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