Finding novel genes by testing G × E interactions in a genome-wide association study.

Area of Science:

• Genetics and Bioinformatics
• Statistical Genomics
• Epidemiology

Background:

• Genome-wide association studies (GWAS) primarily detect SNPs with strong marginal effects.
• Weakly associated SNPs may be missed in primary GWAS scans.
• Gene-environment (G×E) interactions can reveal SNPs important in specific population subgroups.

Purpose of the Study:

• To determine if genome-wide interaction scans (GWIS) can identify genes missed by primary GWAS.
• To propose and evaluate a novel two-step screening and testing method (EDG×E) for GWIS.
• To compare the performance of EDG×E against existing GWIS methods.

Main Methods:

• Review of common GWIS approaches for case-control studies.
• Development and simulation of the EDG×E two-step screening and testing method.
• Application of EDG×E to a G×Sex scan for childhood asthma.

Main Results:

• The EDG×E method demonstrated 70-80% power to detect disease loci with weak marginal effects, compared to <5% for marginal scans.
• Simulations showed EDG×E outperformed other GWIS methods, including case-only and previous two-step approaches.
• Two potentially significant SNPs for childhood asthma, missed by marginal scans, were identified using G×Sex analysis.

Conclusions:

• GWIS, particularly using the EDG×E method, can successfully identify important genetic associations missed by standard GWAS.
• The EDG×E method is optimized for detecting genes with weak marginal effects.
• The study introduces G×Escan software for implementing these GWIS approaches.