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

Majumdar, Arunabha and et al, . (2021) Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. PLoS Computational Biology, 17 (5). pp. 1-33. ISSN 1553-734X

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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. Copyright: © 2021 Majumdar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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IITH Creators:
IITH CreatorsORCiD
Majumdar, ArunabhaUNSPECIFIED
Item Type: Article
Additional Information: UK Biobank Resource under applications 24129 and 33297
Uncontrolled Keywords: adipose tissue; adult; article; biobank; body mass; brain; controlled study; expression quantitative trait locus; human tissue; muscle tissue; quantitative analysis; simulation; waist hip ratio; algorithm; antibody specificity; Bayes theorem; biological model; biology; computer simulation; gene expression; genetic predisposition; genetics; human; metabolism; multifactorial inheritance; obesity; pathology; phenotype; quantitative trait locus; single nucleotide polymorphism; software; tissue distribution
Subjects: Mathematics
Divisions: Department of Mathematics
Depositing User: Mrs Haseena VKKM
Date Deposited: 21 Jun 2022 09:14
Last Modified: 21 Jun 2022 09:14
URI: http://raiith.iith.ac.in/id/eprint/9330
Publisher URL: https://doi.org/10.1371/journal.pcbi.1008915
OA policy: https://v2.sherpa.ac.uk/id/publication/17595
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