Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits. Author summary A significant component of the genetic susceptibility to complex traits is mediated through genetic control of gene expression in one or multiple tissues. Several studies have highlighted the relevance of tissue specific biological mechanisms underlying the pathogenesis of complex traits, and have often identified multiple tissues relevant to a given phenotype in the population. Since existing methods only prioritize tissues for a complex phenotype in the population, it remains an open question whether certain classes of individuals have their genetic predisposition for the phenotype mediated primarily through a specific tissue. We present an efficient statistical approach that integrates tissue-specific eQTLs (i.e., eQTLs for tissue-specific genes) with genetic association data for a complex trait to probabilistically quantify the tissue-wise genetic contribution to the phenotype of each individual in the study. Using simulations we show that the proposed approach accurately infers the simulated tissue of interest for each individual. Integrating expression data from the GTEx consortium, we apply the proposed approach to two obesity related phenotypes in the UK Biobank. Our approach identified subgroups of individuals with their genetic susceptibility to the phenotype mediated in a tissue-specific manner. Interestingly, multiple metabolic traits, neuropsychiatric traits, and other traits were found to be differentially distributed between the tissue-specific groups of individuals and the remaining population, suggesting a biologically meaningful interpretation for these subgroups of individuals. We provide an R-package 'eGST' for general use of the method: https://cran.r-project.org/web/packages/eGST/index.html.