Testing gene-environment interactions for rare and/or common variants in sequencing association studies

Area of Science:

• Genetics
• Bioinformatics
• Complex disease risk

Background:

• Complex disease risk arises from gene-environment (GE) interactions.
• Next-generation sequencing enables identification of GE interactions for common and rare variants.
• Existing methods often overlook GE interactions, particularly for rare variants.

Purpose of the Study:

• To develop novel statistical approaches for testing GE interactions in sequencing association studies.
• To specifically address GE interactions involving rare and/or common genetic variants.
• To improve the power and robustness of GE interaction analyses.

Main Methods:

• Proposed two novel methods: TOW-GE for rare variants and VW-TOW-GE for both rare and common variants.
• Utilized optimally weighted combinations of GE interaction effects.
• Evaluated methods using simulations based on Genetic Analysis Workshop 17 data and real data from the COPDGene Study.

Main Results:

• TOW-GE and VW-TOW-GE demonstrated well-controlled type I error rates.
• TOW-GE showed increased power for GE interactions involving rare variants compared to ISKAT.
• VW-TOW-GE exhibited greater power for GE interactions involving both rare and common variants, robust to effect directions.

Conclusions:

• The proposed TOW-GE and VW-TOW-GE methods effectively identify gene-environment interactions in sequencing studies.
• These novel approaches offer enhanced power for detecting GE interactions involving diverse genetic variant types.
• The methods are robust and applicable to real-world genetic datasets like COPDGene.