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Watershed implementation for African American samples from GTEx

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WatershedAFR

Watershed implementation for African American samples from GTEx

Parts of this code were originally written by:

Setup

Dependencies

Install python and dependencies from environment.yml

conda env create -f environment.yml

Directory structure

.
├── data
│   ├── data_prep
│   ├── figures
│   ├── models
│   ├── outlier_calling
│   └── rare_variants
│   └── RIVER
├── raw_data
│   ├── 1KG
│   ├── GTEx
│   └── hg38.phyloP100way.bw
└── WatershedAFR
    ├── code
    │   ├── analysis
    │   ├── figures
    │   ├── preprocessing
    │   │   ├── data_prep
    │   │   ├── outlier_calling
    │   │   └── rare_variants
    │   └── Watershed
    ├── data_sources.md
    ├── environment.yml
    ├── LICENSE
    ├── pipelines
    └── README.md

No absolute paths are hard coded into scripts. data and raw_data directories are assigned local variable names.

# Defining root, data, and raw data directories
rootdir=/scratch/groups/abattle4/victor/WatershedAFR
datadir=${rootdir}/data
rawdir=${rootdir}/raw_data

## Code location for executable Watershed Repo
watershed_dir=/scratch/groups/abattle4/jessica/RareVar_AFR/Watershed
# Making directories
mkdir ${rootdir}

mkdir ${datadir}
mkdir ${datadir}/data_prep
mkdir ${datadir}/figures
mkdir ${datadir}/models
mkdir ${datadir}/outlier_calling
mkdir ${datadir}/rare_variants
mkdir ${datadir}/rare_variants/1KG
mkdir ${datadir}/rare_variants/1KG/genes_padded10kb_PCandlinc_only
mkdir ${datadir}/rare_variants/rv_bed_EUR
mkdir ${datadir}/rare_variants/rv_bed_AFR


mkdir ${rawdir}
mkdir ${rawdir}/1KG
mkdir ${rawdir}/GTEx

# Clone the repo
git clone https://github.com/battle-lab/WatershedAFR.git ${rootdir}/WatershedAFR

Raw data

See data_sources.md

Preprocessing pipeline

Goal: Preprocess raw data to be used as input for training Watershed models.

Make lists of individuals

Make list of all individuals from GTEx v8. Make list of all individuals with reported African American ancestry and European ancestry. Requires bcftools

  • ${datadir}/data_prep/gtex_v8_individuals_all.txt - All 948 individuals from GTEx v8
  • ${datadir}/data_prep/gtex_v8_wgs_individuals.txt - All 838 individuals from GTEx v8 with WGS data
  • ${datadir}/data_prep/gtex_v8_individuals_AFR.txt - 121 African American individuals from GTEx v8
  • ${datadir}/data_prep/gtex_v8_wgs_individuals_AFR.txt - 103 African American individuals from GTEx v8 with WGS data
  • ${datadir}/data_prep/gtex_v8_individuals_EUR.txt - 804 European individuals from GTEx v8
  • ${datadir}/data_prep/gtex_v8_wgs_individuals_EUR.txt - 714 European individuals from GTEx v8 with WGS
# All individuals
## list of all individuals from GTEx v8 (some do not have WGS data)
cat ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_Annotations_SubjectPhenotypesDS.txt | awk -F "\t" 'NR>1{print $1}' \
> ${datadir}/data_prep/gtex_v8_individuals_all.txt


## list of all individuals from GTEx v8 with WGS data
bcftools query -l ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_WholeGenomeSeq_838Indiv_Analysis_Freeze.SHAPEIT2_phased.vcf.gz \
> ${datadir}/data_prep/gtex_v8_wgs_individuals_all.txt

# African American individuals
## list of all African American individuals from GTEx v8 (some do not have WGS data)
cat ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_Annotations_SubjectPhenotypesDS.txt | awk -F '\t' '{if ($5 == 2) print $1;}' \
> ${datadir}/data_prep/gtex_v8_individuals_AFR.txt

## list of all African American individuals from GTEx v8 with WGS data
awk 'NR==FNR { lines[$0]=1; next } $0 in lines' ${datadir}/data_prep/gtex_v8_wgs_individuals_all.txt \
${datadir}/data_prep/gtex_v8_individuals_AFR.txt > ${datadir}/data_prep/gtex_v8_wgs_individuals_AFR.txt

# European individuals
# list of all European individuals from GTEx v8 (some do not have WGS data)
cat ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_Annotations_SubjectPhenotypesDS.txt | awk -F '\t' '{if ($5 == 3) print $1;}' \
> ${datadir}/data_prep/gtex_v8_individuals_EUR.txt

# list of all European individuals from GTEx v8 with WGS data
awk 'NR==FNR { lines[$0]=1; next } $0 in lines' ${datadir}/data_prep/gtex_v8_wgs_individuals_all.txt \
${datadir}/data_prep/gtex_v8_individuals_EUR.txt > ${datadir}/data_prep/gtex_v8_wgs_individuals_EUR.txt

Expression data correction and normalization

Generate tpm and read count matrices from GTEx V8

Generate file mapping sample identifiers to tissues. Restrict to samples that pass RNA-seq QC (marked as RNASEQ in SMAFRZE column).

cat ${rawdir}/GTEx/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt | tail -n+2 | cut -f1,7,17 | \
  sed 's/ - /_/' | sed 's/ /_/g' | sed 's/(//' | sed 's/)//' | sed 's/c-1/c1/' | \
  awk '$3=="RNASEQ" {print $1"\t"$2}' | sort -k 1 > ${datadir}/data_prep/gtex_v8_samples_tissues.txt

Splits the combined TPM file and read counts file by tissue.

# split TPM
GTEX_tpm=${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz
OUT=${datadir}/data_prep/PEER
SAMPLE=${datadir}/data_prep/gtex_v8_samples_tissues.txt
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_tpm --out $OUT --sample $SAMPLE --end .tpm.txt

# split read counts
GTEX_reads=${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_reads.gct.gz
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_reads --out $OUT --sample $SAMPLE --end .reads.txt

Population specific TPM and read counts

# Subset `gtex_v8_samples_tissues.txt` by African and European populations
Rscript code/preprocessing/data_prep/population_sample_tissue_map.R \
  --MAP ${datadir}/data_prep/gtex_v8_samples_tissues.txt \
  --POP.LIST ${datadir}/data_prep/gtex_v8_individuals_AFR.txt \
  --OUT ${datadir}/data_prep/gtex_v8_samples_tissues_AFR.txt
  
Rscript code/preprocessing/data_prep/population_sample_tissue_map.R \
  --MAP ${datadir}/data_prep/gtex_v8_samples_tissues.txt \
  --POP.LIST ${datadir}/data_prep/gtex_v8_individuals_EUR.txt \
  --OUT ${datadir}/data_prep/gtex_v8_samples_tissues_EUR.txt

# split TMP
GTEX_tpm=${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz

OUT_AFR=${datadir}/data_prep/PEER_AFR
SAMPLE_AFR=${datadir}/data_prep/gtex_v8_samples_tissues_AFR.txt
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_tpm --out $OUT_AFR --sample $SAMPLE_AFR --end .tpm.txt

OUT_EUR=${datadir}/data_prep/PEER_EUR
SAMPLE_EUR=${datadir}/data_prep/gtex_v8_samples_tissues_EUR.txt
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_tpm --out $OUT_EUR --sample $SAMPLE_EUR --end .tpm.txt

# split read counts
GTEX_reads=${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_reads.gct.gz
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_reads --out $OUT_AFR --sample $SAMPLE_AFR --end .reads.txt
python code/preprocessing/data_prep/split_expr_by_tissues.py --gtex $GTEX_reads --out $OUT_EUR --sample $SAMPLE_EUR --end .reads.txt

Generate PEER corrected data (includes non-EAs) with covariates removed

Build covariate matrix with PC's 1 - 5 and sex from the eQTL covariates by tissue.

Rscript code/preprocessing/data_prep/combine_covariates_across_tissues.R \
  --COV ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL_covariates \
  --OUT ${datadir}/data_prep

For each tissue, filter for genes with > 20% individuals with TPM > 0.1 and read count > 6, Log2(tpm + 2) transfrom the data, and then z-transform.

# All populations
#Rscript code/preprocessing/data_prep/preprocess_expr.R \
#  --COV ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
#  --MAP ${datadir}/data_prep/gtex_v8_samples_tissues.txt \
#  --PEER ${datadir}/data_prep/PEER

# African (min samples per tissue is 11 because there are fewer African samples. Double check that 11 is approrpiate)
Rscript code/preprocessing/data_prep/preprocess_expr.R \
  --COV ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  --MAP ${datadir}/data_prep/gtex_v8_samples_tissues_AFR.txt \
  --MIN.SAMPLE 11 \
  --PEER ${datadir}/data_prep/PEER_AFR
  
# European (script has default min sample per tissue of 70)
Rscript code/preprocessing/data_prep/preprocess_expr.R \
  --COV ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  --MAP ${datadir}/data_prep/gtex_v8_samples_tissues_EUR.txt \
  --PEER ${datadir}/data_prep/PEER_EUR

Rename eQTL files from "cervical_c-1" to "cervical_c1" for consistency

rename c-1 c1 ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL/*c-1*

TODO update get_eqtl_genotypes to remove hardcoding

Generate list of top eQTLs for each gene in each tissue, extract from VCF, convert to number alternative alleles

bash code/preprocessing/data_prep/get_eqtl_genotypes.sh

Run PEER correction and compute residuals

## All populations
# Compute PEER factors (use PEER dockerimage if unable to install peer natively https://hub.docker.com/r/bryancquach/peer)
bash code/preprocessing/data_prep/calculate_PEER_factors.sh \
  -p ${datadir}/data_prep/PEER \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch \
  -d ~/code/PEER/peer-1.3.simg
  
# Compute residuals (does not need PEER package)
bash code/preprocessing/data_prep/calculate_PEER_residuals.sh \
  -p ${datadir}/data_prep/PEER \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch


## African
# Compute PEER factors (use PEER dockerimage if unable to install peer natively https://hub.docker.com/r/bryancquach/peer)
bash code/preprocessing/data_prep/calculate_PEER_factors.sh \
  -p ${datadir}/data_prep/PEER_AFR \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch \
  -d ~/code/PEER/peer-1.3.simg
  
# Compute residuals (does not need PEER package)
bash code/preprocessing/data_prep/calculate_PEER_residuals.sh \
  -p ${datadir}/data_prep/PEER_AFR \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch


## European
# Compute PEER factors (use PEER dockerimage if unable to install peer natively https://hub.docker.com/r/bryancquach/peer)
bash code/preprocessing/data_prep/calculate_PEER_factors.sh \
  -p ${datadir}/data_prep/PEER_EUR \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch \
  -d ~/code/PEER/peer-1.3.simg
  
# Compute residuals (does not need PEER package)
bash code/preprocessing/data_prep/calculate_PEER_residuals.sh \
  -p ${datadir}/data_prep/PEER_EUR \
  -e ${rawdir}/GTEx/GTEx_Analysis_v8_eQTL \
  -c ${datadir}/data_prep/gtex_v8_eQTL_covariates.txt \
  -g ${datadir}/data_prep/gtex_2017-06-05_v8_genotypes_cis_eQTLs_012_processed.txt \
  -t ~/scratch

Combine PEER-corrected data into a single flat file

Generate files with data on what tissues are available per individual after PEER correction

# All populations
bash code/preprocessing/data_prep/get_tissue_by_individual.sh \
  -p ${datadir}/data_prep/PEER \
  -o ${datadir}/data_prep/gtex_2017-06-05_tissue_by_ind.txt \
  -t ${datadir}/data_prep/gtex_2017-06-05_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_2017-06-05_individuals_all_normalized_samples.txt

# African
bash code/preprocessing/data_prep/get_tissue_by_individual.sh \
  -p ${datadir}/data_prep/PEER_AFR \
  -o ${datadir}/data_prep/gtex_AFR_tissue_by_ind.txt \
  -t ${datadir}/data_prep/gtex_AFR_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_AFR_individuals_all_normalized_samples.txt
  
# European
bash code/preprocessing/data_prep/get_tissue_by_individual.sh \
  -p ${datadir}/data_prep/PEER_EUR \
  -o ${datadir}/data_prep/gtex_EUR_tissue_by_ind.txt \
  -t ${datadir}/data_prep/gtex_EUR_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_EUR_individuals_all_normalized_samples.txt

Combine the PEER-corrected data

# All populations
# output located in ${datadir}/data_prep/gtex_2017-06-05_normalized_expression.txt.gz
python code/preprocessing/data_prep/gather_filter_normalized_expression.py \
  -p ${datadir}/data_prep/PEER \
  -t ${datadir}/data_prep/gtex_2017-06-05_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_2017-06-05_individuals_all_normalized_samples.txt \
  -o ${datadir}/data_prep/gtex_2017-06-05_normalized_expression.txt

gzip ${datadir}/data_prep/gtex_2017-06-05_normalized_expression.txt


# African
# output located in ${datadir}/data_prep/gtex_AFR_normalized_expression.txt.gz
python code/preprocessing/data_prep/gather_filter_normalized_expression.py \
  -p ${datadir}/data_prep/PEER_AFR \
  -t ${datadir}/data_prep/gtex_AFR_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_AFR_individuals_all_normalized_samples.txt \
  -o ${datadir}/data_prep/gtex_AFR_normalized_expression.txt

gzip ${datadir}/data_prep/gtex_AFR_normalized_expression.txt


# European
# output located in ${datadir}/data_prep/gtex_EUR_normalized_expression.txt.gz
python code/preprocessing/data_prep/gather_filter_normalized_expression.py \
  -p ${datadir}/data_prep/PEER_EUR \
  -t ${datadir}/data_prep/gtex_EUR_tissues_all_normalized_samples.txt \
  -i ${datadir}/data_prep/gtex_EUR_individuals_all_normalized_samples.txt \
  -o ${datadir}/data_prep/gtex_EUR_normalized_expression.txt

gzip ${datadir}/data_prep/gtex_EUR_normalized_expression.txt

Outlier calling

Call outliers on all individuals

Saved to ${datadir}/outlier_calling/test/gtexV8.outlier.controls.v8ciseQTLs_globalOutliersRemoved.txt

Rscript code/preprocessing/outlier_calling/call_outliers.R \
  --Z.SCORES=${datadir}/data_prep/gtex_2017-06-05_normalized_expression.txt.gz \
  --OUT=${datadir}/outlier_calling/test/gtexV8.outlier.controls.v8ciseQTLs.txt \
  --N.PHEN=5 --ZTHRESH=3


# Remove global outliers
Rscript code/preprocessing/outlier_calling/identify_global_outliers.R \
  --OUTLIERS=${datadir}/outlier_calling/test/gtexV8.outlier.controls.v8ciseQTLs.txt \
  --METHOD=proportion

Call outliers on African individuals

Saved to ${datadir}/outlier_calling/AFR/gtexV8.AFR.outlier.controls.v8ciseQTLs_globalOutliersRemoved.txt

Rscript code/preprocessing/outlier_calling/call_outliers.R \
  --Z.SCORES=${datadir}/data_prep/gtex_AFR_normalized_expression.txt.gz \
  --OUT=${datadir}/outlier_calling/AFR/gtexV8.AFR.outlier.controls.v8ciseQTLs.txt \
  --POP=${datadir}/data_prep/gtex_v8_wgs_individuals_AFR.txt \
  --N.PHEN=5 --ZTHRESH=3

Rscript code/preprocessing/outlier_calling/identify_global_outliers.R \
  --OUTLIERS=${datadir}/outlier_calling/AFR/gtexV8.AFR.outlier.controls.v8ciseQTLs.txt \
  --METHOD=proportion

Call outliers on European individuals

Saved to ${datadir}/outlier_calling/EUR/gtexV8.EUR.outlier.controls.v8ciseQTLs_globalOutliersRemoved.txt

Rscript code/preprocessing/outlier_calling/call_outliers.R \
  --Z.SCORES=${datadir}/data_prep/gtex_EUR_normalized_expression.txt.gz \
  --OUT=${datadir}/outlier_calling/EUR/gtexV8.EUR.outlier.controls.v8ciseQTLs.txt \
  --POP=${datadir}/data_prep/gtex_v8_wgs_individuals_EUR.txt \
  --N.PHEN=5 --ZTHRESH=3


# Remove global outliers
Rscript code/preprocessing/outlier_calling/identify_global_outliers.R \
  --OUTLIERS=${datadir}/outlier_calling/EUR/gtexV8.EUR.outlier.controls.v8ciseQTLs.txt \
  --METHOD=proportion

GTEx v8 outliers from Watershed paper are located in /work-zfs/abattle4/bstrober/rare_variant/gtex_v8/splicing/input_data/outlier_calls/gtexV8.outlier.controls.v8ciseQTLs.globalOutliers.removed.medz.txt Save as ${datadir}/outlier_calling/WATERSHED.gtexV8.outlier.controls.v8ciseQTLs.globalOutliers.removed.medz.txt

Prepare gene annotations from gencode

Manipulate annotation files in various ways to make them easier to use downstream. Outputs

  • ${datadir}/data_prep/gencode.v26.GRCh38.genes.bed - Gene bed file
  • ${datadir}/data_prep/gencode.v26.GRCh38.genes_padded10kb.bed - Gene bed file with 10kb added on either side
  • ${datadir}/data_prep/gencode.v26.GRCh38.genes_genetypes_autosomal.txt - Genetypes
  • ${datadir}/data_prep/gencode.v26.GRCh38.genes_padded10kb_PCandlinc_only.bed - Gene bed file with protein coding and lincRNA coding genes padded by 10kb
bash ${rootdir}/WatershedAFR/code/preprocessing/data_prep/process_reference_files.sh \
${rawdir}/GTEx/gencode.v26.GRCh38.genes.gtf \
${datadir}/data_prep

Finding rare variants

Requires bcftools and bedtools

European Individuals

Filter with gnomAD. Rare variants file will be saved to {datadir}/rare_variants_gnomad/gene-EUR-rv.txt
arguments to find_rare_variants_gnomad.sh:
-g: raw gtex vcf
-r: bed file with protein coding and lnc rna coding regions (generated during outlier calling earlier)
-f gnomad raw file
-l list of individuals in population
-p: prefix/suffix also used to specify population. Select from "EUR" or "AFR"

bash code/preprocessing/rare_variants/find_rare_variants_gnomad.sh \
-d ${datadir}/rare_variants_gnomad \
-g ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_WholeGenomeSeq_838Indiv_Analysis_Freeze.SHAPEIT2_phased.vcf.gz \
-r ${datadir}/data_prep/gencode.v26.GRCh38.genes_padded10kb_PCandlinc_only.bed \
-f /work-zfs/abattle4/lab_data/gnomAD_v2.1/gnomad.genomes.r2.1.1.sites.liftover_grch38.vcf.bgz \
-l ${datadir}/data_prep/gtex_v8_wgs_individuals_EUR.txt \
-p "EUR"

African Individuals

Filter with gnomAD. Rare variants file will be saved to {datadir}/rare_variants_gnomad/gene-AFR-rv.txt

bash code/preprocessing/rare_variants/find_rare_variants_gnomad.sh \
-d ${datadir}/rare_variants_gnomad \
-g ${rawdir}/GTEx/GTEx_Analysis_2017-06-05_v8_WholeGenomeSeq_838Indiv_Analysis_Freeze.SHAPEIT2_phased.vcf.gz \
-r ${datadir}/data_prep/gencode.v26.GRCh38.genes_padded10kb_PCandlinc_only.bed \
-f /work-zfs/abattle4/lab_data/gnomAD_v2.1/gnomad.genomes.r2.1.1.sites.liftover_grch38.vcf.bgz \
-l ${datadir}/data_prep/gtex_v8_wgs_individuals_AFR.txt \
-p "AFR"

Create Annotations

Create a file that countains all rare variants within 10kb of an outlier gene???

Training Watershed models

Prep files for RIVER and Watershed.

Rscript code/preprocessing/data_prep/dataprep_watershed.R \
--ZSCORES ${datadir}/outlier_calling/AFR/gtexV8.AFR.outlier.controls.v8ciseQTLs_globalOutliersRemoved.txt \
--ANNOT /scratch/groups/abattle4/victor/GTExV6PRareVariationData/archive/processed/fully_observed_merged_outliers_0.01_genes_intersection_between_te_ase_splicing_features_filter_no_tissue_anno_N2_pairs_3.txt \
--OUT ${datadir}/data_prep/RIVER/river_input_v8_african_all_07-19-2021.txt

#--ANNOT /work-zfs/abattle4/bstrober/random_projects/feature_generation_for_victor_and_jessica/river_input_gene_level.txt
Run RIVER on prepped AFR data:

afr_all=${datadir}/data_prep/RIVER/river_input_v8_african_all_07-19-2021.txt
Rscript ${watershed_dir}/evaluate_watershed.R --input $afr_all --number_dimensions 1 --output_prefix ${outdir}/eval_afr_all --model_name RIVER --dirichlet_prior_parameter 30 --l2_prior_parameter .001 --n2_pair_pvalue_fraction .01 --binary_pvalue_threshold .01

look at RIVER folder in RIVER repo for RIVER.Rmd

Analysis and Figures

look at RIVER folder in RIVER repo for enrichment_AFR.Rmd

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Watershed implementation for African American samples from GTEx

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