“Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases” published in Nature Genetics

Statistical fine-mapping helps narrow down a GWAS hit to a smaller set of potentially causal genetic variants and works well on data from relatively homogeneous populations. Kai Yuan, Tian Ge, Hailiang Huang, and colleagues have developed a technique, SuSiEx, for cross-population fine-mapping. The method integrates data from multiple ancestries, models population specific allele frequencies and linkage disequilibrium patterns, and can be applied to GWAS summary statistics. In Nature Genetics, the team showed how SuSiEx improved fine-mapping for various traits in both the UK Biobank and Taiwan Biobank and the fine-mapping of schizophrenia loci across East Asian and European ancestries.