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.

Genetics of 35 blood and urine biomarkers in the UK Biobank

Nature Genetics volume 53, pages 185–194 (2021)Cite this article

Subjects

An Author Correction to this article was published on 04 October 2021

This article has been updated

Abstract

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 5,794 independent loci associated with at least one trait (p < 5 × 10−9), containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build ‘multi-PRS’ models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Fig. 1: Schematic overview of the study.
Fig. 2: Genetics of 35 biomarkers.
Fig. 3: Summary of fine-mapped associations across 35 biomarker traits.
Fig. 4: Causal inference, transferability of PRSs and complex trait association in polygenic risk tails.
Fig. 5: Multiple regression with biomarker polygenic scores improves prevalent and incident disease prediction.

Data availability

Summary-level data generated in this work are available at the NIH’s instance of figshare (the meta-analyzed GWAS summary statistics (https://doi.org/10.35092/yhjc.12355382), the fine-mapped associations (https://doi.org/10.35092/yhjc.12344351), the snpnet PRS coefficients (https://doi.org/10.35092/yhjc.12298838) and the multi-PRS weights (https://doi.org/10.35092/yhjc.12355424), please see the Supplementary Note for details)77. Other data are displayed in the Global Biobank Engine (https://biobankengine.stanford.edu).

Code availability

Analysis scripts and notebooks are available on GitHub at https://github.com/rivas-lab/biomarkers/.

Change history

References

  1. 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 

  2. 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  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Wheeler, E. et al. Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 14, e1002383 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Spracklen, C. N. et al. Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet. 14, e1007275 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Graham, S. E. et al. Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis. Nat. Commun. 10, 1847 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51, 957–972 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in East Asian populations. Nat. Genet. 44, 904–909 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Fry, D., Almond, R., Moffat, S., Gordon, M. & Singh, P. UK Biobank Biomarker Project: Companion Document to Accompany Serum Biomarker Data (UK Biobank Document Showcase, 2019); https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf

  12. 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 

  13. Snell-Bergeon, J. K. et al. Evaluation of urinary biomarkers for coronary artery disease, diabetes, and diabetic kidney disease. Diabetes Technol. Ther. 11, 1–9 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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 

  16. Kettunen, J. et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. 7, 11122 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Horikoshi, M. et al. Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet. 11, e1005230 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Teumer, A. et al. Genome-wide association studies identify genetic loci associated with albuminuria in diabetes. Diabetes 65, 803–817 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Gorski, M. et al. 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci. Rep. 7, 45040 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jiang, X. et al. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat. Commun. 9, 260 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Köttgen, A. et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet. 45, 145–154 (2012).

    PubMed Central  Google Scholar 

  23. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  PubMed  Google Scholar 

  24. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shi, H., Mancuso, N., Spendlove, S. & Pasaniuc, B. Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am. J. Hum. Genet. 101, 737–751 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tanigawa, Y. et al. Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nat. Commun. 10, 4064 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. 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 

  29. van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144 (2016).

  31. DeBoever, C. et al. Assessing digital phenotyping to enhance genetic studies of human diseases. Am. J. Hum. Genet. 106, 611–622 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. McComb, R. B., Bowers, G. N. & Posen, S. Alkaline Phosphatase (Springer, 1979).

  33. Aguirre, M., Rivas, M. A. & Priest, J. Phenome-wide burden of copy-number variation in the UK Biobank. Am. J. Hum. Genet. 105, 373–383 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Horikawa, Y. et al. Mutation in hepatocyte nuclear factor-1β gene (TCF2) associated with MODY. Nat. Genet. 17, 384–385 (1997).

    Article  CAS  PubMed  Google Scholar 

  35. Iwasaki, N. et al. Liver and kidney function in Japanese patients with maturity-onset diabetes of the young. Diabetes Care 21, 2144–2148 (1998).

    Article  CAS  PubMed  Google Scholar 

  36. Nishigori, H. et al. Frameshift mutation, A263fsinsGG, in the hepatocyte nuclear factor-1β gene associated with diabetes and renal dysfunction. Diabetes 47, 1354–1355 (1998).

    CAS  PubMed  Google Scholar 

  37. Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hayes, M. G. et al. Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies. Diabetes 56, 3033–3044 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Abhary, S. et al. Common sequence variation in the VEGFA gene predicts risk of diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 50, 5552–5558 (2009).

    Article  PubMed  Google Scholar 

  41. Barton, J. C. & Acton, R. T. Diabetes in HFE hemochromatosis. J. Diabetes Res. 2017, 9826930 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Raju, K. & Venkataramappa, S. M. Primary hemochromatosis presenting as type 2 diabetes mellitus: a case report with review of literature. Int. J. Appl. Basic Med. Res. 8, 57–60 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Marks, J. D. et al. Pressure indices of myocardial oxygen consumption during pulsatile ventricular assistance. ASAIO Trans. 35, 436–439 (1989).

    Article  CAS  PubMed  Google Scholar 

  44. Alkhatatbeh, M. J., Enjeti, A. K., Acharya, S., Thorne, R. F. & Lincz, L. F. The origin of circulating CD36 in type 2 diabetes. Nutr. Diabetes 3, e59 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gonzalez, Y. et al. High glucose concentrations induce TNF-α production through the down-regulation of CD33 in primary human monocytes. BMC Immunol. 13, 19 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Larson, N. B. et al. Trans-ethnic meta-analysis identifies common and rare variants associated with hepatocyte growth factor levels in the Multi-Ethnic Study of Atherosclerosis (MESA). Ann. Hum. Genet. 79, 264–274 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Woodward, O. M. et al. Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc. Natl Acad. Sci. USA 106, 10338–10342 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 2100 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Rücker, G., Schwarzer, G., Carpenter, J. R., Binder, H. & Schumacher, M. Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics 12, 122–142 (2011).

    Article  PubMed  Google Scholar 

  51. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Curhan, G. C., Willett, W. C., Rimm, E. B. & Stampfer, M. J. A prospective study of dietary calcium and other nutrients and the risk of symptomatic kidney stones. N. Engl. J. Med. 328, 833–838 (1993).

    Article  CAS  PubMed  Google Scholar 

  53. Ruth, K. S. et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat. Med. 26, 252–258 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, F. et al. Apolipoprotein A-IV improves glucose homeostasis by enhancing insulin secretion. Proc. Natl Acad. Sci. USA 109, 9641–9646 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Pietzsch, J., Julius, U., Nitzsche, S. & Hanefeld, M. In vivo evidence for increased apolipoprotein A-I catabolism in subjects with impaired glucose tolerance. Diabetes 47, 1928–1934 (1998).

    Article  CAS  PubMed  Google Scholar 

  56. Zhang, P., Gao, J., Pu, C. & Zhang, Y. Apolipoprotein status in type 2 diabetes mellitus and its complications (Review). Mol. Med. Rep. 16, 9279–9286 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Vozarova, B. et al. High alanine aminotransferase is associated with decreased hepatic insulin sensitivity and predicts the development of type 2 diabetes. Diabetes 51, 1889–1895 (2002).

    Article  CAS  PubMed  Google Scholar 

  58. De Silva, N. M. G. et al. Liver function and risk of type 2 diabetes: bidirectional Mendelian randomization study. Diabetes 68, 1681–1691 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Huang, G.-X., Ji, X.-M., Ding, Y.-C. & Huang, H.-Y. Association between serum cystatin C levels and the severity or potential risk factors of acute ischemic stroke. Neurol. Res. 38, 518–523 (2016).

    Article  CAS  PubMed  Google Scholar 

  60. van der Laan, S. W. et al. Cystatin C and cardiovascular disease: a Mendelian randomization study. J. Am. Coll. Cardiol. 68, 934–945 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Colijn, J. M. et al. Increased high-density lipoprotein levels associated with age-related macular degeneration: evidence from the eye-risk and European Eye Epidemiology consortia. Ophthalmology 126, 393–406 (2019).

    Article  PubMed  Google Scholar 

  62. Kay, P. et al. Age-related changes of cystatin C expression and polarized secretion by retinal pigment epithelium: potential age-related macular degeneration links. Invest. Ophthalmol. Vis. Sci. 55, 926–934 (2014).

    Article  CAS  PubMed  Google Scholar 

  63. Klein, R., Knudtson, M. D., Lee, K. E. & Klein, B. E. K. Serum cystatin C level, kidney disease markers, and incidence of age-related macular degeneration: the Beaver Dam Eye Study. Arch. Ophthalmol. 127, 193–199 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zurdel, J., Finckh, U., Menzer, G., Nitsch, R. M. & Richard, G. CST3 genotype associated with exudative age related macular degeneration. Br. J. Ophthalmol. 86, 214–219 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Khalili, H. et al. Endogenous levels of circulating androgens and risk of Crohn’s disease and ulcerative colitis among women: a nested case–control study from the nurses’ health study cohorts. Inflamm. Bowel Dis. 21, 1378–1385 (2015).

    PubMed  Google Scholar 

  66. Nasser, M. et al. Testosterone therapy in men with Crohn’s disease improves the clinical course of the disease: data from long-term observational registry study. Horm. Mol. Biol. Clin. Investig. 22, 111–117 (2015).

    CAS  PubMed  Google Scholar 

  67. Kühn, T. et al. Albumin, bilirubin, uric acid and cancer risk: results from a prospective population-based study. Br. J. Cancer 117, 1572–1579 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wray, N. R., Kemper, K. E., Hayes, B. J., Goddard, M. E. & Visscher, P. M. Complex trait prediction from genome data: contrasting EBV in livestock to PRS in humans. Genetics 211, 1131–1141 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288 (1996).

    Google Scholar 

  70. Qian, J. et al. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet. 16, e1009141 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 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 

  72. Bild, D. E. et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–881 (2002).

    Article  PubMed  Google Scholar 

  73. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    Article  CAS  PubMed  Google Scholar 

  75. DeBoever, C. et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat. Commun. 9, 1612 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Amar, D., Ashley, E. & Rivas, M. A. Constraint-based analysis for causal discovery in population-based biobanks. Preprint at bioRxiv https://doi.org/10.1101/566133 (2019).

  77. Tanigawa, Y., Sinnott-Armstrong, N., Benner, C. & Rivas, M. A. Datasets described in ‘Genetics of 35 blood and urine biomarkers in the UK Biobank’ (2020); https://doi.org/10.35092/yhjc.c.5043872.v1

  78. Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Wainberg, M. et al. Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: a Mendelian randomization study. PLoS Med. 16, e1002982 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Eastwood, S. V. et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS ONE 11, e0162388 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  81. 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 

  82. 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 

  83. Carvalho-Silva, D. et al. Open targets platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).

    Article  CAS  PubMed  Google Scholar 

  84. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

  85. Slob, E. A. W., Groenen, P. J. F., Thurik, A. R. & Rietveld, C. A. A note on the use of Egger regression in Mendelian randomization studies. Int. J. Epidemiol. 46, 2094–2097 (2017).

    Article  PubMed  Google Scholar 

  86. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  87. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Joshi, A. D. et al. Four susceptibility loci for gallstone disease identified in a meta-analysis of genome-wide association studies. Gastroenterology 151, 351–363 (2016).

    Article  CAS  PubMed  Google Scholar 

  91. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Läll, K., Mägi, R., Morris, A., Metspalu, A. & Fischer, K. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet. Med. 19, 322–329 (2017).

    Article  PubMed  Google Scholar 

  93. Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 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 

Download references

Acknowledgements

This research was conducted using the UK Biobank Resource under application number 24983, ‘Generating effective therapeutic hypotheses from genomic and hospital linkage data’ (http://www.ukbiobank.ac.uk/wp-content/uploads/2017/06/24983-Dr-Manuel-Rivas.pdf). Based on the information provided in protocol 44532, the Stanford IRB has determined that the research does not involve human subjects as defined in 45 CFR 46.102(f) or 21 CFR 50.3(g). All participants in the UK Biobank study provided written informed consent (more information is available at https://www.ukbiobank.ac.uk/2018/02/gdpr/). The FinnGen project is approved by the Finnish Institute for Health and Welfare (THL), approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018,THL/2222/6.02.00/2018, THL/283/6.02.00/2019), Digital and population data service agency VRK43431/2017-3, VRK/6909/2018-3, the Social Insurance Institution (KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019 and Statistics Finland TK-53-1041-17. The Biobank Access Decisions for FinnGen samples and data include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7 Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealisof Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017, and Terveystalo Biobank STB 2018001. The following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki), THL Biobank (https://thl.fi/fi/web/thl-biopankki), Helsinki Biobank (https://www.terveyskyla.fi/helsinginbiopankki), Biobank Borealis of Northern Finland (https://www.oulu.fi/university/node/38474), Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (https://ita-suomenbiopankki.fi), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.veripalvelu.fi/verenluovutus/biopankkitoiminta) and Terveystalo Biobank (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish Biobanks are members of the BBMRI.fi infrastructure (www.bbmri.fi). Statin adjustment analyses were further conducted via UK Biobank application 7089 using a protocol approved by the Partners HealthCare Institutional Review Board. We thank all the participants in the UK Biobank and FinnGen studies. We thank A. Paterson and members of the Rivas, Pritchard and Bejerano labs for their feedback. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH) and by the NCI, the National Human Genome Research Institute (NHGRI), NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 19 October 2020. This work was supported by the NHGRI of the NIH under awards R01HG010140 (M.A.R.), R01EB001988-21 (T.H.) and R01HG008140 (J.K.P.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Some of the computing for this project was performed on the Sherlock cluster at Stanford University. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. N.S.-A. is supported by the Department of Defense through a National Defense Science and Engineering grant and by a Stanford Graduate Fellowship. Y.T. is supported by a Funai Overseas Scholarship from the Funai Foundation for Information Technology and the Stanford University School of Medicine. N.M. is supported by the Academy of Finland (no. 331671). H.M.O. is supported by the Academy of Finland (no. 309643). F.R. is supported by a National Heart, Lung, and Blood Institute grant (1K01HL144607). The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and by 12 industry partners (AbbVie Inc, AstraZeneca UK Ltd., Biogen MA Inc., Celgene Corporation, Celgene International II Sàrl, Genentech Inc, Merck Sharp & Dohme Corp, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc. and Novartis AG). M.A.R. is in part supported by the NHGRI of the NIH under award R01HG010140 (M.A.R.) and an NIH Center for Multi- and Trans-ethnic Mapping of Mendelian and Complex Diseases grant (5U01 HG009080). The land upon which some of this work was performed is the ancestral and unceded land of the Muwekma Ohlone, and we pay our respects to their elders past and present.

Author information

Author notes

  1. These authors contributed equally: Nasa Sinnott-Armstrong, Yosuke Tanigawa, Manuel A. Rivas.

  2. A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA

    Nasa Sinnott-Armstrong & Jonathan K. Pritchard

  2. Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland

    Nasa Sinnott-Armstrong, Nina Mars, Christian Benner, Hanna M. Ollila, Tuomo Kiiskinen, Aki S. Havulinna, Anna Shcherbina, Samuli Ripatti & Mark J. Daly

  3. VA Palo Alto Health Care System, Palo Alto, CA, USA

    Nasa Sinnott-Armstrong & Themistocles L. Assimes

  4. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA

    Yosuke Tanigawa, David Amar, Matthew Aguirre, Guhan Ram Venkataraman, Robert Tibshirani, Trevor Hastie & Manuel A. Rivas

  5. Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University, Stanford, CA, USA

    David Amar, Anna Shcherbina, Fatima Rodriguez, Themistocles L. Assimes & Vineeta Agarwala

  6. Department of Computer Science, Stanford University, Stanford, CA, USA

    Michael Wainberg

  7. Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA

    Hanna M. Ollila

  8. Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hanna M. Ollila

  9. Finnish Institute for Health and Welfare, Helsinki, Finland

    Tuomo Kiiskinen & Aki S. Havulinna

  10. Massachusetts General Hospital Division of Cardiology, Boston, MA, USA

    James P. Pirruccello

  11. Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    James P. Pirruccello, Samuli Ripatti & Mark J. Daly

  12. Department of Statistics, Stanford University, Stanford, CA, USA

    Junyang Qian, Robert Tibshirani & Trevor Hastie

  13. Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland

    Samuli Ripatti

  14. Department of Biology, Stanford University, Stanford, CA, USA

    Jonathan K. Pritchard

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

    Mark J. Daly

Authors
  1. Nasa Sinnott-Armstrong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yosuke Tanigawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Amar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nina Mars
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christian Benner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew Aguirre
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guhan Ram Venkataraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael Wainberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hanna M. Ollila
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tuomo Kiiskinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Aki S. Havulinna
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. James P. Pirruccello
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Junyang Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Anna Shcherbina
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Fatima Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Themistocles L. Assimes
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Vineeta Agarwala
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Robert Tibshirani
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Trevor Hastie
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Samuli Ripatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jonathan K. Pritchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Mark J. Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Manuel A. Rivas
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

FinnGen

  • Nina Mars
  • , Tuomo Kiiskinen
  • , Aki S. Havulinna
  • , Samuli Ripatti
  •  & Mark J. Daly

Contributions

M.A.R., Y.T. and N.S.-A. conceived and designed the study. N.S.-A., Y.T., D.A., N.M., C.B., M.A., G.R.V., J.P.P., J.Q., A.S. and M.A.R. carried out the statistical and computational analyses with advice from M.W., H.M.O., F.R., T.L.A., V.A., R.T., T.H., S.R., J.K.P. and M.J.D. T.K., A.S.H. and T.L.A. organized reagents. N.S.-A., Y.T., D.A., M.A., G.R.V. and M.A.R. carried out quality control of the data. M.A.R., Y.T. and N.S.-A. supervised computational and statistical aspects of the study. The manuscript was written by N.S.-A., Y.T., D.A., M.A., G.R.V., V.A. and M.A.R. and revised by all the co-authors. All co-authors approved of the final version of the manuscript.

Corresponding authors

Correspondence to Nasa Sinnott-Armstrong, Yosuke Tanigawa or Manuel A. Rivas.

Ethics declarations

Competing interests

The Board of Trustees of the Leland Stanford Junior University filed a US Provisional Application ‘Methods for diagnosis of polygenic diseases and phenotypes from genetic variation’ (serial no. 62/852,738) describing this work. J.K.P., M.A.R., N.S.-A. and Y.T. are designated as inventors of the patent. M.A.R. is on the SAB of 54gene and the computational advisory board for Goldfinch Bio and has advised BioMarin, Third Rock Ventures, MazeTx and Related Sciences. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Additional information

Peer review information Nature Genetics thanks Guillaume Lettre, and the other, anonymous, reviewer(s) 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.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–14

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 1–26

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sinnott-Armstrong, N., Tanigawa, Y., Amar, D. et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet 53, 185–194 (2021). https://doi.org/10.1038/s41588-020-00757-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-020-00757-z

This article is cited by

Search

Advanced 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