Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases

[[abstract]]Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. These loci may contain one or a handful of causal variants, while the associations of other variants are driven by their linkage disequilibrium (LD) with the causal variant(s). Statistical fine-mapping refines a GWAS locus to a smaller set of likely causal variants (i.e., credible set) to facilitate interpretation and prioritize laboratory-based functional studies. Fine-mapping studies in samples of European ancestry have made important advances, with some disease-associated loci resolved to single-variant resolution. Since non-causal variants tagging causal signals have marginally different effects across populations where LD differs, capitalizing on the genomic diversity across ancestries holds the promise to further improve the resolution of fine-mapping. However, to date, cross-population fine-mapping efforts have been limited, partly due to the lack of statistical methods that can appropriately integrate data from multiple ancestries. Building on the Sum of Single Effects (SuSiE), a single-population fine-mapping model, we have developed SuSiEx, an accurate and computationally efficient method for trans-ancestry fine-mapping. Our model can integrate data from an arbitrary number of ancestries, explicitly models population-specific LD patterns, and accounts for multiple causal variants in a genomic region. Our simulation studies revealed that SuSiEx outperformed single-population fine-mapping methods in terms of power, resolution, and scalability. Additionally, we conducted a comparison of SuSiEx with two published Bayesian cross-population fine-mapping methods, PAINTOR and MsCAVIAR. The results clearly demonstrate that SuSiEx is more efficient, user-friendly, high-power, and well-calibrated. Encouraged by simulation results, we applied SuSiEx to data from the Pan-UKBB project and the Taiwan Biobank (TWB). We included summary statistics of EUR and AFR (NEU = 419,807; NAFR = 6,570) ancestries from Pan-UKBB. We additionally included TWB, one of the largest biomedical databases in East Asia (NEAS = 92,615). We selected 25 quantitative traits shared between Pan-UKBB and TWB and defined 13,420 candidate loci. In the biobank analysis, by using SuSiEx, the power, and resolution were Improved. We applied SuSiEx to schizophrenia GWAS summary statistics of EUR (Ncase = 53,251, Ncontrol = 77,127) and EAS (Ncase = 14,004, Ncontrol = 16,757) ancestries from the Psychiatric Genomics Consortium (PGC), and fine-mapped the same 250 autosomal loci in the recent PGC publication4. SuSiEx successfully identified 215 credible sets out of 193 loci, among which 11 had a SNP with PIP >95%. As expected, SuSiEx outperformed published PGC fine-mapping results, which applied a single-population fine-mapping method, FINEMAP, to meta-analyzed GWAS summary statistics and sample size weighted LD. Specifically, SuSiEx mapped 57% (33 vs. 21) more signals to a single variant with PIP >50% in single-credible-set loci. Furthermore, SuSiEx substantially increased the resolution of fine-mapping by reducing the average size of credible sets from 87.1 to 60.3 (P = 0.015; paired two-sided t-test), and increasing the average of maximum PIP across credible sets from 0.25 to 0.27 (P = 0.012; paired two-sided t-test)