Analyzing complex traits and diseases using GxE PRS: genotype-environment interaction in polygenic risk score models

Area of Science:

• Genetics
• Environmental Health
• Biostatistics

Background:

• Understanding gene-environment interplay is vital for health.
• Polygenic risk scores (PRS) traditionally lack GxE components.
• Obesity is a complex trait influenced by genetics and environment.

Purpose of the Study:

• To apply the novel GxEprs model for detecting genotype-environment (GxE) interactions in obesity-related traits.
• To assess the impact of GxE interactions on the predictive accuracy of PRS.
• To validate the GxEprs method's ability to minimize spurious GxE signals.

Main Methods:

• Application of the GxEprs model to UK Biobank data.
• Analysis of various obesity-related complex traits and diseases.
• Statistical modeling to capture significant GxE signals and assess prediction enhancement.

Main Results:

• Significant GxE signals were identified for quantitative obesity phenotypes like BMI, WHR, BF, and WC.
• The GxEprs model demonstrated enhanced prediction accuracy for most analyzed traits.
• The study confirmed the importance of the GxE component in PRS models.

Conclusions:

• Incorporating GxE interactions into PRS models significantly improves prediction accuracy for obesity-related traits.
• The GxEprs method effectively captures meaningful GxE signals, reducing model misspecification.
• This approach provides a foundation for personalized medicine by better understanding genetic risks in environmental contexts.