Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis.

ABSTRACT: Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Ba

SUBMITTER: Zhuang YH 

PROVIDER: S-EPMC7384761 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature