ArticlePLoS computational biology2021
Leveraging eQTLs to identify individual-level tissue of interest for a complex trait.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Single-cell genomics meets human genetics.Nature reviews. Genetics · 2023Review
- Functional genomics in stem cell models: considerations and applications.Frontiers in cell and developmental biology · 2023Review
- Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity.American journal of human genetics · 2022Article
- Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies.Human molecular genetics · 2020Review
- The GTEx Consortium atlas of genetic regulatory effects across human tissues.Science (New York, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.