101
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Trifu SC, Vlăduţi A, Trifu AI. Genetic aspects in schizophrenia. Receptoral theories. Metabolic theories. ROMANIAN JOURNAL OF MORPHOLOGY AND EMBRYOLOGY 2021; 61:25-32. [PMID: 32747892 PMCID: PMC7728101 DOI: 10.47162/rjme.61.1.03] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Ties between schizophrenia (SCZ) and genetics are undeniably significant issue prone to be discussed in the nowadays psychology. Recent research on this domain focuses more on specific genes and heredity (specifically monozygotic pairs of twins) for diagnosing SCZ, than on environmental influences. SCZ is considered a multifactorial disease, thought to convert from a merger of risk and biological genes and environmental factors that could alter and reshape the trajectory of brain development. On this regard, this review sums up recent and innovative methods of distinguishing schizophrenic features from other mental illnesses in patients, based on chromosomal and genes changes. The term “reverse genetics” is no longer up to date, being replaced with “genome scanning” and “positional cloning”. For many researchers, genome scanning is continuing the reverse of the sensible strategy for detecting various important biological disorders, which may start from the discovery of a protein or any other molecule involved in a biological process, being followed by its gene cloning. Genes being discovered in this manner could become candidate genes for the disease. However, genome scanning occurs through testing each chromosomal segment (or mitochondrial genome) for the counter transmission of the disease.
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Affiliation(s)
- Simona Corina Trifu
- Department of Neurosciences, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania;
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102
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Serra V, Orrù V, Steri M, Fiorillo E, Cucca F, Zoledziewska M. Genetic variant within CDK6 regulates immune response to palbociclib treatment. Clin Immunol 2021; 235:108777. [PMID: 34116212 DOI: 10.1016/j.clim.2021.108777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/26/2021] [Accepted: 06/06/2021] [Indexed: 11/03/2022]
Abstract
Everyone carries a set of genetic variants that contribute to regulation of the levels of blood cells, with unknown clinical impact. One of them, rs445 within the cell-cycle checkpoint gene CDK6, reduces the levels of myeloid cell types including granulocytes. We treated CD3+ T cells and whole blood with palbociclib in 41 individuals, who were stratified by genotype for analyses. In T cells we assessed cell cycle and apoptosis, whereas in whole blood, apoptosis in activated (CD11b+), unactivated (CD11b-) granulocytes, cytotoxic (CD8 + CD4-), and helper (CD8-CD4+) T cells. We find that rs445 modulates the immune response of CD8+ T cells. It also increases the level of apoptotic CD11b + activated granulocytes after palbociclib treatment, which, in synergy with neutropenia, may affect drug related adverse events. These results suggest that the effect of palbociclib treatment may depend on underlying genetically encoded individual immune response as well as the direct response to the drug.
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Affiliation(s)
- Valentina Serra
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy
| | - Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy
| | - Maristella Steri
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy; Department of Biomedical Sciences, Sassari University, Sassari, Italy
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), 09042 Monserrato, Sardinia, Italy.
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103
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Voronin VP, Nemova NN, Ruokolainen TR, Artemenkov DV, Rolskii AY, Orlov AM, Murzina SA. Into the Deep: New Data on the Lipid and Fatty Acid Profile of Redfish Sebastes mentella Inhabiting Different Depths in the Irminger Sea. Biomolecules 2021; 11:704. [PMID: 34065058 PMCID: PMC8151303 DOI: 10.3390/biom11050704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 11/17/2022] Open
Abstract
New data on lipid and fatty acid profiles are presented, and the dynamics of the studied components in muscles in the males and females of the beaked redfish, Sebastes mentella, in the depth gradient of the Irminger Sea (North Atlantic) is discussed. The contents of the total lipids (TLs), total phospholipids (PLs), monoacylglycerols (MAGs), diacylglycerols (DAGs), triacylglycerols (TAGs), cholesterol (Chol), Chol esters, non-esterified fatty acids (NEFAs), and wax esters were determined by HPTLC; the phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylcholine (PC), and lysophosphatidylcholine (LPC) were determined by HPLC; and fatty acids of total lipids were determined using GC. The Chol esters prevailed in muscles over the storage TAGs, and the wax ester content was high, which is a characteristic trait of vertically migrating species. Specific dynamics in certain PL in redfish were found to be depended on depth, suggesting that PLs are involved in the re-arrangement of the membrane physicochemical state and the maintenance of motor activity under high hydrostatic pressure. The high contents of DHA and EPA were observed in beaked redfish muscles is the species' characteristic trait. The MUFAs in muscles include dietary markers of zooplankton (copepods)-20:1(n-9) and 22:1(n-11), whose content was found to be lower in fish sampled from greater depths.
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Affiliation(s)
- Viktor P Voronin
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Nina N Nemova
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Tatjana R Ruokolainen
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
| | - Dmitrii V Artemenkov
- Russian Federal Research Institute of Fisheries and Oceanography (VNIRO), 17 V. Krasnoselskaya Street, 107140 Moscow, Russia
| | - Aleksei Y Rolskii
- Polar Branch of the "Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)" ("PINRO" named after N.M. Knipovich), 6 Akademika Knipovicha Street, 183038 Murmansk, Russia
| | - Alexei M Orlov
- Shirshov Institute of Oceanology of the Russian Academy of Sciences (IO RAS), 36 Nakhimovsky Prospekt, 117997 Moscow, Russia
- A.N. Severtsov Institute of Ecology and Evolution of the Russian Academy of Sciences (IPEE RAS), 33 Leninsky Prospekt, 119071 Moscow, Russia
- Tomsk State University (TSU), 36 Lenin Avenue, 634050 Tomsk, Russia
| | - Svetlana A Murzina
- Institute of Biology of the Karelian Research Centre of the Russian Academy of Sciences (IB KarRC RAS), 11 Pushkinskaya Street, 185910 Petrozavodsk, Russia
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104
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Syreeni A, Sandholm N, Sidore C, Cucca F, Haukka J, Harjutsalo V, Groop PH. Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes. J Intern Med 2021; 289:662-674. [PMID: 33179336 PMCID: PMC8247053 DOI: 10.1111/joim.13187] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/07/2020] [Accepted: 09/09/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Type 1 diabetes (T1D) is an autoimmune disease affecting individuals in the early years of life. Although previous studies have identified genetic loci influencing T1D diagnosis age, these studies did not investigate the genome with high resolution. OBJECTIVE AND METHODS We performed a genome-wide meta-analysis for age at diagnosis with cohorts from Finland (Finnish Diabetic Nephropathy Study), the United Kingdom (UK Genetic Resource Investigating Diabetes) and Sardinia. Through SNP associations, transcriptome-wide association analysis linked T1D diagnosis age and gene expression. RESULTS We identified two chromosomal regions associated with T1D diagnosis age: multiple independent variants in the HLA region on chromosome 6 and a locus on chromosome 17q12. We performed gene-level association tests with transcriptome prediction models from two whole blood datasets, lymphocyte cell line, spleen, pancreas and small intestine tissues. Of the non-HLA genes, lower PNMT expression in whole blood, and higher IKZF3 and ZPBP2, and lower ORMDL3 and GSDMB transcription levels in multiple tissues were associated with lower T1D diagnosis age (FDR = 0.05). These genes lie on chr17q12 which is associated with T1D, other autoimmune diseases, and childhood asthma. Additionally, higher expression of PHF20L1, a gene not previously implicated in T1D, was associated with lower diagnosis age in lymphocytes, pancreas, and spleen. Altogether, the non-HLA associations were enriched in open chromatin in various blood cells, blood vessel tissues and foetal thymus tissue. CONCLUSION Multiple genes on chr17q12 and PHF20L1 on chr8 were associated with T1D diagnosis age and only further studies may elucidate the role of these genes for immunity and T1D onset.
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Affiliation(s)
- A Syreeni
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - N Sandholm
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - C Sidore
- Instituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy
| | - F Cucca
- Instituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - J Haukka
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - V Harjutsalo
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,National Institute for Health and Welfare, Helsinki, Finland
| | - P-H Groop
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
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105
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Ahluwalia TS, Prins BP, Abdollahi M, Armstrong NJ, Aslibekyan S, Bain L, Jefferis B, Baumert J, Beekman M, Ben-Shlomo Y, Bis JC, Mitchell BD, de Geus E, Delgado GE, Marek D, Eriksson J, Kajantie E, Kanoni S, Kemp JP, Lu C, Marioni RE, McLachlan S, Milaneschi Y, Nolte IM, Petrelis AM, Porcu E, Sabater-Lleal M, Naderi E, Seppälä I, Shah T, Singhal G, Standl M, Teumer A, Thalamuthu A, Thiering E, Trompet S, Ballantyne CM, Benjamin EJ, Casas JP, Toben C, Dedoussis G, Deelen J, Durda P, Engmann J, Feitosa MF, Grallert H, Hammarstedt A, Harris SE, Homuth G, Hottenga JJ, Jalkanen S, Jamshidi Y, Jawahar MC, Jess T, Kivimaki M, Kleber ME, Lahti J, Liu Y, Marques-Vidal P, Mellström D, Mooijaart SP, Müller-Nurasyid M, Penninx B, Revez JA, Rossing P, Räikkönen K, Sattar N, Scharnagl H, Sennblad B, Silveira A, Pourcain BS, Timpson NJ, Trollor J, van Dongen J, Van Heemst D, Visvikis-Siest S, Vollenweider P, Völker U, Waldenberger M, Willemsen G, Zabaneh D, Morris RW, Arnett DK, Baune BT, Boomsma DI, Chang YPC, Deary IJ, Deloukas P, Eriksson JG, Evans DM, Ferreira MA, Gaunt T, Gudnason V, Hamsten A, Heinrich J, Hingorani A, Humphries SE, Jukema JW, Koenig W, Kumari M, Kutalik Z, Lawlor DA, Lehtimäki T, März W, Mather KA, Naitza S, Nauck M, Ohlsson C, Price JF, Raitakari O, Rice K, Sachdev PS, Slagboom E, Sørensen TIA, Spector T, Stacey D, Stathopoulou MG, Tanaka T, Wannamethee SG, Whincup P, Rotter JI, Dehghan A, Boerwinkle E, Psaty BM, Snieder H, Alizadeh BZ. Genome-wide association study of circulating interleukin 6 levels identifies novel loci. Hum Mol Genet 2021; 30:393-409. [PMID: 33517400 PMCID: PMC8098112 DOI: 10.1093/hmg/ddab023] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/02/2020] [Accepted: 01/04/2021] [Indexed: 12/13/2022] Open
Abstract
Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10-11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10-10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10-122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.
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Affiliation(s)
- Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Gentofte DK2820, Denmark.,Department of Biology, The Bioinformatics Center, University of Copenhagen, Copenhagen DK2200, Denmark
| | - Bram P Prins
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Mohammadreza Abdollahi
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | | | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, Alabama 35233, USA
| | - Lisa Bain
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Barbara Jefferis
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London NW3 2PF, UK
| | - Jens Baumert
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol BS8 2PS, UK
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Eco de Geus
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany
| | - Diana Marek
- SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Joel Eriksson
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Eero Kajantie
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, PO Box 30, Helsinki 00271, Finland.,Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, Helsinki 00014, Finland
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts & the London Medical School, Queen Mary University of London, London EC1M 6BQ, UK
| | - John P Kemp
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland 4102, Australia.,MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Chen Lu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Stela McLachlan
- Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam 1081 HJ, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | | | - Eleonora Porcu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato (CA) 09042, Italy
| | - Maria Sabater-Lleal
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden.,Unit of Genomics of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona 08041, Spain
| | - Elnaz Naderi
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Tina Shah
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Gaurav Singhal
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,Division of Metabolic Diseases and Nutritional Medicine, Ludwig-Maximilians-University of Munich, Dr. von Hauner Children's Hospital, Munich 80337, Germany
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | | | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA.,Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Catherine Toben
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - George Dedoussis
- 44Department of Nutrition-Dietetics, Harokopio University, Athens 17671, Greece
| | - Joris Deelen
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Max Planck Institute for Biology of Ageing, Cologne 50931, Germany
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Jorgen Engmann
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD), Neuherberg 85764, Germany
| | - Ann Hammarstedt
- The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg SE-41345, Sweden
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Sirpa Jalkanen
- MediCity Research Laboratory, University of Turku, Turku 20520, Finland.,Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Yalda Jamshidi
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St George's University of London, London SW17 0RE, UK
| | - Magdalene C Jawahar
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - Tine Jess
- 55Department of Epidemiology Research, Statens Serum Institute, Copenhagen DK2300, Denmark
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London WC1E 7HB, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany
| | - Jari Lahti
- Turku Institute for Advanced Studies, University of Turku, Turku 20014, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki 00014, Finland
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Pedro Marques-Vidal
- Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne 1011, Switzerland.,University of Lausanne, Lausanne 1011, Switzerland
| | - Dan Mellström
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Simon P Mooijaart
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Martina Müller-Nurasyid
- IBE, Faculty of Medicine, Ludwig Maximilians University (LMU) Munich, Munich 81377, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johhanes Gutenberg University, Mainz 55101, Germany
| | - Brenda Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam 1081 HJ, The Netherlands
| | - Joana A Revez
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte DK2820, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen DK2200, Denmark
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki 00014, Finland
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow G12 8TA, UK
| | - Hubert Scharnagl
- 66Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz 8036, Austria
| | - Bengt Sennblad
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden.,Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala 75124, Sweden
| | - Angela Silveira
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK.,Max Planck Institute for Psycholinguistics, Nijmegen XD 6525, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 AJ, The Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney 2031, Australia
| | | | - Jenny van Dongen
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | | | | | - Peter Vollenweider
- Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne 1011, Switzerland.,University of Lausanne, Lausanne 1011, Switzerland
| | - Uwe Völker
- MediCity Research Laboratory, University of Turku, Turku 20520, Finland
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Delilah Zabaneh
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK
| | - Richard W Morris
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
| | - Donna K Arnett
- Dean's Office, College of Public Health, University of Kentucky, Lexington, KY 40536, USA
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Parkville 3000, Australia.,Department of Psychiatry and Psychotherapy, University of Muenster, Muenster 48149, Germany.,The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville 3000, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Yen-Pei C Chang
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts & the London Medical School, Queen Mary University of London, London EC1M 6BQ, UK.,77Centre for Genomic Health, Queen Mary University of London, London EC1M 6BQ, UK
| | - Johan G Eriksson
- National Institute for Health and Welfare, University of Helsinki, Helsinki 00014, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, Helsinki 00014, Finland
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland 4102, Australia.,MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | | | - Tom Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS6 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur 201, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland
| | - Anders Hamsten
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden
| | - Joachim Heinrich
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich 81377, Germany.,Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne 3010, Australia
| | - Aroon Hingorani
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Steve E Humphries
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - J Wouter Jukema
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.,Durrer Center for Cardiogenetic Research, Amsterdam 1105 AZ, The Netherlands
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich 80636, Germany.,88DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich 80336, Germany.,Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm 89081, Germany
| | - Meena Kumari
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London WC1E 7HB, UK.,Institute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, Germany
| | - Zoltan Kutalik
- SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS6 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany.,66Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz 8036, Austria.,SYNLAB Academy, SYNALB Holding Deutschland GmbH, Mannheim 68163, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Neuroscience Research Australia, Sydney 2031, Australia
| | - Silvia Naitza
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato (CA) 09042, Italy
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald 17475, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Jackie F Price
- Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku, Turku University Hospital, Turku 20520, Finland.,Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20014, Finland
| | - Ken Rice
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney 2031, Australia
| | - Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Max Planck Institute for Biology of Ageing, Cologne 50931, Germany
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center For Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK2200, Denmark.,Department of Public Health, Section on Epidemiology, University of Copenhagen, Copenhagen DK1014, Denmark
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - David Stacey
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | | | - Toshiko Tanaka
- Longitudinal Study Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - S Goya Wannamethee
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London NW3 2PF, UK
| | - Peter Whincup
- Population Health Research Institute, St George's, University of London, London SW17 0RE, UK
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC, Rotterdam 3000 CA, The Netherlands
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA.,Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
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106
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Geographic variation in the polygenic score of height in Japan. Hum Genet 2021; 140:1097-1108. [PMID: 33900438 DOI: 10.1007/s00439-021-02281-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 04/12/2021] [Indexed: 10/21/2022]
Abstract
A geographical gradient of height has existed in Japan for approximately 100 years. People in northern Japan tend to be taller than those in southern Japan. The differences in annual temperature and day length between the northern and southern prefectures of Japan have been suggested as possible causes of the height gradient. Although height is well known to be a polygenic trait with high heritability, the genetic contributions to the gradient have not yet been explored. Polygenic score (PS) is calculated by aggregating the effects of genetic variants identified by genome-wide association studies (GWASs) to predict the traits of individual subjects. Here, we calculated the PS of height for 10,840 Japanese individuals from all 47 prefectures in Japan. The median height PS for each prefecture was significantly correlated with the mean height of females and males obtained from another independent Japanese nation-wide height dataset, suggesting genetic contribution to the observed height gradient. We also found that individuals and prefectures genetically closer to continental East Asian ancestry tended to have a higher PS; modern Japanese people are considered to have originated as result of admixture between indigenous Jomon people and immigrants from continental East Asia. Another PS analysis based on the GWAS using only the mainland Japanese was conducted to evaluate the effect of population stratification on PS. The result also supported genetic contribution to height, and indicated that the PS might be affected by a bias due to population stratification even in a relatively homogenous population like Japanese.
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107
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The forensic landscape and the population genetic analyses of Hainan Li based on massively parallel sequencing DNA profiling. Int J Legal Med 2021; 135:1295-1317. [PMID: 33847803 DOI: 10.1007/s00414-021-02590-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/26/2021] [Indexed: 12/30/2022]
Abstract
Due to the formation of the Qiongzhou Strait by climate change and marine transition, Hainan island was isolated from the mainland southern China during the Last Glacial Maximum. Hainan island, located at the southernmost part of China and separated from the Leizhou Peninsula by the Qiongzhou Strait, laid on one of the modern human northward migration routes from Southeast Asia to East Asia. The Hlai language-speaking Li minority, the second largest population after Han Chinese in Hainan island, is the direct descendants of the initial migrants in Hainan island and has unique ethnic properties and derived characteristics; however, the forensic-associated studies on Hainan Li population are still insufficient. Hence, 136 Hainan Li individuals were genotyped in this study using the MPS-based ForenSeq™ DNA Signature Prep Kit (DNA Primer Set A, DPMA) to characterize the forensic genetic polymorphism landscape, and DNA profiles were obtained from 152 different molecular genetic markers (27 autosomal STRs, 24 Y-STRs, 7 X-STRs, and 94 iiSNPs). A total of 419 distinct length variants and 586 repeat sequence sub-variants, with 31 novel alleles (at 17 loci), were identified across the 58 STR loci from the DNA profiles of Hainan Li population. We evaluated the forensic characteristics and efficiencies of DPMA, demonstrating that the STRs and iiSNPs in DPMA were highly polymorphic in Hainan Li population and could be employed in forensic applications. In addition, we set up three datasets, which included the genetic data of (i) iiSNPs (27 populations, 2640 individuals), (ii) Y-STRs (42 populations, 8281 individuals), and (iii) Y haplogroups (123 populations, 4837 individuals) along with the population ancestries and language families, to perform population genetic analyses separately from different perspectives. In conclusion, the phylogenetic analyses indicated that Hainan Li, with a southern East Asia origin and Tai-Kadai language-speaking language, is an isolated population relatively. But the genetic pool of Hainan Li influenced by the limited gene flows from other Tai-Kadai populations and Hainan populations. Furthermore, the establishment of isolated population models will be beneficial to clarify the exquisite population structures and develop specific genetic markers for subpopulations in forensic genetic fields.
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108
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Sidore C, Orrù V, Cocco E, Steri M, Inshaw JR, Pitzalis M, Mulas A, McGurnaghan S, Frau J, Porcu E, Busonero F, Dei M, Lai S, Sole G, Virdis F, Serra V, Poddie F, Delitala A, Marongiu M, Deidda F, Pala M, Floris M, Masala M, Onengut-Gumuscu S, Robertson CC, Leoni L, Frongia A, Ricciardi MR, Chessa M, Olla N, Lovicu M, Loizedda A, Maschio A, Mereu L, Ferrigno P, Curreli N, Balaci L, Loi F, Ferreli LA, Pilia MG, Pani A, Marrosu MG, Abecasis GR, Rich SS, Colhoun H, Todd JA, Schlessinger D, Fiorillo E, Cucca F, Zoledziewska M. PRF1 mutation alters immune system activation, inflammation, and risk of autoimmunity. Mult Scler 2021; 27:1332-1340. [PMID: 33566725 DOI: 10.1177/1352458520963937] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Defective alleles within the PRF1 gene, encoding the pore-forming protein perforin, in combination with environmental factors, cause familial type 2 hemophagocytic lymphohistiocytosis (FHL2), a rare, severe autosomal recessive childhood disorder characterized by massive release of cytokines-cytokine storm. OBJECTIVE The aim of this study was to determine the function of hypomorph PRF1:p.A91V g.72360387 G > A on multiple sclerosis (MS) and type 1 diabetes (T1D). METHODS We cross-compare the association data for PRF1:p.A91V mutation derived from GWAS on adult MS and pediatric T1D in Sardinians. The novel association with T1D was replicated in metanalysis in 12,584 cases and 17,692 controls from Sardinia, the United Kingdom, and Scotland. To dissect this mutation function, we searched through the coincident association immunophenotypes in additional set of general population Sardinians. RESULTS We report that PRF1:p.A91V, is associated with increase of lymphocyte levels, especially within the cytotoxic memory T-cells, at general population level with reduced interleukin 7 receptor expression on these cells. The minor allele increased risk of MS, in 2903 cases and 2880 controls from Sardinia p = 2.06 × 10-4, odds ratio OR = 1.29, replicating a previous finding, whereas it protects from T1D p = 1.04 × 10-5, OR = 0.82. CONCLUSION Our results indicate opposing contributions of the cytotoxic T-cell compartment to MS and T1D pathogenesis.
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Affiliation(s)
- Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Eleonora Cocco
- Department of Medical Sciences and Public health, Multiple Sclerosis Centre, University of Cagliari, Cagliari, Italy
| | - Maristella Steri
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Jamie Rj Inshaw
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Oxford, UK/Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Maristella Pitzalis
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Stuart McGurnaghan
- Diabetes Medical Informatics and Epidemiology, The University of Edinburgh, Edinburgh, Scotland
| | - Jessica Frau
- Department of Medical Sciences and Public health, Multiple Sclerosis Centre, University of Cagliari, Cagliari, Italy
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland/Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Fabio Busonero
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Mariano Dei
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Sandra Lai
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Gabriella Sole
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Francesca Virdis
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Valentina Serra
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Fausto Poddie
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Alessandro Delitala
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy/Department of Surgical, Medical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Michele Marongiu
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Francesca Deidda
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Mauro Pala
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Matteo Floris
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Marco Masala
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | | | - Lidia Leoni
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), Parco Scientifico e Tecnologico della Sardegna, Pula, Italy
| | | | | | - Margherita Chessa
- Struttura Complessa di Pediatria, Azienda Ospedaliera G. Brotzu, Cagliari, Italy
| | - Nazario Olla
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Mario Lovicu
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Annalisa Loizedda
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Andrea Maschio
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Luisa Mereu
- Unità Operativa di Pediatria, Ospedale San Martino di Oristano, Oristano, Italy
| | - Paola Ferrigno
- Reparto di Neurologia, Azienda Ospedaliera G. Brotzu, Cagliari, Italy
| | - Nicolo Curreli
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Lenuta Balaci
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Francesco Loi
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Liana Ap Ferreli
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Maria Grazia Pilia
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Antonello Pani
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy/Struttura Complessa di Nefrologia e Dialisi, Azienda Ospedaliera G. Brotzu, Cagliari, Italy
| | - Maria Giovanna Marrosu
- Department of Medical Sciences and Public health, Multiple Sclerosis Centre, University of Cagliari, Cagliari, Italy
| | - Goncalo R Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Helen Colhoun
- Diabetes Medical Informatics and Epidemiology, The University of Edinburgh, Edinburgh, Scotland
| | - John A Todd
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Oxford, UK/Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - David Schlessinger
- Laboratory of Genetics and Genomics, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy/Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy
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109
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Birolo G, Aneli S, Di Gaetano C, Cugliari G, Russo A, Allione A, Casalone E, Giorgio E, Paraboschi EM, Ardissino D, Duga S, Asselta R, Matullo G. Functional and clinical implications of genetic structure in 1686 Italian exomes. Hum Mutat 2021; 42:272-289. [PMID: 33326653 DOI: 10.1002/humu.24156] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 11/13/2020] [Accepted: 12/11/2020] [Indexed: 12/12/2022]
Abstract
To reconstruct the phenotypical and clinical implications of the Italian genetic structure, we thoroughly analyzed a whole-exome sequencing data set comprised of 1686 healthy Italian individuals. We found six previously unreported variants with remarkable frequency differences between Northern and Southern Italy in the HERC2, OR52R1, ADH1B, and THBS4 genes. We reported 36 clinically relevant variants (submitted as pathogenic, risk factors, or drug response in ClinVar) with significant frequency differences between Italy and Europe. We then explored putatively pathogenic variants in the Italian exome. On average, our Italian individuals carried 16.6 protein-truncating variants (PTVs), with 2.5% of the population having a PTV in one of the 59 American College of Medical Genetics (ACMG) actionable genes. Lastly, we looked for PTVs that are likely to cause Mendelian diseases. We found four heterozygous PTVs in haploinsufficient genes (KAT6A, PTCH1, and STXBP1) and three homozygous PTVs in genes causing recessive diseases (DPYD, FLG, and PYGM). Comparing frequencies from our data set to other public databases, like gnomAD, we showed the importance of population-specific databases for a more accurate assessment of variant pathogenicity. For this reason, we made aggregated frequencies from our data set publicly available as a tool for both clinicians and researchers (http://nigdb.cineca.it; NIG-ExIT).
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Affiliation(s)
- Giovanni Birolo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Serena Aneli
- Department of Medical Sciences, University of Turin, Turin, Italy
| | | | | | - Alessia Russo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | | | | | - Elisa Giorgio
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Elvezia M Paraboschi
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Humanitas Clinical and Research Center-IRCCS, Rozzano, Milan, Italy
| | - Diego Ardissino
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Stefano Duga
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Humanitas Clinical and Research Center-IRCCS, Rozzano, Milan, Italy
| | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Humanitas Clinical and Research Center-IRCCS, Rozzano, Milan, Italy
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
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110
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Mineta K, Goto K, Gojobori T, Alkuraya FS. Population structure of indigenous inhabitants of Arabia. PLoS Genet 2021; 17:e1009210. [PMID: 33428619 PMCID: PMC7799765 DOI: 10.1371/journal.pgen.1009210] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 10/16/2020] [Indexed: 01/19/2023] Open
Abstract
Modern day Saudi Arabia occupies the majority of historical Arabia, which may have contributed to ancient waves of migration out of Africa. This ancient history has left a lasting imprint in the genetics of the region, including the diverse set of tribes that call Saudi Arabia their home. How these tribes relate to each other and to the world's major populations remains an unanswered question. In an attempt to improve our understanding of the population structure of Saudi Arabia, we conducted genomic profiling of 957 unrelated individuals who self-identify with 28 large tribes in Saudi Arabia. Consistent with the tradition of intra-tribal unions, the subjects showed strong clustering along tribal lines with the distance between clusters correlating with their geographical proximities in Arabia. However, these individuals form a unique cluster when compared to the world's major populations. The ancient origin of these tribal affiliations is supported by analyses that revealed little evidence of ancestral origin from within the 28 tribes. Our results disclose a granular map of population structure and have important implications for future genetic studies into Mendelian and common diseases in the region.
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Affiliation(s)
- Katsuhiko Mineta
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Kosuke Goto
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Takashi Gojobori
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- * E-mail: (TG); (FSA)
| | - Fowzan S. Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
- Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
- Saudi Human Genome Program, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
- * E-mail: (TG); (FSA)
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111
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Nielsen JB, Rom O, Surakka I, Graham SE, Zhou W, Roychowdhury T, Fritsche LG, Gagliano Taliun SA, Sidore C, Liu Y, Gabrielsen ME, Skogholt AH, Wolford B, Overton W, Zhao Y, Chen J, Zhang H, Hornsby WE, Acheampong A, Grooms A, Schaefer A, Zajac GJM, Villacorta L, Zhang J, Brumpton B, Løset M, Rai V, Lundegaard PR, Olesen MS, Taylor KD, Palmer ND, Chen YD, Choi SH, Lubitz SA, Ellinor PT, Barnes KC, Daya M, Rafaels N, Weiss ST, Lasky-Su J, Tracy RP, Vasan RS, Cupples LA, Mathias RA, Yanek LR, Becker LC, Peyser PA, Bielak LF, Smith JA, Aslibekyan S, Hidalgo BA, Arnett DK, Irvin MR, Wilson JG, Musani SK, Correa A, Rich SS, Guo X, Rotter JI, Konkle BA, Johnsen JM, Ashley-Koch AE, Telen MJ, Sheehan VA, Blangero J, Curran JE, Peralta JM, Montgomery C, Sheu WHH, Chung RH, Schwander K, Nouraie SM, Gordeuk VR, Zhang Y, Kooperberg C, Reiner AP, Jackson RD, Bleecker ER, Meyers DA, Li X, Das S, Yu K, LeFaive J, Smith A, Blackwell T, Taliun D, Zollner S, Forer L, Schoenherr S, Fuchsberger C, Pandit A, Zawistowski M, Kheterpal S, Brummett CM, Natarajan P, Schlessinger D, Lee S, Kang HM, Cucca F, Holmen OL, Åsvold BO, Boehnke M, Kathiresan S, Abecasis GR, Chen YE, Willer CJ, Hveem K. Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease. Nat Commun 2020; 11:6417. [PMID: 33339817 PMCID: PMC7749177 DOI: 10.1038/s41467-020-20086-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 09/17/2020] [Indexed: 12/15/2022] Open
Abstract
Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.
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Affiliation(s)
- Jonas B Nielsen
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway.
| | - Oren Rom
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Ida Surakka
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Sarah E Graham
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhou
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Tanmoy Roychowdhury
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Lars G Fritsche
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sarah A Gagliano Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Yuhao Liu
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Brooke Wolford
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - William Overton
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Ying Zhao
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Jin Chen
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - He Zhang
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Whitney E Hornsby
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Akua Acheampong
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Austen Grooms
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Amanda Schaefer
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Gregory J M Zajac
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Luis Villacorta
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Jifeng Zhang
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- Department of Dermatology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Pia R Lundegaard
- Laboratory for Molecular Cardiology, Department of Cardiology, Centre for Cardiac, Vascular, Pulmonary and Infectious Diseases, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten S Olesen
- Laboratory for Molecular Cardiology, Department of Cardiology, Centre for Cardiac, Vascular, Pulmonary and Infectious Diseases, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics and Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, CA, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yii-Der Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics and Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, CA, USA
| | - Seung H Choi
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Steven A Lubitz
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Kathleen C Barnes
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Michelle Daya
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA
- Framingham Heart Study, Framingham, MA, USA
| | - L Adrienne Cupples
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Stella Aslibekyan
- The University of Alabama at Birmingham, Birmingham, AL, USA
- 23andMe, Inc., Sunnyvale, CA, USA
| | | | - Donna K Arnett
- Deans Office, College of Public Health, University of Kentucky, Lexington, KY, USA
| | | | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
- Jackson Heart Study, Jackson, MS, USA
| | - Solomon K Musani
- Jackson Heart Study, Jackson, MS, USA
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Adolfo Correa
- Jackson Heart Study, Jackson, MS, USA
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics and Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, CA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics and Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, CA, USA
| | - Barbara A Konkle
- BloodWorks Northwest, University of Washington, Seattle, WA, USA
| | - Jill M Johnsen
- BloodWorks Northwest, University of Washington, Seattle, WA, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Marilyn J Telen
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Vivien A Sheehan
- Department of Pediatrics, Division of Hematology/Oncology, Baylor College of Medicine, Houston, TX, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Juan M Peralta
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma, OK, USA
| | - Wayne H-H Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ren-Hua Chung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Seyed M Nouraie
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Yingze Zhang
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Rebecca D Jackson
- Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH, USA
| | | | - Deborah A Meyers
- Division of Pharmacogenomics University of Arizona, Tucson, AR, USA
| | - Xingnan Li
- Division of Genetics, Genomics and Precision Medicine, Department of Medicine, University of Arizona, Tucson, AR, USA
| | - Sayantan Das
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ketian Yu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Albert Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Tom Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sebastian Zollner
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lukas Forer
- Division of Genetics, Genomics and Precision Medicine, Department of Medicine, University of Arizona, Tucson, AR, USA
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Anita Pandit
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Matthew Zawistowski
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Chad M Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David Schlessinger
- Laboratory of Genetics, National Institute on Aging, US National Institutes of Health, Baltimore, MD, USA
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
- Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Oddgeir L Holmen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Bjørn O Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sekar Kathiresan
- Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MD, USA
| | - Goncalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Y Eugene Chen
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA.
| | - Cristen J Willer
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway.
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway.
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
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Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease. Nat Commun 2020. [PMID: 33339817 DOI: 10.1038/s41467−020−17792−3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.
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113
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Busonero F, Steri M, Orrù V, Sole G, Olla S, Marongiu M, Maschio A, Sidore C, Lai S, Mulas A, Zoledziewska M, Floris M, Pala M, Forabosco P, Asunis I, Pitzalis M, Deidda F, Masala M, Caria CA, Barella S, Abecasis GR, Schlessinger D, Sanna S, Fiorillo E, Cucca F. A Sardinian founder mutation in glycoprotein Ib platelet subunit beta (GP1BB) that impacts thrombocytopenia. Br J Haematol 2020; 191:e124-e128. [PMID: 33216977 DOI: 10.1111/bjh.17090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Fabio Busonero
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Maristella Steri
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Gabriella Sole
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Stefania Olla
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Michele Marongiu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Andrea Maschio
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Sandra Lai
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Matteo Floris
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Mauro Pala
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Paola Forabosco
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Isadora Asunis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Maristella Pitzalis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Francesca Deidda
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Marco Masala
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Cristian Antonio Caria
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Susanna Barella
- Ospedale Pediatrico Microcitemico 'Antonio Cao' (A.O.Brotzu), Cagliari, Italy
| | - Goncalo R Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - David Schlessinger
- Laboratory of Genetics and Genomics, National Institute on Aging, US National Institutes of Health, Baltimore, MD, USA
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato (Cagliari), Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
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114
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The tip of the iceberg for diagnostic dilemmas: Performance of current diagnostics and future complementary screening approaches. Eur J Med Genet 2020; 63:104089. [PMID: 33069933 DOI: 10.1016/j.ejmg.2020.104089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 09/15/2020] [Accepted: 10/12/2020] [Indexed: 11/24/2022]
Abstract
Genetic testing is currently the leading edge of clinical care when it comes to diagnostics. However, many questions remain unanswered even when employing next-generation sequencing techniques due to our inability to decode genetic variations and our limited repertoire of available diagnoses. Accordingly, diagnostic yields for current genomic screenings are <50% and fail to provide the whole picture, leaving the remaining patients without a definitive diagnosis. Human phenotypic/disease expression is explained by alterations not only at the genome, but also at the transcriptome, proteome and metabolome levels. These "higher" complexity levels represent at wealth of information, and diagnostic screenings tests at these levels have been shown to significantly improve diagnostic yields in specific populations compared to conventional diagnostic workup or gold standards in use (7-30% increase in diagnostic yields, depending on the population, approach and gold standard being compared against). However, these are not yet routinely available to clinicians. Due to their dynamic and modifiable nature, tapping into data from different omics will improve our understanding of the pathophysiological bases underlying (many yet to characterize) human disorders. We herein review how alterations at these levels (e.g. post-transcriptional and post-translational) may be pathogenic, how such tests may be implemented and in which situations they are of significant utility.
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115
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Orrù V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, Sole G, Lai S, Dei M, Mulas A, Virdis F, Piras MG, Lobina M, Marongiu M, Pitzalis M, Deidda F, Loizedda A, Onano S, Zoledziewska M, Sawcer S, Devoto M, Gorospe M, Abecasis GR, Floris M, Pala M, Schlessinger D, Fiorillo E, Cucca F. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet 2020; 52:1036-1045. [PMID: 32929287 DOI: 10.1038/s41588-020-0684-4] [Citation(s) in RCA: 248] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 07/27/2020] [Indexed: 01/28/2023]
Abstract
We report on the influence of ~22 million variants on 731 immune cell traits in a cohort of 3,757 Sardinians. We detected 122 significant (P < 1.28 × 10-11) independent association signals for 459 cell traits at 70 loci (53 of them novel) identifying several molecules and mechanisms involved in cell regulation. Furthermore, 53 signals at 36 loci overlapped with previously reported disease-associated signals, predominantly for autoimmune disorders, highlighting intermediate phenotypes in pathogenesis. Collectively, our findings illustrate complex genetic regulation of immune cells with highly selective effects on autoimmune disease risk at the cell-subtype level. These results identify drug-targetable pathways informing the design of more specific treatments for autoimmune diseases.
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Affiliation(s)
- Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Maristella Steri
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Michele Marongiu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Valentina Serra
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Stefania Olla
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Gabriella Sole
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Sandra Lai
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Mariano Dei
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Francesca Virdis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Maria Grazia Piras
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Monia Lobina
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Mara Marongiu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Maristella Pitzalis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Francesca Deidda
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Annalisa Loizedda
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Stefano Onano
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Stephen Sawcer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Marcella Devoto
- Division of Genetics, The Children's Hospital of Philadelphia, and Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA.,Dipartimento di Medicina Traslazionale e di Precisione, Sapienza Università di Roma, Rome, Italy
| | - Myriam Gorospe
- Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Gonçalo R Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Matteo Floris
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Mauro Pala
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - David Schlessinger
- Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy. .,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy.
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116
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Whole genome sequencing of elite athletes. Biol Sport 2020; 37:295-304. [PMID: 32879552 PMCID: PMC7433326 DOI: 10.5114/biolsport.2020.96272] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 05/26/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022] Open
Abstract
Whole genome sequencing (WGS) has great potential to explore all possible DNA variants associated with physical performance, psychological traits and health conditions of athletes. Here we present, for the first time, annotation of genomic variants of elite athletes, based on the WGS of 20 Tatar male wrestlers. The maximum number of high-quality variants per sample was over 3.8 M for single nucleotide polymorphisms (SNPs) and about 0.64 M for indels. The maximum number of nonsense mutations was 148 single nucleotide variants (SNVs) per individual. Athletes' genomes on average contained 18.9 nonsense SNPs in a homozygous state per sample, while non-athletes' exomes (Tatar controls, n = 19) contained 18 nonsense SNPs. Finally, we applied genomic data for the association analysis and used reaction time (RT) as an example. Out of 1884 known genome-wide significant SNPs related to RT, we identified four SNPs (KIF27 rs10125715, APC rs518013, TMEM229A rs7783359, LRRN3 rs80054135) associated with RT in wrestlers. The cumulative number of favourable alleles (KIF27 A, APC A, TMEM229A T, LRRN3 T) was significantly correlated with RT both in wrestlers (P = 0.0003) and an independent cohort (n = 43) of physically active subjects (P = 0.029). Furthermore, we found that the frequencies of the APC A (53.3 vs 44.0%, P = 0.033) and LRRN3 T (7.5 vs 2.8%, P = 0.009) alleles were significantly higher in elite athletes (n = 107) involved in sports with RT as an essential component of performance (combat sports, table tennis and volleyball) compared to less successful (n = 176) athletes. The LRRN3 T allele was also over-represented in elite athletes (7.5%) in comparison with 189 controls (2.9%, P = 0.009). In conclusion, we present the first WGS study of athletes showing that WGS can be applied in sport and exercise science.
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117
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Quick C, Anugu P, Musani S, Weiss ST, Burchard EG, White MJ, Keys KL, Cucca F, Sidore C, Boehnke M, Fuchsberger C. Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations. Genet Epidemiol 2020; 44:537-549. [PMID: 32519380 PMCID: PMC7449570 DOI: 10.1002/gepi.22326] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 04/02/2020] [Accepted: 05/22/2020] [Indexed: 01/03/2023]
Abstract
A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.
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Affiliation(s)
- Corbin Quick
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Pramod Anugu
- University of Mississippi Medical CenterJacksonMississippi
| | - Solomon Musani
- University of Mississippi Medical CenterJacksonMississippi
| | - Scott T. Weiss
- Harvard Medical SchoolBostonMassachusetts
- Channing Department of Network MedicineBrigham and Women's HospitalBostonCalifornia
- Partners HealthCare Personalized MedicineBostonMassachusetts
| | - Esteban G. Burchard
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCalifornia
| | - Marquitta J. White
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Kevin L. Keys
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
- Dipartimento di Scienze BiomedicheUniversità di SassariSassariItaly
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
- Department of Genetics and Pharmacology, Institute of Genetic EpidemiologyMedical University of InnsbruckInnsbruckAustria
- Institute for Biomedicine, Eurac ResearchAffiliated Institute of the University of LübeckBolzanoItaly
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118
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Kim HI, Ye B, Gosalia N, Köroğlu Ç, Hanson RL, Hsueh WC, Knowler WC, Baier LJ, Bogardus C, Shuldiner AR, Van Hout CV, Van Hout CV. Characterization of Exome Variants and Their Metabolic Impact in 6,716 American Indians from the Southwest US. Am J Hum Genet 2020; 107:251-264. [PMID: 32640185 DOI: 10.1016/j.ajhg.2020.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 06/10/2020] [Indexed: 12/21/2022] Open
Abstract
Applying exome sequencing to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from a Southwestern American Indian (SWAI) population with well-characterized metabolic traits. We found that the SWAI population has distinct allelic architecture compared to populations of European and East Asian ancestry, and there were many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in the SWAI population. We used pLOF and nonsynonymous variants in the SWAI population to evaluate gene-burden associations of candidate genes from European genome-wide association studies (GWASs) for type 2 diabetes, body mass index, and four major plasma lipids. We found 19 significant gene-burden associations for 11 genes, providing additional evidence for prioritizing candidate effector genes of GWAS signals. Interestingly, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in the SWAI population. Particularly, we found four pLOF or nonsynonymous variants in APOB, APOE, PCSK9, and TM6SF2 that are private or enriched in the SWAI population and associated with low-density lipoprotein (LDL) cholesterol levels. Their large estimated effects on LDL cholesterol levels suggest strong impacts on protein function and potential clinical implications of these variants in cardiovascular health. In summary, our study illustrates the utility and potential of exome sequencing in genetically unique populations, such as the SWAI population, to prioritize candidate effector genes within GWAS loci and to find additional variants in known disease genes with potential clinical impact.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Cristopher V Van Hout
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc., Tarrytown, NY 10591, USA.
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119
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Chen M, Sidore C, Akiyama M, Ishigaki K, Kamatani Y, Schlessinger D, Cucca F, Okada Y, Chiang CWK. Evidence of Polygenic Adaptation in Sardinia at Height-Associated Loci Ascertained from the Biobank Japan. Am J Hum Genet 2020; 107:60-71. [PMID: 32533944 PMCID: PMC7332648 DOI: 10.1016/j.ajhg.2020.05.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 05/19/2020] [Indexed: 01/31/2023] Open
Abstract
Adult height is one of the earliest putative examples of polygenic adaptation in humans. However, this conclusion was recently challenged because residual uncorrected stratification from large-scale consortium studies was considered responsible for the previously noted genetic difference. It thus remains an open question whether height loci exhibit signals of polygenic adaptation in any human population. We re-examined this question, focusing on one of the shortest European populations, the Sardinians, in addition to mainland European populations. We utilized height-associated loci from the Biobank Japan (BBJ) dataset to further alleviate concerns of biased ascertainment of GWAS loci and showed that the Sardinians remain significantly shorter than expected under neutrality (∼0.22 standard deviation shorter than Utah residents with ancestry from northern and western Europe [CEU] on the basis of polygenic height scores, p = 3.89 × 10-4). We also found the trajectory of polygenic height scores between the Sardinian and the British populations diverged over at least the last 10,000 years (p = 0.0082), consistent with a signature of polygenic adaptation driven primarily by the Sardinian population. Although the polygenic score-based analysis showed a much subtler signature in mainland European populations, we found a clear and robust adaptive signature in the UK population by using a haplotype-based statistic, the trait singleton density score (tSDS), driven by the height-increasing alleles (p = 9.1 × 10-4). In summary, by ascertaining height loci in a distant East Asian population, we further supported the evidence of polygenic adaptation at height-associated loci among the Sardinians. In mainland Europeans, the adaptive signature was detected in haplotype-based analysis but not in polygenic score-based analysis.
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Affiliation(s)
- Minhui Chen
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato 09042, Cagliari, Italy
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - David Schlessinger
- Laboratory of Genetics and Genomics, National Institute on Aging, US National Institutes of Health, Baltimore, MD 21224, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato 09042, Cagliari, Italy
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Quantitative and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
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120
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Abstract
Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.
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Affiliation(s)
- Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, MA, USA
- Department of Cardiology, University of Groningen, University Medical Center Groningen Groningen, the Netherlands
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen Groningen, the Netherlands
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
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121
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Im SW, Chae J, Son HY, Cho B, Kim JI, Park JH. A population-specific low-frequency variant of SLC22A12 (p.W258*) explains nearby genome-wide association signals for serum uric acid concentrations among Koreans. PLoS One 2020; 15:e0231336. [PMID: 32271837 PMCID: PMC7145145 DOI: 10.1371/journal.pone.0231336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/20/2020] [Indexed: 11/30/2022] Open
Abstract
Prolonged hyperuricemia is a cause of gout and an independent risk factor for chronic health conditions including diabetes and chronic kidney diseases. Genome-wide association studies (GWASs) for serum uric acid (SUA) concentrations have repeatedly confirmed genetic loci including those encoding uric acid transporters such as ABCG2 and SLC9A2. However, many single nucleotide polymorphisms (SNPs) found in GWASs have been common variants with small effects and unknown functions. In addition, there is still much heritability to be explained. To identify the causative genetic variants for SUA concentrations in Korean subjects, we conducted a GWAS (1902 males) and validation study (2912 males and females) and found four genetic loci reaching genome-wide significance on chromosomes 4 (ABCG2) and 11 (FRMD8, EIF1AD and SLC22A12-NRXN2). Three loci on chromosome 11 were distributed within a distance of 1.3 megabases and they were in weak or moderate linkage disequilibrium (LD) states (r2 = 0.02–0.68). In a subsequent association analysis on the GWAS loci of chromosome 11 using closely positioned markers derived from whole genome sequencing data, the most significant variant to be linked with the nearby GWAS signal was rs121907892 (c.774G>A, p.W258*) of the SLC22A12 gene. This variant, and each of the three GWAS SNPs, were in LD (r2 = 0.06–0.32). The strength of association of SNPs with SUA concentration (negative logarithm of P-values) and the genetic distance (r2 of LD) between rs121907892 and the surrounding SNPs showed a quantitative correlation. This variant has been found only in Korean and Japanese subjects and is known to lower the SUA concentration in the general population. Thus, this low-frequency variant, rs121907892, known to regulate SUA concentrations in previous studies, is responsible for the nearby GWAS signals.
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Affiliation(s)
- Sun-Wha Im
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Jeesoo Chae
- Department of Biomedical Science, Seoul National University Graduate School, Seoul, Republic of Korea
| | - Ho-Young Son
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Belong Cho
- Departments of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Jong-Il Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Science, Seoul National University Graduate School, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
- * E-mail: (JK); (JP)
| | - Jin-Ho Park
- Departments of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- * E-mail: (JK); (JP)
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122
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Floris M, Sanna D, Castiglia P, Putzu C, Sanna V, Pazzola A, De Miglio MR, Sanges F, Pira G, Azara A, Lampis E, Serra A, Carru C, Steri M, Costanza F, Bisail M, Muroni MR. MTHFR, XRCC1 and OGG1 genetic polymorphisms in breast cancer: a case-control study in a population from North Sardinia. BMC Cancer 2020; 20:234. [PMID: 32192442 PMCID: PMC7083022 DOI: 10.1186/s12885-020-06749-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 03/12/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Despite conflicting results, considerable evidence suggests the association between single nucleotide polymorphisms in MTHFR, XRCC1 and OGG1 genes and, risk of developing breast cancer. Here a case-control study is reported, including 135 breat cancer patients and 112 healthy women, all representative of Northern Sardinian population. METHODS Polymerase chain reaction/restriction fragment length polymorphism method was used to determine the genotypes of five polymorphisms: MTHFR C677T (rs1801133) and A1298C (rs1801131), XRCC1 Arg194Trp (rs1799782) and Arg399Gln (rs25487) and OGG1 Ser326Cys (rs1052133). Allelic, genotypic and haplotype association analyses with disease risk and clinicopathological parameters were performed. RESULTS A nominally significant association with breast cancer risk was observed for MTHFR C677T polymorphism heterozygous genotype in the codominant model (OR: 0.57, 95% CI: 0.32-1.00, p = 0.049) and for Cys/Cys genotype of the OGG1 Ser326Cys polymorphism in the recessive model (OR: 0.23, 95% CI: 0.05-1.11, p = 0.0465). No significant differences were found at genotype-level for A1298C polymorphism of the MTHFR gene and Arg194Trp and Arg399Gln of the XRCC1 gene. Furthermore, the OGG1 and XRCC1 rs25487 polymorphisms were nominally associated with PgR, Her2 status and with sporadic breast cancer, respectively. CONCLUSIONS Based on genetic characteristics of individuals included in this study, results suggest that MTHFR CT and OGG1 Cys/Cys genotypes have a protective effect that may have an influence on breast cancer risk in a representative Northern Sardinian population.
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Affiliation(s)
- Matteo Floris
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Paolo Castiglia
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Carlo Putzu
- Division of Medical Oncology, AOU Sassari, Sassari, Italy
| | - Valeria Sanna
- Division of Medical Oncology, AOU Sassari, Sassari, Italy
| | | | - Maria Rosaria De Miglio
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Francesca Sanges
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Giovanna Pira
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Antonio Azara
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Emanuele Lampis
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | | | - Ciriaco Carru
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Maristella Steri
- Institute for Genetic and Biomedical Research, National Research Council (CNR), Monserrato, Cagliari, Italy
| | - Flavia Costanza
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | | | - Maria Rosaria Muroni
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy.
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123
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Marcus JH, Posth C, Ringbauer H, Lai L, Skeates R, Sidore C, Beckett J, Furtwängler A, Olivieri A, Chiang CWK, Al-Asadi H, Dey K, Joseph TA, Liu CC, Der Sarkissian C, Radzevičiūtė R, Michel M, Gradoli MG, Marongiu P, Rubino S, Mazzarello V, Rovina D, La Fragola A, Serra RM, Bandiera P, Bianucci R, Pompianu E, Murgia C, Guirguis M, Orquin RP, Tuross N, van Dommelen P, Haak W, Reich D, Schlessinger D, Cucca F, Krause J, Novembre J. Genetic history from the Middle Neolithic to present on the Mediterranean island of Sardinia. Nat Commun 2020; 11:939. [PMID: 32094358 PMCID: PMC7039977 DOI: 10.1038/s41467-020-14523-6] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 01/08/2020] [Indexed: 11/30/2022] Open
Abstract
The island of Sardinia has been of particular interest to geneticists for decades. The current model for Sardinia's genetic history describes the island as harboring a founder population that was established largely from the Neolithic peoples of southern Europe and remained isolated from later Bronze Age expansions on the mainland. To evaluate this model, we generate genome-wide ancient DNA data for 70 individuals from 21 Sardinian archaeological sites spanning the Middle Neolithic through the Medieval period. The earliest individuals show a strong affinity to western Mediterranean Neolithic populations, followed by an extended period of genetic continuity on the island through the Nuragic period (second millennium BCE). Beginning with individuals from Phoenician/Punic sites (first millennium BCE), we observe spatially-varying signals of admixture with sources principally from the eastern and northern Mediterranean. Overall, our analysis sheds light on the genetic history of Sardinia, revealing how relationships to mainland populations shifted over time.
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MESH Headings
- Archaeology/methods
- Body Remains
- Chromosomes, Human, X/genetics
- Chromosomes, Human, Y/genetics
- DNA, Ancient
- DNA, Mitochondrial/genetics
- Datasets as Topic
- Female
- Genetics, Population/history
- History, 15th Century
- History, 16th Century
- History, 17th Century
- History, 18th Century
- History, 19th Century
- History, 20th Century
- History, 21st Century
- History, Ancient
- History, Medieval
- Human Migration
- Humans
- Italy
- Male
- Models, Genetic
- Sequence Analysis, DNA
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Affiliation(s)
- Joseph H Marcus
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Cosimo Posth
- Max Planck Institute for the Science of Human History, Jena, Germany
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
| | - Harald Ringbauer
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Luca Lai
- Department of Anthropology, University of South Florida, Tampa, FL, USA
- Department of Anthropology, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Robin Skeates
- Department of Archaeology, Durham University, Durham, UK
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica - CNR, Cagliari, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | | | - Anja Furtwängler
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
| | - Anna Olivieri
- Dipartimento di Biologia e Biotecnologie "L. Spallanzani", Università di Pavia, Pavia, Italy
| | - Charleston W K Chiang
- 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
| | - Hussein Al-Asadi
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Committee on Evolutionary Biology, University of Chicago, Chicago, IL, USA
| | - Kushal Dey
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Tyler A Joseph
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Chi-Chun Liu
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Clio Der Sarkissian
- Laboratoire d'Anthropologie Moléculaire et d'Imagerie de Synthèse, CNRS UMR 5288, Université de Toulouse 3, Toulouse, France
| | - Rita Radzevičiūtė
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Megan Michel
- Max Planck Institute for the Science of Human History, Jena, Germany
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | | | - Patrizia Marongiu
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | - Salvatore Rubino
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | | | - Daniela Rovina
- Soprintendenza Archeologia, belle arti e paesaggio delle province di Sassari e Nuoro, Sassari, Italy
| | - Alessandra La Fragola
- Departamento de Geografía, Historia y Humanidades Escuela Internacional de Doctorado de la Universidad de Almería, Almería, Spain
| | - Rita Maria Serra
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
- Center for Anthropological, Paleopathological and Historical Studies of the Sardinian and Mediterranean Populations, University of Sassari, Sassari, Italy
| | - Pasquale Bandiera
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
- Center for Anthropological, Paleopathological and Historical Studies of the Sardinian and Mediterranean Populations, University of Sassari, Sassari, Italy
| | - Raffaella Bianucci
- Department of Sciences and Technological Innovation, University of Eastern Piedmont, 15121, Alessandria, Italy
- Legal Medicine Section, Department of Public Health and Paediatric Sciences, University of Turin, 10126, Turin, Italy
| | - Elisa Pompianu
- Department of History, Human Sciences and Education, University of Sassari, 07100, Sassari, Italy
| | - Clizia Murgia
- Universitat Autònoma de Barcelona, Departament de Biologia Animal, Biologia Vegetal i Ecologia, 08193, Barcelona, Spain
| | - Michele Guirguis
- Department of History, Human Sciences and Education, University of Sassari, 07100, Sassari, Italy
| | - Rosana Pla Orquin
- Department of History, Human Sciences and Education, University of Sassari, 07100, Sassari, Italy
| | - Noreen Tuross
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Peter van Dommelen
- Joukowsky Institute for Archaeology and the Ancient World, Brown University, Providence, RI, 02912, USA
| | - Wolfgang Haak
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - David Reich
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
- Max Planck-Harvard Research Center for the Archaeoscience of the Ancient Mediterranean, Munich, Germany
| | | | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica - CNR, Cagliari, Italy.
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy.
| | - Johannes Krause
- Max Planck Institute for the Science of Human History, Jena, Germany.
- Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany.
- Max Planck-Harvard Research Center for the Archaeoscience of the Ancient Mediterranean, Munich, Germany.
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
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124
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A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping. Transl Psychiatry 2020; 10:50. [PMID: 32066715 PMCID: PMC7026437 DOI: 10.1038/s41398-020-0738-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/02/2020] [Accepted: 01/10/2020] [Indexed: 12/18/2022] Open
Abstract
Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD.
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125
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Bauer J, Broom M, Alonso E. The stabilization of equilibria in evolutionary game dynamics through mutation: mutation limits in evolutionary games. Proc Math Phys Eng Sci 2019; 475:20190355. [PMID: 31824216 DOI: 10.1098/rspa.2019.0355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/01/2019] [Indexed: 11/12/2022] Open
Abstract
The multi-population replicator dynamics is a dynamic approach to coevolving populations and multi-player games and is related to Cross learning. In general, not every equilibrium is a Nash equilibrium of the underlying game, and the convergence is not guaranteed. In particular, no interior equilibrium can be asymptotically stable in the multi-population replicator dynamics, e.g. resulting in cyclic orbits around a single interior Nash equilibrium. We introduce a new notion of equilibria of replicator dynamics, called mutation limits, based on a naturally arising, simple form of mutation, which is invariant under the specific choice of mutation parameters. We prove the existence of mutation limits for a large class of games, and consider a particularly interesting subclass called attracting mutation limits. Attracting mutation limits are approximated in every (mutation-)perturbed replicator dynamics, hence they offer an approximate dynamic solution to the underlying game even if the original dynamic is not convergent. Thus, mutation stabilizes the system in certain cases and makes attracting mutation limits near attainable. Hence, attracting mutation limits are relevant as a dynamic solution concept of games. We observe that they have some similarity to Q-learning in multi-agent reinforcement learning. Attracting mutation limits do not exist in all games, however, raising the question of their characterization.
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Affiliation(s)
- Johann Bauer
- Department of Mathematics, University of London, London, UK
| | - Mark Broom
- Department of Mathematics, University of London, London, UK
| | - Eduardo Alonso
- Department of Computer Science, City, University of London, London, UK
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126
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Chiara M, Zambelli F, Picardi E, Horner DS, Pesole G. Critical assessment of bioinformatics methods for the characterization of pathological repeat expansions with single-molecule sequencing data. Brief Bioinform 2019; 21:1971-1986. [DOI: 10.1093/bib/bbz099] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/22/2019] [Accepted: 07/09/2019] [Indexed: 01/19/2023] Open
Abstract
Abstract
A number of studies have reported the successful application of single-molecule sequencing technologies to the determination of the size and sequence of pathological expanded microsatellite repeats over the last 5 years. However, different custom bioinformatics pipelines were employed in each study, preventing meaningful comparisons and somewhat limiting the reproducibility of the results. In this review, we provide a brief summary of state-of-the-art methods for the characterization of expanded repeats alleles, along with a detailed comparison of bioinformatics tools for the determination of repeat length and sequence, using both real and simulated data. Our reanalysis of publicly available human genome sequencing data suggests a modest, but statistically significant, increase of the error rate of single-molecule sequencing technologies at genomic regions containing short tandem repeats. However, we observe that all the methods herein tested, irrespective of the strategy used for the analysis of the data (either based on the alignment or assembly of the reads), show high levels of sensitivity in both the detection of expanded tandem repeats and the estimation of the expansion size, suggesting that approaches based on single-molecule sequencing technologies are highly effective for the detection and quantification of tandem repeat expansions and contractions.
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Affiliation(s)
- Matteo Chiara
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milan, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Via Amendola e, 70126 Bari, Italy
| | - Federico Zambelli
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milan, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Via Amendola e, 70126 Bari, Italy
| | - Ernesto Picardi
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Via Amendola e, 70126 Bari, Italy
- Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari “A. Moro”, Via Orabona 4, 70126 Bari, Italy
| | - David S Horner
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milan, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Via Amendola e, 70126 Bari, Italy
| | - Graziano Pesole
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Via Amendola e, 70126 Bari, Italy
- Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari “A. Moro”, Via Orabona 4, 70126 Bari, Italy
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127
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A bird's-eye view of Italian genomic variation through whole-genome sequencing. Eur J Hum Genet 2019; 28:435-444. [PMID: 31784700 PMCID: PMC7080768 DOI: 10.1038/s41431-019-0551-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 09/30/2019] [Accepted: 10/29/2019] [Indexed: 11/30/2022] Open
Abstract
The genomic variation of the Italian peninsula populations is currently under characterised: the only Italian whole-genome reference is represented by the Tuscans from the 1000 Genome Project. To address this issue, we sequenced a total of 947 Italian samples from three different geographical areas. First, we defined a new Italian Genome Reference Panel (IGRP1.0) for imputation, which improved imputation accuracy, especially for rare variants, and we tested it by GWAS analysis on red blood traits. Furthermore, we extended the catalogue of genetic variation investigating the level of population structure, the pattern of natural selection, the distribution of deleterious variants and occurrence of human knockouts (HKOs). Overall the results demonstrate a high level of genomic differentiation between cohorts, different signatures of natural selection and a distinctive distribution of deleterious variants and HKOs, confirming the necessity of distinct genome references for the Italian population.
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128
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Höglund J, Rafati N, Rask-Andersen M, Enroth S, Karlsson T, Ek WE, Johansson Å. Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers. Sci Rep 2019; 9:16844. [PMID: 31727947 PMCID: PMC6856527 DOI: 10.1038/s41598-019-53111-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 10/26/2019] [Indexed: 02/07/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.
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Affiliation(s)
- Julia Höglund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Nima Rafati
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Mathias Rask-Andersen
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Stefan Enroth
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Torgny Karlsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Weronica E Ek
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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129
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Abney M, ElSherbiny A. Kinpute: using identity by descent to improve genotype imputation. Bioinformatics 2019; 35:4321-4326. [PMID: 30918937 PMCID: PMC6821425 DOI: 10.1093/bioinformatics/btz221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 02/21/2019] [Accepted: 03/26/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Genotype imputation, though generally accurate, often results in many genotypes being poorly imputed, particularly in studies where the individuals are not well represented by standard reference panels. When individuals in the study share regions of the genome identical by descent (IBD), it is possible to use this information in combination with a study-specific reference panel (SSRP) to improve the imputation results. Kinpute uses IBD information-due to recent, familial relatedness or distant, unknown ancestors-in conjunction with the output from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype probabilities. Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigree, and results in substantially improved imputation quality. RESULTS Given initial estimates of average IBD between subjects in the study sample, Kinpute uses a novel algorithm to select an optimal set of individuals to sequence and use as an SSRP. Kinpute is designed to use as input both this SSRP and the genotype probabilities output from other LD-based imputation software, and uses a new method to combine the LD imputed genotype probabilities with IBD configurations to substantially improve imputation. We tested Kinpute on a human population isolate where 98 individuals have been sequenced. In half of this sample, whose sequence data was masked, we used Impute2 to perform LD-based imputation and Kinpute was used to obtain higher accuracy genotype probabilities. Measures of imputation accuracy improved significantly, particularly for those genotypes that Impute2 imputed with low certainty. AVAILABILITY AND IMPLEMENTATION Kinpute is an open-source and freely available C++ software package that can be downloaded from. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Abney
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Aisha ElSherbiny
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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130
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Yoo SK, Kim CU, Kim HL, Kim S, Shin JY, Kim N, Yang JSW, Lo KW, Cho B, Matsuda F, Schuster SC, Kim C, Kim JI, Seo JS. NARD: whole-genome reference panel of 1779 Northeast Asians improves imputation accuracy of rare and low-frequency variants. Genome Med 2019; 11:64. [PMID: 31640730 PMCID: PMC6805399 DOI: 10.1186/s13073-019-0677-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/11/2019] [Indexed: 12/30/2022] Open
Abstract
Here, we present the Northeast Asian Reference Database (NARD), including whole-genome sequencing data of 1779 individuals from Korea, Mongolia, Japan, China, and Hong Kong. NARD provides the genetic diversity of Korean (n = 850) and Mongolian (n = 384) ancestries that were not present in the 1000 Genomes Project Phase 3 (1KGP3). We combined and re-phased the genotypes from NARD and 1KGP3 to construct a union set of haplotypes. This approach established a robust imputation reference panel for Northeast Asians, which yields the greatest imputation accuracy of rare and low-frequency variants compared with the existing panels. NARD imputation panel is available at https://nard.macrogen.com/ .
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Affiliation(s)
- Seong-Keun Yoo
- Precision Medicine Center, Seoul National University Bundang Hospital, 172 Dolma-ro, Seongnam, Bundang-gu, Gyeonggi-do, 13605, Republic of Korea
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
| | - Chang-Uk Kim
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
| | - Hie Lim Kim
- The Asian School of the Environment, Nanyang Technological University, Singapore, Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
| | - Sungjae Kim
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
| | - Jong-Yeon Shin
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
| | - Namcheol Kim
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
| | | | - Kwok-Wai Lo
- Department of Anatomical & Cellular Pathology and State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China
| | - Belong Cho
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Stephan C Schuster
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
| | - Changhoon Kim
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, 172 Dolma-ro, Seongnam, Bundang-gu, Gyeonggi-do, 13605, Republic of Korea.
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea.
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea.
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
- Gong-Wu Genomic Medicine Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
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131
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Dutta D, Gagliano Taliun SA, Weinstock JS, Zawistowski M, Sidore C, Fritsche LG, Cucca F, Schlessinger D, Abecasis GR, Brummett CM, Lee S. Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test. Genet Epidemiol 2019; 43:800-814. [PMID: 31433078 DOI: 10.1002/gepi.22248] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 06/13/2019] [Indexed: 12/17/2022]
Abstract
The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10-6 . Further simulations under different models of association show that Meta-MultiSKAT can improve the power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.
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Affiliation(s)
- Diptavo Dutta
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Sarah A Gagliano Taliun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Joshua S Weinstock
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Matthew Zawistowski
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Lars G Fritsche
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - David Schlessinger
- Laboratory of Genetics, National Institute on Aging, US National Institutes of Health, Baltimore, Maryland
| | - Gonçalo R Abecasis
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Chad M Brummett
- Division of Pain Medicine, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Seunggeun Lee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.,Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan
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132
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Wang C, Deng S, Sun L, Li L, Hu YQ. A nonparametric test for association with multiple loci in the retrospective case-control study. Stat Methods Med Res 2019; 29:589-602. [PMID: 30987531 DOI: 10.1177/0962280219842892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance-covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.
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Affiliation(s)
- Chan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China.,Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Shufang Deng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Leiming Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Liming Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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133
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Ulirsch JC, Lareau CA, Bao EL, Ludwig LS, Guo MH, Benner C, Satpathy AT, Kartha VK, Salem RM, Hirschhorn JN, Finucane HK, Aryee MJ, Buenrostro JD, Sankaran VG. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat Genet 2019; 51:683-693. [PMID: 30858613 PMCID: PMC6441389 DOI: 10.1038/s41588-019-0362-6] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here, we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding for proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait-cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations. Fine mapping of blood cell traits in UK Biobank identifies putative causal variants and enrichment of fine-mapped variants in accessible chromatin of hematopoietic progenitor cells. The study provides an analytical framework for single-variant and single-cell analyses of genetic associations.
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Affiliation(s)
- Jacob C Ulirsch
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Caleb A Lareau
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Erik L Bao
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Leif S Ludwig
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael H Guo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Endocrinology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA.,Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Christian Benner
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Vinay K Kartha
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Rany M Salem
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Endocrinology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA.,Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Endocrinology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA.,Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Schmidt Fellows Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Martin J Aryee
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jason D Buenrostro
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Harvard Stem Cell Institute, Cambridge, MA, USA.
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134
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Urru SAM, Antonelli A, Sechi GM. Prevalence of multiple sclerosis in Sardinia: A systematic cross-sectional multi-source survey. Mult Scler 2019; 26:372-380. [DOI: 10.1177/1352458519828600] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Partial surveys in sub-regions of Sardinia have suggested a high prevalence of multiple sclerosis (MS) on the island, relative to other Mediterranean populations. We assessed the island-wide prevalence of MS and its detailed distribution in Sardinia. Methods: The study population consisted of 5677 MS patients, 1735 men and 3942 women, living in Sardinia. Neurologists retrospectively examined electronic and paper-based records of patients with a diagnosis of MS. The data were then linked to the administrative health information systems. Crude, age-, and sex-specific prevalence estimates of disease were calculated. Results: The overall age-adjusted MS prevalence was 330 per 100,000 (95% confidence interval (CI) 321–338) in individuals older than 15 years, 447 in women (95% CI 433–461), and 205 in men (95% CI 195–214). The prevalence was highest in the Ogliastra and Nuoro districts, respectively, 425 (95% CI 372–478) and 419 (95% CI 387–451), and lowest in the Olbia-Tempio district, 217 (95% CI 195–239). Most cases had relapsing–remitting MS (79.3%), 16.3% had secondary-progressive MS, and 4.4% had primary-progressive MS. Conclusion: These prevalence are among the highest reported so far worldwide. They provide estimates for comparative analyses in other populations and are essential for public health interventions.
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Affiliation(s)
- Silvana AM Urru
- Biosciences Sector, CRS4, Science and Technology Park Polaris—Piscina Manna, Pula, Italy
| | - Antonello Antonelli
- Department of Clinical Governance, Sardinia Region Health Service, Cagliari, Italy
| | - Giuseppe M Sechi
- Department of Clinical Governance, Sardinia Region Health Service, Cagliari, Italy
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135
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Mozaffari SV, DeCara JM, Shah SJ, Sidore C, Fiorillo E, Cucca F, Lang RM, Nicolae DL, Ober C. Parent-of-origin effects on quantitative phenotypes in a large Hutterite pedigree. Commun Biol 2019; 2:28. [PMID: 30675526 PMCID: PMC6338666 DOI: 10.1038/s42003-018-0267-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 12/14/2018] [Indexed: 12/22/2022] Open
Abstract
The impact of the parental origin of associated alleles in GWAS has been largely ignored. Yet sequence variants could affect traits differently depending on whether they are inherited from the mother or the father, as in imprinted regions, where identical inherited DNA sequences can have different effects based on the parental origin. To explore parent-of-origin effects (POEs), we studied 21 quantitative phenotypes in a large Hutterite pedigree to identify variants with single parent (maternal-only or paternal-only) effects, and then variants with opposite parental effects. Here we show that POEs, which can be opposite in direction, are relatively common in humans, have potentially important clinical effects, and will be missed in traditional GWAS. We identified POEs with 11 phenotypes, most of which are risk factors for cardiovascular disease. Many of the loci identified are characteristic of imprinted regions and are associated with the expression of nearby genes.
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Affiliation(s)
- Sahar V. Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
| | - Jeanne M. DeCara
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Sanjiv J. Shah
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 USA
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, 07100 Italy
| | - Roberto M. Lang
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Dan L. Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
- Department of Statistics, University of Chicago, Chicago, IL 60637 USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
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136
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Mills MC, Rahal C. A scientometric review of genome-wide association studies. Commun Biol 2019; 2:9. [PMID: 30623105 PMCID: PMC6323052 DOI: 10.1038/s42003-018-0261-x] [Citation(s) in RCA: 225] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 12/10/2018] [Indexed: 02/01/2023] Open
Abstract
This scientometric review of genome-wide association studies (GWAS) from 2005 to 2018 (3639 studies; 3508 traits) reveals extraordinary increases in sample sizes, rates of discovery and traits studied. A longitudinal examination shows fluctuating ancestral diversity, still predominantly European Ancestry (88% in 2017) with 72% of discoveries from participants recruited from three countries (US, UK, Iceland). US agencies, primarily NIH, fund 85% and women are less often senior authors. We generate a unique GWAS H-Index and reveal a tight social network of prominent authors and frequently used data sets. We conclude with 10 evidence-based policy recommendations for scientists, research bodies, funders, and editors.
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Affiliation(s)
- Melinda C. Mills
- University of Oxford and Nuffield College, New Road, Oxford, OX1 1NF UK
| | - Charles Rahal
- University of Oxford and Nuffield College, New Road, Oxford, OX1 1NF UK
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137
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Gagliano SA, Sengupta S, Sidore C, Maschio A, Cucca F, Schlessinger D, Abecasis GR. Relative impact of indels versus SNPs on complex disease. Genet Epidemiol 2018; 43:112-117. [PMID: 30565766 PMCID: PMC6330128 DOI: 10.1002/gepi.22175] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 09/07/2018] [Accepted: 10/29/2018] [Indexed: 11/30/2022]
Abstract
It is unclear whether insertions and deletions (indels) are more likely to influence complex traits than abundant single‐nucleotide polymorphisms (SNPs). We sought to understand which category of variation is more likely to impact health. Using the SardiNIA study as an exemplar, we characterized 478,876 common indels and 8,246,244 common SNPs in up to 5,949 well‐phenotyped individuals from an isolated valley in Sardinia. We assessed association between 120 traits, resulting in 89 nonoverlapping‐associated loci.We evaluated whether indels were enriched among credible sets of potential causal variants. These credible sets included 1,319 SNPs and 88 indels. We did not find indels to be significantly enriched. Indels were the most likely causal variant in seven loci, including one locus associated with monocyte count where an indel with causality and mechanism previously demonstrated (rs200748895:TGCTG/T) had a 0.999 posterior probability. Overall, our results show a very modest and nonsignificant enrichment for common indels in associated loci.
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Affiliation(s)
- Sarah A Gagliano
- Center for Statistical Genetics, and Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Sebanti Sengupta
- Center for Statistical Genetics, and Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Andrea Maschio
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - David Schlessinger
- Laboratory of Genetics, National Institute on Aging, US National Institutes of Health, Baltimore, Maryland
| | - Gonçalo R Abecasis
- Center for Statistical Genetics, and Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
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138
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Sivadas A, Scaria V. Population-scale genomics-Enabling precision public health. ADVANCES IN GENETICS 2018; 103:119-161. [PMID: 30904093 DOI: 10.1016/bs.adgen.2018.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.
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Affiliation(s)
- Ambily Sivadas
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
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139
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Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, Gagnon DR, DuVall SL, Li J, Peloso GM, Chaffin M, Small AM, Huang J, Tang H, Lynch JA, Ho YL, Liu DJ, Emdin CA, Li AH, Huffman JE, Lee JS, Natarajan P, Chowdhury R, Saleheen D, Vujkovic M, Baras A, Pyarajan S, Di Angelantonio E, Neale BM, Naheed A, Khera AV, Danesh J, Chang KM, Abecasis G, Willer C, Dewey FE, Carey DJ, Concato J, Gaziano JM, O'Donnell CJ, Tsao PS, Kathiresan S, Rader DJ, Wilson PWF, Assimes TL. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet 2018; 50:1514-1523. [PMID: 30275531 PMCID: PMC6521726 DOI: 10.1038/s41588-018-0222-9] [Citation(s) in RCA: 404] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 08/03/2018] [Indexed: 01/17/2023]
Abstract
The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).
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Affiliation(s)
- Derek Klarin
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
| | - Scott M Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA
| | | | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Scott L DuVall
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jin Li
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mark Chaffin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aeron M Small
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Jie Huang
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Julie A Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- University of Massachusetts College of Nursing and Health Sciences, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, USA
| | - Connor A Emdin
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Jennifer S Lee
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rajiv Chowdhury
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Danish Saleheen
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marijana Vujkovic
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Saiju Pyarajan
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emanuele Di Angelantonio
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aliya Naheed
- Initiative for Noncommunicable Diseases, Health Systems and Population Studies Division, International Centre for Diarrheal Disease Research, Dhaka, Bangladesh
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John Danesh
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyong-Mi Chang
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gonçalo Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Cristen Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | | | - John Concato
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Clinical Epidemiology Research Center, VA Connecticut Healthcare System, West Haven, CT, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Christopher J O'Donnell
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter W F Wilson
- Atlanta VA Medical Center, Decatur, GA, USA
- Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- VA Palo Alto Health Care System, Palo Alto, CA, USA.
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140
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Chiang CWK, Mangul S, Robles C, Sankararaman S. A Comprehensive Map of Genetic Variation in the World's Largest Ethnic Group-Han Chinese. Mol Biol Evol 2018; 35:2736-2750. [PMID: 30169787 PMCID: PMC6693441 DOI: 10.1093/molbev/msy170] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
As are most non-European populations, the Han Chinese are relatively understudied in population and medical genetics studies. From low-coverage whole-genome sequencing of 11,670 Han Chinese women we present a catalog of 25,057,223 variants, including 548,401 novel variants that are seen at least 10 times in our data set. Individuals from this data set came from 24 out of 33 administrative divisions across China (including 19 provinces, 4 municipalities, and 1 autonomous region), thus allowing us to study population structure, genetic ancestry, and local adaptation in Han Chinese. We identified previously unrecognized population structure along the East-West axis of China, demonstrated a general pattern of isolation-by-distance among Han Chinese, and reported unique regional signals of admixture, such as European influences among the Northwestern provinces of China. Furthermore, we identified a number of highly differentiated, putatively adaptive, loci (e.g., MTHFR, ADH7, and FADS, among others) that may be driven by immune response, climate, and diet in the Han Chinese. Finally, we have made available allele frequency estimates stratified by administrative divisions across China in the Geography of Genetic Variant browser for the broader community. By leveraging the largest currently available genetic data set for Han Chinese, we have gained insights into the history and population structure of the world's largest ethnic group.
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Affiliation(s)
- Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA
| | - Serghei Mangul
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA
- Institute for Quantitative and Computational Bioscience, University of California Los Angeles, Los Angeles, CA
| | - Christopher Robles
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Sriram Sankararaman
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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141
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Genome-wide enrichment of m6A-associated single-nucleotide polymorphisms in the lipid loci. THE PHARMACOGENOMICS JOURNAL 2018; 19:347-357. [DOI: 10.1038/s41397-018-0055-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 07/07/2018] [Accepted: 08/10/2018] [Indexed: 12/17/2022]
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142
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Is the third molar maturity index (I 3M) useful for a genetic isolate population? Study of a Sardinian sample of children and young adults. Int J Legal Med 2018; 132:1787-1794. [PMID: 30232544 DOI: 10.1007/s00414-018-1933-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 09/05/2018] [Indexed: 02/04/2023]
Abstract
This work aims to assess the validity of the cut-off value (0.08) of the third molar maturity index (I3M) for discriminating minors from adults in Sardinian population. A sample of 336 digital panoramic radiographs of healthy Sardinian children and young minors (165 females and 171 males), aged between 15 and 23 years (mean age, 19.35 years in females and 18.80 years in males), was retrospectively evaluated. The left lower third molars were analysed by applying a specific cut-off value of 0.08 determined by Cameriere et al. in 2008. The reliability and reproducibility of the test was also studied: the intra-class correlation coefficients (ICC) were 0.91 (95% CI, 0.89-0.93) and 0.88 (95% CI, 0.86-0.90), for the intra- and inter-observer reliability, respectively. The I3M gradually decreased as the real age gradually increased in both sexes. According to the pooled results of the diagnostic test, the accuracy (ACC) was 0.86 (95% CI, 0.82-0.89); the proportion of correctly classified subjects (Se = sensitivity) was 0.82 (95% CI: 0.76-0.86); and specificity (Sp = specificity) was 0.95 (95% CI, 0.89-0.97). The positive predictive values (PPV) and the negative predictive values (NPVs) were 0.97 (95% CI, 0.94-0.99) and 0.70 (95% CI, 0.62-0.77). The LR+ and the LR- were 17.12 (95% CI, 7.27 to 40.36) and 0.19 (95% CI, 0.14 to 0.25). In spite of this, significant differences in the early mineralisation of the third molar were found between sexes as well as in the results of the diagnostic test, showing a better sensitivity in males than in females. The results showed that, although the third molar teeth are highly variable in development, and with differences between females and males as compare to other teeth, the I3M is a reliable method to distinguish between minors and adults even in such a genetic isolate population.
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143
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Abstract
The population of the Mediterranean island of Sardinia has made important contributions to genome-wide association studies of complex disease traits and, based on ancient DNA (aDNA) studies of mainland Europe, Sardinia is hypothesized to be a unique refuge for early Neolithic ancestry. To provide new insights on the genetic history of this flagship population, we analyzed 3,514 whole-genome sequenced individuals from Sardinia. We find Sardinian samples show elevated levels of shared ancestry with Basque individuals, especially samples from the more historically isolated regions of Sardinia. Our analysis also uniquely illuminates how levels of genetic similarity with mainland aDNA samples varies subtly across the island. Together, our results indicate within-island sub-structure and sex-biased processes have substantially impacted the genetic history of Sardinia. These results give new insight to the demography of ancestral Sardinians and help further the understanding of sharing of disease risk alleles between Sardinia and mainland populations.
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144
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Cox JW, Patel D, Chung J, Zhu C, Lent S, Fisher V, Pitsillides A, Farrer L, Zhang X. An efficient analytic approach in genome-wide identification of methylation quantitative trait loci response to fenofibrate treatment. BMC Proc 2018; 12:44. [PMID: 30275893 PMCID: PMC6157188 DOI: 10.1186/s12919-018-0152-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Background The study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach. Methods Subjects (n = 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included. Following the quality control steps of removing certain cytosine-phosphate-guanine (CpG) probes, the post−/premethylation changes (post/pre) were log transformed and the association was performed on 208,449 CpG sites. An additive linear mixed-effects model was used to test the association between each CpG probe and single nucleotide polymorphisms (SNPs) around ±1 Mb region, with age, sex, smoke, batch effect, and principal components included as covariates. Bonferroni correction was applied to define the significance threshold (p < 5.6 × 10− 10, given a total of 89,217,303 tests). Finally, we integrated our response-meQTL (re-meQTL) findings with the published genome-wide association study (GWAS) catalog of human diseases/traits. Results We identified 1087 SNPs as cis re-meQTLs associated with 610 CpG probes/sites located in 351 unique gene loci. Among these 1087 cis re-meQTL SNPs, 229 were unique and 6 were co-localized at 8 unique disease/trait loci reported in the GWAS catalog (enrichment p = 1.51 × 10− 23). Specifically, a lipid SNP, rs10903129, located in intron regions of gene TMEM57, was a re-meQTL (p = 3.12 × 10− 36) associated with the CpG probe cg09222892, which is in the upstream region of the gene RHCE, indicating a new target gene for rs10903129. In addition, we found that SNP rs12710728 has a suggestive association with cg17097782 (p = 1.77 × 10− 4), and that this SNP is in high linkage disequilibrium (LD) (R2 > 0.8) with rs7443270, which was previously reported to be associated with fenofibrate response (p = 5.00 × 10− 6). Conclusions By using a novel analytic approach, we efficiently identified thousands of cis re-meQTLs that provide a unique resource for further characterizing functional roles and gene targets of the SNPs that are most responsive to fenofibrate treatment. Our efficient analytic approach can be extended to large response quantitative trait locus studies with large sample sizes and multiple time points data.
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Affiliation(s)
- Jiayi Wu Cox
- 1Department of Genetics and Genomics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Devanshi Patel
- 2Department of Bioinformatics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Jaeyoon Chung
- 2Department of Bioinformatics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Congcong Zhu
- 4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Samantha Lent
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Virginia Fisher
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Achilleas Pitsillides
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Lindsay Farrer
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Xiaoling Zhang
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
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145
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Morin A, Madore AM, Kwan T, Ban M, Partanen J, Rönnblom L, Syvänen AC, Sawcer S, Stunnenberg H, Lathrop M, Pastinen T, Laprise C. Exploring rare and low-frequency variants in the Saguenay-Lac-Saint-Jean population identified genes associated with asthma and allergy traits. Eur J Hum Genet 2018; 27:90-101. [PMID: 30206357 PMCID: PMC6303288 DOI: 10.1038/s41431-018-0266-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 08/08/2018] [Accepted: 08/19/2018] [Indexed: 12/13/2022] Open
Abstract
The Saguenay–Lac-Saint-Jean (SLSJ) region is located in northeastern Quebec and is known for its unique demographic history and founder effect. As founder populations are enriched with population-specific variants, we characterized the variants distribution in SLSJ and compared it with four European populations (Finnish, Sweden, United Kingdom and France), of which the Finnish population is another founder population. Targeted sequencing of the coding and non-coding immune regulatory regions of the SLSJ asthma familial cohort and the four European populations were performed. Rare and low-frequency coding and non-coding regulatory variants identified in the SLSJ population were then investigated for variant- and gene-level associations with asthma and allergy-related traits (eosinophil percentage, immunoglobulin (Ig) E levels and lung function). Our data showed that (1) rare or deleterious variants were not enriched in the two founder populations as compared with the three non-founder European populations; (2) a larger proportion of founder population-specific variants occurred with higher frequencies; and (3) low-frequency variants appeared to be more deleterious. Furthermore, a rare variant, rs1386931, located in the 3ʹ-UTR of CXCR6 and intron of FYCO1 was found to be associated with eosinophil percentage. Gene-based analyses identified NRP2, MRPL44 and SERPINE2 to be associated with various asthma and allergy-related traits. Our study demonstrated the usefulness of using a founder population to identify new genes associated with asthma and allergy-related traits; thus better understand the genes and pathways implicated in pathophysiology.
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Affiliation(s)
- Andréanne Morin
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,McGill University and Genome Québec Innovation Centre, Montréal, QC, Canada.,Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada
| | - Anne-Marie Madore
- Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada
| | - Tony Kwan
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,McGill University and Genome Québec Innovation Centre, Montréal, QC, Canada
| | - Maria Ban
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jukka Partanen
- Research & Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Lars Rönnblom
- Department of Medical Sciences, Section of Rheumatology, Uppsala University, Uppsala, Sweden
| | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Stephen Sawcer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Hendrik Stunnenberg
- Department of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, The Netherlands
| | - Mark Lathrop
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,McGill University and Genome Québec Innovation Centre, Montréal, QC, Canada
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,McGill University and Genome Québec Innovation Centre, Montréal, QC, Canada.,Center for Pediatric Genomic Medicine, Kansas City, MO, USA
| | - Catherine Laprise
- Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada. .,Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay-Lac-Saint-Jean, Saguenay, QC, Canada.
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146
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Kocarnik JM, Richard M, Graff M, Haessler J, Bien S, Carlson C, Carty CL, Reiner AP, Avery CL, Ballantyne CM, LaCroix AZ, Assimes TL, Barbalic M, Pankratz N, Tang W, Tao R, Chen D, Talavera GA, Daviglus ML, Chirinos-Medina DA, Pereira R, Nishimura K, Bůžková P, Best LG, Ambite JL, Cheng I, Crawford DC, Hindorff LA, Fornage M, Heiss G, North KE, Haiman CA, Peters U, Le Marchand L, Kooperberg C. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study. Hum Mol Genet 2018; 27:2940-2953. [PMID: 29878111 PMCID: PMC6077792 DOI: 10.1093/hmg/ddy211] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 05/02/2018] [Accepted: 05/28/2018] [Indexed: 12/11/2022] Open
Abstract
C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
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Affiliation(s)
- Jonathan M Kocarnik
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Institute of Translational Health Sciences, University of Washington, Seattle, WA, USA
| | - Melissa Richard
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Misa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey Haessler
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stephanie Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Carlson
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Alexander P Reiner
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christie M Ballantyne
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Andrea Z LaCroix
- Department of Epidemiology, University of San Diego, San Diego, CA, USA
| | | | - Maja Barbalic
- Division of Epidemiology, Human Genetics & Environmental Sciences, The University of Texas, Houston, TX, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Weihong Tang
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dongquan Chen
- Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gregory A Talavera
- Division of Health Promotion and Behavioral Science, San Diego State University, San Diego, CA, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois College of Medicine, Chicago, IL, USA
| | - Diana A Chirinos-Medina
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rocio Pereira
- Division of Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Katie Nishimura
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Petra Bůžková
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lyle G Best
- Missouri Breaks Industries Research, Inc., Eagle Butte, SD, USA
| | - José Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Iona Cheng
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Dana C Crawford
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | | | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Charles Kooperberg
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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147
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Floris M, Olla S, Schlessinger D, Cucca F. Genetic-Driven Druggable Target Identification and Validation. Trends Genet 2018; 34:558-570. [PMID: 29803319 PMCID: PMC6088790 DOI: 10.1016/j.tig.2018.04.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/13/2018] [Accepted: 04/23/2018] [Indexed: 12/19/2022]
Abstract
Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.
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Affiliation(s)
- Matteo Floris
- Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy; IRGB-CNR, Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Stefania Olla
- IRGB-CNR, Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - David Schlessinger
- Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Francesco Cucca
- Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy; IRGB-CNR, Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy.
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148
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Lin Y, Liu L, Yang S, Li Y, Lin D, Zhang X, Yin X. Genotype imputation for Han Chinese population using Haplotype Reference Consortium as reference. Hum Genet 2018; 137:431-436. [DOI: 10.1007/s00439-018-1894-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/26/2018] [Indexed: 11/30/2022]
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149
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Steri M, Idda ML, Whalen MB, Orrù V. Genetic variants in mRNA untranslated regions. WILEY INTERDISCIPLINARY REVIEWS-RNA 2018; 9:e1474. [PMID: 29582564 DOI: 10.1002/wrna.1474] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/05/2018] [Accepted: 02/11/2018] [Indexed: 12/24/2022]
Abstract
Genome Wide Association Studies (GWAS) have mapped thousands of genetic variants associated with complex disease risk and regulating quantitative traits, thus exploiting an unprecedented high-resolution genetic characterization of the human genome. A small fraction (3.7%) of the identified associations is located in untranslated regions (UTRs), and the molecular mechanism has been elucidated for few of them. Genetic variations at UTRs may modify regulatory elements affecting the interaction of the UTRs with proteins and microRNAs. The overall functional consequences include modulation of messenger RNA (mRNA) transcription, secondary structure, stability, localization, translation, and access to regulators like microRNAs (miRNAs) and RNA-binding proteins (RBPs). Alterations of these regulatory mechanisms are known to modify molecular pathways and cellular processes, potentially leading to disease processes. Here, we analyze some examples of genetic risk variants mapping in the UTR regulatory elements. We describe a recently identified genetic variant localized in the 3'UTR of the TNFSF13B gene, associated with autoimmunity risk and responsible of an increased stability and translation of TNFSF13B mRNA. We discuss how the correct use and interpretation of public GWAS repositories could lead to a better understanding of etiopathogenetic mechanisms and the generation of robust biological hypothesis as starting point for further functional studies. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Maristella Steri
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - M Laura Idda
- Laboratory of Genetics and Genomics, National Institute on Aging, National Institute of Health, Baltimore, Maryland
| | - Michael B Whalen
- Istituto di Biofisica, Consiglio Nazionale delle Ricerche (CNR), Trento, Italy
| | - Valeria Orrù
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
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150
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Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, Kvale MN, Kwok PY, Schaefer C, Krauss RM, Iribarren C, Risch N. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet 2018; 50:401-413. [PMID: 29507422 PMCID: PMC5942247 DOI: 10.1038/s41588-018-0064-5] [Citation(s) in RCA: 189] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 01/19/2018] [Indexed: 12/16/2022]
Abstract
A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
| | | | - Tanushree Haldar
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Dilrini K Ranatunga
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Marisa W Medina
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Mark N Kvale
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Pui-Yan Kwok
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Ronald M Krauss
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Neil Risch
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. .,Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA.
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