS_all_final$cs1_6),
+ sum(GWAS_all_final$cs1_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_5_',names(mpp.pp$MPP)[5]),
+ # paste0('Trait_6_',names(mpp.pp$MPP)[6]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[6],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_1')
+ fig_ch4_radar <- fig_ch4_radar %>%
+ add_trace(r = c(sum(GWAS_all_final$csM_1),
+ sum(GWAS_all_final$csM_2),
+ sum(GWAS_all_final$csM_3),
+ sum(GWAS_all_final$csM_4),
+ sum(GWAS_all_final$csM_5),
+ sum(GWAS_all_final$csM_6),
+ sum(GWAS_all_final$csM_1)),
+ # theta = c(paste0('Trait_1_',names(mpp.pp$MPP)[1]),
+ # paste0('Trait_2_',names(mpp.pp$MPP)[2]),
+ # paste0('Trait_3_',names(mpp.pp$MPP)[3]),
+ # paste0('Trait_4_',names(mpp.pp$MPP)[4]),
+ # paste0('Trait_5_',names(mpp.pp$MPP)[5]),
+ # paste0('Trait_6_',names(mpp.pp$MPP)[6]),
+ # paste0('Trait_1_',names(mpp.pp$MPP)[1])),
+ theta = c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[6],
+ names(mpp.pp$MPP)[1]),
+ name = 'cs_M')
+ }
+ fig_ch4_radar <- fig_ch4_radar %>%
+ layout(polar = list(radialaxis = list(visible = T, range = c(0,max(c(sum(GWAS_all_final$csM_1),
+ sum(GWAS_all_final$csM_2),
+ sum(GWAS_all_final$csM_3),
+ sum(GWAS_all_final$csM_4),
+ sum(GWAS_all_final$csM_5),
+ sum(GWAS_all_final$csM_6),
+ sum(GWAS_all_final$cs1_1),
+ sum(GWAS_all_final$cs1_2),
+ sum(GWAS_all_final$cs1_3),
+ sum(GWAS_all_final$cs1_4),
+ sum(GWAS_all_final$cs1_5),
+ sum(GWAS_all_final$cs1_6)))))))
+ fig_ch4_radar
+ })
+ #Venn diagram cs_1----
+ fig_ch4_tab2_venn_cs1_data <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ M_ch4=input$control_widgets_ch_4 ##CHECK
+ x_cs1_1 <- x_cs1_2 <- x_cs1_3 <- x_cs1_4 <- x_cs1_5 <- x_cs1_6 <- c();
+ if (M_ch4 == 3){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$cs1_1[i]==1){ x_cs1_1 = c(x_cs1_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_2[i]==1){ x_cs1_2 = c(x_cs1_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_3[i]==1){ x_cs1_3 = c(x_cs1_3, GWAS_all_final$rs[i]) }
+ }
+ x_cs1_all <- list(cs1_1_a = x_cs1_1,
+ cs1_2_a = x_cs1_2,
+ cs1_3_a = x_cs1_3)
+ # names(x_cs1_all) <- c(paste0('cs1_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('cs1_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('cs1_trait3_',names(mpp.pp$MPP)[3]))
+ names(x_cs1_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3])
+ x_cs1_all
+ }else if (M_ch4 == 4){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$cs1_1[i]==1){ x_cs1_1 = c(x_cs1_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_2[i]==1){ x_cs1_2 = c(x_cs1_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_3[i]==1){ x_cs1_3 = c(x_cs1_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_4[i]==1){ x_cs1_4 = c(x_cs1_4, GWAS_all_final$rs[i]) }
+ }
+ x_cs1_all <- list(cs1_1 = x_cs1_1,
+ cs1_2 = x_cs1_2,
+ cs1_3 = x_cs1_3,
+ cs1_4 = x_cs1_4)
+ # names(x_cs1_all) <- c(paste0('cs1_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('cs1_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('cs1_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('cs1_trait4_',names(mpp.pp$MPP)[4]))
+ names(x_cs1_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4])
+ x_cs1_all
+ }else if (M_ch4 == 5){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$cs1_1[i]==1){ x_cs1_1 = c(x_cs1_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_2[i]==1){ x_cs1_2 = c(x_cs1_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_3[i]==1){ x_cs1_3 = c(x_cs1_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_4[i]==1){ x_cs1_4 = c(x_cs1_4, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_5[i]==1){ x_cs1_5 = c(x_cs1_5, GWAS_all_final$rs[i]) }
+ }
+ x_cs1_all <- list(cs1_1 = x_cs1_1,
+ cs1_2 = x_cs1_2,
+ cs1_3 = x_cs1_3,
+ cs1_4 = x_cs1_4,
+ cs1_5 = x_cs1_5)
+ # names(x_cs1_all) <- c(paste0('cs1_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('cs1_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('cs1_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('cs1_trait4_',names(mpp.pp$MPP)[4]),
+ # paste0('cs1_trait5_',names(mpp.pp$MPP)[5]))
+ names(x_cs1_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5])
+ x_cs1_all
+ }else if (M_ch4 == 6){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$cs1_1[i]==1){ x_cs1_1 = c(x_cs1_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_2[i]==1){ x_cs1_2 = c(x_cs1_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_3[i]==1){ x_cs1_3 = c(x_cs1_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_4[i]==1){ x_cs1_4 = c(x_cs1_4, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_5[i]==1){ x_cs1_5 = c(x_cs1_5, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$cs1_6[i]==1){ x_cs1_6 = c(x_cs1_6, GWAS_all_final$rs[i]) }
+ }
+ x_cs1_all <- list(cs1_1 = x_cs1_1,
+ cs1_2 = x_cs1_2,
+ cs1_3 = x_cs1_3,
+ cs1_4 = x_cs1_4,
+ cs1_5 = x_cs1_5,
+ cs1_6 = x_cs1_6)
+ # names(x_cs1_all) <- c(paste0('cs1_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('cs1_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('cs1_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('cs1_trait4_',names(mpp.pp$MPP)[4]),
+ # paste0('cs1_trait5_',names(mpp.pp$MPP)[5]),
+ # paste0('cs1_trait6_',names(mpp.pp$MPP)[6]))
+ names(x_cs1_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[6])
+ x_cs1_all
+ }
+ })
+
+ output$fig_ch4_tab2_venn_cs1 <- renderPlotly({
+ ggVennDiagram(fig_ch4_tab2_venn_cs1_data(),
+ show_intersect = TRUE)
+ # ggplotly(ggVennDiagram(fig_ch4_tab2_venn_cs1_data()) +
+ # scale_color_brewer(palette = "Paired"))
+ })
+
+ output$table_ch4_tab2_venn_cs1_table <- DT::renderDataTable({
+ check_x_cs1_table = process_region_data(Venn(fig_ch4_tab2_venn_cs1_data()))[,c(1,2,5,4,3)]
+ DT::datatable(data.frame(check_x_cs1_table),
+ extensions = 'Buttons',
+ options = list(
+ paging = FALSE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ ordering = TRUE,
+ dom = 'tB',
+ buttons = list(
+ list(extend = 'csv', filename = "cs1_table"),
+ list(extend = 'excel', filename = "cs1_table"))
+ ),
+ class = "display"
+ )
+ })
+
+ #Venn diagram cs_M----
+ fig_ch4_tab2_venn_csM_data <- reactive({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ M_ch4=input$control_widgets_ch_4 ##CHECK
+ x_csM_1 <- x_csM_2 <- x_csM_3 <- x_csM_4 <- x_csM_5 <- x_csM_6 <- c();
+ if (M_ch4 == 3){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$csM_1[i]==1){ x_csM_1 = c(x_csM_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_2[i]==1){ x_csM_2 = c(x_csM_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_3[i]==1){ x_csM_3 = c(x_csM_3, GWAS_all_final$rs[i]) }
+ }
+ x_csM_all <- list(csM_1_a = x_csM_1,
+ csM_2_a = x_csM_2,
+ csM_3_a = x_csM_3)
+ # names(x_csM_all) <- c(paste0('csM_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('csM_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('csM_trait3_',names(mpp.pp$MPP)[3]))
+ names(x_csM_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3])
+ x_csM_all
+ }else if (M_ch4 == 4){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$csM_1[i]==1){ x_csM_1 = c(x_csM_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_2[i]==1){ x_csM_2 = c(x_csM_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_3[i]==1){ x_csM_3 = c(x_csM_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_4[i]==1){ x_csM_4 = c(x_csM_4, GWAS_all_final$rs[i]) }
+ }
+ x_csM_all <- list(csM_1 = x_csM_1,
+ csM_2 = x_csM_2,
+ csM_3 = x_csM_3,
+ csM_4 = x_csM_4)
+ # names(x_csM_all) <- c(paste0('csM_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('csM_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('csM_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('csM_trait4_',names(mpp.pp$MPP)[4]))
+ names(x_csM_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4])
+ x_csM_all
+ }else if (M_ch4 == 5){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$csM_1[i]==1){ x_csM_1 = c(x_csM_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_2[i]==1){ x_csM_2 = c(x_csM_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_3[i]==1){ x_csM_3 = c(x_csM_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_4[i]==1){ x_csM_4 = c(x_csM_4, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_5[i]==1){ x_csM_5 = c(x_csM_5, GWAS_all_final$rs[i]) }
+ }
+ x_csM_all <- list(csM_1 = x_csM_1,
+ csM_2 = x_csM_2,
+ csM_3 = x_csM_3,
+ csM_4 = x_csM_4,
+ csM_5 = x_csM_5)
+ # names(x_csM_all) <- c(paste0('csM_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('csM_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('csM_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('csM_trait4_',names(mpp.pp$MPP)[4]),
+ # paste0('csM_trait5_',names(mpp.pp$MPP)[5]))
+ names(x_csM_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5])
+ x_csM_all
+ }else if (M_ch4 == 6){
+ for (i in 1:length(GWAS_all_final$rs)){
+ if (GWAS_all_final$csM_1[i]==1){ x_csM_1 = c(x_csM_1, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_2[i]==1){ x_csM_2 = c(x_csM_2, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_3[i]==1){ x_csM_3 = c(x_csM_3, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_4[i]==1){ x_csM_4 = c(x_csM_4, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_5[i]==1){ x_csM_5 = c(x_csM_5, GWAS_all_final$rs[i]) }
+ if (GWAS_all_final$csM_6[i]==1){ x_csM_6 = c(x_csM_6, GWAS_all_final$rs[i]) }
+ }
+ x_csM_all <- list(csM_1 = x_csM_1,
+ csM_2 = x_csM_2,
+ csM_3 = x_csM_3,
+ csM_4 = x_csM_4,
+ csM_5 = x_csM_5,
+ csM_6 = x_csM_6)
+ # names(x_csM_all) <- c(paste0('csM_trait1_',names(mpp.pp$MPP)[1]),
+ # paste0('csM_trait2_',names(mpp.pp$MPP)[2]),
+ # paste0('csM_trait3_',names(mpp.pp$MPP)[3]),
+ # paste0('csM_trait4_',names(mpp.pp$MPP)[4]),
+ # paste0('csM_trait5_',names(mpp.pp$MPP)[5]),
+ # paste0('csM_trait6_',names(mpp.pp$MPP)[6]))
+ names(x_csM_all) <- c(names(mpp.pp$MPP)[1],
+ names(mpp.pp$MPP)[2],
+ names(mpp.pp$MPP)[3],
+ names(mpp.pp$MPP)[4],
+ names(mpp.pp$MPP)[5],
+ names(mpp.pp$MPP)[6])
+ x_csM_all
+ }
+ })
+
+ output$fig_ch4_tab2_venn_csM <- renderPlotly({
+ ggVennDiagram(fig_ch4_tab2_venn_csM_data(), show_intersect = TRUE)
+ })
+
+ output$table_ch4_tab2_venn_csM_table <- DT::renderDataTable({
+ check_x_csM_table = process_region_data(Venn(fig_ch4_tab2_venn_csM_data()))[,c(1,2,5,4,3)]
+ DT::datatable(data.frame(check_x_csM_table),
+ extensions = 'Buttons',
+ options = list(
+ paging = FALSE,
+ searching = TRUE,
+ fixedColumns = TRUE,
+ autoWidth = TRUE,
+ scrollX=TRUE,
+ ordering = TRUE,
+ dom = 'tB',
+ buttons = list(
+ list(extend = 'csv', filename = "csM_table"),
+ list(extend = 'excel', filename = "csM_table"))
+ ),
+ class = "display"
+ )
+ })
+
+
+
+
+
+ #ch5: ----
+ #Sankey Diagram in R and plotly (all 4 traits)----
+ output$ch5_sankey_all <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[1],"_Single")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[2],"_Single")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[3],"_Single")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[4],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi")][i],
+ GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_all <- plot_ly(type = "sankey", arrangement = "perpendicular", #arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_all <- fig_ch5_sankey_all %>% layout(title = "Sankey Diagram of SNP groups (all traits)")
+
+ fig_ch5_sankey_all
+
+ })
+
+ #Sankey Diagram in R and plotly (trait_1)----
+ output$ch5_sankey_t1 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[1],"_Single")][i]), na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[1],"_Multi")][i]), na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t1 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t1 <- fig_ch5_sankey_t1 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 1): ", names(mpp.pp$MPP)[1]))
+
+ fig_ch5_sankey_t1
+
+ })
+
+ #Sankey Diagram in R and plotly (trait_2)----
+ output$ch5_sankey_t2 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[2],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[2],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t2 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t2 <- fig_ch5_sankey_t2 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 2): ", names(mpp.pp$MPP)[2]))
+
+ fig_ch5_sankey_t2
+
+ })
+
+ #Sankey Diagram in R and plotly (trait_3)----
+ output$ch5_sankey_t3 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[3],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[3],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t3 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t3 <- fig_ch5_sankey_t3 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 3): ", names(mpp.pp$MPP)[3]))
+
+ fig_ch5_sankey_t3
+
+ })
+
+ #Sankey Diagram in R and plotly (trait_4)----
+ output$ch5_sankey_t4 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[4],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[4],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t4 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t4 <- fig_ch5_sankey_t4 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 4): ", names(mpp.pp$MPP)[4]))
+
+ fig_ch5_sankey_t4
+
+ })
+
+
+ #Sankey Diagram in R and plotly (trait_5)----
+ output$ch5_sankey_t5 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[5],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[5],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t5 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t5 <- fig_ch5_sankey_t5 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 5): ", names(mpp.pp$MPP)[5]))
+
+ fig_ch5_sankey_t5
+
+ })
+
+
+ #Sankey Diagram in R and plotly (trait_6)----
+ output$ch5_sankey_t6 <- renderPlotly({
+ GWAS_all_final = tt()$GWAS_all_final
+ mpp.pp = tt()$mpp.pp
+
+ node_labels_snp = GWAS_all_final$rs
+ node_labels_groupfm = unique(GWAS_all_final$snpGroups_fm)
+ node_labels_groupfm_2 = paste("fm", node_labels_groupfm, sep="_")
+ node_labels_groupflashfm = unique(GWAS_all_final$snpGroups_flashfm)
+ node_labels_groupflashfm_2 = paste("flashfm", node_labels_groupflashfm, sep="_")
+
+ node_labels = c(node_labels_snp, node_labels_groupfm, node_labels_groupflashfm)
+ node_labels_2 = c(node_labels_snp, node_labels_groupfm_2, node_labels_groupflashfm_2)
+
+ node_x = c(rep(0.01,times=length(node_labels_snp)),
+ rep(0.4,times=length(node_labels_groupfm)),
+ rep(0.8,times=length(node_labels_groupflashfm)))
+ node_y = c(seq(from = 1, to = length(node_labels_snp), by = 1)/length(node_labels_snp),
+ seq(from = 1, to = length(node_labels_groupfm), by = 1)/length(node_labels_groupfm),
+ seq(from = 1, to = length(node_labels_groupflashfm), by = 1)/length(node_labels_groupflashfm))
+
+ node_table = as.data.frame(cbind(node_labels, node_x, node_y));
+
+ link_source_fm = c();
+ link_target_fm = c();
+ link_value_fm = c();
+ link_source_flashfm = c();
+ link_target_flashfm = c();
+ link_value_flashfm = c();
+ #group_fm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_fm[i] != '0'){
+ link_source_fm = c(link_source_fm, i-1)
+ link_value_fm = c(link_value_fm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[6],"_Single")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupfm)){
+ if (node_table$node_labels[length(node_labels_snp)+ii]==GWAS_all_final$snpGroups_fm[i]){
+ link_target_fm = c(link_target_fm, length(node_labels_snp)+ii-1)
+ }
+ }
+ }
+ }
+ #group_flashfm
+ for (i in 1:length(node_labels_snp)){
+ if (GWAS_all_final$snpGroups_flashfm[i] != '0'){
+ link_source_flashfm = c(link_source_flashfm, i-1)
+ link_value_flashfm = c(link_value_flashfm,
+ mean(c(GWAS_all_final[ , paste0("MPP_",names(mpp.pp$MPP)[6],"_Multi")][i]),
+ na.rm=F) #updated20220208
+ )
+ for (ii in 1:length(node_labels_groupflashfm)){
+ if (node_table$node_labels[length(c(node_labels_snp,node_labels_groupfm))+ii]==GWAS_all_final$snpGroups_flashfm[i]){
+ link_target_flashfm = c(link_target_flashfm, length(c(node_labels_snp,node_labels_groupfm))+ii-1)
+ }
+ }
+ }
+ }
+ #check direction carefully: fm->flashfm
+ # link_source = c(link_source_fm, link_source_flashfm)
+ # link_target = c(link_target_fm, link_target_flashfm)
+ # link_value = c(link_value_fm, link_value_flashfm)
+ #check direction carefully: flashfm->fm
+ link_source = c(link_source_flashfm, link_source_fm)
+ link_target = c(link_target_flashfm, link_target_fm)
+ link_value = c(link_value_flashfm, link_value_fm)
+
+ link_source_updated = link_source
+ for (i in 2:length(link_source)){
+ if (sum(link_source[i]==link_source[1:(i-1)])!=0){
+ link_source_updated[i] = link_target[which(link_source[i]==link_source[1:(i-1)])]
+ }
+ }
+
+ fig_ch5_sankey_t6 <- plot_ly(type = "sankey", arrangement = "snap",
+ domain = list(x = c(0,1),y = c(0,1)),
+ orientation = "h",
+ valueformat = "0.0000",
+ #valuesuffix = "TWh",
+ node = list(label = node_labels_2,
+ pad = 15,
+ thickness = 15,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = link_source_updated, #c(0, 0, 1, 2, 5, 4, 3, 5),
+ target = link_target, #c(5, 3, 4, 3, 0, 2, 2, 3),
+ value = link_value)) #c(1, 2, 1, 1, 1, 1, 1, 2)))
+ fig_ch5_sankey_t6 <- fig_ch5_sankey_t6 %>% layout(title = paste0("Sankey Diagram of SNP groups (trait 6): ", names(mpp.pp$MPP)[6]))
+
+ fig_ch5_sankey_t6
+
+ })
+
+ #ch6: tables----
+ output$web_inputs_cs1 <- renderPrint({
+ cs1 = tt()$cs1
+ str(cs1)
+ })
+ output$web_inputs_csM <- renderPrint({
+ csM = tt()$csM
+ str(csM)
+ })
+ output$web_inputs_GWAS <- renderPrint({
+ GWAS = tt()$GWAS
+ str(GWAS)
+ })
+ output$web_inputs_LD <- renderPrint({
+ LD = tt()$LD
+ str(LD)
+ })
+ output$web_inputs_mpp.pp <- renderPrint({
+ mpp.pp = tt()$mpp.pp
+ str(mpp.pp)
+ })
+ output$web_inputs_snpGroups <- renderPrint({
+ snpGroups = tt()$snpGroups
+ str(snpGroups)
+ })
+ output$GWAS_all_final_table_done <- DT::renderDataTable({
+ GWAS_all_final = tt()$GWAS_all_final
+ DT::datatable(head(data.frame(GWAS_all_final),10),
+ options = list(dom = 't'))
+ })
+ output$LD_data<- DT::renderDataTable({
+ LD = tt()$LD
+ DT::datatable(data.frame(LD[1:5,1:5]),
+ options = list(dom = 't'))
+ })
+
+
+
+ # Downloadable csv of selected dataset ----
+ output$downloadData_3 <- downloadHandler(
+ filename = function() {
+ paste("GWAS_all_data-", Sys.Date(), ".csv", sep = "")
+ },
+ content = function(file) {
+ write.csv(tt()$GWAS_all_final, file, row.names = FALSE)
+ }
+ )
+
+ # output$downloadData <- downloadHandler(
+ # filename = function() {
+ # paste("data-", Sys.Date(), ".csv", sep="")
+ # },
+ # content = function(file) {
+ # write.csv(data, file)
+ # }
+ # )
+
+
+} #End of Shiny server ##
+
+
+####----Shiny app----
+shinyApp(ui, server)
\ No newline at end of file
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