Skip to content

Exploration of saddlepoint approximations in GWAS with a binary response.

Notifications You must be signed in to change notification settings

palVJ/SaddlePointApproxInBinaryGWAS

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 

Repository files navigation

Exploration of saddlepoint approximations in binary GWAS

Detailed analysis of saddlepoint approximation of the score test statistic in a GWAS with a binary reponse such as presence or absence of a disease.

Usage

Perform the GWAS using normal approximation, single saddlepoint approximation of the efficient score statistic with second continuity correction, or double saddlepoint approximation with second continuity correction. For the saddlepoint approximations, choose either using the CDF approximation from Lugananni-Rice of from Barndorff-Nielsen.

Given genotype matrix genos with each variant along the columns, covariate matrix cov with covariates along the columns, and a significance level alpha where the null hypothesis is rejected. Choose methods which is either normal approximation "noraprx", single saddlepoint of efficient score "SPASCC", or double saddlepoint with second contiuity correction "DSPASCC". For a fast version of double saddlepoint with second continuity correction, use "DSPASCC_FAST". Compute p-value using Lugananni-Rice "LR" or Barndorff-Nielsen "BN". For instance, with double saddlepoint approximation with second continuity correction using Barndorff-Nielsen p-value computation:

p.values = ScoreTest(genos,pheno,cov,alpha=5*10^-8,pval.comp = "BN",methods = "DSPASCC")

About

Exploration of saddlepoint approximations in GWAS with a binary response.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages