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Updated: Jun 27, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Gene-environment interaction in genome-wide association studies.
Cassandra E Murcray1, Juan Pablo Lewinger, W James Gauderman
1Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089-9010, USA. murcray@usc.edu
This study introduces a new two-step method for analyzing genome-wide association studies to find gene-environment interactions. This approach can identify new genetic signals for complex diseases beyond marginal effects.
Area of Science:
- Genetics
- Environmental Health
- Biostatistics
Background:
- Complex diseases like diabetes, asthma, and cancer often result from gene-environment interactions.
- Genome-wide association studies (GWAS) traditionally focus on marginal genetic effects, potentially missing crucial interaction signals.
Purpose of the Study:
- To propose an efficient and easily implemented two-step analysis for GWAS data.
- To identify genes involved in gene-environment interactions relevant to complex diseases.
Main Methods:
- A novel two-step analytical procedure for genome-wide association study data.
- The method is designed to complement existing screening for marginal genetic effects.
Main Results:
- The proposed method efficiently identifies genes involved in gene-environment interactions.
- This approach can uncover novel genetic signals missed by traditional GWAS.
Conclusions:
- The developed two-step analysis is a valuable tool for uncovering gene-environment interactions in GWAS.
- This method enhances the potential to identify new genetic contributors to complex diseases.
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