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

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.