A unified powerful set-based test for sequencing data analysis of GxE interactions

Area of Science:

• Genetics and Genomics
• Biostatistics
• Computational Biology

Background:

• Next-generation sequencing enables comprehensive study of rare variants in complex diseases.
• Current analyses often overlook environmental factors' role in modifying genetic risk.
• Analyzing gene-environment interaction (GxE) for rare variants presents significant challenges due to rarity and limited exposed subjects.

Purpose of the Study:

• To propose a novel hierarchical model for jointly assessing GxE effects of rare variants.
• To leverage information across a set of rare variants within a gene or regulatory region.
• To address challenges in rare variant GxE analysis by accounting for variant rarity and exposure.

Main Methods:

• A hierarchical model with two components: fixed effects for weighted variant burdens and environmental factors, and a variance component for residual GxE effects.
• Development of a novel testing procedure with two independent score statistics for fixed and variance components.
• Two data-adaptive combination approaches for score statistics and establishment of asymptotic distributions.

Main Results:

• Extensive simulations demonstrate the proposed methods maintain correct type I error rates.
• The power of the proposed approaches is comparable to or exceeds existing methods across various scenarios.
• Illustration via an exome-wide GxE analysis of NSAIDs use in colorectal cancer.

Conclusions:

• The proposed hierarchical model and testing procedure effectively analyze GxE effects for rare variants.
• The methods offer a robust framework for understanding genetic and environmental contributions to complex diseases.
• This approach advances the analysis of rare variants in genetic epidemiology and precision medicine.