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. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.
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Data availability
Publicly available data are available from the following sites: 1000 Genomes Project Phase 3 reference panels: https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html; genetic map for each subpopulation: https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130507_omni_recombination_rates; Pan-UKB summary statistics: https://pan.ukbb.broadinstitute.org/downloads. Individual-level genotypes for the UKB samples were obtained under application no. 32568; the TWB data used in this study contain protected health information and are thus under controlled access. Request for data access can be made to the TWB (https://www.twbiobank.org.tw/). The PGC schizophrenia GWAS can be found at https://pgc.unc.edu/for-researchers/download-results. The in-sample LD of individuals with EUR and EAS ancestry for the PGC schizophrenia analysis were obtained from the Schizophrenia Working Group of the PGC. The Ensembl variant effect predictor can be found at https://ftp.ensembl.org/pub/release-95/.
Code availability
The code used in this study is available from the following websites: SuSiEx (v.1.1.2): https://github.com/getian107/SuSiEx (https://doi.org/10.5281/zenodo.11211744)41; PAINTOR (v.3.0): https://github.com/gkichaev/PAINTOR_V3.0; MsCAVIAR (v.0.1): https://github.com/nlapier2/MsCAVIAR; HAPGEN2 (v.2.2.0): https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html; FlashPCA2 (v.2.0): https://github.com/gabraham/flashpca; KING (v.2.3.2): https://www.kingrelatedness.com/index.shtml; PLINK 1.90 (v.1.9): https://www.cog-genomics.org/plink; LDmergeFM: https://github.com/Pintaius/LDmergeFM; and METASOFT (v.2.0.1): https://zarlab.cs.ucla.edu/software/.
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Acknowledgements
We thank M. Kanai for helpful discussions. The UKB EUR and AFR GWAS summary statistics were obtained from the Pan-UKB project. H.H. acknowledges support from National Institute of Diabetes and Digestive and Kidney Diseases (nos K01DK114379 and R01DK129364), National Institute of Mental Health (NIMH) (nos U01MH109539 and R01MH130675), Brain and Behavior Research Foundation Young Investigator Grant (no. 28450), the Zhengxu and Ying He Foundation, and the Stanley Center for Psychiatric Research. T.G. is supported by a National Human Genome Research Institute grant no. R01HG012354. A.F.P. was supported by the Academy of Medical Sciences ‘Springboard’ award (no. SBF005\1083). M.C.O. acknowledges support from Medical Research Council grants to Cardiff University: center (no. MR/L010305/1), program (no. MR/P005748/1) and project (nos MR/L011794/1, MC_PC_17212). Y.-F.L. is supported by the National Health Research Institutes (nos NP-110, 111, 112-PP-09) and the National Science and Technology Council (Ministry of Science and Technology nos 109-2314-B-400-017 and 110-2314-B-400-028-MY3) of Taiwan. Y.-C.A.F. acknowledges support from the National Science and Technology Council (no. 112-2314-B-002-200-MY3) and the Ministry of Education (the Yushan Young Fellow Program, no. MOE-111-YSFAG-0003-001-P1; the Population Health Research Center from Featured Areas Research Center Program within the framework of the Higher Education Sprout Project, no. NTU-113L9004). B.M.N. acknowledges support from the NIMH (nos R37MH107649 and R01MH101244). The Schizophrenia Workgroup of Psychiatric Genomics Consortium acknowledges support from the NIMH (no. R01MH124873).
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Contributions
T.G. and H.H. designed and supervised the project. T.G. developed the statistical methods. K.Y. and T.G. programmed the code for SuSiEx. K.Y., R.J.L. and T.G. conducted the simulation studies. K.Y., R.J.L. and M.Y. performed the biobank fine-mapping analysis. T.-T.C., S.-C.L. and Y.-F.L. performed the analysis in the TWB. Y.-C.A.F., Y.-F.L. and C.-Y.C. supervised the work in the TWB. K.Y. and A.F.P. performed the analysis of the cohorts with schizophrenia. M.J.D. and B.M.N. provided critical suggestions for the study design. M.Y., Y.C., M.L., R.L. and Y.X. took part in the testing of the code. M.C.O. and Z.G. made substantial contributions to the generation and management of the schizophrenia data. W.S. and C.S. provided support to the computational infrastructure. K.Y., T.G. and H.H. wrote the manuscript. All authors reviewed and approved the final version of the manuscript.
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Competing interests
W.S. and C.S. are employees of Digital Health China Technologies. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and Neumora. C.-Y.C. is an employee of Biogen. R.J.L. is an employee of Ionis Pharmaceuticals. M.C.O. was supported by a collaborative research grant from Takeda Pharmaceuticals for a project unrelated to work presented in this article. Takeda played no part in the conception, design, implementation or interpretation of this study. H.H. received consultancy fees from Ono Pharmaceutical and an honorarium from Xian Janssen Pharmaceutical. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Schematic illustration of the meta-analysis-based fine-mapping method, single-population combining method, and SuSiEx.
All panels were created following the LocusZoom style. Variant positions are shown on the x-axis. The gold diamond for each locus represents the lead (most associated) variant. The association strengths for other variants are colored by descending degrees of linkage disequilibrium (LD) with the lead variant (ordered red, orange, green, and blue dots). The purple bars represent the posterior inclusion probabilities (PIPs) inferred by fine-mapping methods. The light gray boxes represent the credible sets estimated by fine-mapping. a, Example of a strong causal signal shared across populations. b, Example of a weak causal signal shared across populations. c, Example of a population-specific causal signal.
Extended Data Fig. 2 Comparison of SuSiEx, PAINTOR and MsCAVIAR in simulations.
a, The job completion summary for the three Bayesian fine-mapping methods using different parameters and input datasets. Red represents jobs taking longer than 24 hours. Yellow represents jobs returning unreasonable results, defined as the sum of PIPs across variants in the genomic locus >5 or <0.1 (1 is expected). Green represents jobs that were completed within 24 hours and returned reasonable results. The lower panel represents different sample size combinations of the discovery GWAS. P-values are from linear regression with no multiple testing correction applied. b, Number of identified true causal SNPs with PIP > 0.5 (x-axis) versus the coverage of the credible sets (y-axis) for different input datasets and fine-mapping methods. Color represents the combination of discovery populations, the size of the symbols represents the total discovery sample size, and the shape of the symbols represents different methods and parameters. Only simulation runs that were completed within 24 hours and returned reasonable results were included.
Extended Data Fig. 3 Examples of the improvement of SuSiEx over single-population fine-mapping in the biobank analysis.
Each of the three sub-figures consists of eight panels, which are aligned vertically, with the x-axis showing the genomic position. The top six panels visualize GWAS association statistics and single-population fine-mapping results within the European (Pan-UKBB European), African (Pan-UKBB African) and East Asian (Taiwan biobank) populations. For association statistics, the left y-axis shows the −log10(P-value) of each SNP. The color represents the descending degrees of LD with the lead SNP (from red, orange to blue). The right y-axis shows the recombination rate in centimorgan per Megabase. The solid line indicates the population-specific recombination maps obtained from the 1000 Genomes Project. Different colors are used to distinguish different credible sets in the fine-mapping results. The second to bottom panel visualizes the results from SuSiEx. ‘Null’ indicates that single-population fine-mapping did not obtain any reliable credible set. The bottom panel shows gene annotations, if any. P-values are from linear regression with no multiple testing correction applied. a, Association with albumin on chr8:9,170,000-9,190,000, an example of a strong causal signal shared across populations. b, Association with platelets count on chr12:104,900,000-105,050,000, an example of a weak causal signal shared across populations. c, Association with albumin on chr12:13,100,000-13,400,000, an example of population-specific causal signals.
Extended Data Fig. 4 Association with total bilirubin on chr11: 5,100,000-5,700,000.
Panels are aligned vertically, with the x-axis showing the genomic position. The top six panels visualize GWAS association statistics and single-population fine-mapping results of the European (Pan-UKBB European), African (Pan-UKBB African) and East Asian (Taiwan biobank) populations following the LocusZoom style. The second to bottom panel visualizes the fine-mapping results from SuSiEx, which integrated GWAS summary statistics from the three populations. The bottom panel shows gene annotations. For GWAS panels, the left y-axis shows the −log10(P-value) of each SNP. The gray horizontal dash line represents the genome-wide significance threshold (P = 5 × 10−8). The purple rectangle for each locus represents the lead (most associated) variant. Variants are colored by descending LD with the lead variant (ordered red, orange, green, light blue, and dark blue dots). For fine-mapping panels, different colors are used to distinguish different credible sets. The diamond represents the variant with the maximum PIP in each credible set. The left y-axis shows the PIP from fine-mapping, and the right y-axis shows the recombination map obtained from the 1000 Genomes Project. For the SuSiEx panel, the average recombination rate across three populations is used. P-values are from linear regression with no multiple testing correction applied.
Extended Data Fig. 5 Association with albumin on chr13: 31,150,000-31,450,000.
Panels are aligned vertically, with the x-axis showing the genomic position. The top six panels visualize GWAS association statistics and single-population fine-mapping results of the European (Pan-UKBB European), African (Pan-UKBB African) and East Asian (Taiwan biobank) populations following the LocusZoom style. The second to bottom panel visualizes the fine-mapping results from SuSiEx, which integrated GWAS summary statistics from the three populations. The bottom panel shows gene annotations. For GWAS panels, the left y-axis shows the −log10(P-value) of each SNP. The gray horizontal dash line represents the genome-wide significance threshold (P = 5 × 10−8). The purple rectangle for each locus represents the lead (most associated) variant. Variants are colored by descending LD with the lead variant (ordered red, orange, green, light blue, and dark blue dots). For fine-mapping panels, different colors are used to distinguish different credible sets. The diamond represents the variant with the maximum PIP in each credible set. The left y-axis shows the PIP from fine-mapping, and the right y-axis shows the recombination map obtained from the 1000 Genomes Project. For the SuSiEx panel, the average recombination rate across three populations is used. P-values are from linear regression with no multiple testing correction applied.
Extended Data Fig. 6 Proportion of variants showing quality issues binned by the drop in PIP between single- and multi-population fine-mapping.
Quality issues were defined as (i) the best PIP variant is in the low complexity region; (ii) the best PIP variant is in allelic imbalance or violates Hardy Weinberg equilibrium in gnomAD; or (iii) the best PIP variant is multi-allelic or colocalizes with indels at the same genomic position, which might influence imputation quality.
Extended Data Fig. 7 Proportion of variants with high/moderate functional impact in cross-population biobank fine-mapping analyses.
The functional impact of each variant was annotated using VEP, with the definition and classification of functional impact obtained from https://useast.ensembl.org/info/genome/variation/prediction/predicted_data.html. The high impact category includes transcript ablation, splice acceptor variants and splice donor variants; the moderate impact category includes missense variants and protein-altering variants; the low impact category includes synonymous variants and splice region variants; the modifier impact category includes introns and intergenic variants among others.
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Yuan, K., Longchamps, R.J., Pardiñas, A.F. et al. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Nat Genet 56, 1841–1850 (2024). https://doi.org/10.1038/s41588-024-01870-z
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DOI: https://doi.org/10.1038/s41588-024-01870-z
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