A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and...

Area of Science:

• Genetics
• Biostatistics
• Complex disease research

Background:

• Gene-environment interactions are crucial for understanding complex diseases.
• Current methods for testing these interactions face challenges with statistical power and environmental effect modeling.
• Existing aggregated approaches, like polygenic risk scores, improve power but hinder identification of specific contributing genes.

Purpose of the Study:

• To develop a novel framework, RITSS (Robust Interaction Testing using Sample Splitting), for testing gene-environment interactions in quantitative traits.
• To enhance statistical power and address model misspecification issues in interaction testing.
• To improve the interpretability of findings by identifying contributing genes and pathways.

Main Methods:

• RITSS employs sample splitting and robust test statistics for interaction analysis.
• The framework accommodates sets of genetic variants and multiple environmental factors.
• A screening step selects and combines potential interactions, followed by a testing step using robust statistics to minimize misspecification effects.

Main Results:

• RITSS effectively controls the type 1 error rate across various scenarios in simulations.
• The screening strategy in RITSS influences statistical power.
• Application to UK Biobank data revealed significant gene-environment interactions for lung function and height, highlighting aggregated effects.

Conclusions:

• RITSS provides a robust and interpretable method for gene-environment interaction analysis.
• The framework identifies significant interaction patterns with strong aggregated effects, even with weak individual variant signals.
• RITSS offers valuable insights into the mechanisms of complex diseases driven by gene-environment interactions.