GxEscan

GxEscan enhances detection of gene–environment (G×E) interactions in genome-wide association studies (GWAS) by applying the EDG×E two-step screening and testing approach to reveal SNPs with weak marginal effects when interacting with environmental or personal factors.

Key Features:

• EDG×E two-step screening and testing: Implements the EDG×E method as a two-step screening and testing procedure for genome-wide interaction scans.
• Detection of SNPs with weak marginal effects: Targets SNPs that have weak marginal associations but exhibit stronger effects within subgroups defined by environmental or personal factors.
• Integration of environmental and personal covariates: Incorporates environmental and personal factors to define subgroups for interaction analysis.
• Increased statistical power: Simulation studies reported 70–80% power to detect disease-associated loci under various scenarios compared with less than 5% power using marginal scans.
• Performance versus other GWIS methods: Simulation results indicate improved performance relative to case-only analyses and previously proposed two-step methodologies.
• Empirical application: Applied to a G×Sex interaction analysis for childhood asthma, identifying two SNPs not detected by marginal-association scans.

Scientific Applications:

• Genome-wide interaction scans (GWIS): Detects G×E interactions across the genome in GWAS datasets.
• Discovery of disease-associated loci: Identifies loci missed by marginal-association scans by leveraging interactions with environmental or personal factors.
• Subgroup-specific interaction discovery: Reveals genetic effects specific to subgroups defined by environmental or personal covariates.
• Sex-specific genetic effect analysis: Enables analysis of G×Sex interactions, exemplified by findings in childhood asthma.

Methodology:

Implements the EDG×E two-step screening and testing method and evaluates performance using simulation studies comparing power against marginal scans, case-only analyses, and previously proposed two-step methods.