A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity

TL;DR

This paper introduces a robust, model-free Mendelian randomization framework that effectively addresses population heterogeneity in two-sample summary-data studies, improving causal inference accuracy.

Contribution

The authors develop a new estimator that is consistent and asymptotically normal under heterogeneity, avoiding parametric assumptions and enhancing robustness in diverse populations.

Findings

Estimator remains consistent under population heterogeneity.

Method shows efficiency gains over classic estimators.

Real data analysis confirms robustness and practical utility.

Abstract

Mendelian randomization is a powerful tool for causal inference in observational studies. The two-sample summary-data design, which estimates genetic associations with exposures and outcomes in separate cohorts, is the most widely used Mendelian randomization approach in large-scale genomic studies. However, this approach relies on a strong assumption of population homogeneity across the two samples. In practice, available samples often differ in ancestry, demographics, socioeconomic factors, covariate adjustment, and measurement protocols. Violations of the homogeneity assumption can bias causal effect estimates and undermine the credibility of Mendelian randomization findings. We introduce a robust, model-free Mendelian randomization framework that directly addresses population heterogeneity in the two-sample summary-data setting. Our method avoids parametric assumptions about population differences and is designed to address real-world challenges, including measurement error, weak instruments, and pleiotropy. We show that the proposed estimator is consistent and asymptotically normal under heterogeneous designs, and may offer efficiency gains over the classic estimator even in homogeneous settings. Through numerical simulations and a real data analysis for estimating the causal effect of body mass index on high-density lipoprotein cholesterol across ancestrally diverse populations, we demonstrate the practical utility, stability, and robustness of our approach.