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Related Experiment Video

Updated: May 13, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

Test for interactions between a genetic marker set and environment in generalized linear models.

Xinyi Lin¹, Seunggeun Lee, David C Christiani

¹Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.

Biostatistics (Oxford, England)

|March 7, 2013

Summary

This study addresses biases in gene-environment (GE) interaction analysis by proposing a new method, GESAT. GESAT accurately tests for interactions within genetic marker sets and environmental factors, improving upon traditional single SNP analysis.

Keywords:

Asymptotic bias analysis Gene–environment interactions Genome-wide association studies Score statistic Single-nucleotide polymorphism Variance component test

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

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Last Updated: May 13, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genetics
Epidemiology
Biostatistics

Background:

Gene-environment (GE) interactions are crucial for understanding complex diseases.
Current single nucleotide polymorphism (SNP) based GE interaction analysis may be biased when multiple SNPs in a set have main effects.
Existing SNP-set GE analysis methods, like minimum p-value approaches, can lead to biased results and inflated Type 1 error rates.

Purpose of the Study:

To identify biases in classical single SNP and simple SNP-set GE interaction analyses.
To develop a computationally efficient and powerful method for testing GE interactions within biologically defined SNP sets.
To investigate GE interactions between SNPs in the 15q24-25.1 region and smoking in lung cancer risk.

Main Methods:

Derivation of asymptotic bias for classical single SNP-GE interaction analysis.
Development of the Gene-Environment Set Association Test (GESAT) using generalized linear models.
GESAT employs a variance component test for SNP-set by environment interactions and ridge regression for estimating main SNP effects under the null hypothesis.

Main Results:

Demonstrated bias in classical single SNP-GE interaction analysis when multiple SNPs in a set are associated with a disease/trait.
Showed that simple minimum p-value SNP-set GE analysis can be biased with inflated Type 1 error rates.
Simulation studies confirmed the performance of GESAT.
Application of GESAT to lung cancer data revealed GE interactions in the 15q24-25.1 region with smoking.

Conclusions:

The proposed GESAT method provides a statistically sound and powerful approach for analyzing GE interactions in SNP sets.
GESAT overcomes limitations of traditional methods, offering improved accuracy and reliability in GE interaction studies.
The findings highlight the importance of considering SNP sets and potential GE interactions in genetic association studies.