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Leveraging eQTLs to identify individual-level tissue of interest for a complex trait
Arunabha Majumdar1,2, Claudia Giambartolomei1, Na Cai3,4
1Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.
Plos Computational Biology
|May 21, 2021
Summary
This study introduces a new method to pinpoint which tissues are most affected by genetic predispositions for complex traits in individuals. The approach accurately identifies these tissues and reveals distinct individual subgroups based on their genetic architecture.
Area of Science:
- Genetics
- Systems Biology
- Bioinformatics
Background:
- Complex traits are influenced by genetic predispositions acting across multiple tissues and developmental stages.
- Understanding tissue-specific genetic contributions is crucial for dissecting trait etiology.
- Current methods often focus on population-level tissue relevance, not individual-specific contributions.
Purpose of the Study:
- To develop a statistical approach for prioritizing relevant tissues underlying an individual's genetic predisposition to complex traits.
- To probabilistically quantify tissue-wise genetic contributions at the individual level.
- To identify subgroups of individuals with distinct tissue-specific genetic architectures.
Main Methods:
- Leveraging tissue-specific expression quantitative trait loci (eQTLs) and tissue-specific genes.
- Developing a probabilistic framework to quantify individual-level, tissue-wise genetic contributions.
- Utilizing UK Biobank data for simulations and analyses of body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI).
Main Results:
- The developed approach accurately predicts relevant tissues for individuals.
- The method successfully clusters individuals based on their tissue-specific genetic architecture.
- Analysis of BMI and WHRadjBMI identified subgroups with genetic predispositions primarily acting through brain vs. adipose tissue, and adipose vs. muscle tissue.
Conclusions:
- The new statistical approach effectively identifies individual-specific tissue contributions to complex traits.
- Distinct subgroups of individuals exhibit unique tissue-specific genetic architectures.
- These tissue-specific genetic contributions are associated with distinct phenotypic features, suggesting biological relevance.
