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

Autor: Majumdar A; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.; Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, Telangana, India., Giambartolomei C; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America., Cai N; Wellcome Sanger Institute, Wellcome genome campus, Hinxton, United Kingdom.; European Bioinformatics Institute (EMBL-EBI), Wellcome genome campus, Hinxton, United Kingdom., Haldar T; Institute for Human Genetics, University of California, San Francisco, California, United States of America., Schwarz T; Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America., Gandal M; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America., Flint J; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America., Pasaniuc B; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.; Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2021 May 21; Vol. 17 (5), pp. e1008915. Date of Electronic Publication: 2021 May 21 (Print Publication: 2021).
DOI: 10.1371/journal.pcbi.1008915
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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