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MACHINE: a robust and scalable multi-ancestry fine-mapping method using a continuous global-local shrinkage prior

2025-09-29 genetic and genomic medicine Title + abstract only
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Fine mapping aims to identify causal genetic variants with nonzero phenotypic effects. Leveraging genome-wide association study (GWAS) data from diverse ancestries enhances fine-mapping accuracy and resolution by exploiting differences in linkage disequilibrium (LD) and increasing sample sizes. However, existing multi-ancestry fine-mapping methods rely on discrete priors and assume that all causal variants are shared across ancestries - an assumption that may not hold in practice. Although MESuS...

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