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Carrasco-Zanini J, Wheeler E, Uluvar B, Kerrison N, Koprulu M, Wareham NJ, Pietzner M, Langenberg C. Mapping biological influences on the human plasma proteome beyond the genome. Nat Metab 2024; 6:2010-2023. [PMID: 39327534 PMCID: PMC11496106 DOI: 10.1038/s42255-024-01133-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 08/23/2024] [Indexed: 09/28/2024]
Abstract
Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.
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Affiliation(s)
- Julia Carrasco-Zanini
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nicola Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
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2
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Ottensmann L, Tabassum R, Ruotsalainen SE, Gerl MJ, Klose C, Widén E, Simons K, Ripatti S, Pirinen M. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun 2023; 14:6934. [PMID: 37907536 PMCID: PMC10618167 DOI: 10.1038/s41467-023-42532-8] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023] Open
Abstract
The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.
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Affiliation(s)
- Linda Ottensmann
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Rubina Tabassum
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | | | - Elisabeth Widén
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Samuli Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
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3
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Smith JL, Wilson ML, Nilson SM, Rowan TN, Schnabel RD, Decker JE, Seabury CM. Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle. BMC Genomics 2022; 23:517. [PMID: 35842584 PMCID: PMC9287884 DOI: 10.1186/s12864-022-08667-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 05/27/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated. Results Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight. Conclusions Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08667-6.
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Affiliation(s)
- Johanna L Smith
- Department of Veterinary Pathobiology, Texas A&M University, College Station, 77843, USA
| | - Miranda L Wilson
- Department of Veterinary Pathobiology, Texas A&M University, College Station, 77843, USA
| | - Sara M Nilson
- Division of Animal Sciences, University of Missouri, Columbia, 65211, USA
| | - Troy N Rowan
- Division of Animal Sciences, University of Missouri, Columbia, 65211, USA.,Genetics Area Program, University of Missouri, Columbia, 65211, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, 65211, USA.,Genetics Area Program, University of Missouri, Columbia, 65211, USA.,Informatics Institute, University of Missouri, Columbia, 65211, USA
| | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, 65211, USA.,Genetics Area Program, University of Missouri, Columbia, 65211, USA.,Informatics Institute, University of Missouri, Columbia, 65211, USA
| | - Christopher M Seabury
- Department of Veterinary Pathobiology, Texas A&M University, College Station, 77843, USA.
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4
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An Amish founder population reveals rare-population genetic determinants of the human lipidome. Commun Biol 2022; 5:334. [PMID: 35393526 PMCID: PMC8989972 DOI: 10.1038/s42003-022-03291-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/17/2022] [Indexed: 12/02/2022] Open
Abstract
Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants. A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids.
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5
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Pietzner M, Wheeler E, Carrasco-Zanini J, Raffler J, Kerrison ND, Oerton E, Auyeung VPW, Luan J, Finan C, Casas JP, Ostroff R, Williams SA, Kastenmüller G, Ralser M, Gamazon ER, Wareham NJ, Hingorani AD, Langenberg C. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 2020; 11:6397. [PMID: 33328453 PMCID: PMC7744536 DOI: 10.1038/s41467-020-19996-z] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK
- UCL BHF Research Accelerator centre, London, UK
| | - Juan P Casas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Eric R Gamazon
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK.
- UCL BHF Research Accelerator centre, London, UK.
- Health Data Research UK, Institute of Health Informatics, University College London, London, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany.
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6
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Pietzner M, Wheeler E, Carrasco-Zanini J, Raffler J, Kerrison ND, Oerton E, Auyeung VP, Luan J, Finan C, Casas JP, Ostroff R, Williams SA, Kastenmüller G, Ralser M, Gamazon ER, Wareham NJ, Hingorani AD, Langenberg C. Genetic architecture of host proteins interacting with SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.07.01.182709. [PMID: 32637948 PMCID: PMC7337378 DOI: 10.1101/2020.07.01.182709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
- UCL BHF Research Accelerator centre
| | - Juan P. Casas
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Eric R. Gamazon
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
- UCL BHF Research Accelerator centre
- Health Data Research UK, Institute of Health Informatics, University College London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, UK
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7
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Tabassum R, Rämö JT, Ripatti P, Koskela JT, Kurki M, Karjalainen J, Palta P, Hassan S, Nunez-Fontarnau J, Kiiskinen TTJ, Söderlund S, Matikainen N, Gerl MJ, Surma MA, Klose C, Stitziel NO, Laivuori H, Havulinna AS, Service SK, Salomaa V, Pirinen M, Jauhiainen M, Daly MJ, Freimer NB, Palotie A, Taskinen MR, Simons K, Ripatti S. Genetic architecture of human plasma lipidome and its link to cardiovascular disease. Nat Commun 2019; 10:4329. [PMID: 31551469 PMCID: PMC6760179 DOI: 10.1038/s41467-019-11954-8] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 08/13/2019] [Indexed: 01/07/2023] Open
Abstract
Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.
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Affiliation(s)
- Rubina Tabassum
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Joel T Rämö
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Pietari Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jukka T Koskela
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Mitja Kurki
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics and Genetic Analysis Platform, Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Priit Palta
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Shabbeer Hassan
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Javier Nunez-Fontarnau
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo T J Kiiskinen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Sanni Söderlund
- Research Programs Unit, Diabetes & Obesity, University of Helsinki and Department of Internal Medicine, Helsinki University Hospital, Helsinki, Finland
| | - Niina Matikainen
- Research Programs Unit, Diabetes & Obesity, University of Helsinki and Department of Internal Medicine, Helsinki University Hospital, Helsinki, Finland
- Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
| | | | - Michal A Surma
- Lipotype GmbH, Dresden, Germany
- Łukasiewicz Research Network-PORT Polish Center for Technology Development, Stablowicka 147 Str., 54-066, Wroclaw, Poland
| | | | - Nathan O Stitziel
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital and Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Susan K Service
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Matti Jauhiainen
- National Institute for Health and Welfare, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum, Helsinki, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Marja-Riitta Taskinen
- Research Programs Unit, Diabetes & Obesity, University of Helsinki and Department of Internal Medicine, Helsinki University Hospital, Helsinki, Finland
| | - Kai Simons
- Lipotype GmbH, Dresden, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
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8
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Locke AE, Steinberg KM, Chiang CWK, Service SK, Havulinna AS, Stell L, Pirinen M, Abel HJ, Chiang CC, Fulton RS, Jackson AU, Kang CJ, Kanchi KL, Koboldt DC, Larson DE, Nelson J, Nicholas TJ, Pietilä A, Ramensky V, Ray D, Scott LJ, Stringham HM, Vangipurapu J, Welch R, Yajnik P, Yin X, Eriksson JG, Ala-Korpela M, Järvelin MR, Männikkö M, Laivuori H, Dutcher SK, Stitziel NO, Wilson RK, Hall IM, Sabatti C, Palotie A, Salomaa V, Laakso M, Ripatti S, Boehnke M, Freimer NB. Exome sequencing of Finnish isolates enhances rare-variant association power. Nature 2019; 572:323-328. [PMID: 31367044 PMCID: PMC6697530 DOI: 10.1038/s41586-019-1457-z] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 07/02/2019] [Indexed: 12/30/2022]
Abstract
Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.
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Affiliation(s)
- Adam E Locke
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Karyn Meltz Steinberg
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Charleston W K Chiang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Quantitative and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Laurel Stell
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Haley J Abel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Colby C Chiang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Robert S Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Chul Joo Kang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Krishna L Kanchi
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Daniel C Koboldt
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - David E Larson
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Joanne Nelson
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Thomas J Nicholas
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- USTAR Center for Genetic Discovery and Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Arto Pietilä
- National Institute for Health and Welfare, Helsinki, Finland
| | - Vasily Ramensky
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
- Federal State Institution "National Medical Research Center for Preventive Medicine" of the Ministry of Healthcare of the Russian Federation, Moscow, Russia
| | - Debashree Ray
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Departments of Epidemiology and Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Pranav Yajnik
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Johan G Eriksson
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia
| | - Marjo-Riitta Järvelin
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Minna Männikkö
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital and University of Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Susan K Dutcher
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Richard K Wilson
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- The Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Ira M Hall
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Chiara Sabatti
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Analytical and Translational Genetics Unit (ATGU), Psychiatric & Neurodevelopmental Genetics Unit, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.
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