Trex-QTL: A mixture-model for identification of genetic effects with global effects on molecular phenotypes

While thousands of cis expression quantitative trait loci (cis-eQTLs) have been reliably identified, detecting trans-eQTL effects has proven to be challenging due to insufficient statistical power, lack of comparable tissues and cohorts, and low reproducibility across studies. Here, we present Trex-QTL, a novel trans-eQTL detection method that models eQTL summary statistics as a mixture consisting of both target gene and null associations. Compared to other recently developed methods, Trex-QTL has improved power for trans-eQTL detection and employs a simplified framework, requiring only eQTL association summary statistics as input. We performed extensive simulations to characterize the conditions under which trans-eQTLs are detectable by Trex-QTL across a range of effect sizes and numbers of target genes. We applied Trex-QTL to the Depression Genes and Networks (DGN) dataset and replicated two well-established trans-eQTLs at ARHGEF3 and IKZF1. We then applied Trex-QTL to the deeply characterized heterogeneous stock (HS) rat cohort with matched brain transcriptomic and genomic data, identifying 7 top-scoring, linkage disequilibrium-independent trans-eQTLs. One previously unreported trans-eQTL is at the locus harboring Jag2, a critical ligand for the Notch signaling pathway, which is associated with decreased Jag2 expression and decreased expression of multiple downstream genes including known Notch targets. A second example is a strong trans-eQTL overlapping a cluster of interferon genes associated with interferon-response genes including C4a and Parp14. We show evidence that this signal is mediated by a cis-eQTL for a cluster of interferon ligand genes that operate upstream of interferon receptor signaling. Both of these signals co-localize with association signals for a range of other phenotypes measured in this cohort. Overall, we demonstrate that Trex-QTL represents a powerful method to identify trans-eQTLs with global effects on molecular phenotypes and identify novel biologically compelling examples of such loci.