Gene-environment interactions in case-control studies with silent disease

Area of Science:

• Genetics
• Epidemiology
• Biostatistics

Background:

• Genome-wide association studies (GWAS) frequently assess gene-environment interactions (G × E).
• Misclassification of disease status in control groups can bias G × E estimates.
• Silent or undiagnosed disease in controls, varying with environmental factors, presents a significant challenge.

Purpose of the Study:

• To develop a method for accurately estimating G × E in case-control GWAS.
• To address bias introduced by undiagnosed disease in the control population.
• To provide a robust approach for analyzing genetic associations influenced by environmental factors.

Main Methods:

• Proposed a pseudolikelihood approach to correct for misdiagnosis in controls.
• Evaluated the method's performance through extensive simulations.
• Applied the developed method to a real-world GWAS dataset for prostate cancer.

Main Results:

• Uncorrected case-control status leads to biased G × E estimates.
• The pseudolikelihood method effectively removes bias from misdiagnosed controls.
• Accurate estimation of G × E is achievable even with varying frequencies of silent disease.

Conclusions:

• Accounting for undiagnosed disease in controls is crucial for accurate G × E estimation in GWAS.
• The proposed pseudolikelihood method offers a reliable solution for correcting bias in G × E analyses.
• This approach enhances the understanding of gene-environment interplay in diseases like prostate cancer.