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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| Published in: | Nature genetics Vol. 53; no. 2; pp. 185 - 194 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.02.2021
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| Bibliography: | 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 |
| ISSN: | 1061-4036 1546-1718 1546-1718 |
| DOI: | 10.1038/s41588-020-00757-z |