Estimating the causal tissues for complex traits and diseases

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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Published on: August 24, 2013

Halit Ongen^1,2,3, Andrew A Brown^1,2,3, Olivier Delaneau^1,2,3

¹Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

Nature Genetics

|October 24, 2017

Abstract:

How to interpret the biological causes underlying the predisposing markers identified through genome-wide association studies (GWAS) remains an open question. One direct and powerful way to assess the genetic causality behind GWAS is through analysis of expression quantitative trait loci (eQTLs). Here we describe a new approach to estimate the tissues behind the genetic causality of a variety of GWAS traits, using the cis-eQTLs in 44 tissues from the Genotype-Tissue Expression (GTEx) Consortium. We have adapted the regulatory trait concordance (RTC) score to measure the probability of eQTLs being active in multiple tissues and to calculate the probability that a GWAS-associated variant and an eQTL tag the same functional effect. By normalizing the GWAS-eQTL probabilities by the tissue-sharing estimates for eQTLs, we generate relative tissue-causality profiles for GWAS traits. Our approach not only implicates the gene likely mediating individual GWAS signals, but also highlights tissues where the genetic causality for an individual trait is likely manifested.

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