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Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects

Nature Genetics volume 57, pages 2408–2417 (2025)Cite this article

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Abstract

Large biobanks, such as the UK Biobank (UKB), enable massive phenome by genome-wide association studies that elucidate genetic etiology of complex traits. However, people from diverse genetic ancestry groups are often excluded from association analyses due to concerns about population structure introducing false positive associations. Here we generate mixed model associations and meta-analyses across genetic ancestry groups, inclusive of a larger fraction of the UK Biobank than previous efforts, to produce freely available summary statistics for 7,266 traits. We build a quality control and analysis framework informed by genetic architecture. Overall, we identify 14,676 significant loci (P < 5 × 10−8) in the meta-analysis that were not found in the EUR genetic ancestry group alone, including new associations, for example between CAMK2D and triglycerides. We also highlight associations from ancestry-enriched variation, including a known pleiotropic missense variant in G6PD associated with several biomarker traits. We release these results publicly alongside frequently asked questions that describe caveats for interpretation of results, enhancing available resources for interpretation of risk variants across diverse populations.

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Fig. 1: Genetic ancestry in the Pan-UKB.
Fig. 2: Pan-UKB GWAS resource facilitates multiancestry multitrait analyses.
Fig. 3: Heritability informs robustness of GWAS across ancestry–trait pairs.
Fig. 4: UKB-wide analysis improves genetic discovery.
Fig. 5: Differences in allele frequencies across ancestries yield new genetic discoveries.
Fig. 6: Meta-analysis identifies pleiotropic signals from non-European populations.

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Data availability

All data are available at https://pan.ukbb.broadinstitute.org/, as well as on the AWS Open Data program (https://aws.amazon.com/marketplace/pp/prodview-2efssfw2ezyq6). Sample metadata is available in the UKB showcase under https://biobank.ndph.ox.ac.uk/ukb/dset.cgi?id=2442.

Code availability

All analysis code is available via GitHub at https://github.com/atgu/ukbb_pan_ancestry and via Zenodo at https://doi.org/10.5281/zenodo.15420125 (ref. 73).

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Acknowledgements

We thank P. Kraft and J.-A. Dias for helpful discussions. This work was supported by the Novo Nordisk Foundation (NNF21SA0072102; K.J.K., B.M.N.), NIH grants R37MH107649 (B.M.N.), R00MH117229 (A.R.M.), K01MH121659 (E.G.A.), F31HL167378 (K.T.) and F30AG074507 (R.G.) and BroadIgnite funding (A.R.M.).

Author information

Author notes

  1. These authors contributed equally: Konrad J. Karczewski, Rahul Gupta, Masahiro Kanai.

Authors and Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Konrad J. Karczewski, Rahul Gupta, Masahiro Kanai, Wenhan Lu, Kristin Tsuo, Ying Wang, Nikolas Baya, Duncan S. Palmer, Jacqueline I. Goldstein, Gopal Sarma, Matthew Solomonson, Nathan Cheng, Sam Bryant, Claire Churchhouse, Caroline M. Cusick, Timothy Poterba, John Compitello, Daniel King, Wei Zhou, Cotton Seed, Hilary K. Finucane, Mark J. Daly, Benjamin M. Neale & Alicia R. Martin

  2. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Konrad J. Karczewski, Rahul Gupta, Masahiro Kanai, Wenhan Lu, Kristin Tsuo, Ying Wang, Raymond K. Walters, Nikolas Baya, Duncan S. Palmer, Jacqueline I. Goldstein, Gopal Sarma, Matthew Solomonson, Nathan Cheng, Sam Bryant, Claire Churchhouse, Caroline M. Cusick, Timothy Poterba, John Compitello, Daniel King, Wei Zhou, Cotton Seed, Hilary K. Finucane, Mark J. Daly, Benjamin M. Neale & Alicia R. Martin

  3. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Konrad J. Karczewski & Benjamin M. Neale

  4. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA

    Rahul Gupta & Kristin Tsuo

  5. Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA

    Masahiro Kanai

  6. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Wenhan Lu, Kristin Tsuo, Ying Wang, Raymond K. Walters, Nikolas Baya, Duncan S. Palmer, Jacqueline I. Goldstein, Gopal Sarma, Nathan Cheng, Sam Bryant, Claire Churchhouse, Caroline M. Cusick, Timothy Poterba, John Compitello, Daniel King, Wei Zhou, Cotton Seed, Hilary K. Finucane, Mark J. Daly, Benjamin M. Neale & Alicia R. Martin

  7. Department of Economics, University of Southern California, Los Angeles, CA, USA

    Patrick Turley

  8. Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA

    Patrick Turley

  9. Department of Clinical Research and Leadership, The George Washington University, Washington, DC, USA

    Shawneequa Callier

  10. Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Shawneequa Callier

  11. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Nirav N. Shah & Elizabeth G. Atkinson

  12. Institute for Molecular Medicine, Helsinki, Finland

    Mark J. Daly

Authors
  1. Konrad J. Karczewski
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  2. Rahul Gupta
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  29. Alicia R. Martin
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Contributions

K.J.K. developed pipelines and performed association analysis, variant and association QC, and summary statistics analyses. K.J.K., R.G., M.K., W.L., N.N.S. and A.R.M. generated figures. R.G. performed heritability analysis and QC. M.K. created LD matrices and LD scores, and performed locus definition analysis, meta-analysis and fine-mapping. W.L. performed additional association analyses. K.T. and N.B. performed LD analyses including clumping. Y.W. performed polygenicity analyses. R.K.W. performed sample QC and advised on association analyses. P.T., S.C., E.G.A. and A.R.M. wrote the FAQs. N.N.S. and E.G.A. performed Tractor analyses. D.S.P. and E.G.A. performed phenotype curation, processing and QC. J.I.G., T.P., J.C., D.K. and C.S. built the Hail infrastructure that enabled association analysis. G.S. curated prescription data. M.S. developed the website. N.C. performed initial comparisons of meta- and mega-analysis. S.B., C.C. and C.M.C. provided data and project management. W.Z. aided in development of association methods. A.R.M., H.K.F., M.J.D., B.M.N. and E.G.A. provided oversight and direction of the project. A.R.M. performed ancestry assignment and pruning analysis. A.R.M., E.G.A., B.M.N. and K.J.K. conceived the study. K.J.K., R.G., M.K., E.G.A. and A.R.M. wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Alicia R. Martin.

Ethics declarations

Competing interests

K.J.K. is a consultant for Tome Biosciences, AlloDx and Vor Biosciences, and a member of the scientific advisory board of Nurture Genomics. 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. The other authors declare no competing interests.

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Nature Genetics thanks Paul Auer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Global and subcontinental PCA.

a, Global PCA projection of UKBB into PCs 1-2 defined by HGDP and 1000 Genomes Project reference panel, which are shown in colored dots on top of UKBB in black. b, Global PCA density plot of UKBB points only, excluding reference panel. c, Map of HGDP, 1000 Genomes Project, and AGVP reference used to define AFR PC space. d, PCs 1-2 within AFR, reference panel colored, UKBB in grey. Inset shows density of UKBB samples assigned to AFR using a random forest. In c and d, colors and shapes are consistent across panels. e, Map of HGDP and 1000 Genomes Project reference used to define CSA PC space. f, PCs 1-2 within CSA, reference panel colored, UKBB in grey. Inset shows density of UKBB samples assigned to CSA using a random forest. In e and f, colors and shapes are consistent across panels. g, Map of HGDP and 1000 Genomes Project reference used to define EAS PC space. h, PCs 1-2 within EAS, reference panel colored, UKBB in grey. Inset shows density of UKBB samples assigned to EAS using a random forest. In g and h, colors and shapes are consistent across panels. i, Map of HGDP and 1000 Genomes Project reference used to define EUR PC space. j, PCs 1-2 within EUR, reference panel colored, UKBB in grey. Inset shows density of UKBB samples assigned to EUR using a random forest. In I and j, colors and shapes are consistent across panels. k, Map of HGDP and 1000 Genomes Project reference used to define MID PC space. l, PCs 1-2 within MID, reference panel colored, UKBB in grey. Inset shows density of UKBB samples assigned to MID using a random forest. In k and l, colors and shapes are consistent across panels.

Extended Data Fig. 2 Heritability informs the robustness of GWAS across ancestry-trait pairs.

a, Heritability estimates are generally concordant in the EUR genetic ancestry group across 64 pilot phenotypes (Supplementary Table 9) and two statistical methods. RHE-mc uses a randomized multi-component version of classical Haseman-Elston regression with a genetic relatedness matrix53, whereas S-LDSC uses GWAS summary statistics52. For binary phenotypes, heritability estimates are reported on the liability scale. All pilot phenotypes are shown, except for sepsis, which had negative heritability estimates by both methods. The dotted line shows y = x, while the dashed line is a fitted linear regression (slope = 0.87, intercept = 0.05, P = 7 × 10−13). Error bars indicate one standard error. b, Across the same non-EUR ancestry-trait pairs, heritability estimated with RHE-mc have higher z-scores due to the smaller standard errors compared to S-LDSC. Dashed line at z = 4 was used as a QC filter. c, As in Fig. 2b, without filtering to phenotypes passing QC, but instead only filtering to EUR z > 4 and defined heritability in both genetic ancestry groups. Dotted line shows y = x and dashed line shows York regression fit (n phenotypes = 147, slope = 0.66, intercept = 0.17, P < 10−100). Points indicate the point estimate of heritability, and error bars indicate one standard error.

Extended Data Fig. 3 Heritability summaries across trait types and genetic ancestry groups.

a, The confidence metrics (heritability z score) across traits (columns) and ancestry groups (rows) are shown for the final heritability metrics used (S-LDSC for EUR, otherwise RHE-mc). Dashed line indicates inclusion criteria (z ≥ 4). b, The mean observed heritability (h2) is plotted by ancestry group and trait type. For ancestry groups with smaller sample sizes, heritabilities are likely inflated due to a combination of residual stratification and winner’s curse, as only significantly heritable phenotypes in each ancestry group are shown. Error bars are standard deviations of the distribution of the heritability point estimates.

Extended Data Fig. 4 Improved identification of associations by EFO category.

Number (left) and percentage (right) of known and novel variants identified in this study compared to the GWAS catalog across EFO categories.

Extended Data Fig. 5 GWAS hits near haploinsufficient genes.

a, The percentage of novel associations by gene category. 66% of haploinsufficient genes have a novel significant hit nearby, compared to 34% of all genes. b, Locuszoom plots of a 1-Mb region around rs1379871 (purple diamond; DMD), for whole body fat mass (P = 1.84 × 10−41; n = 431,792). The −log10(P-value) is plotted along chromosomal position, with neighboring variants colored by sample-size weighted LD (with lead SNP) for ancestries included in meta-analysis (gray: LD not defined for at least one ancestry group). This variant has recently been identified in a larger study of BMI74.

Extended Data Fig. 6 Comparison of meta-analysis and EUR summary statistics.

a, As in Fig. 3c, the P-value in EUR is plotted compared to the P-value in the meta-analysis, as a density plot to indicate the relative number of points in each region of the plot. Three quadrants are highlighted for significant in meta-analysis only (green), both meta-analysis and EUR (purple), and EUR-only (blue). b, Summaries and meta-data of the variants in each of these three quadrants are shown. Heterogeneous is defined as Cochran’s Q P < 0.01, low INFO score is defined as INFO < 0.9, and low quality is defined as failing quality filters from gnomAD or allele frequency significantly differing between gnomAD and Pan-UKB in at least one ancestry group (see Supplementary Note, QC of summary statistics). Common is defined as frequency > 1%.

Extended Data Fig. 7 Fine-mapping of the G6PD locus.

a,b, Fine-mapping results for rs1050828 (G6PD) in AFR (a) and meta-analysis (b). a, AFR fine-mapping results highlight the missense variant (rs1050828) in a credible set, with a second independent signal for some phenotypes. b, Meta-analysis fine-mapped results show instability as the major signal at rs1050828 is discovered in a group with a relatively small sample size, which results in a small contribution to the LD panel and thus, poor performance in fine-mapping. Detailed results are shown in Supplementary Table 12.

Extended Data Fig. 8 Manhattan and QQ plot comparison for SAIGE and Tractor GWAS for mean corpuscular hemoglobin concentration.

a,b, Original GWAS performed using SAIGE for AFR. c-f, Among AFR individuals, Tractor GWAS results are shown for AFR haplotypes (c,d) and EUR haplotypes (e,f). QQ plots in b, d and f include bands indicating the confidence bounds based on the normal distribution.

Extended Data Table 1 Comparison of λ1000 and λGC for five phenotypes across three association study paradigms
Full size table
Extended Data Table 2 Number of significant associations for height in AFR and CSA
Full size table

Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–34 and Tables 1–12.

Reporting Summary

Peer Review File

Supplementary Data 1

Assigned genetic ancestry labels correlate with the country of birth or known migration events. The number of people by genetic ancestry and country of birth (non-UK) are shown.

Supplementary Data 2

Summary of all phenotypes in Pan-UKB. Phenotypes are keyed by five keys: trait type, phenocode, pheno_sex, coding and modifier. Where available, description and coding_description are provided from the UKB showcase. For each ancestry group, we include the number of cases, heritability estimates (observed, liability, standard errors and z scores), whether the phenotype passes QC, and lambda GC. We provide QC flags, whether the phenotype is in the maximal independent set, and filename information, including a download link for the phenotype-specific file and tabix index on Amazon S3 and md5 checksums for each.

Supplementary Data 3

Summary of all heritability metrics. Phenotypes are keyed as in Supplementary Data 2. For each ancestry group, we provide heritability estimates (observed, liability, standard errors and z scores) for LDSC and S-LDSC, and for ancestry groups other than EUR, also RHE-mc, as well as details of QC flags.

Supplementary Data 4

Pairwise genetic correlations. Genetic correlations (rg) from S-LDSC are computed for pairs of 528 phenotypes (phenotype_code_1 and phenotype_code_2) using summary statistics from EUR.

Supplementary Data 5

Pairwise phenotypic correlations. Covariates were regressed out from each of the 452 high-quality phenotypes, and pairwise correlations (entry) were computed for each pair of phenotypes (residuals), i (with phenotype identifier in i_data) and j (identifier in j_data). The correlation for all phenotypes is available at gs://ukb-diverse-pops-public/misc/pairwise/pairwise_correlations_regressed.txt.bgz.

Supplementary Data 6

Polygenicity estimates. Polygenicity estimates (mean and s.d.) from SBayesS for 451 phenotypes, along with convergence criteria (R_GelmanRubin).

Supplementary Data 7

Summary statistics for key loci across GWAS methods. SAIGE AFR and SAIGE EUR refer to the SAIGE analyses performed on the AFR and EUR genetically inferred ancestry groups of UKB. Tractor AFR and Tractor EUR indicate the Tractor GWAS conducted on the AFR or EUR haplotype tracts, respectively, within the AFR group. Variants are filtered as described above in Tractor GWAS analysis.

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Karczewski, K.J., Gupta, R., Kanai, M. et al. Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat Genet 57, 2408–2417 (2025). https://doi.org/10.1038/s41588-025-02335-7

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