Genetics of 35 blood and urine biomarkers in the UK Biobank
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 ), contain...
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| Veröffentlicht in: | Nature genetics Jg. 53; H. 2; S. 185 - 194 |
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Nature Publishing Group US
01.02.2021
Nature Publishing Group |
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| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| Online-Zugang: | Volltext |
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| 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.
Genetic analysis of 35 blood and urine laboratory measurements from 363,228 individuals identifies 1,857 loci associated with at least one laboratory value. |
|---|---|
| AbstractList | 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 1,857 loci associated with at least one trait, 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. 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. Genetic analysis of 35 blood and urine laboratory measurements from 363,228 individuals identifies 1,857 loci associated with at least one laboratory value. 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 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations, and additional sets of large-effect (> 0.1 sd) protein-altering, HLA, and copy-number variant associations. Through Mendelian Randomization 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 for each biomarker and built ‘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, their causal influences on diseases, and improve genetic risk stratification for common diseases. 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 1,857 loci associated with at least one trait, 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.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 1,857 loci associated with at least one trait, 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. 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 1,857 loci associated with at least one trait, 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. Genetic analysis of 35 blood and urine laboratory measurements from 363,228 individuals identifies 1,857 loci associated with at least one laboratory value. |
| Audience | Academic |
| Author | Amar, David Qian, Junyang Hastie, Trevor Benner, Christian Mars, Nina Kiiskinen, Tuomo Pirruccello, James P. Aguirre, Matthew Rodriguez, Fatima Wainberg, Michael Shcherbina, Anna Assimes, Themistocles L. Ripatti, Samuli Daly, Mark J. Sinnott-Armstrong, Nasa Agarwala, Vineeta Ollila, Hanna M. Havulinna, Aki S. Venkataraman, Guhan Ram Pritchard, Jonathan K. Tibshirani, Robert Rivas, Manuel A. Tanigawa, Yosuke |
| AuthorAffiliation | 4. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA 13. Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland 11. Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA 5. Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA 9. Finnish Institute for Health and Welfare, Helsinki, Finland 10. Massachusetts General Hospital Division of Cardiology, Boston, MA, USA 3. VA Palo Alto Health Care System, Palo Alto, CA, USA 6. Department of Computer Science, Stanford University, Stanford, CA, USA 1. Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA 8. Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA 2. Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland 12. Department of Statistics, S |
| AuthorAffiliation_xml | – name: 2. Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland – name: 11. Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA – name: 4. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA – name: 6. Department of Computer Science, Stanford University, Stanford, CA, USA – name: 13. Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland – name: 14. Department of Biology, Stanford University, Stanford, CA, USA – name: 7. Stanford University, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, USA – name: 9. Finnish Institute for Health and Welfare, Helsinki, Finland – name: 12. Department of Statistics, Stanford University, Stanford, CA, USA – name: 15. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA., USA – name: 8. Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA – name: 5. Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA – name: 10. Massachusetts General Hospital Division of Cardiology, Boston, MA, USA – name: 3. VA Palo Alto Health Care System, Palo Alto, CA, USA – name: 1. Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA |
| Author_xml | – sequence: 1 givenname: Nasa orcidid: 0000-0003-4490-0601 surname: Sinnott-Armstrong fullname: Sinnott-Armstrong, Nasa email: nasa@stanford.edu organization: Department of Genetics, School of Medicine, Stanford University, Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, VA Palo Alto Health Care System – sequence: 2 givenname: Yosuke orcidid: 0000-0001-9759-157X surname: Tanigawa fullname: Tanigawa, Yosuke email: ytanigaw@stanford.edu organization: Department of Biomedical Data Science, School of Medicine, Stanford University – sequence: 3 givenname: David orcidid: 0000-0002-9719-0837 surname: Amar fullname: Amar, David organization: Department of Biomedical Data Science, School of Medicine, Stanford University, Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University – sequence: 4 givenname: Nina orcidid: 0000-0002-7259-5993 surname: Mars fullname: Mars, Nina organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki – sequence: 5 givenname: Christian surname: Benner fullname: Benner, Christian organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki – sequence: 6 givenname: Matthew surname: Aguirre fullname: Aguirre, Matthew organization: Department of Biomedical Data Science, School of Medicine, Stanford University – sequence: 7 givenname: Guhan Ram surname: Venkataraman fullname: Venkataraman, Guhan Ram organization: Department of Biomedical Data Science, School of Medicine, Stanford University – sequence: 8 givenname: Michael surname: Wainberg fullname: Wainberg, Michael organization: Department of Computer Science, Stanford University – sequence: 9 givenname: Hanna M. surname: Ollila fullname: Ollila, Hanna M. organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Department of Psychiatry and Behavioral Sciences, Stanford University, Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School – sequence: 10 givenname: Tuomo orcidid: 0000-0002-6306-8227 surname: Kiiskinen fullname: Kiiskinen, Tuomo organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Finnish Institute for Health and Welfare – sequence: 11 givenname: Aki S. surname: Havulinna fullname: Havulinna, Aki S. organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Finnish Institute for Health and Welfare – sequence: 12 givenname: James P. orcidid: 0000-0001-6088-4037 surname: Pirruccello fullname: Pirruccello, James P. organization: Massachusetts General Hospital Division of Cardiology, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT – sequence: 13 givenname: Junyang surname: Qian fullname: Qian, Junyang organization: Department of Statistics, Stanford University – sequence: 14 givenname: Anna surname: Shcherbina fullname: Shcherbina, Anna organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University – sequence: 16 givenname: Fatima surname: Rodriguez fullname: Rodriguez, Fatima organization: Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University – sequence: 17 givenname: Themistocles L. orcidid: 0000-0003-2349-0009 surname: Assimes fullname: Assimes, Themistocles L. organization: VA Palo Alto Health Care System, Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University – sequence: 18 givenname: Vineeta surname: Agarwala fullname: Agarwala, Vineeta organization: Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University – sequence: 19 givenname: Robert surname: Tibshirani fullname: Tibshirani, Robert organization: Department of Biomedical Data Science, School of Medicine, Stanford University, Department of Statistics, Stanford University – sequence: 20 givenname: Trevor surname: Hastie fullname: Hastie, Trevor organization: Department of Biomedical Data Science, School of Medicine, Stanford University, Department of Statistics, Stanford University – sequence: 21 givenname: Samuli orcidid: 0000-0002-0504-1202 surname: Ripatti fullname: Ripatti, Samuli organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Department of Public Health, Clinicum, University of Helsinki – sequence: 22 givenname: Jonathan K. orcidid: 0000-0002-8828-5236 surname: Pritchard fullname: Pritchard, Jonathan K. organization: Department of Genetics, School of Medicine, Stanford University, Department of Biology, Stanford University – sequence: 23 givenname: Mark J. surname: Daly fullname: Daly, Mark J. organization: Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Analytic and Translational Genetics Unit, Massachusetts General Hospital – sequence: 24 givenname: Manuel A. orcidid: 0000-0003-1457-9925 surname: Rivas fullname: Rivas, Manuel A. email: mrivas@stanford.edu organization: Department of Biomedical Data Science, School of Medicine, Stanford University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33462484$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2021. corrected publication 2021 COPYRIGHT 2021 Nature Publishing Group Copyright Nature Publishing Group Feb 2021 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 A list of authors and their affiliations appear at the end of the paper. A full list of members and their affiliations appears in the Supplementary Information. Nina Mars2, Tuomo Kiiskinen2,9, Aki S. Havulinna2,9, Samuli Ripatti2,11,13, Mark J. Daly2,11,15 These authors contributed equally FinnGen Consortium 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.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 have approved of the final version of the manuscript. Author contributions |
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| Title | Genetics of 35 blood and urine biomarkers in the UK Biobank |
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