Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The power of genetic diversity in genome-wide association studies of lipids

An Author Correction to this article was published on 26 May 2023

This article has been updated

Abstract

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Comparison of identified loci across ancestry groups.
Fig. 2: Inclusion of multiple ancestries drives improved fine-mapping.
Fig. 3: Multi-ancestry LDL-C PRS show similar performance across ancestry groups.

Similar content being viewed by others

Data availability

The GWAS meta-analysis results (including both ancestry-specific and multi-ancestry analyses) and risk score weights are available at http://csg.sph.umich.edu/willer/public/glgc-lipids2021. The optimized multi-ancestry and single-ancestry PRS weights are deposited in the PGS Catalogue (https://www.pgscatalog.org/) accession numbers PGS000886PGS000897 (all intervening numbers).

Code availability

The code EasyQC is available at www.genepi-regensburg.de/easyqc, and Raremetal is available at https://github.com/SailajaVeda/raremetal.

Change history

References

  1. Taddei, C. et al. Repositioning of the global epicentre of non-optimal cholesterol. Nature 582, 73–77 (2020).

    Article  Google Scholar 

  2. Ference, B. A. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur. Heart J. 38, 2459–2472 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roth, G. A. et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1736–1788 (2018).

    Article  Google Scholar 

  4. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lu, X. et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat. Genet. 49, 1722–1730 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kathiresan, S. et al. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. BMC Med. Genet. 8, S17 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240–1249 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Peloso, G. M. et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hoffmann, T. J. et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat. Genet. 50, 401–413 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Holmen, O. L. et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat. Genet. 46, 345–351 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Asselbergs, F. W. et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am. J. Hum. Genet. 91, 823–838 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Saxena, R. et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Iotchkova, V. et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat. Genet. 48, 1303–1312 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tachmazidou, I. et al. A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat. Commun. 4, 2872 (2013).

    Article  PubMed  Google Scholar 

  20. Tang, C. S. et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat. Commun. 6, 10206 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  21. van Leeuwen, E. M. et al. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels. Nat. Commun. 6, 6065 (2015).

    Article  PubMed  Google Scholar 

  22. Spracklen, C. N. et al. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum. Mol. Genet. 26, 1770–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 26–31 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Buniello, A. et al. The NHGRI–EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Tishkoff, S. A. et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mägi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26, 3639–3650 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M. & Wray, N. R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brown, B. C., Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Guo, J. et al. Quantifying genetic heterogeneity between continental populations for human height and body mass index. Sci. Rep. 11, 5240 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  34. Majara, L. et al. Low generalizability of polygenic scores in African populations due to genetic and environmental diversity. Preprint at bioRxiv https://doi.org/10.1101/2021.01.12.426453 (2021).

  35. Lehmann, B. C. L., Mackintosh, M., McVean, G. & Holmes, C. C. High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts. Preprint at bioRxiv https://doi.org/10.1101/2021.01.15.426781 (2021).

  36. Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 1098 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Cavazos, T. B. & Witte, J. S. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2, 100017 (2021).

    CAS  PubMed  Google Scholar 

  39. Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bentley, A. R. et al. Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet. 51, 636–648 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kowalski, M. H. et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 15, e1008500 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baigent, C. et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90 056 participants in 14 randomised trials of statins. Lancet 366, 1267–1278 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Feng, S., Liu, D., Zhan, X., Wing, M. K. & Abecasis, G. R. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Loh, P.-R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Liu, X. et al. WGSA: an annotation pipeline for human genome sequencing studies. J. Med. Genet. 53, 111–112 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Liu, D. J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  58. Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  62. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  63. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Finer, S. et al. Cohort Profile: East London Genes &Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2019).

    Article  PubMed Central  Google Scholar 

  66. Moon, S. et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci. Rep. 9, 1382 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  67. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research was conducted using the UK Biobank Resource under application number 24460. Computing support and file management for central meta-analysis by S. Caron is acknowledged. This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. Study-specific acknowledgements are provided in the Supplementary Information.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

S.L.C., K.-H.H.W., S. Kanoni, G.J.M.Z. and S. Ramdas contributed equally to this work as co-second authors. All authors reviewed the manuscript. Consortium management: G.M.P., P.N., T.L.A., M.B., S.Kathiresan and C.J.W. Study design, interpretation of results and drafting of the manuscript: S.E.G., S.L.C., K.-H.H.W., S. Kanoni, G.J.M.Z., S. Ramdas, I.S., I.N., E.M., K.L.M., T.M.F., J.N.H., S. Kathiresan, M. Boehnke, P.N., G.M.P., C.D.B., A.P.M., Y.V.S., P.D., T.L.A. and C.J.W. Primary meta-analysis and QC: S.E.G., S. Vedantam, T.W.W. and A.E.L. PRS analysis and development: S.E.G., S.L.C., K.-H.H.W., S. Kanoni, M.Y.H., S.H., A.N., A. Choudhury, A.R.B., K.E., A.V., B.T., H.C.M., K.A.H., C.N.R., S.H., M.R., R.C.T., D.A.v.H., G.T., M.Y. and B.-J.K. Individual study genetic analysis: S.E.G., S. Kanoni, S. Vedantam, A.E.L., K.L.M., G.M.P., P.D., C.J.W., Q.H., D.K., X. Zhu, G.T., A. Helgadottir, D.F.G., H. Holm, I.O., M. Akiyama, S.S., C. Terao, M. Kanai, W. Zhou, B.M.B., H.R., S.E.R., A.S.H., Y.V., Q.F., E.A.R., T. Lingren, J.A.P., S.A.P., J. Haessler, F.G., Y.B., J.E.M., A. Campbell, K. Lin, I.Y.M., G. Hindy, A.R., J.D.F., W. Zhao, D.R.W., C. Turman, H. Huang, M. Graff, A. Mahajan, M.R.B., W. Zhang, K. Yu, E.M.S., A. Pandit, S.G., X.Y., J. Luan, J.-H. Zhao, F.M., H.-M.J., K. Yoon, C.M.-G., A. Pitsillides, J.J.H., G.W., A.R. Wood, Y.J., Z.G., S. Haworth, R.E.M., J.F.C., M. Aadahl, J.Yao, A. Manichaikul, H.R.W., J.R., J.B.-J., L.L.K., A.G., M.S.-L., R.N., C. Sidore, E.F., A.F.M., P.M.-V., M. Wielscher, S.T., N.S., L.T.M., B.H.T., M. Munz, L.Z., J. Huang, B.Y., A. Poveda, A.K., C. Lamina, L.F., M. Scholz, T.E.G., J.P.B., E.W.D., J.M.Z., J.S.M., C.F., H. Christensen, J.A.B., M.F.F., M.K.W., M. Preuss, M. Mangino, P.C., N.V., J.W. Benjamins, J. Engmann, R.L.K., R.C.S., K.S.L., N.R.Z., P.L., M.E.K., G.E.D., S. Huo, D.D.I., H.I., J. Yang, Jun Liu, H.L.L., J.M., B.S., M. Arendt, L.J.S., M.C.-G., C.W., M. Nakatochi, A.W., N.H.-K., X.Sim, R.X., A.H.-C., J.C.F.-L., V.L., M. Ahmed, A.U.J., N.A.Y., M.R.I., C. Oldmeadow, H.-N.K., S. Ryu, P.R.H.J.T., L.A., R.D., L.A.L., X.C., G. Prasad, L.L.-M., M. Pauper, J. Long, X. Li, E. Theusch, F.T., C.N.S., A. Loukola, S. Bollepalli, S.C.W., Y.X.W., W.B.W., T. Nutile, D. Ruggiero, Y.J.S., Y.-J. Hung, S.C., F. Liu, Jingyun Yang, K.A.K., M. Gorski, M. Brumat, K.M., L.F.B., J.A.S., P.H., A.-E.F., E.H., M. Lin, C.X., J. Zhang, M.P.C., S. Vaccargiu, P.J.v.d.M., N. Pitkänen, B.E.C., J. Lee, S.W.v.d.L., K.N.C., S.W., M.E.Z., J.Y.L., H.S.C., M. Nethander, S.F.-W., L.S., N.W.R., C.A.W., S.-Y.L., J.-S.W., C. Couture, L.-P.L., K.N., G.C.-P., H. Vestergaard, B.H., O.G., Q.C., M.O.O., J.v.S., Xiaoyin Li, K. Schwander, N.T., J.H.S., R.D.J., A.P.R., L.W.M., Z.C., L.Li, H.M.H., K.L.Y., T. Kawaguchi, J. Thiery, J.C.B., G.N.N., L.J.L., H.Li, M.A.N., O.T.R., S.I., S.H.W., C.P.N., H. Campbell, S.J., T. Nabika, F.A.-M., H.N., P.S.B., I.K., P. Kovacs, T.G., T. Katsuya, K.F.B., D.d.K., G.J.d.B., E.K.K., H.H.H.A., M.A.I., Xiaofeng Zhu, F.W.A., A.O.K., J.W.J.B., X.-O.S., L.S.R., O. Pedersen, T.H., P. Mitchell, A.W.H., M. Kkähönen, L.P., C. Bouchard, A.T., Y.-D.I.C., C.E.P., T.A.M., W.L., A. Franke, C. Ohlsson, D.M., Y.S.C., H. Lee, J.-M.Y., W.-P.K., S.Y.R., J.-T.W., I.M.H., K.J.S., H. Völzke, G. Homuth, M.K.E., A.B.Z., O. Polasek, G. Pasterkamp, I.E.H., S. Redline, K.P., A.J.O., H. Snieder, G.B., R.S., H. Schmidt, Y.E.C., S. Bandinelli, G. Dedoussis, T.A.T., S.L.R.K., N.K., M.B.S., G.G., B.J., C.A.B., P.K.J., D.A.B., P.L.D.J., X. Lu, V.M., M. Brown, M.J.C., P.B.M., X.G., M. Ciullo, J.B.J., N.J.S., J. Kaprio, P.P., L.S.A., S.A.B., H.J.d.S., A.R.W., R.M.K., J.-Y.W., W. Zheng, A.I.d.H., D.B., A. Correa, J.G.W., L. Lind, C.-K.H., A.E.N., Y.M.G., J.F.W., B.P., H.-L.K., J.A., R.J.S., D.C.R., D.K.A., S.C.H., M. Walker, H.A.K., G.R.C., C.S.Y., J.M.M., T.T.-L., C.A.-S., C.G.V., L.O., M.F., E.S.T., R.M.v.D., T. Lehtimäki, N.C., M.Y., Jianjun Liu, D.F.R., A.J.M., F. Kee, K.-H.J., M.I.M., C.N.A.P., V.V., C. Hayward, E.S., C.M.v.D., F. Lu, J.Q., H. Hishigaki, X. Lin, W.M., E.J.P., M. Cruz, V.G., J.-C.T., G.L., L.M.t.H., P.J.M.E., S.M.D., M. Kumari, M. Kivimaki, P.v.d.H., T.D.S., R.J.F.L., M.A.P., B.M.P., I.B., P.P.P., K. Christensen, S. Ripatti, E.W., H. Hakonarson, S.F.A.G., L.A.L.M.K., J.d.G., M. Loeffler, F. Kronenberg, D.G., J. Erdmann, H. Schunkert, P.W.F., A. Linneberg, J.W.J., A.V.K., M. Männikkö, M.-R.J., Z.K., F.C., D.O.M.-K., K.W.v.D., H.W., D.P.S., N.G., P.S., N. Poulter, J.I.R., T.M.D., F. Karpe, M.J.N., N.J.T., C.-Y.C., T.-Y.W., C.C.K., C. Sabanayagam, A. Peters, C.G., A.T.H., N.L.P., P.K.E.M., D.I.B., E.J.C.d.G., L.A.C., J.B.J.v.M., M. Ghanbari, P.G.-L., W.H., Y.J.K., Y.T., N.J.W., C. Langenberg, E.Z., J. Kuusisto, M. Laakso, E.I., G.A., J.C.C., J.S.K., P.S.d.V., A.C.M., K.E.N., M.D., P. Kraft, N.G.M., J.B.W., S.A., D.S., R.G.W., M.V.H., C.Black, B.H.S., A.E.J., A.B., J.E.B., P.M.R., D.I.C., C. Kooperberg, W.-Q.W., G.P.J., B.N., M.G.H., M.D.R., P.J., V.S., K.H., B.O.A., M. Kubo, Y. Kamatani, Y.O., Y.M., U.T., K. Stefansson, Y.-L.H., J.A.L., D. Rader, P.S.T., K.-M.C., K. Cho, C.J.O., J.M.G., and P.W.

Corresponding authors

Correspondence to Themistocles L. Assimes or Cristen J. Willer.

Ethics declarations

Competing interests

G.J.M.Z. is an employee of Incyte Corporation. G.C.-P. is currently an employee of 23andMe. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G.T., A. Helgadottir, D.F.G., H. Holm., U.T. and K. Stefansson are employees of deCODE/Amgen. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer. M. McCarthy has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. M. McCarthy and A. Mahajan are employees of Genentech and are holders of Roche stock. M. Scholz receives funding from Pfizer for a project unrelated to this work. M.E.K. is employed by Synlab. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from Amgen, grants from AstraZeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital, grants from bestbion dx, grants from Boehringer Ingelheim, grants from Immundiagnostik, grants from Merck Chemicals, grants from MSD Sharp and Dohme, grants from Novartis Pharma, grants from Olink Proteomics, and is employed by Synlab Holding Deutschland, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen and Color Genomics; received speaking fees from Illumina, the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research; and reports a patent related to a genetic risk predictor (20190017119). S.K. is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest and Medscape; and reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M.-K. is a part-time clinical research consultant for Metabolon. D.S. has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech and Eli Lilly pharmaceuticals. The spouse of C.J.W. is employed by Regeneron.

Additional information

Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Effect sizes of identified index variants from multi-ancestry meta-analysis.

Index variants associated with a) HDL cholesterol, b) LDL cholesterol, c) triglycerides, d) nonHDL cholesterol and e) total cholesterol include both common variants of small to moderate effect and low frequency variants of moderate to large effect.

Extended Data Fig. 2 Comparison of the number of index variants by sample size.

a) Comparison of the number of index variants reaching genome-wide significance (p < 5x10−8) from meta-analysis of LDL-C in each ancestry group. A meta-analysis of five random subsets of European cohorts selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals is also shown. b) Comparison of chi-squared values from meta-analysis of LDL-C for each possible combination of ancestry groups (without genomic-control correction) for variants with minor allele frequency (MAF) ≥ 5%. The colored lines indicate a linear regression model of all meta-analyses for a specific ancestry (eg. all analyses including European individuals). c) Comparison of chi-squared values from meta-analysis of LDL-C for variants with MAF ≤ 5%. d) Comparison of chi-squared valued for variants with MAF ≥ 5% for LDL-C without genomic-control correction in a meta-analysis of all European cohorts as well as five subsets selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals.

Extended Data Fig. 3 Effect sizes by ancestry for unique index variants from ancestry-specific meta-analysis.

Comparison of effect sizes and standard errors for the 389 unique variants reaching genome-wide significance (p-value < 5x10−8 as given by RAREMETAL) in two ancestry groups. Variants with discordant directions of effect between ancestries are labeled by chromosome and position (build 37). Association results for all index variants are given in Supplementary Table 3. The red line depicts an equivalent European ancestry and non-European ancestry effect size while the black line depicts a linear regression model. R2 = 0.93.

Extended Data Fig. 4 Comparison of credible set size.

The number of variants in the 99% credible sets for each association signal are compared between a) admixed African ancestry and multi-ancestry analysis and b) European ancestry and multi-ancestry analysis.

Extended Data Fig. 5 Overview of LDL-C polygenic score generation and validation.

Polygenic scores were calculated separately in each ancestry group (or in all ancestries) using either pruning and thresholding or PRS-CS. The polygenic scores were then taken forward for testing in ancestry-matched participants followed by validation in independent data sets.

Extended Data Fig. 6 Optimal polygenic score threshold by ancestry group for either PRS-CS or pruning and thresholding based LDL-C polygenic scores.

Adjusted R2 estimated upon testing in UK Biobank ancestry-matched participants (who were not included in GWAS summary statistics). a) Admixed African, East Asian and South Asian ancestry polygenic scores. b) European and multi-ancestry polygenic scores. c) European ancestry (GLGC 2010) and multi-ancestry polygenic scores. d) All polygenic scores across all thresholds used for score construction. e) Comparison of adjusted R2 across ancestry groups relative to the maximum for covariates alone, polygenic scores from PRS-CS or polygenic scores from pruning and thresholding.

Extended Data Fig. 7 Improvement in PRS performance in African Americans when starting with ancestry-mismatched European ancestry scores by updating weights, updating variant lists, or updating both variants and weights to be ancestry-matched.

By comparison to the gold-standard performance of the multi-ancestry-derived PRS in African Americans (adjusted R2 = 0.12), a European ancestry derived score capture only 47% of the variance explained by the multi-ancestry PRS. When LD and association information from the target population is used to optimize the list of variants for inclusion in the PRS, but with ancestry-mismatched weights from European ancestry GWAS, the variance explained reaches 71% of the gold standard. If the PRS variant list selected in European ancestry individuals were genotyped in the target population, and PRS weights were updated using a GWAS from the target population, the variance explained reached 87% of the gold standard. Finally, deriving both the marker list and weights from the target population (single-ancestry GWAS of admixed African individuals) explained 94% of the variance relative to the gold-standard trans-ancestry PRS.

Extended Data Fig. 8 Comparison of PRS performance by admixture quartile.

We divided the testing cohorts into quartiles by proportion of African ancestry and estimated the performance of the PRS separately within each quartile in a) the Michigan Genomics Initiative (N = 1,341), and b) the Million Veteran Program (N = 18,251). Error bars represent 95% confidence intervals.

Supplementary information

Supplementary Information

This file contains acknowledgements for each cohort, VA Million Veteran Program and Global Lipids Genetics Consortium authors, Supplementary Tables 2, 4, 8, 13 and 21–23, Supplementary Figs. 1–10, the Supplementary Notes and Supplementary Methods.

Reporting Summary

Peer Review File

Supplementary Tables

This file contains Supplementary Tables 1, 3, 5–7, 9–12 and 14–20.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Graham, S.E., Clarke, S.L., Wu, KH.H. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021). https://doi.org/10.1038/s41586-021-04064-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04064-3

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing