A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.

Area of Science:

• Genetics
• Biostatistics
• Epidemiology

Background:

• Gene-environment interaction (G×E) is crucial for complex diseases, but genome-wide detection is underpowered.
• Existing G×E tests often rely on single-study data, limiting statistical power.
• Large consortia offer opportunities to increase power by pooling data across studies.

Purpose of the Study:

• To develop a meta-analysis framework for detecting gene-based G×E effects.
• To introduce meta-analysis-based filtering statistics to enhance G×E test power.
• To identify genes with age-dependent penetrance in breast cancer using consortium data.

Main Methods:

• Proposed a novel meta-analysis framework for gene-based G×E detection.
• Incorporated meta-analysis-based filtering statistics into G×E tests.
• Developed an R software package named ofGEM for the proposed methods.
• Applied the methods to breast cancer consortium data.

Main Results:

• Simulations demonstrated the superior power of the proposed ofGEM test.
• The ofGEM test successfully identified genes with age-dependent penetrance in breast cancer.
• The R package ofGEM facilitates the application of these meta-analysis G×E tests.

Conclusions:

• The proposed meta-analysis framework significantly improves the power of genome-wide G×E detection.
• The ofGEM test is effective in identifying gene-age interaction effects in complex diseases.
• The ofGEM R package provides a valuable tool for G×E research in large consortia.