PolyQTL: Bayesian multiple eQTL detection with control for population structure and sample relatedness

Motivation Expression quantitative loci (eQTL) are being used widely to annotate and interpret GWAS hits. Recent studies have demonstrated that individual gene expression is often regulated by multiple independent cis-acting eQTL. Diverse methods, frequentist and Bayesian, have already been developed to simultaneously detect and fine-map such multiple eQTL, but most of these ignore sample relatedness and potential population structure. This can result in false positives and disrupt the accuracy of fine-mapping. Here we introduce PolyQTL software for identifying and estimating eQTL effects. The package incorporates a genetic relatedness matrix to remove the influence of population structure and sample relatedness, while utilizing a Bayesian multiple eQTL detection pipeline to identify the most plausible candidate causal variants at one or more independent loci influencing abundance of a transcript. Results Simulations demonstrate that our approach improves the rate of discovery of causal variants relative to methods that do not account for relatedness. Availability and implementation The software is written in C++, and freely available for download at https://github.com/jxzb1988/PolyQTL.