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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
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High-throughput allele-specific expression across 250 environmental conditions.
Gregory A Moyerbrailean1, Allison L Richards1, Daniel Kurtz1
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA.
Genome Research
|December 10, 2016
Summary
Gene-environment interactions significantly impact complex traits and disease risk. This study used allele-specific expression analysis to identify 215 genes with such interactions, revealing their crucial role in human health.
Area of Science:
- Genomics
- Systems Biology
- Human Genetics
Background:
- Gene-environment (GxE) interactions are key to understanding complex traits and disease.
- Quantifying GxE effects in humans is challenging due to environmental complexity.
- Cellular models offer a tractable approach to study GxE interactions on gene expression.
Purpose of the Study:
- To characterize genetic effects on transcriptional responses to diverse cellular environments.
- To identify gene-by-environment interactions using allele-specific expression (ASE) analysis.
- To assess the role of GxE interactions in human complex traits and genome-wide association studies (GWAS).
Main Methods:
- Utilized allele-specific expression (ASE) analysis to detect GxE interactions.
- Analyzed transcriptional responses to 50 different treatments across five cell types.
- Cross-referenced identified genes with existing GWAS data.
Main Results:
- Discovered 1455 genes with ASE and 215 genes exhibiting GxE interactions.
- Genes responding transcriptionally to environmental perturbations were sevenfold more likely to be in GWAS.
- 49% of identified GxE genes were previously linked to complex traits in GWAS, including GIPR-caffeine and obesity, and LAMP3-selenium and Parkinson's disease.
Conclusions:
- GxE interactions play a substantial role in human complex traits.
- Comprehensive catalogs of GxE interactions are essential for gene annotation.
- Findings bridge cellular studies with epidemiological and GWAS data to enhance understanding of disease etiology.
