LocusPackRat: a Semi-Automated Framework for Prioritizing Candidate Genes from Large GWAS Intervals

Abstract

Genome-wide association studies (GWAS) routinely implicate broad loci that span tens of megabases and contain dozens of genes, making the leap from locus to causal gene challenging, especially in model organism cohorts with reduced mapping resolution. We developed LocusPackRat, a semi-automated, easily extendible package that assembles standardized packets of evidence to accelerate candidate gene prioritization. Each packet merges study-specific information for each gene in a locus such as differential expression between conditions or presence of cis-eQTLs with functional/disease annotations pulled from InterMine and Open Targets. Packets are identically structured and easily disseminated to support side-by-side comparison and team review. We demonstrate LocusPackRats efficacy on a recent GWAS study of cardiac hypertrophy and failure in the Collaborative Cross. LocusPackRat shortens the path from statistical association to mechanistic hypotheses and improves the likelihood of successful experimental validation and is easily adaptable to other genetic reference populations or even human cohorts.