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Jason F, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, McCarthy MI. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data 2017; 4:170179. [PMID: 29257133 PMCID: PMC5735917 DOI: 10.1038/sdata.2017.179] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 11/02/2017] [Indexed: 02/08/2023] Open
Abstract
To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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Affiliation(s)
- Flannick Jason
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,
J.F. ()
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tanya M. Teslovich
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Vineeta Agarwala
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kyle J. Gaulton
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lizz Caulkins
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Ryan Koesterer
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Clement Ma
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Loukas Moutsianas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Davis J. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Statistics, University of Oxford, Oxford, UK
| | - Manuel A. Rivas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - John R. B. Perry
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK,MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Xueling Sim
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Thomas W. Blackwell
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Neil R. Robertson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - N William Rayner
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Pablo Cingolani
- School of Computer Science, McGill University, Montreal, Quebec, Canada,McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada
| | - Adam E. Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Juan Fernandez Tajes
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Heather M. Highland
- Human Genetics Center, The University of Texas Graduate School of Biomedical Sciences at Houston, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA,National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Peter S. Chines
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Cecilia M. Lindgren
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christopher Hartl
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Han Chen
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Jeroen R. Huyghe
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Richard D. Pearson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Ashish Kumar
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Eric R. Gamazon
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Jaehoon Lee
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Yuhui Chen
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Jennifer E. Below
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Jinyan Huang
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Min Jin Go
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Michael L. Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen C. J. Parker
- Departments of Computational Medicine & Bioinformatics and Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Tibor V. Varga
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Todd Green
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Nicola L. Beer
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Aaron G. Day-Williams
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Teresa Ferreira
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tasha Fingerlin
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Momoko Horikoshi
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cheng Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Iksoo Huh
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Mohammad Kamran Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Bong-Jo Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Young Jin Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Min-Seok Kwon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Juyoung Lee
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Selyeong Lee
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Keng-Han Lin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Taylor J. Maxwell
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yoshihiko Nagai
- McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada,Department of Human Genetics, McGill University, Montreal, Quebec, Canada,Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Xu Wang
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Ryan P. Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Joon Yoon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK
| | - Nir Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, USA
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania—Perelman School of Medicine, Philadelphia, Pennsylvania, USA,Department of Genetics, University of Pennsylvania—Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Bok-Ghee Han
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Christopher P. Jenkinson
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA,Research, South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Teemu Kuulasmaa
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Johanna Kuusisto
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland,Kuopio University Hospital, Kuopio, Finland
| | - Alisa Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Nicholette D. Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Beverley Balkau
- Centre for Research in Epidemiology and Population Health, Inserm U1018, Villejuif, France
| | - Alena Stančáková
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Hanna E. Abboud
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Heiner Boeing
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | | | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - James Scott
- National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Jason Carey
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Phoenix Kwan
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - George Grant
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Joshua D. Smith
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shaun Purcell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Psychiatry, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Adam S. Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joanna M. M. Howson
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Heung Man Lee
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Wei Zhao
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Danesh
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Vincent K. L. Lam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Kyong Soo Park
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Uzma Afzal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - David Aguilar
- Cardiovascular Division, Baylor College of Medicine, Houston, Texas, USA
| | - Rector Arya
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Edmund Chan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - Carmen Navarro
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain,CIBER Epidemiología y Salud Pública (CIBERESP), Spain,Unit of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Spain
| | - Ching-Yu Cheng
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Domenico Palli
- Cancer Research and Prevention Institute (ISPO), Florence, Italy
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Joanne E. Curran
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Dennis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Vidya S. Farook
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Sharon P. Fowler
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Barry I. Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Michael Griswold
- Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Daniel Esten Hale
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Pamela J. Hicks
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Chiea-Chuen Khor
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Division of Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Satish Kumar
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | | | - Wei Yen Lim
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Division of Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Institute of Health Sciences, University of Oulu, Oulu, Finland,Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Solomon K. Musani
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - William R. Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Loïc Yengo
- CNRS-UMR8199, Lille University, Lille Pasteur Institute, Lille, France
| | - Sian-Tsung Tan
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Herman A. Taylor
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Farook Thameem
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Gregory Wilson
- College of Public Services, Jackson State University, Jackson, Mississippi, USA
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Pål Rasmus Njølstad
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway,Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Jonathan C. Levy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK,NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Lori L. Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Thomas Schwarzmayr
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - João Fadista
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | | | - Christian Herder
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas Wieland
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark,Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Cramer Christensen
- Department of Internal Medicine and Endocrinology, Vejle Hospital, Vejle, Denmark
| | - Heikki A. Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland,Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Minerva Foundation Institute for Medical Research, Helsinki, Finland,Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Alex S. F. Doney
- Division of Cardiovascular and Diabetes Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - Leena Kinnunen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Tõnu Esko
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Estonian Genome Center, University of Tartu, Tartu, Estonia,Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA,Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Andrew J. Farmer
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Liisa Hakaste
- Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Folkhälsan Research Centre, Helsinki, Finland,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Dylan Hodgkiss
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jasmina Kravic
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Valeri Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mette Hollensted
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Torben Jørgensen
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark,Department of Public Health, Institute of Health Sciences, University of Copenhagen, Copenhagen, Denmark,Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Johanne Marie Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Annemari Käräjämäki
- Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland,Diabetes Center, Vaasa Health Care Center, Vaasa, Finland
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Lars Lannfelt
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Torsten Lauritzen
- Department of Public Health, Section of General Practice, Aarhus University, Aarhus, Denmark
| | - Narisu Narisu
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark,Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Olle Melander
- Department of Clinical Sciences, Hypertension and Cardiovascular Disease, Lund University, Malmö, Sweden
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Marju Orho-Melander
- Department of Clinical Sciences, Diabetes and Cardiovascular Disease, Genetic Epidemiology, Lund University, Malmö, Sweden
| | - Lu Qi
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Qibin Qi
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, USA
| | - Michael Roden
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Amy Swift
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Anders H. Rosengren
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Kathleen Stirrups
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Andrew R. Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | - Christine Blancher
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mauricio O. Carneiro
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Jared Maguire
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Ryan Poplin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Khalid Shakir
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Timothy Fennell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Mark DePristo
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Martin Hrabé de Angelis
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
| | - Panos Deloukas
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK,Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Anette P. Gjesing
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Goo Jun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter Nilsson
- Department of Clinical Sciences, Medicine, Lund University, Malmö, Sweden
| | - Jacquelyn Murphy
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Robert Onofrio
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Christa Meisinger
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Frank B. Hu
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Bo Isomaa
- Folkhälsan Research Centre, Helsinki, Finland,Department of Social Services and Health Care, Jakobstad, Finland
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Liming Liang
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Annette Peters
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Cornelia Huth
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Stephen P O'Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rainer Rauramaa
- Foundation for Research in Health, Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Jaakko Tuomilehto
- Center for Vascular Prevention, Danube University Krems, Krems, Austria,Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia,Dasman Diabetes Institute, Dasman, Kuwait,National Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Richard M. Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,Department of Physiology & Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,Diabetes and Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Richard N. Bergman
- Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, California, USA
| | - Dwaipayan Bharadwaj
- Functional Genomics Unit, CSIR-Institute of Genomics & Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Republic of Korea
| | - Giriraj R. Chandak
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, India
| | - Juliana CN Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Kathleen A. Jablonski
- The Biostatistics Center, The George Washington University, Rockville, Maryland, USA
| | - Donna M. Lehman
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Toni I. Pollin
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, and Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Manjinder Sandhu
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, India
| | - Philippe Froguel
- CNRS-UMR8199, Lille University, Lille Pasteur Institute, Lille, France,Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Life Sciences Institute, National University of Singapore, Singapore, Singapore,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ralph A. DeFronzo
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Harald Grallert
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Benjamin Glaser
- Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | | | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Mark Walker
- The Medical School, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Eric Banks
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Hae Kyung Im
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Hannover Unified Biobank, Hannover Medical School, Hanover, Germany,Department of Human Genetics, Hannover Medical School, Hanover, Germany
| | - Paul W. Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Gemma Buck
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joseph Trakalo
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Buck
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yossi Farjoun
- Data Sciences and Data Engineering, Broad Institute, Cambridge, Massachusetts, USA
| | - Katharine R. Owen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Anna L. Gloyn
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany
| | - Tiinamaija Tuomi
- Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Folkhälsan Research Centre, Helsinki, Finland,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland,Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Jaspal Singh Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK,Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Jong-Young Lee
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Republic of Korea,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Peter Donnelly
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Statistics, University of Oxford, Oxford, UK
| | - Andrew D. Morris
- Clinical Research Centre, Centre for Molecular Medicine, Ninewells Hospital and Medical School, Dundee, UK,The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | | | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Francis S. Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gil Atzmon
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, USA,Department of Natural Science, University of Haifa, Haifa, Israel
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Markku Laakso
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland,Kuopio University Hospital, Kuopio, Finland
| | - Tim M. Strom
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Graeme I. Bell
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, Illinois, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | | | - E. Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Cardiovascular & Metabolic Disorders Program, Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Gilean McVean
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Craig L. Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Mark Seielstad
- Department of Laboratory Medicine & Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA,Blood Systems Research Institute, San Francisco, California, USA
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - James B. Meigs
- General Medicine Division, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Nancy J. Cox
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Rob Sladek
- McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada,Department of Human Genetics, McGill University, Montreal, Quebec, Canada,Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Stacey Gabriel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden,Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Goncalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura J. Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Estonian Genome Center, University of Tartu, Tartu, Estonia,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Hyun Min Kang
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David Altshuler
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA,Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Noël P. Burtt
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Jose C. Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA,Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
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102
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Abstract
Background Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure's causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage. Results In this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting. Conclusions Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.
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103
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Hoya S, Watanabe Y, Hishimoto A, Nunokawa A, Kaneko N, Muratake T, Shinmyo N, Otsuka I, Okuda S, Inoue E, Igeta H, Shibuya M, Egawa J, Orime N, Sora I, Someya T. Rare PDCD11 variations are not associated with risk of schizophrenia in Japan. Psychiatry Clin Neurosci 2017; 71:780-788. [PMID: 28657695 DOI: 10.1111/pcn.12549] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 06/06/2017] [Accepted: 06/20/2017] [Indexed: 12/26/2022]
Abstract
AIM Rare gene variations are thought to confer substantial risk for schizophrenia. We performed a three-stage study to identify rare variations that have a strong impact on the risk of developing schizophrenia. METHODS In the first stage, we prioritized rare missense variations using whole-exome sequencing (WES) data from three families, consisting of a proband, an affected sibling, and parents. In the second stage, we performed targeted resequencing of the PDCD11 coding region in 96 patients. In the third stage, we conducted an association study of rare PDCD11 variations with schizophrenia in a total of 1357 patients and 1394 controls. RESULTS Via WES, we identified two rare missense PDCD11 variations, p.(Asp961Asn) and p.(Val1240Leu), shared by two affected siblings within families. Targeted resequencing of the PDCD11 coding region identified three rare non-synonymous variations: p.(Asp961Asn), p.(Phe1835del), and p.(Arg1837His). The case-control study demonstrated no significant associations between schizophrenia and four rare PDCD11 variations: p.(Asp961Asn), p.(Val1240Leu), p.(Phe1835del), and p.(Arg1837His). CONCLUSION Our data do not support the role of rare PDCD11 variations in conferring substantial risk for schizophrenia in the Japanese population.
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Affiliation(s)
- Satoshi Hoya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuichiro Watanabe
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Ayako Nunokawa
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Oojima Hospital, Niigata, Japan
| | - Naoshi Kaneko
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Oojima Hospital, Niigata, Japan
| | - Tatsuyuki Muratake
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Furumachi Mental Clinic, Niigata, Japan
| | - Naofumi Shinmyo
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Ikuo Otsuka
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Shujiro Okuda
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Emiko Inoue
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hirofumi Igeta
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masako Shibuya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Jun Egawa
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Naoki Orime
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Ichiro Sora
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Toshiyuki Someya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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104
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He Z, Xu B, Lee S, Ionita-Laza I. Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data. Am J Hum Genet 2017; 101:340-352. [PMID: 28844485 PMCID: PMC5590864 DOI: 10.1016/j.ajhg.2017.07.011] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 07/18/2017] [Indexed: 12/14/2022] Open
Abstract
Substantial progress has been made in the functional annotation of genetic variation in the human genome. Integrative analysis that incorporates such functional annotations into sequencing studies can aid the discovery of disease-associated genetic variants, especially those with unknown function and located outside protein-coding regions. Direct incorporation of one functional annotation as weight in existing dispersion and burden tests can suffer substantial loss of power when the functional annotation is not predictive of the risk status of a variant. Here, we have developed unified tests that can utilize multiple functional annotations simultaneously for integrative association analysis with efficient computational techniques. We show that the proposed tests significantly improve power when variant risk status can be predicted by functional annotations. Importantly, when functional annotations are not predictive of risk status, the proposed tests incur only minimal loss of power in relation to existing dispersion and burden tests, and under certain circumstances they can even have improved power by learning a weight that better approximates the underlying disease model in a data-adaptive manner. The tests can be constructed with summary statistics of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of multiple studies without sharing individual-level data. We applied the proposed tests to a meta-analysis of noncoding rare variants in Metabochip data on 12,281 individuals from eight studies for lipid traits. By incorporating the Eigen functional score, we detected significant associations between noncoding rare variants in SLC22A3 and low-density lipoprotein and total cholesterol, associations that are missed by standard dispersion and burden tests.
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Affiliation(s)
- Zihuai He
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Bin Xu
- Department of Psychiatry, Columbia University, New York, NY 10032, USA
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
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105
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Tanisawa K, Arai Y, Hirose N, Shimokata H, Yamada Y, Kawai H, Kojima M, Obuchi S, Hirano H, Yoshida H, Suzuki H, Fujiwara Y, Ihara K, Sugaya M, Arai T, Mori S, Sawabe M, Sato N, Muramatsu M, Higuchi M, Liu YW, Kong QP, Tanaka M. Exome-wide Association Study Identifies CLEC3B Missense Variant p.S106G as Being Associated With Extreme Longevity in East Asian Populations. J Gerontol A Biol Sci Med Sci 2017; 72:309-318. [PMID: 27154906 PMCID: PMC5861862 DOI: 10.1093/gerona/glw074] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 04/06/2016] [Indexed: 02/05/2023] Open
Abstract
Life span is a complex trait regulated by multiple genetic and environmental factors; however, the genetic determinants of extreme longevity have been largely unknown. To identify the functional coding variants associated with extreme longevity, we performed an exome-wide association study (EWAS) on a Japanese population by using an Illumina HumanExome Beadchip and a focused replication study on a Chinese population. The EWAS on two independent Japanese cohorts consisting of 530 nonagenarians/centenarians demonstrated that the G allele of CLEC3B missense variant p.S106G was associated with extreme longevity at the exome-wide level of significance (p = 2.33×10–7, odds ratio [OR] = 1.50). The CLEC3B gene encodes tetranectin, a protein implicated in the mineralization process in osteogenesis as well as in the prognosis and metastasis of cancer. The replication study consisting of 448 Chinese nonagenarians/centenarians showed that the G allele of CLEC3B p.S106G was also associated with extreme longevity (p = .027, OR = 1.51), and the p value of this variant reached 1.87×10–8 in the meta-analysis of Japanese and Chinese populations. In conclusion, the present study identified the CLEC3B p.S106G as a novel longevity-associated variant, raising the novel hypothesis that tetranectin, encoded by CLEC3B, plays a role in human longevity and aging.
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Affiliation(s)
- Kumpei Tanisawa
- Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology, Japan.,Faculty of Sport Sciences, Waseda University, Tokorozawa, Japan.,Japan Society for the Promotion of Science, Tokyo, Japan
| | - Yasumichi Arai
- Center for Supercentenarian Research, Keio University School of Medicine, Tokyo, Japan
| | - Nobuyoshi Hirose
- Center for Supercentenarian Research, Keio University School of Medicine, Tokyo, Japan
| | - Hiroshi Shimokata
- Section of Longitudinal Study of Aging, National Institute for Longevity Sciences (NILS-LSA), National Center for Geriatrics and Gerontology, Obu, Japan.,Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences, Nisshin, Japan
| | - Yoshiji Yamada
- Department of Human Functional Genomics, Life Science Research Center, Mie University, Tsu, Japan
| | - Hisashi Kawai
- Human Care Research Team, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Motonaga Kojima
- Human Care Research Team, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Shuichi Obuchi
- Human Care Research Team, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Hirohiko Hirano
- Research Team for Promoting Independence of the Elderly, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Hideyo Yoshida
- Research Team for Promoting Independence of the Elderly, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Hiroyuki Suzuki
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Yoshinori Fujiwara
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Kazushige Ihara
- Department of Public Health, Toho University School of Medicine, Tokyo, Japan
| | - Maki Sugaya
- Department of Genomics for Longevity and Health, Tokyo Metropolitan Institute of Gerontology, Japan
| | - Tomio Arai
- Department of Pathology, Tokyo Metropolitan Geriatric Hospital, Japan
| | - Seijiro Mori
- Center for Promotion of Clinical Investigation, Tokyo Metropolitan Geriatric Hospital, Japan
| | - Motoji Sawabe
- Section of Molecular Pathology, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Japan
| | - Noriko Sato
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University, Japan
| | - Masaaki Muramatsu
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University, Japan
| | - Mitsuru Higuchi
- Faculty of Sport Sciences, Waseda University, Tokorozawa, Japan.,Institute of Advanced Active Aging Research, Waseda University, Tokorozawa, Japan
| | - Yao-Wen Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, China
| | - Qing-Peng Kong
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, China
| | - Masashi Tanaka
- Department of Clinical Laboratory, Tokyo Metropolitan Geriatric Hospital, Japan
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106
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Wang H, Cade BE, Chen H, Gleason KJ, Saxena R, Feng T, Larkin EK, Vasan RS, Lin H, Patel SR, Tracy RP, Liu Y, Gottlieb DJ, Below JE, Hanis CL, Petty LE, Sunyaev SR, Frazier-Wood AC, Rotter JI, Post W, Lin X, Redline S, Zhu X. Variants in angiopoietin-2 (ANGPT2) contribute to variation in nocturnal oxyhaemoglobin saturation level. Hum Mol Genet 2017; 25:5244-5253. [PMID: 27798093 DOI: 10.1093/hmg/ddw324] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/19/2016] [Indexed: 12/30/2022] Open
Abstract
Genetic determinants of sleep-disordered breathing (SDB), a common set of disorders that contribute to significant cardiovascular and neuropsychiatric morbidity, are not clear. Overnight nocturnal oxygen saturation (SaO2) is a clinically relevant and easily measured indicator of SDB severity but its genetic contribution has never been studied. Our recent study suggests nocturnal SaO2 is heritable. We performed linkage analysis, association analysis and haplotype analysis of average nocturnal oxyhaemoglobin saturation in participants in the Cleveland Family Study (CFS), followed by gene-based association and additional tests in four independent samples. Linkage analysis identified a peak (LOD = 4.29) on chromosome 8p23. Follow-up association analysis identified two haplotypes in angiopoietin-2 (ANGPT2) that significantly contributed to the variation of SaO2 (P = 8 × 10-5) and accounted for a portion of the linkage evidence. Gene-based association analysis replicated the association of ANGPT2 and nocturnal SaO2. A rare missense SNP rs200291021 in ANGPT2 was associated with serum angiopoietin-2 level (P = 1.29 × 10-4), which was associated with SaO2 (P = 0.002). Our study provides the first evidence for the association of ANGPT2, a gene previously implicated in acute lung injury syndromes, with nocturnal SaO2, suggesting that this gene has a broad range of effects on gas exchange, including influencing oxygenation during sleep.
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Affiliation(s)
- Heming Wang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Han Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kevin J Gleason
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Richa Saxena
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.,Center for Human Genetic Research and Department of Anesthesia, Pain, and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Tao Feng
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Emma K Larkin
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ramachandran S Vasan
- Preventive Medicine & Epidemiology, Boston University School of Medicine, Boston, MA, USA.,Framingham Heart Study, Framingham, MA
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Sanjay R Patel
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.,Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont, Burlington, VT, USA
| | - Yongmei Liu
- Epidemiology and Prevention Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.,Sleep Disorders Center, VA Boston Healthcare System, Boston, MA, USA
| | - Jennifer E Below
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Craig L Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Lauren E Petty
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.,Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wendy Post
- Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.,Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
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107
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Sulovari A, Liu Z, Zhu Z, Li D. Genome-wide meta-analysis of copy number variations with alcohol dependence. THE PHARMACOGENOMICS JOURNAL 2017; 18:398-405. [PMID: 28696413 DOI: 10.1038/tpj.2017.35] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/10/2017] [Accepted: 06/07/2017] [Indexed: 12/26/2022]
Abstract
Genetic association studies and meta-analyses of alcohol dependence (AD) have reported AD-associated single nucleotide polymorphisms (SNPs). These SNPs collectively account for a small portion of estimated heritability in AD. Recent genome-wide copy number variation (CNV) studies have identified CNVs associated with AD and substance dependence, suggesting that a portion of the missing heritability is explained by CNV. We applied PennCNV and QuantiSNP CNV calling algorithms to identify consensus CNVs in five AD cohorts of European and African origins. After rigorous quality control, genome-wide meta-analyses of CNVs were carried out in 3243 well-diagnosed AD cases and 2802 controls. We identified nine CNV regions, including a deletion in chromosome 5q21.3 with a suggestive association with AD (OR=2.15 (1.41-3.29) and P=3.8 × 10-4) and eight nominally significant CNV regions. All regions were replicated with consistent effect sizes across studies and populations. Pathway and gene-drug interaction enrichment analyses based on the resulting genes indicated the mitogen-activated protein kinase signaling pathway and the recombinant insulin and hyaluronidase drugs, which were relevant to AD biology or treatment. To our knowledge, this is the first genome-wide meta-analysis of CNVs with addiction. Further investigation of the AD-associated CNV regions will provide better understanding of the AD genetic mechanism.
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Affiliation(s)
- A Sulovari
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA
| | - Z Liu
- Spine Surgery, Drum Tower Hospital, Nanjing University Medical School, Nanjing, China
| | - Z Zhu
- Spine Surgery, Drum Tower Hospital, Nanjing University Medical School, Nanjing, China
| | - D Li
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA.,Department of Computer Science, University of Vermont, Burlington, VT, USA.,Neuroscience, Behavior, and Health Initiative, University of Vermont, Burlington, VT, USA
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108
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Manning A, Highland HM, Gasser J, Sim X, Tukiainen T, Fontanillas P, Grarup N, Rivas MA, Mahajan A, Locke AE, Cingolani P, Pers TH, Viñuela A, Brown AA, Wu Y, Flannick J, Fuchsberger C, Gamazon ER, Gaulton KJ, Im HK, Teslovich TM, Blackwell TW, Bork-Jensen J, Burtt NP, Chen Y, Green T, Hartl C, Kang HM, Kumar A, Ladenvall C, Ma C, Moutsianas L, Pearson RD, Perry JR, Rayner NW, Robertson NR, Scott LJ, van de Bunt M, Eriksson JG, Jula A, Koskinen S, Lehtimäki T, Palotie A, Raitakari OT, Jacobs SB, Wessel J, Chu AY, Scott RA, Goodarzi MO, Blancher C, Buck G, Buck D, Chines PS, Gabriel S, Gjesing AP, Groves CJ, Hollensted M, Huyghe JR, Jackson AU, Jun G, Justesen JM, Mangino M, Murphy J, Neville M, Onofrio R, Small KS, Stringham HM, Trakalo J, Banks E, Carey J, Carneiro MO, DePristo M, Farjoun Y, Fennell T, Goldstein JI, Grant G, Hrabé de Angelis M, Maguire J, Neale BM, Poplin R, Purcell S, Schwarzmayr T, Shakir K, Smith JD, Strom TM, Wieland T, Lindstrom J, Brandslund I, Christensen C, Surdulescu GL, Lakka TA, Doney AS, Nilsson P, Wareham NJ, Langenberg C, Varga TV, Franks PW, Rolandsson O, Rosengren AH, Farook VS, Thameem F, Puppala S, Kumar S, Lehman DM, Jenkinson CP, Curran JE, Hale DE, Fowler SP, Arya R, DeFronzo RA, Abboud HE, Syvänen AC, Hicks PJ, Palmer ND, Ng MC, Bowden DW, Freedman BI, Esko T, Mägi R, Milani L, Mihailov E, Metspalu A, Narisu N, Kinnunen L, Bonnycastle LL, Swift A, Pasko D, Wood AR, Fadista J, Pollin TI, Barzilai N, Atzmon G, Glaser B, Thorand B, Strauch K, Peters A, Roden M, Müller-Nurasyid M, Liang L, Kriebel J, Illig T, Grallert H, Gieger C, Meisinger C, Lannfelt L, Musani SK, Griswold M, Taylor HA, Wilson G, Correa A, Oksa H, Scott WR, Afzal U, Tan ST, Loh M, Chambers JC, Sehmi J, Kooner JS, Lehne B, Cho YS, Lee JY, Han BG, Käräjämäki A, Qi Q, Qi L, Huang J, Hu FB, Melander O, Orho-Melander M, Below JE, Aguilar D, Wong TY, Liu J, Khor CC, Chia KS, Lim WY, Cheng CY, Chan E, Tai ES, Aung T, Linneberg A, Isomaa B, Meitinger T, Tuomi T, Hakaste L, Kravic J, Jørgensen ME, Lauritzen T, Deloukas P, Stirrups KE, Owen KR, Farmer AJ, Frayling TM, O'Rahilly SP, Walker M, Levy JC, Hodgkiss D, Hattersley AT, Kuulasmaa T, Stančáková A, Barroso I, Bharadwaj D, Chan J, Chandak GR, Daly MJ, Donnelly PJ, Ebrahim SB, Elliott P, Fingerlin T, Froguel P, Hu C, Jia W, Ma RC, McVean G, Park T, Prabhakaran D, Sandhu M, Scott J, Sladek R, Tandon N, Teo YY, Zeggini E, Watanabe RM, Koistinen HA, Kesaniemi YA, Uusitupa M, Spector TD, Salomaa V, Rauramaa R, Palmer CN, Prokopenko I, Morris AD, Bergman RN, Collins FS, Lind L, Ingelsson E, Tuomilehto J, Karpe F, Groop L, Jørgensen T, Hansen T, Pedersen O, Kuusisto J, Abecasis G, Bell GI, Blangero J, Cox NJ, Duggirala R, Seielstad M, Wilson JG, Dupuis J, Ripatti S, Hanis CL, Florez JC, Mohlke KL, Meigs JB, Laakso M, Morris AP, Boehnke M, Altshuler D, McCarthy MI, Gloyn AL, Lindgren CM. A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk. Diabetes 2017; 66:2019-2032. [PMID: 28341696 PMCID: PMC5482074 DOI: 10.2337/db16-1329] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 03/13/2017] [Indexed: 01/04/2023]
Abstract
To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.
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Affiliation(s)
- Alisa Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Heather M. Highland
- Human Genetics Center, The University of Texas MD Anderson Cancer Center and The University of Texas Health Science Center at Houston Graduate School of Biomedical Sciences, Houston, TX
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jessica Gasser
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Xueling Sim
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Taru Tukiainen
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Pierre Fontanillas
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- 23andMe, Mountain View, CA
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manuel A. Rivas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Adam E. Locke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Pablo Cingolani
- School of Computer Science, McGill University, Montreal, Canada
- McGill University and Génome Québec Innovation Centre, Montreal, Canada
| | - Tune H. Pers
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Divisions of Endocrinology and Genetics and Genomics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Ana Viñuela
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, U.K
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Andrew A. Brown
- Wellcome Trust Sanger Institute, Hinxton, U.K
- Norwegian Centre for Mental Disorders Research and KG Jebsen Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ying Wu
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jason Flannick
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Eric R. Gamazon
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL
- Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Kyle J. Gaulton
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL
| | - Tanya M. Teslovich
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Thomas W. Blackwell
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Noël P. Burtt
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Yuhui Chen
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Todd Green
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Christopher Hartl
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Hyun Min Kang
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Ashish Kumar
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Chronic Disease Epidemiology Unit, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Claes Ladenvall
- Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden
| | - Clement Ma
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Loukas Moutsianas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Richard D. Pearson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - John R.B. Perry
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, U.K
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - N. William Rayner
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
| | - Neil R. Robertson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
| | - Laura J. Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
| | - Johan G. Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Unit of General Practice, Helsinki University Central Hospital, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Vaasa Central Hospital, Vaasa, Finland
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Antti Jula
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Seppo Koskinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland
| | - Aarno Palotie
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Olli T. Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | | | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, IN
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Audrey Y. Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Christine Blancher
- High-Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Gemma Buck
- High-Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - David Buck
- High-Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Peter S. Chines
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Stacey Gabriel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Anette P. Gjesing
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
| | - Mette Hollensted
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeroen R. Huyghe
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Goo Jun
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Johanne Marie Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, U.K
| | - Jacquelyn Murphy
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
| | - Robert Onofrio
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Kerrin S. Small
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, U.K
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Joseph Trakalo
- High-Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Eric Banks
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Jason Carey
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | | | - Mark DePristo
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Yossi Farjoun
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Timothy Fennell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Jacqueline I. Goldstein
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - George Grant
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Martin Hrabé de Angelis
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Freising, Germany
| | - Jared Maguire
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Ryan Poplin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Shaun Purcell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Icahn Institute for Genomics & Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Thomas Schwarzmayr
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Khalid Shakir
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Joshua D. Smith
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA
| | - Tim M. Strom
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Neuherberg, Germany
| | - Thomas Wieland
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jaana Lindstrom
- Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Ivan Brandslund
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Cramer Christensen
- Department of Internal Medicine and Endocrinology, Vejle Hospital, Vejle, Denmark
| | | | - Timo A. Lakka
- Department of Physiology, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Alex S.F. Doney
- Division of Cardiovascular & Diabetes Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, U.K
| | - Peter Nilsson
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Tibor V. Varga
- Department of Clinical Sciences, Lund University Diabetes Centre, and Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Paul W. Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, and Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Anders H. Rosengren
- Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden
| | - Vidya S. Farook
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Farook Thameem
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Satish Kumar
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Donna M. Lehman
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
| | - Christopher P. Jenkinson
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
- Research and Development Service, South Texas Veterans Health Care System, San Antonio, TX
| | - Joanne E. Curran
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Daniel Esten Hale
- Department of Pediatrics, The University of Texas Health Science Center, San Antonio, TX
| | - Sharon P. Fowler
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
| | - Rector Arya
- Department of Pediatrics, The University of Texas Health Science Center, San Antonio, TX
| | - Ralph A. DeFronzo
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
| | - Hanna E. Abboud
- Department of Medicine, The University of Texas Health Science Center, San Antonio, TX
| | - Ann-Christine Syvänen
- Molecular Medicine and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Pamela J. Hicks
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Nicholette D. Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Maggie C.Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Barry I. Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - Tõnu Esko
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Genetics, Harvard Medical School, Boston, MA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | | | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Leena Kinnunen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Lori L. Bonnycastle
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Amy Swift
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, U.K
| | - Andrew R. Wood
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, U.K
| | - João Fadista
- Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden
| | - Toni I. Pollin
- Program in Personalized and Genomic Medicine, Department of Medicine, University of Maryland, Baltimore, MD
| | - Nir Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, NY
| | - Gil Atzmon
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, NY
- Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Benjamin Glaser
- Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Genetic Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael Roden
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Genetic Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
| | - Liming Liang
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Illig
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lars Lannfelt
- Geriatrics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Solomon K. Musani
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS
| | - Michael Griswold
- Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS
| | - Herman A. Taylor
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Gregory Wilson
- College of Public Services, Jackson State University, Jackson, MS
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Heikki Oksa
- Pirkanmaa Hospital District, Tampere, Finland
| | - William R. Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
| | - Uzma Afzal
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
| | - Sian-Tsung Tan
- Cardiovascular Sciences, National Heart and Lung Institute, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, U.K
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research (A*STAR), Singapore
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, U.K
- Imperial College Healthcare NHS Trust, Imperial College London, London, U.K
| | - Jobanpreet Sehmi
- Cardiovascular Sciences, National Heart and Lung Institute, Imperial College London, London, U.K
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, U.K
| | - Jaspal Singh Kooner
- Cardiovascular Sciences, National Heart and Lung Institute, Imperial College London, London, U.K
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Republic of Korea
| | - Jong-Young Lee
- Ministry of Health and Welfare, Seoul, Republic of Korea
| | - Bok-Ghee Han
- Center for Genome Science, Korea National Research Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Annemari Käräjämäki
- Vaasa Health Care Center, Vaasa, Finland
- Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland
| | - Qibin Qi
- Department of Nutrition, Harvard School of Public Health, Boston, MA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY
| | - Lu Qi
- Department of Nutrition, Harvard School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Jinyan Huang
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, MA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Olle Melander
- Hypertension and Cardiovascular Disease, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Marju Orho-Melander
- Diabetes and Cardiovascular Disease–Genetic Epidemiology, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Jennifer E. Below
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - David Aguilar
- Cardiovascular Division, Baylor College of Medicine, Houston, TX
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Division of Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Chiea-Chuen Khor
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Division of Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Wei Yen Lim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Office of Clinical Sciences, Centre for Quantitative Medicine, Duke-NUS Graduate Medical School Singapore, Singapore
| | - Edmund Chan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Cardiovascular & Metabolic Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bo Isomaa
- Folkhälsan Research Center, Helsinki, Finland
- Department of Social Services and Health Care, Jakobstad, Finland
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Neuherberg, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Tiinamaija Tuomi
- Folkhälsan Research Center, Helsinki, Finland
- Department of Endocrinology, Helsinki University Central Hospital, Helsinki, Finland
| | | | - Jasmina Kravic
- Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden
| | | | - Torsten Lauritzen
- Section of General Practice, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Panos Deloukas
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
| | - Kathleen E. Stirrups
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, U.K
- Department of Haematology, University of Cambridge, Cambridge, U.K
| | - Katharine R. Owen
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, U.K
| | - Andrew J. Farmer
- Department of Primary Care Health Sciences, University of Oxford, Oxford, U.K
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, U.K
| | - Stephen P. O'Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Mark Walker
- Institute of Cellular Medicine, University of Newcastle, Newcastle, U.K
| | - Jonathan C. Levy
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
| | - Dylan Hodgkiss
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, U.K
| | | | - Teemu Kuulasmaa
- Internal Medicine, Institute of Clinical Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Alena Stančáková
- Internal Medicine, Institute of Clinical Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Dwaipayan Bharadwaj
- Functional Genomics Unit, CSIR-Institute of Genomics & Integrative Biology, New Delhi, India
| | - Juliana Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Mark J. Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Peter J. Donnelly
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Department of Statistics, University of Oxford, Oxford, U.K
| | | | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, U.K
- MRC-PHE Centre for Environment & Health, Imperial College London, London, U.K
| | - Tasha Fingerlin
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO
| | - Philippe Froguel
- Genomics and Molecular Physiology, CNRS Institut de Biologie de Lille, Lille, France
| | - Cheng Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Ronald C.W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Gilean McVean
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | | | - Manjinder Sandhu
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
- Institute of Public Health, Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
| | - James Scott
- Cardiovascular Sciences, National Heart and Lung Institute, Imperial College London, London, U.K
| | - Rob Sladek
- McGill University and Génome Québec Innovation Centre, Montreal, Canada
- Department of Human Genetics, McGill University, Montreal, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, Canada
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, India
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
| | - Richard M. Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Heikki A. Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine and Abdominal Center, Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Y. Antero Kesaniemi
- Institute of Clinical Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Timothy D. Spector
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, U.K
| | - Veikko Salomaa
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Colin N.A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, U.K
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, U.K
| | - Andrew D. Morris
- Division for Molecular Medicine, Clinical Research Centre, Ninewells Hospital and Medical School, Dundee, U.K
| | - Richard N. Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Francis S. Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Molecular Medicine and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Jaakko Tuomilehto
- Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Center for Vascular Prevention, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
- Dasman Diabetes Institute, Dasman, Kuwait
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, U.K
| | - Leif Groop
- Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
- Faculty of Medicine, University of Aalborg, Aalborg, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Johanna Kuusisto
- Internal Medicine, Institute of Clinical Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Gonçalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Graeme I. Bell
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, IL
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Nancy J. Cox
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL
| | | | - Mark Seielstad
- Department of Laboratory Medicine, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA
- Blood Systems Research Institute, San Francisco, CA
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA
| | - Samuli Ripatti
- Wellcome Trust Sanger Institute, Hinxton, U.K
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Hjelt Institute, University of Helsinki, Helsinki, Finland
| | - Craig L. Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jose C. Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Karen L. Mohlke
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - James B. Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, U.K
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - David Altshuler
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, U.K
| | - Anna L. Gloyn
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, U.K
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, U.K
| | - Cecilia M. Lindgren
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, U.K
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Lee S, Kim S, Fuchsberger C. Improving power for rare-variant tests by integrating external controls. Genet Epidemiol 2017; 41:610-619. [PMID: 28657150 DOI: 10.1002/gepi.22057] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 03/16/2017] [Accepted: 04/25/2017] [Indexed: 11/07/2022]
Abstract
Due to the drop in sequencing cost, the number of sequenced genomes is increasing rapidly. To improve power of rare-variant tests, these sequenced samples could be used as external control samples in addition to control samples from the study itself. However, when using external controls, possible batch effects due to the use of different sequencing platforms or genotype calling pipelines can dramatically increase type I error rates. To address this, we propose novel summary statistics based single and gene- or region-based rare-variant tests that allow the integration of external controls while controlling for type I error. Our approach is based on the insight that batch effects on a given variant can be assessed by comparing odds ratio estimates using internal controls only vs. using combined control samples of internal and external controls. From simulation experiments and the analysis of data from age-related macular degeneration and type 2 diabetes studies, we demonstrate that our method can substantially improve power while controlling for type I error rate.
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Affiliation(s)
- Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Sehee Kim
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.,Center for Biomedicine, European Academy of Bolzano/Bozen, affiliated to the University of Lübeck, Bolzano/Bozen, Italy
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110
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Liu Z, Lin X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 2017; 74:165-175. [PMID: 28653391 DOI: 10.1111/biom.12735] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 05/01/2017] [Accepted: 05/01/2017] [Indexed: 12/13/2022]
Abstract
We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
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111
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Tachmazidou I, Süveges D, Min JL, Ritchie GRS, Steinberg J, Walter K, Iotchkova V, Schwartzentruber J, Huang J, Memari Y, McCarthy S, Crawford AA, Bombieri C, Cocca M, Farmaki AE, Gaunt TR, Jousilahti P, Kooijman MN, Lehne B, Malerba G, Männistö S, Matchan A, Medina-Gomez C, Metrustry SJ, Nag A, Ntalla I, Paternoster L, Rayner NW, Sala C, Scott WR, Shihab HA, Southam L, St Pourcain B, Traglia M, Trajanoska K, Zaza G, Zhang W, Artigas MS, Bansal N, Benn M, Chen Z, Danecek P, Lin WY, Locke A, Luan J, Manning AK, Mulas A, Sidore C, Tybjaerg-Hansen A, Varbo A, Zoledziewska M, Finan C, Hatzikotoulas K, Hendricks AE, Kemp JP, Moayyeri A, Panoutsopoulou K, Szpak M, Wilson SG, Boehnke M, Cucca F, Di Angelantonio E, Langenberg C, Lindgren C, McCarthy MI, Morris AP, Nordestgaard BG, Scott RA, Tobin MD, Wareham NJ, Burton P, Chambers JC, Smith GD, Dedoussis G, Felix JF, Franco OH, Gambaro G, Gasparini P, Hammond CJ, Hofman A, Jaddoe VWV, Kleber M, Kooner JS, Perola M, Relton C, Ring SM, Rivadeneira F, Salomaa V, Spector TD, Stegle O, Toniolo D, Uitterlinden AG, Barroso I, Greenwood CMT, Perry JRB, Walker BR, Butterworth AS, Xue Y, Durbin R, Small KS, Soranzo N, Timpson NJ, Zeggini E. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. Am J Hum Genet 2017; 100:865-884. [PMID: 28552196 PMCID: PMC5473732 DOI: 10.1016/j.ajhg.2017.04.014] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 04/21/2017] [Indexed: 01/05/2023] Open
Abstract
Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.
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Affiliation(s)
- Ioanna Tachmazidou
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Dániel Süveges
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Graham R S Ritchie
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Usher Institute of Population Health Sciences & Informatics, University of Edinburgh, Edinburgh EH16 4UX, UK; MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH16 4UX, UK
| | - Julia Steinberg
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Klaudia Walter
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Valentina Iotchkova
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | | | - Jie Huang
- Boston VA Research Institute, Boston, MA 02130, USA
| | - Yasin Memari
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Shane McCarthy
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Andrew A Crawford
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Cristina Bombieri
- Department of Neurological, Biomedical and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Massimiliano Cocca
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste 34100, Italy
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Pekka Jousilahti
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Marjolein N Kooijman
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Giovanni Malerba
- Department of Neurological, Biomedical and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Satu Männistö
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Angela Matchan
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Carolina Medina-Gomez
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Sarah J Metrustry
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Nigel W Rayner
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - William R Scott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK
| | - Hashem A Shihab
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Lorraine Southam
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; Max Planck Institute for Psycholinguistics, Nijmegen 6500, the Netherlands
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - Katerina Trajanoska
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Gialuigi Zaza
- Renal Unit, Department of Medicine, Verona University Hospital, Verona 37126, Italy
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK
| | - María S Artigas
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Narinder Bansal
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Marianne Benn
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Zhongsheng Chen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Petr Danecek
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Wei-Yu Lin
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Adam Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Alisa K Manning
- Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine, Harvard University Medical School, Boston, MA 02115, USA
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy; Università degli Studi di Sassari, Sassari 07100, Italy
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy
| | - Anne Tybjaerg-Hansen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Anette Varbo
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | | | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
| | | | - Audrey E Hendricks
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - John P Kemp
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD 4072, Australia
| | - Alireza Moayyeri
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; Institute of Health Informatics, University College London, London NW1 2DA, UK
| | | | - Michal Szpak
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Scott G Wilson
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; School of Medicine and Pharmacology, The University of Western Australia, Crawley, WA 6009, Australia; Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy; Università degli Studi di Sassari, Sassari 07100, Italy
| | - Emanuele Di Angelantonio
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK; Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK; Estonian Genome Center, University of Tartu, Tartu, Tartumaa 51010, Estonia
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Martin D Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK; National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | | | - Paul Burton
- D2K Research Group, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK; Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Janine F Felix
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Giovanni Gambaro
- Division of Nephrology and Dialysis, Columbus-Gemelli University Hospital, Catholic University, Rome 00168, Italy
| | - Paolo Gasparini
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste 34100, Italy; Medical Genetics, Institute for Maternal and Child Health IRCCS "Burlo Garofolo", Trieste 34100, Italy
| | - Christopher J Hammond
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Marcus Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK; Imperial College Healthcare NHS Trust, London W2 1NY, UK; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Markus Perola
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland; Estonian Genome Center, University of Tartu, Tartu, Tartumaa 51010, Estonia; Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki 00290, Finland
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Veikko Salomaa
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | | | | | | | - Inês Barroso
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; University of Cambridge Metabolic Research Laboratories, and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC H3A 1A2, Canada; Department of Oncology, McGill University, Montréal, QC H2W 1S6, Canada
| | - John R B Perry
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Brian R Walker
- BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Adam S Butterworth
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK
| | - Yali Xue
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Richard Durbin
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Nicole Soranzo
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK; Department of Haematology, University of Cambridge, Cambridge CB2 0AH, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Eleftheria Zeggini
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
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112
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Links Between the Sequence Kernel Association and the Kernel-Based Adaptive Cluster Tests. STATISTICS IN BIOSCIENCES 2017. [DOI: 10.1007/s12561-016-9175-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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113
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Common coding variant in SERPINA1 increases the risk for large artery stroke. Proc Natl Acad Sci U S A 2017; 114:3613-3618. [PMID: 28265093 DOI: 10.1073/pnas.1616301114] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Large artery atherosclerotic stroke (LAS) shows substantial heritability not explained by previous genome-wide association studies. Here, we explore the role of coding variation in LAS by analyzing variants on the HumanExome BeadChip in a total of 3,127 cases and 9,778 controls from Europe, Australia, and South Asia. We report on a nonsynonymous single-nucleotide variant in serpin family A member 1 (SERPINA1) encoding alpha-1 antitrypsin [AAT; p.V213A; P = 5.99E-9, odds ratio (OR) = 1.22] and confirm histone deacetylase 9 (HDAC9) as a major risk gene for LAS with an association in the 3'-UTR (rs2023938; P = 7.76E-7, OR = 1.28). Using quantitative microscale thermophoresis, we show that M1 (A213) exhibits an almost twofold lower dissociation constant with its primary target human neutrophil elastase (NE) in lipoprotein-containing plasma, but not in lipid-free plasma. Hydrogen/deuterium exchange combined with mass spectrometry further revealed a significant difference in the global flexibility of the two variants. The observed stronger interaction with lipoproteins in plasma and reduced global flexibility of the Val-213 variant most likely improve its local availability and reduce the extent of proteolytic inactivation by other proteases in atherosclerotic plaques. Our results indicate that the interplay between AAT, NE, and lipoprotein particles is modulated by the gate region around position 213 in AAT, far away from the unaltered reactive center loop (357-360). Collectively, our findings point to a functionally relevant balance between lipoproteins, proteases, and AAT in atherosclerosis.
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Larson NB, McDonnell S, Cannon Albright L, Teerlink C, Stanford J, Ostrander EA, Isaacs WB, Xu J, Cooney KA, Lange E, Schleutker J, Carpten JD, Powell I, Bailey-Wilson JE, Cussenot O, Cancel-Tassin G, Giles GG, MacInnis RJ, Maier C, Whittemore AS, Hsieh CL, Wiklund F, Catalona WJ, Foulkes W, Mandal D, Eeles R, Kote-Jarai Z, Ackerman MJ, Olson TM, Klein CJ, Thibodeau SN, Schaid DJ. gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels. Genet Epidemiol 2017; 41:297-308. [PMID: 28211093 DOI: 10.1002/gepi.22036] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 11/16/2016] [Accepted: 12/09/2016] [Indexed: 01/28/2023]
Abstract
Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.
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Affiliation(s)
- Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Shannon McDonnell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Lisa Cannon Albright
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Craig Teerlink
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Janet Stanford
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Elaine A Ostrander
- National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - William B Isaacs
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jianfeng Xu
- NorthShore University HealthSystem Research Institute, Chicago, Illinois, United States of America
| | - Kathleen A Cooney
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.,Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.,Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Ethan Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland
| | - John D Carpten
- Department of Translational Genomics, University of Southern California, Los Angeles, California, United States of America
| | - Isaac Powell
- Department of Urology, Wayne State University, Detroit, Michigan, United States of America
| | - Joan E Bailey-Wilson
- Statistical Genetics Section, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | | | | | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.,Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.,Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | | | - Alice S Whittemore
- Department of Health Research and Policy, Stanford University, Stanford, California, United States of America
| | - Chih-Lin Hsieh
- Department of Urology, University of Southern California, Los Angeles, California, United States of America
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - William J Catalona
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - William Foulkes
- Department of Oncology, Montreal General Hospital, Montreal, Quebec, Canada.,Department of Human Genetics, Montreal General Hospital, Montreal, Quebec, Canada
| | - Diptasri Mandal
- Department of Genetics, LSU Health Sciences Center, New Orleans, Louisiana, United States of America
| | | | - Zsofia Kote-Jarai
- The Institute of Cancer Research, London, UK.,The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London
| | - Michael J Ackerman
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Timothy M Olson
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Christopher J Klein
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Stephen N Thibodeau
- Department of Laboratory Medicine/Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Daniel J Schaid
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
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115
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Tang ZZ, Bunn P, Tao R, Liu Z, Lin DY. PreMeta: a tool to facilitate meta-analysis of rare-variant associations. BMC Genomics 2017; 18:160. [PMID: 28196472 PMCID: PMC5310051 DOI: 10.1186/s12864-017-3573-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Accepted: 02/09/2017] [Indexed: 11/10/2022] Open
Abstract
Background Meta-analysis is essential to the discovery of rare variants that influence complex diseases and traits. Four major software packages, namely MASS, MetaSKAT, RAREMETAL, and seqMeta, have been developed to perform meta-analysis of rare-variant associations. These packages first generate summary statistics for each study and then perform the meta-analysis by combining the summary statistics. Because of incompatible file formats and non-equivalent summary statistics, the output files from the study-level analysis of one package cannot be directly used to perform meta-analysis in another package. Results We developed a computationally efficient software program, PreMeta, to resolve the non-compatibility of the four software packages and to facilitate meta-analysis of large-scale sequencing studies in a consortium setting. PreMeta reformats the output files of study-level summary statistics generated by the four packages (text files produced by MASS and RAREMETAL, binary files produced by MetaSKAT, and R data files produced by seqMeta) and translates the summary statistics from one form to another, such that the summary statistics from any package can be used to perform meta-analysis in any other package. With this tool, consortium members are not required to use the same software for study-level analyses. In addition, PreMeta checks for allele mismatches, corrects summary statistics, and allows the rescaled inverse normal transformation to be performed at the meta-analysis stage by rescaling summary statistics. Conclusions PreMeta processes summary statistics from the four packages to make them compatible and avoids the need to redo study-level analyses. PreMeta documentation and executable are available at: http://dlin.web.unc.edu/software/premeta.
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Affiliation(s)
- Zheng-Zheng Tang
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA.
| | - Paul Bunn
- Department of Biostatistics, University of North Carolina, Chapel Hill, 37203, NC, USA
| | - Ran Tao
- Department of Biostatistics, University of North Carolina, Chapel Hill, 37203, NC, USA
| | - Zhouwen Liu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, 37203, NC, USA
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116
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Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 2017; 18:117-127. [PMID: 27840428 PMCID: PMC5449190 DOI: 10.1038/nrg.2016.142] [Citation(s) in RCA: 257] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.
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Affiliation(s)
- Bogdan Pasaniuc
- Departments of Human Genetics, and Pathology and Laboratory Medicine, University of California, Los Angeles, California 90095, USA
| | - Alkes L Price
- Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
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117
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de Vlaming R, Okbay A, Rietveld CA, Johannesson M, Magnusson PKE, Uitterlinden AG, van Rooij FJA, Hofman A, Groenen PJF, Thurik AR, Koellinger PD. Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies. PLoS Genet 2017; 13:e1006495. [PMID: 28095416 PMCID: PMC5240919 DOI: 10.1371/journal.pgen.1006495] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 11/17/2016] [Indexed: 11/18/2022] Open
Abstract
Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing heritability’. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51–62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36–38%). Hence, cross-study heterogeneity contributes to the missing heritability. Large-scale genome-wide association studies are uncovering the genetic architecture of traits which are affected by many genetic variants. In such efforts, one typically meta-analyzes association results from multiple studies spanning different regions and/or time periods. Results from such efforts do not yet capture a large share of the heritability. The origins of this so-called ‘missing heritability’ have been strongly debated. One factor exacerbating the missing heritability is heterogeneity in the effects of genetic variants across studies. The effect of this type of heterogeneity on statistical power to detect associated genetic variants and the accuracy of polygenic predictions is poorly understood. In the current study, we derive the precise effects of heterogeneity in genetic effects across studies on both the statistical power to detect associated genetic variants as well as the accuracy of polygenic predictions. We present an online calculator, available at www.devlaming.eu, which accounts for these effects. By means of this calculator, we show that imperfect genetic correlations between studies substantially decrease statistical power and predictive accuracy and, thereby, contribute to the missing heritability. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies. If strong heterogeneity is expected, random-effects meta-analysis methods should be used instead of fixed-effects methods.
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Affiliation(s)
- Ronald de Vlaming
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands
| | - Aysu Okbay
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands
| | - Cornelius A. Rietveld
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - André G. Uitterlinden
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Frank J. A. van Rooij
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Albert Hofman
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Patrick J. F. Groenen
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Econometric Institute, Erasmus School of Economics, Rotterdam, the Netherlands
| | - A. Roy Thurik
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
- Montpellier Business School, Montpellier, France
| | - Philipp D. Koellinger
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands
- * E-mail:
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118
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Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. Eur J Hum Genet 2016; 25:350-359. [PMID: 28000696 DOI: 10.1038/ejhg.2016.170] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 07/26/2016] [Accepted: 09/27/2016] [Indexed: 11/09/2022] Open
Abstract
To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.
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119
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Chen S, Nunez S, Reilly MP, Foulkes AS. Bayesian variable selection for post-analytic interrogation of susceptibility loci. Biometrics 2016; 73:603-614. [PMID: 27858978 DOI: 10.1111/biom.12620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Revised: 09/01/2016] [Accepted: 09/01/2016] [Indexed: 11/26/2022]
Abstract
Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material.
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Affiliation(s)
- Siying Chen
- Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A
| | - Sara Nunez
- Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A
| | - Muredach P Reilly
- Department of Medicine, Division of Cardiology, and the Irving Institute for Clinical and Translational Research at Columbia University, New York City, New York, U.S.A
| | - Andrea S Foulkes
- Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A
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120
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Lindström S, Ablorh A, Chapman B, Gusev A, Chen G, Turman C, Eliassen AH, Price AL, Henderson BE, Le Marchand L, Hofmann O, Haiman CA, Kraft P. Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities. Breast Cancer Res 2016; 18:109. [PMID: 27814745 PMCID: PMC5097387 DOI: 10.1186/s13058-016-0772-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 10/18/2016] [Indexed: 12/30/2022] Open
Abstract
Background Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. Methods Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. Results After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. Conclusions This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sara Lindström
- Department of Epidemiology, University of Washington, 1959 N.E. Pacific Street, Health Sciences Building, Room F247B, Seattle, WA, 98195, USA. .,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - Akweley Ablorh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Brad Chapman
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,HSPH Bioinformatics Core, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alexander Gusev
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Gary Chen
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Brian E Henderson
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Loic Le Marchand
- Cancer Research Center of Hawai'i, University of Hawai'i, Honolulu, HI, 96813, USA
| | - Oliver Hofmann
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,HSPH Bioinformatics Core, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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121
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Wang S, Fisher VA, Chen Y, Dupuis J. Comparison of multiple single-nucleotide variant association tests in a meta-analysis of Genetic Analysis Workshop 19 family and unrelated data. BMC Proc 2016; 10:187-191. [PMID: 27980634 PMCID: PMC5133513 DOI: 10.1186/s12919-016-0028-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Meta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis approaches for haplotype analysis have not been extensively developed and used, and have not been compared with other ways of jointly analysing multiple genetic variants. Methods We propose a novel meta-analysis approach for a gene-based haplotype association test, and compare it with an existing meta-analysis approach of the sequence kernel association test (SKAT), using the unrelated samples and family samples of the Genetic Analysis Workshop 19 data sets. We performed association tests with diastolic blood pressure and restricted our analyses to all variants in exonic regions on all odd chromosomes. Results Meta-analysis of haplotype results and SKAT identified different genes. The most significantly associated gene identified by SKAT was the ALCAM gene on chromosome 3 with a p value of 7.0 × 10− 5. Two of the most associated genes identified by the haplotype method were FPGT (p = 6.7 × 10− 8) on chromosome 1 and SPARC (p = 3.3 × 10− 7) on chromosome 5. Both genes were previously implicated in blood pressure regulation and hypertension. Conclusion We compared two meta-analysis approaches to jointly analyze multiple variants: SKAT and haplotype tests. The difference in observed results may be because the haplotype method considered all observed haplotypes, whereas SKAT weighted variants inversely to their minor allele frequency, masking the effects of common variants. The two approaches identified different top genes, and appear to be complementary.
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122
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Egawa J, Hoya S, Watanabe Y, Nunokawa A, Shibuya M, Ikeda M, Inoue E, Okuda S, Kondo K, Saito T, Kaneko N, Muratake T, Igeta H, Iwata N, Someya T. Rare UNC13B variations and risk of schizophrenia: Whole-exome sequencing in a multiplex family and follow-up resequencing and a case-control study. Am J Med Genet B Neuropsychiatr Genet 2016; 171:797-805. [PMID: 26990377 DOI: 10.1002/ajmg.b.32444] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 02/25/2016] [Indexed: 01/03/2023]
Abstract
Rare genomic variations inherited in multiplex schizophrenia families are suggested to play a role in the genetic etiology of the disease. To identify rare variations with large effects on the risk of developing schizophrenia, we performed whole-exome sequencing (WES) in two affected and one unaffected individual of a multiplex family with 10 affected individuals. We also performed follow-up resequencing of the unc-13 homolog B (Caenorhabditis elegans) (UNC13B) gene, a potential risk gene identified by WES, in the multiplex family and undertook a case-control study to investigate association between UNC13B and schizophrenia. UNC13B coding regions (39 exons) from 15 individuals of the multiplex family and 111 affected offspring for whom parental DNA samples were available were resequenced. Rare missense UNC13B variations identified by resequencing were further tested for association with schizophrenia in two independent case-control populations comprising a total of 1,753 patients and 1,602 controls. A rare missense variation (V1525M) in UNC13B was identified by WES in the multiplex family; this variation was present in five of six affected individuals, but not in eight unaffected individuals or one individual of unknown disease status. Resequencing UNC13B coding regions identified five rare missense variations (T103M, M813T, P1349T, I1362T, and V1525M). In the case-control study, there was no significant association between rare missense UNC13B variations and schizophrenia, although single-variant meta-analysis indicated that M813T was nominally associated with schizophrenia. These results do not support a contribution of rare missense UNC13B variations to the genetic etiology of schizophrenia in the Japanese population. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Jun Egawa
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Satoshi Hoya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuichiro Watanabe
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Ayako Nunokawa
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Oojima Hospital, Sanjo, Niigata, Japan
| | - Masako Shibuya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masashi Ikeda
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Emiko Inoue
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shujiro Okuda
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kenji Kondo
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takeo Saito
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Naoshi Kaneko
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Oojima Hospital, Sanjo, Niigata, Japan
| | - Tatsuyuki Muratake
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.,Furumachi Mental Clinic, Niigata, Japan
| | - Hirofumi Igeta
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Toshiyuki Someya
- Department of Psychiatry, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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123
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Abstract
The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
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124
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Transethnic Genetic-Correlation Estimates from Summary Statistics. Am J Hum Genet 2016; 99:76-88. [PMID: 27321947 DOI: 10.1016/j.ajhg.2016.05.001] [Citation(s) in RCA: 192] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 05/03/2016] [Indexed: 11/22/2022] Open
Abstract
The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.
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125
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Wang K. Boosting the Power of the Sequence Kernel Association Test by Properly Estimating Its Null Distribution. Am J Hum Genet 2016; 99:104-14. [PMID: 27292111 DOI: 10.1016/j.ajhg.2016.05.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 05/02/2016] [Indexed: 01/22/2023] Open
Abstract
The sequence kernel association test (SKAT) is probably the most popular statistical test used in rare-variant association studies. Its null distribution involves unknown parameters that need to be estimated. The current estimation method has a valid type I error rate, but the power is compromised given that all subjects are used for estimation. I have developed an estimation method that uses only control subjects. Named SKAT+, this method uses the same test statistic as SKAT but differs in the way the null distribution is estimated. Extensive simulation studies and applications to data from the Genetic Analysis Workshop 17 and the Ocular Hypertension Treatment Study demonstrated that SKAT+ has superior power over SKAT while maintaining control over the type I error rate. This method is applicable to extensions of SKAT in the literature.
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Affiliation(s)
- Kai Wang
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA 52242, USA.
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126
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Wang L, Lee S, Gim J, Qiao D, Cho M, Elston RC, Silverman EK, Won S. Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis. Genet Epidemiol 2016; 40:502-11. [PMID: 27312886 DOI: 10.1002/gepi.21985] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Revised: 05/01/2016] [Accepted: 05/08/2016] [Indexed: 12/19/2022]
Abstract
Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.
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Affiliation(s)
- Longfei Wang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Sungyoung Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Jungsoo Gim
- Institute of Health and Environment, Seoul National University, Seoul, Korea
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Michael Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Robert C Elston
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Sungho Won
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Graduate School of Public Health, Seoul National University, Seoul, Korea
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127
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Discovery of rare variants for complex phenotypes. Hum Genet 2016; 135:625-34. [PMID: 27221085 DOI: 10.1007/s00439-016-1679-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/28/2016] [Indexed: 12/27/2022]
Abstract
With the rise of sequencing technologies, it is now feasible to assess the role rare variants play in the genetic contribution to complex trait variation. While some of the earlier targeted sequencing studies successfully identified rare variants of large effect, unbiased gene discovery using exome sequencing has experienced limited success for complex traits. Nevertheless, rare variant association studies have demonstrated that rare variants do contribute to phenotypic variability, but sample sizes will likely have to be even larger than those of common variant association studies to be powered for the detection of genes and loci. Large-scale sequencing efforts of tens of thousands of individuals, such as the UK10K Project and aggregation efforts such as the Exome Aggregation Consortium, have made great strides in advancing our knowledge of the landscape of rare variation, but there remain many considerations when studying rare variation in the context of complex traits. We discuss these considerations in this review, presenting a broad range of topics at a high level as an introduction to rare variant analysis in complex traits including the issues of power, study design, sample ascertainment, de novo variation, and statistical testing approaches. Ultimately, as sequencing costs continue to decline, larger sequencing studies will yield clearer insights into the biological consequence of rare mutations and may reveal which genes play a role in the etiology of complex traits.
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128
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A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants. Am J Hum Genet 2016; 98:525-540. [PMID: 26942286 DOI: 10.1016/j.ajhg.2016.01.017] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 01/29/2016] [Indexed: 11/20/2022] Open
Abstract
Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.
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129
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A pharmacogenetic investigation of intravenous furosemide in decompensated heart failure: a meta-analysis of three clinical trials. THE PHARMACOGENOMICS JOURNAL 2016; 17:192-200. [PMID: 26927285 PMCID: PMC5009007 DOI: 10.1038/tpj.2016.4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 10/22/2015] [Accepted: 01/20/2016] [Indexed: 01/11/2023]
Abstract
We conducted a meta-analysis of pharmacogenomic substudies of three randomized trials conducted in patients with decompensated heart failure (HF) that were led by National Heart Lung and Blood Institute (NHLBI)-funded HF Network to test the hypothesis that candidate genes modulate net fluid loss and weight change in patients with decompensated HF treated with a furosemide-based diuretic regimen. Although none of the genetic variants previously shown to modulate the effects of loop diuretics in healthy individuals were associated with net fluid loss after 72 h of treatment, a set of rare variants in the APOL1 gene, which codes for apolipoprotein L1 (P=0.0005 in the random effects model), was associated with this end point. Moreover, a common variant in the multidrug resistance protein-4 coding gene (ABCC4, rs17268282) was associated with weight loss with furosemide use (P=0.0001). Our results suggest that both common and rare genetic variants modulate the response to a furosemide-based diuretic regimen in patients with decompensated HF.
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130
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Shi J, Lee S. A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis. Biometrics 2016; 72:945-54. [PMID: 26916671 DOI: 10.1111/biom.12481] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 11/01/2015] [Accepted: 11/01/2015] [Indexed: 11/28/2022]
Abstract
Meta-analysis of trans-ethnic genome-wide association studies (GWAS) has proven to be a practical and profitable approach for identifying loci that contribute to the risk of complex diseases. However, the expected genetic effect heterogeneity cannot easily be accommodated through existing fixed-effects and random-effects methods. In response, we propose a novel random effect model for trans-ethnic meta-analysis with flexible modeling of the expected genetic effect heterogeneity across diverse populations. Specifically, we adopt a modified random effect model from the kernel regression framework, in which genetic effect coefficients are random variables whose correlation structure reflects the genetic distances across ancestry groups. In addition, we use the adaptive variance component test to achieve robust power regardless of the degree of genetic effect heterogeneity. Simulation studies show that our proposed method has well-calibrated type I error rates at very stringent significance levels and can improve power over the traditional meta-analysis methods. We reanalyzed the published type 2 diabetes GWAS meta-analysis (Consortium et al., 2014) and successfully identified one additional SNP that clearly exhibits genetic effect heterogeneity across different ancestry groups. Furthermore, our proposed method provides scalable computing time for genome-wide datasets, in which an analysis of one million SNPs would require less than 3 hours.
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Affiliation(s)
- Jingchunzi Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A..
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A..
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131
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A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci. PLoS One 2016; 11:e0148218. [PMID: 26859766 PMCID: PMC4747495 DOI: 10.1371/journal.pone.0148218] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 01/14/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs. METHODS We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1. RESULTS We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes. CONCLUSIONS We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.
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132
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Abstract
Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, methods for rare variant association have been an active area of research for the past decade. Here, we provide a survey of the current literature and developments from the Genetics Analysis Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we present the generalized linear regression framework and associated score statistic for the 2 major types of methods: burden and variance components methods. We further show that by simply modifying weights within these frameworks we arrive at many of the popular existing methods, for example, the cohort allelic sums test and sequence kernel association test. Meta-analysis techniques are also described. Next, we describe the 6 contributions from the GAW19 Collapsing Rare Variants working group. These included development of new methods, such as a retrospective likelihood for family data, a method using genomic structure to compare cases and controls, a haplotype-based meta-analysis, and a permutation-based method for combining different statistical tests. In addition, one contribution compared a mega-analysis of family-based and population-based data to meta-analysis. Finally, the power of existing family-based methods for binary traits was compared. We conclude with suggestions for open research questions.
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Affiliation(s)
- Stephanie A Santorico
- Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, 80217-3364, USA.
| | - Audrey E Hendricks
- Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, 80217-3364, USA.
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133
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Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project. Eur J Hum Genet 2016; 24:1181-7. [PMID: 26757982 DOI: 10.1038/ejhg.2015.272] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Revised: 11/10/2015] [Accepted: 11/26/2015] [Indexed: 02/06/2023] Open
Abstract
Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.
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134
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Ware EB, Smith JA, Mukherjee B, Lee S, Kardia SLR, Diez-Roux AV. Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis. Behav Genet 2016; 46:89-99. [PMID: 26254610 PMCID: PMC4720563 DOI: 10.1007/s10519-015-9734-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 08/04/2015] [Indexed: 12/13/2022]
Abstract
Complex illnesses, like depression, are thought to arise from the interplay between psychosocial stressors and genetic predispositions. Approaches that take into account both personal and neighborhood factors and that consider gene regions as well as individual SNPs may be necessary to capture these interactions across race and ethnic groups. We used novel gene-region based analysis methods [Sequence Kernel Association Test (SKAT) and meta-analysis (MetaSKAT), gene-environment set association test (GESAT)], as well as traditional linear models to identify gene region and SNP × psychosocial factor interactions at the individual- and neighborhood-level, across multiple race/ethnicities. Multiple regions identified in SKAT analyses showed evidence of a significant gene-region association with averaged depressive symptom scores across race/ethnicity (MetaSKAT p values <0.001). One region × neighborhood-environment interaction was significantly associated with averaged depressive symptom score across race/ethnicity after multiple testing correction (chr 18:21454070-21494070, Fisher's combined p value = 0.001). The examination of gene regions jointly with environmental factors measured at multiple levels (individuals and their contexts) may shed light on the etiology of depressive illness across race/ethnicities.
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Affiliation(s)
- Erin B Ware
- Institute for Social Research, University of Michigan, #4614 SPH Tower, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
- 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
| | - Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Seunggeun Lee
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ana V Diez-Roux
- Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, USA
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135
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Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models. Genetics 2015; 202:457-70. [PMID: 26715663 DOI: 10.1534/genetics.115.180869] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Accepted: 12/09/2015] [Indexed: 11/18/2022] Open
Abstract
We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [i.e., minor allele frequencies (MAF) < 0.03], the Rao's efficient score test statistics have similar or slightly lower power than MetaSKATs. The LRT statistics generate accurate type I error rates for homogeneous genetic-effect models and may inflate type I error rates for heterogeneous genetic-effect models owing to the large numbers of degrees of freedom and have similar or slightly higher power than the Rao's efficient score test statistics. GFLMs were applied to analyze genetic data of 22 gene regions of type 2 diabetes data from a meta-analysis of eight European studies and detected significant association for 18 genes (P < 3.10 × 10(-6)), tentative association for 2 genes (HHEX and HMGA2; P ≈ 10(-5)), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX. GFLMs and related tests can analyze rare or common variants or a combination of the two and can be useful in whole-genome and whole-exome association studies.
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136
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Mensah-Ablorh A, Lindstrom S, Haiman CA, Henderson BE, Marchand LL, Lee S, Stram DO, Eliassen AH, Price A, Kraft P. Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations. Genet Epidemiol 2015; 40:57-65. [PMID: 26639010 DOI: 10.1002/gepi.21939] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 09/15/2015] [Accepted: 09/19/2015] [Indexed: 12/30/2022]
Abstract
Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.
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Affiliation(s)
- Akweley Mensah-Ablorh
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Sara Lindstrom
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Research Center, Honolulu, Hawaii, United States of America
| | - Seunngeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Brigham & Women's Hospital, Boston, Massachusetts, United States of America
| | - Alkes Price
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
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Timofeeva MN, Kinnersley B, Farrington SM, Whiffin N, Palles C, Svinti V, Lloyd A, Gorman M, Ooi LY, Hosking F, Barclay E, Zgaga L, Dobbins S, Martin L, Theodoratou E, Broderick P, Tenesa A, Smillie C, Grimes G, Hayward C, Campbell A, Porteous D, Deary IJ, Harris SE, Northwood EL, Barrett JH, Smith G, Wolf R, Forman D, Morreau H, Ruano D, Tops C, Wijnen J, Schrumpf M, Boot A, Vasen HFA, Hes FJ, van Wezel T, Franke A, Lieb W, Schafmayer C, Hampe J, Buch S, Propping P, Hemminki K, Försti A, Westers H, Hofstra R, Pinheiro M, Pinto C, Teixeira M, Ruiz-Ponte C, Fernández-Rozadilla C, Carracedo A, Castells A, Castellví-Bel S, Campbell H, Bishop DT, Tomlinson IPM, Dunlop MG, Houlston RS. Recurrent Coding Sequence Variation Explains Only A Small Fraction of the Genetic Architecture of Colorectal Cancer. Sci Rep 2015; 5:16286. [PMID: 26553438 PMCID: PMC4639776 DOI: 10.1038/srep16286] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 09/21/2015] [Indexed: 12/21/2022] Open
Abstract
Whilst common genetic variation in many non-coding genomic regulatory regions are known to impart risk of colorectal cancer (CRC), much of the heritability of CRC remains unexplained. To examine the role of recurrent coding sequence variation in CRC aetiology, we genotyped 12,638 CRCs cases and 29,045 controls from six European populations. Single-variant analysis identified a coding variant (rs3184504) in SH2B3 (12q24) associated with CRC risk (OR = 1.08, P = 3.9 × 10(-7)), and novel damaging coding variants in 3 genes previously tagged by GWAS efforts; rs16888728 (8q24) in UTP23 (OR = 1.15, P = 1.4 × 10(-7)); rs6580742 and rs12303082 (12q13) in FAM186A (OR = 1.11, P = 1.2 × 10(-7) and OR = 1.09, P = 7.4 × 10(-8)); rs1129406 (12q13) in ATF1 (OR = 1.11, P = 8.3 × 10(-9)), all reaching exome-wide significance levels. Gene based tests identified associations between CRC and PCDHGA genes (P < 2.90 × 10(-6)). We found an excess of rare, damaging variants in base-excision (P = 2.4 × 10(-4)) and DNA mismatch repair genes (P = 6.1 × 10(-4)) consistent with a recessive mode of inheritance. This study comprehensively explores the contribution of coding sequence variation to CRC risk, identifying associations with coding variation in 4 genes and PCDHG gene cluster and several candidate recessive alleles. However, these findings suggest that recurrent, low-frequency coding variants account for a minority of the unexplained heritability of CRC.
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Affiliation(s)
- Maria N. Timofeeva
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Susan M. Farrington
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Nicola Whiffin
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Claire Palles
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Victoria Svinti
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Amy Lloyd
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Maggie Gorman
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Li-Yin Ooi
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Fay Hosking
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Ella Barclay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Lina Zgaga
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Sara Dobbins
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Lynn Martin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Evropi Theodoratou
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, United Kingdom
| | - Peter Broderick
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Albert Tenesa
- Roslin Institute, University of Edinburgh, Easter Bush, Roslin EH25 9RG, United Kingdom
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Claire Smillie
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Graeme Grimes
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Caroline Hayward
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Archie Campbell
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Generation Scotland, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - David Porteous
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Generation Scotland, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Ian J. Deary
- University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Sarah E. Harris
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Emma L. Northwood
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Jennifer H. Barrett
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Gillian Smith
- Medical Research Institute, University of Dundee, Dundee, UK
| | - Roland Wolf
- Medical Research Institute, University of Dundee, Dundee, UK
| | | | - Hans Morreau
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Dina Ruano
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Carli Tops
- Department of Clinical Genetics, Leiden University Medical Center, The Netherlands
| | - Juul Wijnen
- Department of Human Genetics, Leiden University Medical Center, The Netherlands
| | - Melanie Schrumpf
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Arnoud Boot
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Hans F A Vasen
- Department of Gastroenterology, Leiden University Medical Center, The Netherlands
| | - Frederik J. Hes
- Department of Clinical Genetics, Leiden University Medical Center, The Netherlands
| | - Tom van Wezel
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Andre Franke
- Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Wolgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University Kiel, Kiel
| | - Clemens Schafmayer
- Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden, Germany
| | - Stephan Buch
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden, Germany
| | - Peter Propping
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | - Asta Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | - Helga Westers
- University of Groningen, University Medical Centre Groningen, Department of Genetics, PO Box 30001, 9700 RB Groningen, the Netherlands
| | - Robert Hofstra
- University of Groningen, University Medical Centre Groningen, Department of Genetics, PO Box 30001, 9700 RB Groningen, the Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Manuela Pinheiro
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Carla Pinto
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Manuel Teixeira
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Clara Ruiz-Ponte
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Ceres Fernández-Rozadilla
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Angel Carracedo
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Antoni Castells
- Servei de Gastroenterologia, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, 08036 Barcelona, Catalonia, Spain
| | - Sergi Castellví-Bel
- Servei de Gastroenterologia, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, 08036 Barcelona, Catalonia, Spain
| | - Harry Campbell
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, United Kingdom
| | - D. Timothy Bishop
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Ian P M Tomlinson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Malcolm G. Dunlop
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Richard S. Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
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Rosenberger A, Friedrichs S, Amos CI, Brennan P, Fehringer G, Heinrich J, Hung RJ, Muley T, Müller-Nurasyid M, Risch A, Bickeböller H. META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies. PLoS One 2015; 10:e0140179. [PMID: 26501144 PMCID: PMC4621033 DOI: 10.1371/journal.pone.0140179] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 09/21/2015] [Indexed: 01/31/2023] Open
Abstract
INTRODUCTION Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher's inverse χ2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns. SIMULATION AND POWER We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon's rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs. APPLICATION We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 "transmembrane transporter activity" as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 "acetylcholine receptor activity" but only when not corrected for multiple testing (all GSA-methods applied; p ≈ 0.02).
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Affiliation(s)
- Albert Rosenberger
- Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
| | - Stefanie Friedrichs
- Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
| | - Christopher I. Amos
- Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America
| | - Paul Brennan
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Gordon Fehringer
- Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rayjean J. Hung
- Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Thomas Muley
- Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Martina Müller-Nurasyid
- Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-University, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Angela Risch
- Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
- Division of Molecular Biology, University Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
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139
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Kim S, Lee K, Sun H. Statistical selection strategy for risk and protective rare variants associated with complex traits. J Comput Biol 2015; 22:1034-43. [PMID: 26469994 DOI: 10.1089/cmb.2015.0091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
In genetic association studies with deep sequencing data, it is a challenging statistical problem to precisely locate rare variants associated with complex diseases or traits due to the limited number of observed genetic mutations. In particular, both risk and protective rare variants can be present in the same gene or genetic region. There currently exist very few statistical methods to separate casual rare variants from noncausal variants within a disease/trait-related gene or a genetic region, while there are relatively many statistical tests to detect a phenotypic association of a group of rare variants such as a gene or a genetic region. In this article, we propose a new statistical selection strategy that is able to locate causal rare variants within the disease/trait-related gene or a genetic region. The proposed procedure is to linearly combine potential risk and protective variants in order to find the optimal combination of rare variants that can have the strongest association signal. It is also computationally very efficient since the procedure is based on forward selection. In simulation studies we demonstrate that the selection performance of the proposed procedure is more powerful than other existing methods when both risk and protective variants are present. We also applied it to the real sequencing data on the ANGPTL gene family from the Dallas Heart Study.
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Affiliation(s)
- Sera Kim
- Department of Statistics, Pusan National University , Busan, Korea
| | - Kyeongjun Lee
- Department of Statistics, Pusan National University , Busan, Korea
| | - Hokeun Sun
- Department of Statistics, Pusan National University , Busan, Korea
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140
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Zhan X, Liu DJ. SEQMINER: An R-Package to Facilitate the Functional Interpretation of Sequence-Based Associations. Genet Epidemiol 2015; 39:619-23. [PMID: 26394715 PMCID: PMC4794281 DOI: 10.1002/gepi.21918] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 07/01/2015] [Accepted: 07/17/2015] [Indexed: 11/23/2022]
Abstract
Next‐generation sequencing has enabled the study of a comprehensive catalogue of genetic variants for their impact on various complex diseases. Numerous consortia studies of complex traits have publically released their summary association statistics, which have become an invaluable resource for learning the underlying biology, understanding the genetic architecture, and guiding clinical translations. There is great interest in the field in developing novel statistical methods for analyzing and interpreting results from these genotype‐phenotype association studies. One popular platform for method development and data analysis is R. In order to enable these analyses in R, it is necessary to develop packages that can efficiently query files of summary association statistics, explore the linkage disequilibrium structure between variants, and integrate various bioinformatics databases. The complexity and scale of sequence datasets and databases pose significant computational challenges for method developers. To address these challenges and facilitate method development, we developed the R package SEQMINER for annotating and querying files of sequence variants (e.g., VCF/BCF files) and summary association statistics (e.g., METAL/RAREMETAL files), and for integrating bioinformatics databases. SEQMINER provides an infrastructure where novel methods can be distributed and applied to analyzing sequence datasets in practice. We illustrate the performance of SEQMINER using datasets from the 1000 Genomes Project. We show that SEQMINER is highly efficient and easy to use. It will greatly accelerate the process of applying statistical innovations to analyze and interpret sequence‐based associations. The R package, its source code and documentations are available from http://cran.r‐project.org/web/packages/seqminer and http://seqminer.genomic.codes/.
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Affiliation(s)
- Xiaowei Zhan
- Department of Clinical Sciences, Quantitative Biomedical Research Center, Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Dajiang J Liu
- Institute for Personalized Medicine, College of Medicine, Pennsylvania State University, Pennsylvania, Hershey, United States of America.,Division of Biostatistics, Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, United States of America
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141
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Shriner D. Mixed Ancestry and Disease Risk Transferability. CURRENT GENETIC MEDICINE REPORTS 2015. [DOI: 10.1007/s40142-015-0080-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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142
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Schmidt EM, Willer CJ. Insights into blood lipids from rare variant discovery. Curr Opin Genet Dev 2015; 33:25-31. [PMID: 26241468 DOI: 10.1016/j.gde.2015.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 06/19/2015] [Accepted: 06/22/2015] [Indexed: 12/18/2022]
Abstract
Large-scale genome wide screens have discovered over 160 common variants associated with plasma lipids, which are risk factors often linked to heart disease. A large fraction of lipid heritability remains unexplained, and it is hypothesized that rare variants of functional consequence may account for some of the missing heritability. Finding lipid-associated variants that occur less frequently in the human population poses a challenge, primarily due to lack of power and difficulties to identify and test them. Interrogation of the protein-coding regions of the genome using array and sequencing techniques has led to important discoveries of rare variants that affect lipid levels and related disease risk. Here, we summarize the latest methods and findings that contribute to our current understanding of rare variant lipid genetics.
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Affiliation(s)
- Ellen M Schmidt
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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143
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Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs. Am J Hum Genet 2015; 97:35-53. [PMID: 26094574 DOI: 10.1016/j.ajhg.2015.05.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Accepted: 05/01/2015] [Indexed: 01/01/2023] Open
Abstract
There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.
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144
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Larson NB, Berardi C, Decker PA, Wassel CL, Kirsch PS, Pankow JS, Sale MM, de Andrade M, Sicotte H, Tang W, Hanson NQ, Tsai MY, Taylor KD, Bielinski SJ. Trans-ethnic meta-analysis identifies common and rare variants associated with hepatocyte growth factor levels in the Multi-Ethnic Study of Atherosclerosis (MESA). Ann Hum Genet 2015; 79:264-74. [PMID: 25998175 PMCID: PMC4474777 DOI: 10.1111/ahg.12119] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Accepted: 04/08/2015] [Indexed: 01/03/2023]
Abstract
Hepatocyte growth factor (HGF) is a mesenchyme-derived pleiotropic factor that regulates cell growth, motility, mitogenesis, and morphogenesis in a variety of cells, and increased serum levels of HGF have been linked to a number of clinical and subclinical cardiovascular disease phenotypes. However, little is currently known regarding which genetic factors influence HGF levels, despite evidence of substantial genetic contributions to HGF variation. Based upon ethnicity-stratified single-variant association analysis and trans-ethnic meta-analysis of 6201 participants of the Multi-Ethnic Study of Atherosclerosis (MESA), we discovered five statistically significant common and low-frequency variants: HGF missense polymorphism rs5745687 (p.E299K) as well as four variants (rs16844364, rs4690098, rs114303452, rs3748034) within or in proximity to HGFAC. We also identified two significant ethnicity-specific gene-level associations (A1BG in African Americans; FASN in Chinese Americans) based upon low-frequency/rare variants, while meta-analysis of gene-level results identified a significant association for HGFAC. However, identified single-variant associations explained modest proportions of the total trait variation and were not significantly associated with coronary artery calcium or coronary heart disease. Our findings indicate that genetic factors influencing circulating HGF levels may be complex and ethnically diverse.
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Affiliation(s)
- Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Cecilia Berardi
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Paul A. Decker
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Phillip S. Kirsch
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Michele M. Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Naomi Q. Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Michael Y. Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute and Department of Pediatrics at Harbor-UCLA, Torrance, CA, USA
| | - Suzette J. Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models. Genetics 2015; 200:1089-104. [PMID: 26058849 DOI: 10.1534/genetics.115.178343] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 06/05/2015] [Indexed: 11/18/2022] Open
Abstract
Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.
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146
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Lopes MS, El-Basyoni I, Baenziger PS, Singh S, Royo C, Ozbek K, Aktas H, Ozer E, Ozdemir F, Manickavelu A, Ban T, Vikram P. Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change. JOURNAL OF EXPERIMENTAL BOTANY 2015; 66:3477-86. [PMID: 25821073 DOI: 10.1093/jxb/erv122] [Citation(s) in RCA: 170] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Climate change has generated unpredictability in the timing and amount of rain, as well as extreme heat and cold spells that have affected grain yields worldwide and threaten food security. Sources of specific adaptation related to drought and heat, as well as associated breeding of genetic traits, will contribute to maintaining grain yields in dry and warm years. Increased crop photosynthesis and biomass have been achieved particularly through disease resistance and healthy leaves. Similarly, sources of drought and heat adaptation through extended photosynthesis and increased biomass would also greatly benefit crop improvement. Wheat landraces have been cultivated for thousands of years under the most extreme environmental conditions. They have also been cultivated in lower input farming systems for which adaptation traits, particularly those that increase the duration of photosynthesis, have been conserved. Landraces are a valuable source of genetic diversity and specific adaptation to local environmental conditions according to their place of origin. Evidence supports the hypothesis that landraces can provide sources of increased biomass and thousand kernel weight, both important traits for adaptation to tolerate drought and heat. Evaluation of wheat landraces stored in gene banks with highly beneficial untapped diversity and sources of stress adaptation, once characterized, should also be used for wheat improvement. Unified development of databases and promotion of data sharing among physiologists, pathologists, wheat quality scientists, national programmes, and breeders will greatly benefit wheat improvement for adaptation to climate change worldwide.
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Affiliation(s)
| | - Ibrahim El-Basyoni
- Department of Agronomy and Horticulture, 362D Plant Science Building, 1875 N. 38th Street, University of Nebraska, Lincoln, NE 68583-0915, USA Crop Science Department, 15 F, Mogamaa El-Abadia, Faculty of Agriculture, Damanhour University, Damanhour 22516, Egypt
| | - Peter S Baenziger
- Department of Agronomy and Horticulture, 362D Plant Science Building, 1875 N. 38th Street, University of Nebraska, Lincoln, NE 68583-0915, USA
| | | | - Conxita Royo
- IRTA, Avda Rovira Roure 191, 25198 Lleida, Spain
| | - Kursad Ozbek
- Central Field Crops Research Institute, Şehit Cem Ersever Cad. No: 9-11 Yenimahalle, 06520 Ankara, Turkey
| | - Husnu Aktas
- Gap Uluslararası Tarımsal Araştırma ve Eğitim Merkezi Silvan Yolu Üzeri 9. Km PK.72, 21110 Diyarbakir, Turkey
| | - Emel Ozer
- Bahri Dagdas Uluslararasi Tarimsal Arastirma Enstitüsü, PK:125, Karatay, 42020 Konya, Turkey
| | - Fatih Ozdemir
- Bahri Dagdas Uluslararasi Tarimsal Arastirma Enstitüsü, PK:125, Karatay, 42020 Konya, Turkey
| | - Alagu Manickavelu
- Kihara Institute for Biological Research, Yokohama City University, Yokohama 244-0813, Japan
| | - Tomohiro Ban
- Kihara Institute for Biological Research, Yokohama City University, Yokohama 244-0813, Japan
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147
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Wang Q, Lu Q, Zhao H. A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Front Genet 2015; 6:149. [PMID: 25941534 PMCID: PMC4403555 DOI: 10.3389/fgene.2015.00149] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 03/30/2015] [Indexed: 12/22/2022] Open
Abstract
Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. For example, although a large number of disease-associated loci have been identified from genome-wide association studies in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. With the help of next-generation sequencing (NGS), diverse types of genomic and epigenetic variations can be detected with high accuracy. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization.
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Affiliation(s)
- Qian Wang
- Program of Computational Biology and Bioinformatics, Yale University New Haven, CT, USA
| | - Qiongshi Lu
- Department of Biostatistics, Yale School of Public Health New Haven, CT, USA
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University New Haven, CT, USA ; Department of Biostatistics, Yale School of Public Health New Haven, CT, USA ; Veterans Affairs Cooperative Studies Program Coordinating Center West Haven, CT, USA
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148
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Zhang Q. Associating rare genetic variants with human diseases. Front Genet 2015; 6:133. [PMID: 25904936 PMCID: PMC4389536 DOI: 10.3389/fgene.2015.00133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 03/19/2015] [Indexed: 11/20/2022] Open
Affiliation(s)
- Qunyuan Zhang
- Division of Statistical Genomics, Washington University School of Medicine St. Louis, MO, USA
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149
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Zeng P, Zhao Y, Liu J, Liu L, Zhang L, Wang T, Huang S, Chen F. Likelihood ratio tests in rare variant detection for continuous phenotypes. Ann Hum Genet 2015; 78:320-32. [PMID: 25117149 DOI: 10.1111/ahg.12071] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2013] [Accepted: 04/22/2014] [Indexed: 12/30/2022]
Abstract
It is believed that rare variants play an important role in human phenotypes; however, the detection of rare variants is extremely challenging due to their very low minor allele frequency. In this paper, the likelihood ratio test (LRT) and restricted likelihood ratio test (ReLRT) are proposed to test the association of rare variants based on the linear mixed effects model, where a group of rare variants are treated as random effects. Like the sequence kernel association test (SKAT), a state-of-the-art method for rare variant detection, LRT and ReLRT can effectively overcome the problem of directionality of effect inherent in the burden test in practice. By taking full advantage of the spectral decomposition, exact finite sample null distributions for LRT and ReLRT are obtained by simulation. We perform extensive numerical studies to evaluate the performance of LRT and ReLRT, and compare to the burden test, SKAT and SKAT-O. The simulations have shown that LRT and ReLRT can correctly control the type I error, and the controls are robust to the weights chosen and the number of rare variants under study. LRT and ReLRT behave similarly to the burden test when all the causal rare variants share the same direction of effect, and outperform SKAT across various situations. When both positive and negative effects exist, LRT and ReLRT suffer from few power reductions compared to the other two competing methods; under this case, an additional finding from our simulations is that SKAT-O is no longer the optimal test, and its power is even lower than that of SKAT. The exome sequencing SNP data from Genetic Analysis Workshop 17 were employed to illustrate the proposed methods, and interesting results are described.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, P. R. China; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu, 221004, P. R. China
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150
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Feng S, Pistis G, Zhang H, Zawistowski M, Mulas A, Zoledziewska M, Holmen OL, Busonero F, Sanna S, Hveem K, Willer C, Cucca F, Liu DJ, Abecasis GR. Methods for association analysis and meta-analysis of rare variants in families. Genet Epidemiol 2015; 39:227-38. [PMID: 25740221 DOI: 10.1002/gepi.21892] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 01/03/2015] [Accepted: 01/26/2015] [Indexed: 11/09/2022]
Abstract
Advances in exome sequencing and the development of exome genotyping arrays are enabling explorations of association between rare coding variants and complex traits. To ensure power for these rare variant analyses, a variety of association tests that group variants by gene or functional unit have been proposed. Here, we extend these tests to family-based studies. We develop family-based burden tests, variable frequency threshold tests and sequence kernel association tests. Through simulations, we compare the performance of different tests. We describe situations where family-based studies provide greater power than studies of unrelated individuals to detect rare variants associated with moderate to large changes in trait values. Broadly speaking, we find that when sample sizes are limited and only a modest fraction of all trait-associated variants can be identified, family samples are more powerful. Finally, we illustrate our approach by analyzing the relationship between coding variants and levels of high-density lipoprotein (HDL) cholesterol in 11,556 individuals from the HUNT and SardiNIA studies, demonstrating association for coding variants in the APOC3, CETP, LIPC, LIPG, and LPL genes and illustrating the value of family samples, meta-analysis, and gene-level tests. Our methods are implemented in freely available C++ code.
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Affiliation(s)
- Shuang Feng
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
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