Abstract
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.