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bioRxiv · 10.1101/2025.07.11.664423

Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning

Yang, Y.·Zhu, X.
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Abstract

Multivariable Mendelian randomization (MVMR) has been largely limited to individuals of European ancestry, due to the larger sample sizes available in European genome-wide association studies (GWAS). We introduce MRBEE-TL, one of the first multi-ancestry MVMR 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 outperformed MR methods that relied solely on ancestry-specific GWAS data. In real data analyses, MRBEE-TL not only identified ancestry-consistent and ancestry-specific causal effects missed by conventional methods, but also improved power in African and East Asian ancestries. MRBEE-TL is available through the R package MRBEEX at https://github.com/harryyiheyang/MRBEEX.

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Yang, Y., Zhu, X.. 2025-07-17. Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning. https://doi.org/10.1101/2025.07.11.664423

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