Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.

Joint modeling approaches improve power and resolution for shared causal variants in multi-ancestry fine-mapping studies.

• Why it matters: Understanding the genetic basis of complex traits across diverse populations requires accurate fine-mapping methods that address differences in LD patterns and effect heterogeneity, which remain challenging.
• What they did: Nine fine-mapping methods, including joint probabilistic models and univariate integration approaches, were systematically benchmarked using simulations from UK Biobank data and real data from the All of Us Research Program.
• The result: Joint modeling methods showed enhanced accuracy for shared variants, while post-hoc integration better recovered population-specific variants; an adaptive decision strategy was proposed to optimize method selection based on locus evidence.