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Integrative Mendelian randomization for detecting exposure-by-group interactions using group-specific and combined
Ke Xu1,2, Nathaniel Maydanchik1, Bowei Kang1
1Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, United States of America.
Plos Genetics
|September 11, 2025
Summary
A new method, int2MR, uses genetic data to find how risk factors interact with groups in complex diseases. This approach reveals insights into ADHD and Alzheimer's disease, even with limited individual data.
Area of Science:
- Genetics
- Epidemiology
- Computational Biology
Background:
- Complex diseases often involve interactions between risk factors and specific population groups.
- Current methods for detecting these interactions typically require individual-level data, which is often unavailable or limited.
- This limitation restricts the power and applicability of interaction assessments in genetic and epidemiological studies.
Purpose of the Study:
- To introduce int2MR, an integrative Mendelian randomization (MR) method designed to overcome limitations of individual-level data.
- To enable the assessment of interactions between risk exposures and covariate-defined groups using genome-wide association study (GWAS) summary statistics.
- To provide a robust tool for uncovering disease mechanisms and risk factors specific to different population subgroups.
Main Methods:
- Developed int2MR, a novel integrative Mendelian randomization (MR) approach.
- Leveraged GWAS summary statistics for exposure traits and group-separated/combined GWAS statistics for outcome traits.
- Validated the method through simulation studies assessing type I error rates and power gains.
Main Results:
- int2MR effectively controls type I error rates and demonstrates considerable power gains, especially with integrated group-combined GWAS data.
- Applied int2MR to identify sex-interaction effects on ADHD, suggesting elevated inflammation in males.
- Detected age-group-specific risk factors for Alzheimer's disease (AD) in individuals aged 95+, many linked to immune/inflammatory processes.
Conclusions:
- int2MR is a robust and flexible tool for assessing group-specific or interaction effects in complex diseases.
- Findings suggest reduced chronic inflammation may underlie distinct AD pathological mechanisms in the oldest-old.
- The method provides valuable insights into disease mechanisms, overcoming limitations of traditional individual-level data analysis.
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