RT Journal Article
SR Electronic
T1 Estimating the causal tissues for complex traits and diseases
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 074682
DO 10.1101/074682
A1 Halit Ongen
A1 Andrew A. Brown
A1 Olivier Delaneau
A1 Nikolaos Panousis
A1 Alexandra C. Nica
A1 GTEx Consortium
A1 Emmanouil T. Dermitzakis
YR 2016
UL http://biorxiv.org/content/early/2016/09/11/074682.abstract
AB Interpretation of biological causes of the predisposing markers identified through Genome Wide Association Studies (GWAS) remains an open question1. One direct and powerful way to assess the genetic causality behind GWAS is through expression quantitative trait loci (eQTLs)2. Here we describe a novel approach to estimate the tissues giving rise to the genetic causality behind a wide variety of GWAS traits, using the cis-eQTLs identified in 44 tissues of the GTEx consortium3,4. We have adapted the Regulatory Trait Concordance (RTC) score5, to on the one hand measure the tissue sharing probabilities of eQTLs, and also to calculate the probability that a GWAS and an eQTL variant tag the same underlying functional effect. We show that our tissue sharing estimates significantly correlate with commonly used estimates of tissue sharing. By normalizing the GWAS-eQTL probabilities with the tissue sharing estimates of the eQTLs, we can estimate the tissues from which GWAS genetic causality arises. Our approach not only indicates the gene mediating individual GWAS signals, but also can highlight tissues where the genetic causality for an individual trait is manifested.