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MRBEE-TL: improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer

Yihe Yang1, Xiaofeng Zhu2

  • 1Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.

Genome Biology
|March 31, 2026
PubMed
Summary
This summary is machine-generated.

We developed MRBEE-TL, a novel multi-ancestry method for multivariable Mendelian randomization (MR). This approach enhances statistical power in underrepresented ancestries and reveals cross-ancestry differences in disease risk factors.

Keywords:
Genome-wide association studiesMulti-ancestry Mendelian randomizationMultivariable Mendelian randomizationTransfer learning

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Multivariable Mendelian randomization (MR) studies predominantly use European ancestry data due to larger genome-wide association study (GWAS) sample sizes.
  • This limits the generalizability of findings and the ability to study diverse populations.

Purpose of the Study:

  • To introduce MRBEE-TL, a novel multi-ancestry multivariable MR method.
  • To improve statistical power in underpowered ancestries.
  • To assess cross-ancestry heterogeneity of disease risk factors.

Main Methods:

  • MRBEE-TL integrates transfer learning with bias-corrected estimating equations.
  • It leverages multi-ancestry GWAS data for enhanced causal inference.
  • The method is evaluated through simulations and real-world data analyses.

Main Results:

  • MRBEE-TL demonstrates superior performance compared to traditional MR methods relying on single-ancestry GWAS data.
  • The method successfully identifies both ancestry-consistent and ancestry-specific causal effects.
  • It significantly improves statistical power in ancestries with limited GWAS data.

Conclusions:

  • MRBEE-TL offers a powerful new approach for multi-ancestry MR studies.
  • It enhances the ability to detect causal relationships across diverse populations.
  • This method advances the study of global health disparities and genetic epidemiology.