Using shared genetic controls in studies of gene-environment interactions

Area of Science:

• Genetics
• Epidemiology
• Biostatistics

Background:

• Large-scale genetic association studies often incur high costs.
• External or publicly available controls offer a cost-saving alternative to traditional methods.
• Adjusting for population stratification is crucial for accurate genetic association studies.

Purpose of the Study:

• To develop a method for estimating gene-environment joint effects in case-control studies with mixed control data.
• To address the challenge of utilizing external controls for genetic data and internal controls for environmental data.
• To enable cost-effective large-scale genetic association studies.

Main Methods:

• Developed a semiparametric profile likelihood method.
• Introduced related pseudolikelihood and working likelihood methods.
• Proposed variance estimators based on asymptotic theory.

Main Results:

• The proposed methods allow estimation of gene-environment joint effects under a gene-environment independence assumption.
• The methods effectively adjust for population stratification.
• Simulation studies demonstrated the performance of the developed methods.

Conclusions:

• The developed methods provide a practical approach for analyzing gene-environment interactions in genetic association studies with mixed control data.
• This facilitates cost reduction in large-scale genetic research.
• The methods were successfully illustrated using bladder cancer genome-wide association study data.