BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies.

ABSTRACT:

Background

Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates.

Results

We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery c

SUBMITTER: Williams J 

PROVIDER: S-EPMC10176706 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature