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<p>Multivariable Mendelian randomization has been largely applied to individuals of European ancestry, due to the larger sample sizes available in European GWAS. We introduce MRBEE-TL, one of the first multi-ancestry multivariable Mendelian randomization methods, which combines transfer learning with bias-corrected estimating equations to improve power in underpowered ancestries and to assess cross-ancestry heterogeneity of disease risk factors. In simulations, MRBEE-TL consistently outperforms MR methods that rely solely on ancestry-specific GWAS data. In real data analyses, MRBEE-TL not only identifies ancestry-consistent and ancestry-specific causal effects missed by conventional methods, but also improves power in underpowered ancestries.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Yihe Yang,
  • Xiaofeng Zhu

摘要

Multivariable Mendelian randomization has been largely applied to individuals of European ancestry, due to the larger sample sizes available in European GWAS. We introduce MRBEE-TL, one of the first multi-ancestry multivariable Mendelian randomization methods, which combines transfer learning with bias-corrected estimating equations to improve power in underpowered ancestries and to assess cross-ancestry heterogeneity of disease risk factors. In simulations, MRBEE-TL consistently outperforms MR methods that rely solely on ancestry-specific GWAS data. In real data analyses, MRBEE-TL not only identifies ancestry-consistent and ancestry-specific causal effects missed by conventional methods, but also improves power in underpowered ancestries.