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http://ir.nhri.org.tw/handle/3990099045/13773
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| Title: | Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases |
| Authors: | Yuan, K;Chen, TT;Lin, SC;Longchamps, R;Pardinas, A;Lam, M;Chen, CY;Feng, YCA;Lin, YF;Ge, T;Huang, HL |
| Contributors: | Center for Neuropsychiatric Research |
| Abstract: | Background: 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). 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. Since non-causal variants tagging causal signals have marginally different effects across populations where LD differs, capitalizing on the genomic diversity across ancestries (e.g., smaller LD blocks in African populations) 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.Methods: Building on 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. SuSiEx assumes that causal variants are largely shared across populations, which has been observed for many complex traits and diseases including schizophrenia, while allowing for varying variant effect sizes across populations, retaining modeling flexibility. Our model can integrate data from an arbitrary number of ancestries, explicitly models population-specific LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics without access to individual-level data. Results: We showed, via simulation studies, that compared with fine-mapping 100K European samples, integrating 50K European and 50K African samples using SuSiEx enabled fine-mapping of more association signals, and dramatically increased the resolution of credible sets. Comparing with PAINTOR, a widely used fine-mapping method, SuSiEx had a 37% reduction in the median size of credible sets and a 54% increase in the number of high Posterior Inclusion Probability (PIP) variants across simulation settings. We applied SuSiEx to 25 quantitative traits that are available from both the Taiwan Biobank (TWB, n = 92,615) and UK Biobank (UKBB, n = 361,194) to fine-map genetic loci reaching genome-wide significance. Compared with single-population fine-mapping in UKBB, cross-ancestry fine-mapping significantly reduced the size of credible sets and increased the PIP of the most probable variant. We additionally applied our method to schizophrenia GWAS summary statistics of East Asian and European ancestries. Compared with the published fine-mapping results from PGC using FINEMAP on the same data, SuSiEx reduced the size of credible sets in 70% of the fine-mapped loci. Manual inspection confirmed that SuSiEx provided more sensible results in many loci, particularly those with a marginally genome-wide significant signal (P-value between 5E-8 and 1E-10). Discussion: More and more GWAS of non-Europeans were published recently and these provide us with unprecedented opportunities in exploring the underlying biological mechanism of diseases. As the accumulation of GWAS results from diverse ancestries, the application of this method will be much promising. |
| Date: | 2021-10 |
| Relation: | European Neuropsychopharmacology. 2021 Oct;51:E68. |
| Link to: | http://dx.doi.org/10.1016/j.euroneuro.2021.07.141 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0924-977X&DestApp=IC2JCR |
| Cited Times(WOS): | https://www.webofscience.com/wos/woscc/full-record/WOS:000704035500128 |
| Appears in Collections: | [林彥鋒] 會議論文/會議摘要
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| ISI000704035500128.pdf | | 50Kb | Adobe PDF | 159 | View/Open |
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