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Integrative Mendelian Randomization for Detecting Exposure-by-group Interactions Using Group-Specific and Combined
Medrxiv : the Preprint Server for Health Sciences
|February 20, 2025
Summary
A new method, int2MR, uses GWAS summary statistics to detect gene-environment interactions for complex diseases. This approach enhances power and reveals insights into sex-specific ADHD and age-specific Alzheimer's disease risk factors.
Area of Science:
- Genetics
- Epidemiology
- Biostatistics
Background:
- Complex diseases involve interactions between risk factors and specific population groups.
- Current methods for detecting these interactions often require individual-level data, which can be limited.
- Assessing interactions is crucial for understanding disease mechanisms but faces data availability challenges.
Purpose of the Study:
- To develop an integrative Mendelian randomization (MR) method, int2MR, for detecting interactions using genome-wide association study (GWAS) summary statistics.
- To overcome limitations of individual-level data in assessing risk factor interactions across different groups.
- To provide a robust tool for exploring group-specific or interaction effects in complex traits.
Main Methods:
- Developed int2MR, leveraging GWAS summary statistics for exposure traits and group-separated/combined GWAS statistics for outcome traits.
- Conducted simulation studies to evaluate type I error rates and power gains.
- Applied int2MR to analyze sex-interaction effects on ADHD and age-group-specific risk factors for Alzheimer's disease.
Main Results:
- int2MR effectively controls type I error rates and shows power gains, especially with group-combined GWAS data.
- Identified sex-interaction effects on ADHD, suggesting potential sex differences in inflammation.
- Detected age-group-specific risk factors for Alzheimer's disease in individuals aged 95+, many linked to immune and inflammatory processes.
Conclusions:
- int2MR is a powerful and flexible method for assessing interaction effects using summary-level GWAS data.
- Findings highlight the role of inflammation in sex-specific ADHD and in the oldest-old with Alzheimer's disease.
- The method offers novel insights into complex disease mechanisms previously unattainable with limited data.
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