Efficient Prioritization of Multiple Causal eQTL Variants via Sparse Polygenic Modeling.

ABSTRACT: Expression quantitative trait loci (eQTL) studies have typically used single-variant association analysis to identify genetic variants correlated with gene expression. However, this approach has several drawbacks: causal variants cannot be distinguished from nonfunctional variants in strong linkage disequilibrium, combined effects from multiple causal variants cannot be captured, and low-frequency (<5% MAF) eQTL variants are difficult to identify. While these issues possibly could be overcome by using sparse polygenic models, which associate multiple genetic variants with gene expression simultaneously, the predictive performance of these models for eQTL studies has not been evaluated. Here, we assessed the ability of three sparse polygenic models (Lasso, Elastic Net, and BSLMM) to identif

SUBMITTER: Nariai N 

PROVIDER: S-EPMC5714449 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature