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Taub MA, Conomos MP, Keener R, Iyer KR, Weinstock JS, Yanek LR, Lane J, Miller-Fleming TW, Brody JA, Raffield LM, McHugh CP, Jain D, Gogarten SM, Laurie CA, Keramati A, Arvanitis M, Smith AV, Heavner B, Barwick L, Becker LC, Bis JC, Blangero J, Bleecker ER, Burchard EG, Celedón JC, Chang YPC, Custer B, Darbar D, de las Fuentes L, DeMeo DL, Freedman BI, Garrett ME, Gladwin MT, Heckbert SR, Hidalgo BA, Irvin MR, Islam T, Johnson WC, Kaab S, Launer L, Lee J, Liu S, Moscati A, North KE, Peyser PA, Rafaels N, Seidman C, Weeks DE, Wen F, Wheeler MM, Williams LK, Yang IV, Zhao W, Aslibekyan S, Auer PL, Bowden DW, Cade BE, Chen Z, Cho MH, Cupples LA, Curran JE, Daya M, Deka R, Eng C, Fingerlin TE, Guo X, Hou L, Hwang SJ, Johnsen JM, Kenny EE, Levin AM, Liu C, Minster RL, Naseri T, Nouraie M, Reupena MS, Sabino EC, Smith JA, Smith NL, Lasky-Su J, Taylor JG, Telen MJ, Tiwari HK, Tracy RP, White MJ, Zhang Y, Wiggins KL, Weiss ST, Vasan RS, Taylor KD, Sinner MF, Silverman EK, Shoemaker MB, Sheu WHH, Sciurba F, Schwartz DA, Rotter JI, Roden D, Redline S, Raby BA, Psaty BM, Peralta JM, Palmer ND, Nekhai S, Montgomery CG, Mitchell BD, Meyers DA, McGarvey ST, Mak AC, Loos RJ, Kumar R, Kooperberg C, Konkle BA, Kelly S, Kardia SL, Kaplan R, He J, Gui H, Gilliland FD, Gelb BD, Fornage M, Ellinor PT, de Andrade M, Correa A, Chen YDI, Boerwinkle E, Barnes KC, Ashley-Koch AE, Arnett DK, Albert C, Laurie CC, Abecasis G, Nickerson DA, Wilson JG, Rich SS, Levy D, Ruczinski I, Aviv A, Blackwell TW, Thornton T, O’Connell J, Cox NJ, Perry JA, Armanios M, Battle A, Pankratz N, Reiner AP, Mathias RA. Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed. CELL GENOMICS 2022; 2:S2666-979X(21)00105-1. [PMID: 35530816 PMCID: PMC9075703 DOI: 10.1016/j.xgen.2021.100084] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 09/03/2021] [Accepted: 12/10/2021] [Indexed: 01/16/2023]
Abstract
Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10-9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes.
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Affiliation(s)
- Margaret A. Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matthew P. Conomos
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD, USA
| | - Kruthika R. Iyer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joshua S. Weinstock
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lisa R. Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - John Lane
- Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Tyne W. Miller-Fleming
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Caitlin P. McHugh
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Deepti Jain
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Stephanie M. Gogarten
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Cecelia A. Laurie
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Ali Keramati
- Department of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Marios Arvanitis
- Department of Medicine, Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Albert V. Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Benjamin Heavner
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Lucas Barwick
- LTRC Data Coordinating Center, The Emmes Company, LLC, Rockville, MD, USA
| | - Lewis C. Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eugene R. Bleecker
- Department of Medicine, Division of Genetics, Genomics, and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Esteban G. Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Juan C. Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yen Pei C. Chang
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Dawood Darbar
- Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA
| | - Lisa de las Fuentes
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Barry I. Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Melanie E. Garrett
- Department of Medicine and Duke Comprehensive Sickle Cell Center, Duke University Medical Center, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Mark T. Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Bertha A. Hidalgo
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Talat Islam
- Division of Environmental Health, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - W. Craig Johnson
- Department of Biostatistics, Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA
| | - Stefan Kaab
- Department of Medicine I, University Hospital Munich, Ludwig-Maximilian’s University, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Lenore Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jiwon Lee
- Department of Medicine, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Simin Liu
- Department of Epidemiology and Brown Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Patricia A. Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Nicholas Rafaels
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | | | - Daniel E. Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Fayun Wen
- Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington, DC 20059, USA
| | - Marsha M. Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Ivana V. Yang
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Paul L. Auer
- Zilber School of Public Health, University of Wisconsin, Milwaukee, Milwaukee, WI, USA
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Brian E. Cade
- Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Zhanghua Chen
- Division of Environmental Health, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The National Heart, Lung, and Blood Institute, Boston University’s Framingham Heart Study, Framingham, MA, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Michelle Daya
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Ranjan Deka
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Tasha E. Fingerlin
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
- Department of Biostatistics and Informatics, University of Colorado, Denver, Aurora, CO, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jill M. Johnsen
- Bloodworks Northwest Research Institute, Seattle, WA, USA
- University of Washington, Department of Medicine, Seattle, WA, USA
| | - Eimear E. Kenny
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Albert M. Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Chunyu Liu
- The National Heart, Lung, and Blood Institute, Boston University’s Framingham Heart Study, Framingham, MA, USA
- The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Ryan L. Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
- Department of Epidemiology & International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | - Mehdi Nouraie
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Ester C. Sabino
- Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Nicholas L. Smith
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - James G. Taylor
- Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington, DC 20059, USA
| | - Marilyn J. Telen
- Department of Medicine and Duke Comprehensive Sickle Cell Center, Duke University Medical Center, Durham, NC, USA
- Duke Comprehensive Sickle Cell Center, Duke University Medical Center, Durham, NC, USA
| | - Hemant K. Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Russell P. Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larrner College of Medicine, University of Vermont, Colchester, VT, USA
| | - Marquitta J. White
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Yingze Zhang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ramachandran S. Vasan
- The National Heart, Lung, and Blood Institute, Boston University’s Framingham Heart Study, Framingham, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Moritz F. Sinner
- Department of Medicine I, University Hospital Munich, Ludwig-Maximilian’s University, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - M. Benjamin Shoemaker
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wayne H.-H. Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Frank Sciurba
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A. Schwartz
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Daniel Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Susan Redline
- Division of Sleep Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Benjamin A. Raby
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Juan M. Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sergei Nekhai
- Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington, DC 20059, USA
| | - Courtney G. Montgomery
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Deborah A. Meyers
- Department of Medicine, Division of Genetics, Genomics, and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Stephen T. McGarvey
- Department of Epidemiology & International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | | | - Angel C.Y. Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rajesh Kumar
- Division of Allergy and Clinical Immunology, The Ann and Robert H. Lurie Children’s Hospital of Chicago, and Department of Pediatrics, Northwestern University, Chicago, IL, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Barbara A. Konkle
- Bloodworks Northwest Research Institute, Seattle, WA, USA
- University of Washington, Department of Medicine, Seattle, WA, USA
| | - Shannon Kelly
- Vitalant Research Institute, San Francisco, CA, USA
- UCSF Benioff Children’s Hospital, Oakland, CA, USA
| | - Sharon L.R. Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jiang He
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Frank D. Gilliland
- Division of Environmental Health, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Bruce D. Gelb
- Mindich Child Health and Development Institute, Departments of Pediatrics and Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patrick T. Ellinor
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Adolfo Correa
- Jackson Heart Study and Departments of Medicine and Population Health Science, Jackson, MS, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kathleen C. Barnes
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Allison E. Ashley-Koch
- Department of Medicine and Duke Comprehensive Sickle Cell Center, Duke University Medical Center, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Christine Albert
- Harvard Medical School, Boston, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | | | | | - Cathy C. Laurie
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Goncalo Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Daniel Levy
- The National Heart, Lung, and Blood Institute, Boston University’s Framingham Heart Study, Framingham, MA, USA
- The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abraham Aviv
- Center of Human Development and Aging, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Timothy Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jeff O’Connell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nancy J. Cox
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James A. Perry
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mary Armanios
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD, USA
- Departments of Computer Science and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Rasika A. Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
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Dalvie S, Maihofer AX, Coleman JRI, Bradley B, Breen G, Brick LA, Chen CY, Choi KW, Duncan LE, Guffanti G, Haas M, Harnal S, Liberzon I, Nugent NR, Provost AC, Ressler KJ, Torres K, Amstadter AB, Bryn Austin S, Baker DG, Bolger EA, Bryant RA, Calabrese JR, Delahanty DL, Farrer LA, Feeny NC, Flory JD, Forbes D, Galea S, Gautam A, Gelernter J, Hammamieh R, Jett M, Junglen AG, Kaufman ML, Kessler RC, Khan A, Kranzler HR, Lebois LAM, Marmar C, Mavissakalian MR, McFarlane A, Donnell MO, Orcutt HK, Pietrzak RH, Risbrough VB, Roberts AL, Rothbaum AO, Roy-Byrne P, Ruggiero K, Seligowski AV, Sheerin CM, Silove D, Smoller JW, Stein MB, Teicher MH, Ursano RJ, Van Hooff M, Winternitz S, Wolff JD, Yehuda R, Zhao H, Zoellner LA, Stein DJ, Koenen KC, Nievergelt CM. Genomic influences on self-reported childhood maltreatment. Transl Psychiatry 2020; 10:38. [PMID: 32066696 PMCID: PMC7026037 DOI: 10.1038/s41398-020-0706-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 11/28/2019] [Accepted: 12/11/2019] [Indexed: 12/27/2022] Open
Abstract
Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to (1) identify genetic variation associated with self-reported childhood maltreatment, (2) estimate SNP-based heritability (h2snp), (3) assess predictive value of polygenic risk scores (PRS) for childhood maltreatment, and (4) quantify genetic overlap of childhood maltreatment with mental and physical health-related phenotypes, and condition the top hits from our analyses when such overlap is present. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n = 124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder group (PGC-PTSD) (n = 26,290). h2snp for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression. PRS was calculated using PRSice and mtCOJO was used to perform conditional analysis. Two genome-wide significant loci associated with childhood maltreatment (rs142346759, p = 4.35 × 10-8, FOXP1; rs10262462, p = 3.24 × 10-8, FOXP2) were identified in the discovery dataset but were not replicated in PGC-PTSD. h2snp for childhood maltreatment was ~6% and the PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC-PTSD (r2 = 0.0025; p = 1.8 × 10-15). The most significant genetic correlation of childhood maltreatment was with depressive symptoms (rg = 0.70, p = 4.65 × 10-40), although we show evidence that our top hits may be specific to childhood maltreatment. This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Speculatively, FOXP genes might influence externalizing traits and so be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of phenotypes, may ultimately be useful in in developing targeted treatment and prevention strategies.
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Affiliation(s)
- Shareefa Dalvie
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Jonathan R I Coleman
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR BRC at the Maudsley, London, UK
| | - Bekh Bradley
- Atlanta VA Health Care System, Mental Health Service Line, Decatur, GA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Gerome Breen
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR BRC at the Maudsley, London, UK
| | - Leslie A Brick
- Alpert Medical School of Brown University, Providence, RI, USA
| | - Chia-Yen Chen
- Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Karmel W Choi
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, USA
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Laramie E Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Guia Guffanti
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Magali Haas
- Cohen Veterans Bioscience, Cambridge, MA, USA
| | - Supriya Harnal
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Israel Liberzon
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nicole R Nugent
- Alpert Medical School of Brown University, Providence, RI, USA
- Bradley/Hasbro Children's Research Center of Rhode Island Hospital, Providence, RI, USA
| | | | - Kerry J Ressler
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Katy Torres
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Ananda B Amstadter
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Richmond, VA, USA
| | - S Bryn Austin
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- Harvard School of Public Health, Department of Social and Behavioral Sciences, Boston, MA, USA
- Boston Children's Hospital, Division of Adolescent and Young Adult Medicine, Boston, MA, USA
- Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, MA, USA
| | - Dewleen G Baker
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
| | - Elizabeth A Bolger
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Richard A Bryant
- Department of Psychology, University of New South Wales, Sydney, NSW, Australia
| | | | - Douglas L Delahanty
- Department of Psychological Sciences, Kent State University, Kent, OH, USA
- Research and Sponsored Programs, Kent State University, Kent, OH, USA
| | - Lindsay A Farrer
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Norah C Feeny
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Janine D Flory
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Forbes
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
| | - Sandro Galea
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Aarti Gautam
- US Army Medical Research and Materiel Command, Fort Detrick, MD, USA
| | - Joel Gelernter
- US Department of Veterans Affairs, Department of Psychiatry, West Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rasha Hammamieh
- US Army Medical Research and Materiel Command, USACEHR, Fort Detrick, MD, USA
| | - Marti Jett
- US Army Medical Research and Materiel Command, USACEHR, Fort Detrick, MD, USA
| | - Angela G Junglen
- Department of Psychological Sciences, Kent State University, Kent, OH, USA
| | - Milissa L Kaufman
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Ronald C Kessler
- Harvard Medical School, Department of Health Care Policy, Boston, MA, USA
| | - Alaptagin Khan
- McLean Hospital, Belmont, MA, USA
- Harvard Medical School, Department of Health Care Policy, Boston, MA, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Lauren A M Lebois
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Charles Marmar
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | | | - Alexander McFarlane
- University of Adelaide, Centre for Traumatic Stress Studies, Adelaide, SA, Australia
| | - Meaghan O' Donnell
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
| | - Holly K Orcutt
- Department of Psychology, Northern Illinois University, DeKalb, IL, USA
| | - Robert H Pietrzak
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Victoria B Risbrough
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alex O Rothbaum
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Peter Roy-Byrne
- Department of Psychiatry, University of Washington, Seattle, WA, USA
| | - Ken Ruggiero
- Department of Nursing and Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
| | - Antonia V Seligowski
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Christina M Sheerin
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Richmond, VA, USA
| | - Derrick Silove
- Department of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jordan W Smoller
- Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Million Veteran Program, San Diego, CA, USA
| | - Martin H Teicher
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA
| | - Miranda Van Hooff
- University of Adelaide, Centre for Traumatic Stress Studies, Adelaide, SA, Australia
| | - Sherry Winternitz
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | | | - Rachel Yehuda
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Lori A Zoellner
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Karestan C Koenen
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Harvard School of Public Health, Department of Epidemiology, Boston, MA, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
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3
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Wu W, Wang Z, Xu K, Zhang X, Amei A, Gelernter J, Zhao H, Justice AC, Wang Z. Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use. Genetics 2019; 213:1225-1236. [PMID: 31591132 PMCID: PMC6893384 DOI: 10.1534/genetics.119.302598] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 10/04/2019] [Indexed: 12/15/2022] Open
Abstract
Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.
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Affiliation(s)
- Weimiao Wu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Zhong Wang
- Baker Institute for Animal Health, Cornell University, Ithaca, New York 14850
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
- VA Connecticut Healthcare System, West Haven, Connecticut 06516
| | - Xinyu Zhang
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
- VA Connecticut Healthcare System, West Haven, Connecticut 06516
| | - Amei Amei
- Department of Mathematical Sciences, University of Nevada, Las Vegas, Nevada 89154
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
- VA Connecticut Healthcare System, West Haven, Connecticut 06516
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Amy C Justice
- VA Connecticut Healthcare System, West Haven, Connecticut 06516
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut 06511
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
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4
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Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations. Nat Commun 2019; 10:880. [PMID: 30787307 PMCID: PMC6382865 DOI: 10.1038/s41467-019-08469-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 01/08/2019] [Indexed: 12/15/2022] Open
Abstract
Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.
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5
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Kontou PI, Pavlopoulou A, Bagos PG. Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes. Methods Mol Biol 2018; 1793:183-210. [PMID: 29876898 DOI: 10.1007/978-1-4939-7868-7_12] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.
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Affiliation(s)
- Panagiota I Kontou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Athanasia Pavlopoulou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.,International Biomedicine and Genome Institute (iBG-Izmir), Dokuz Eylul University, Izmir, 35340, Turkey
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
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6
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Lee CH, Cook S, Lee JS, Han B. Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inform 2016; 14:173-180. [PMID: 28154508 PMCID: PMC5287121 DOI: 10.5808/gi.2016.14.4.173] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/03/2016] [Accepted: 12/03/2016] [Indexed: 12/22/2022] Open
Abstract
The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.
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Affiliation(s)
- Cue Hyunkyu Lee
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Seungho Cook
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; School of Systems Biomedical Science, Soongsil University, Seoul 06978, Korea
| | - Ji Sung Lee
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Buhm Han
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
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7
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Bernal Rubio YL, Gualdrón Duarte JL, Bates RO, Ernst CW, Nonneman D, Rohrer GA, King DA, Shackelford SD, Wheeler TL, Cantet RJC, Steibel JP. Implementing meta-analysis from genome-wide association studies for pork quality traits. J Anim Sci 2016; 93:5607-17. [PMID: 26641170 DOI: 10.2527/jas.2015-9502] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a meta-analysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining -scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [] and calpastatin []), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit []). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial []) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [] and ferritin, light polypeptide []). Thus, implementation of MA-GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.
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8
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Wang S, Zhao JH, An P, Guo X, Jensen RA, Marten J, Huffman JE, Meidtner K, Boeing H, Campbell A, Rice KM, Scott RA, Yao J, Schulze MB, Wareham NJ, Borecki IB, Province MA, Rotter JI, Hayward C, Goodarzi MO, Meigs JB, Dupuis J. General Framework for Meta-Analysis of Haplotype Association Tests. Genet Epidemiol 2016; 40:244-52. [PMID: 27027517 PMCID: PMC4869684 DOI: 10.1002/gepi.21959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 11/03/2015] [Accepted: 12/14/2015] [Indexed: 11/24/2022]
Abstract
For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta‐analysis has emerged as the method of choice to combine results from multiple studies. Many meta‐analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta‐analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two‐stage meta‐analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta‐analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype‐specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type‐I error rate, and our approach is more powerful than inverse variance weighted meta‐analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose‐associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.
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Affiliation(s)
- Shuai Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Jing Hua Zhao
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Ping An
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Richard A Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America.,Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Jonathan Marten
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, United Kingdom
| | - Jennifer E Huffman
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, United Kingdom
| | - Karina Meidtner
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Archie Campbell
- Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetic and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, United Kindom
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Robert A Scott
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Caroline Hayward
- Department of Medicine, University of Washington, Seattle, Washington, United States of America.,Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetic and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, United Kindom
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - James B Meigs
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.,National Heart, Lung, Blood Institute (NHLBI), Framingham Heart Study, Framingham, Massachusetts, United States of America
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9
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Asgari MM, Wang W, Ioannidis NM, Itnyre J, Hoffmann T, Jorgenson E, Whittemore AS. Identification of Susceptibility Loci for Cutaneous Squamous Cell Carcinoma. J Invest Dermatol 2016; 136:930-937. [PMID: 26829030 DOI: 10.1016/j.jid.2016.01.013] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 01/04/2016] [Accepted: 01/06/2016] [Indexed: 12/20/2022]
Abstract
We report a genome-wide association study of cutaneous squamous cell carcinoma conducted among non-Hispanic white members of the Kaiser Permanente Northern California health care system. The study includes a genome-wide screen of 61,457 members (6,891 cases and 54,566 controls) genotyped on the Affymetrix Axiom European array and a replication phase involving an independent set of 6,410 additional members (810 cases and 5,600 controls). Combined analysis of screening and replication phases identified 10 loci containing single-nucleotide polymorphisms (SNPs) with P-values < 5 × 10(-8). Six loci contain genes in the pigmentation pathway; SNPs at these loci appear to modulate squamous cell carcinoma risk independently of the pigmentation phenotypes. Another locus contains HLA class II genes studied in relation to elevated squamous cell carcinoma risk following immunosuppression. SNPs at the remaining three loci include an intronic SNP in FOXP1 at locus 3p13, an intergenic SNP at 3q28 near TP63, and an intergenic SNP at 9p22 near BNC2. These findings provide insights into the genetic factors accounting for inherited squamous cell carcinoma susceptibility.
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Affiliation(s)
- Maryam M Asgari
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA; Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Wei Wang
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Nilah M Ioannidis
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Jacqueline Itnyre
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Thomas Hoffmann
- Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Alice S Whittemore
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA.
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10
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Bernal Rubio YL, Gualdrón Duarte JL, Bates RO, Ernst CW, Nonneman D, Rohrer GA, King A, Shackelford SD, Wheeler TL, Cantet RJC, Steibel JP. Meta-analysis of genome-wide association from genomic prediction models. Anim Genet 2015; 47:36-48. [PMID: 26607299 PMCID: PMC4738412 DOI: 10.1111/age.12378] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2015] [Indexed: 12/21/2022]
Abstract
Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.
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Affiliation(s)
- Y L Bernal Rubio
- Departamento de Producción Animal, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina.,Department of Animal Science, Michigan State University, East Lansing, MI, 48824-1225, USA
| | - J L Gualdrón Duarte
- Departamento de Producción Animal, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina
| | - R O Bates
- Departamento de Producción Animal, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina
| | - C W Ernst
- Departamento de Producción Animal, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina
| | - D Nonneman
- USDA/ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933-0166, USA
| | - G A Rohrer
- USDA/ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933-0166, USA
| | - A King
- USDA/ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933-0166, USA
| | - S D Shackelford
- USDA/ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933-0166, USA
| | - T L Wheeler
- USDA/ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933-0166, USA
| | - R J C Cantet
- Department of Animal Science, Michigan State University, East Lansing, MI, 48824-1225, USA.,Consejo Nacional de Investigaciones Cientificas y Tecnicas - CONICET, Buenos Aires, Argentina
| | - J P Steibel
- Departamento de Producción Animal, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina.,Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824-1225, USA
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11
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Xu X, Shi G, Nehorai A. Meta-regression of gene-environment interaction in genome-wide association studies. IEEE Trans Nanobioscience 2014; 12:354-62. [PMID: 25003165 DOI: 10.1109/tnb.2013.2294331] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Genome-wide association studies (GWAS) have created heightened interest in understanding the effects of gene-environment interaction on complex human diseases or traits. Applying methods for analyzing such interaction can help uncover novel genes and identify environmental hazards that influence only certain genetically susceptible groups. However, the number of interaction analysis methods is still limited, so there is a need to develop more efficient and powerful methods. In this paper, we propose two novel meta-analysis methods of studying gene-environment interaction, based on meta-regression of estimated genetic effects on the environmental factor. The two methods can perform joint analysis of a single nucleotide polymorphism's (SNP) main and interaction effects, or analyze only the effect of the interaction. They can readily estimate any linear or non-linear interactions by simply modifying the gene-environment regression function. Thus, they are efficient methods to be applied to different scenarios. We use numerical examples to demonstrate the performance of our methods. We also compare them with two other methods commonly used in current GWAS, i.e., meta-analysis of SNP main effects (MAIN) and joint meta-analysis of SNP main and interaction effects (JMA). The results show that our methods are more powerful than MAIN when the interaction effect exists, and are comparable to JMAin the linear or quadratic interaction cases. In the numerical examples, we also investigate how the number of the divided groups and the sample size of the studies affect the performance of our methods.
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12
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Sung YJ, Schwander K, Arnett DK, Kardia SLR, Rankinen T, Bouchard C, Boerwinkle E, Hunt SC, Rao DC. An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genet Epidemiol 2014; 38:369-78. [PMID: 24719363 DOI: 10.1002/gepi.21800] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Revised: 02/10/2014] [Accepted: 02/28/2014] [Indexed: 11/11/2022]
Abstract
For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.
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Affiliation(s)
- Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
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13
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Wang T, Zhou B, Guo T, Bidlingmaier M, Wallaschofski H, Teumer A, Vasan RS, Kaplan RC. A robust method for genome-wide association meta-analysis with the application to circulating insulin-like growth factor I concentrations. Genet Epidemiol 2013; 38:162-71. [PMID: 24446417 DOI: 10.1002/gepi.21766] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 08/16/2013] [Accepted: 09/17/2013] [Indexed: 11/06/2022]
Abstract
Genome-wide association studies (GWAS) offer an excellent opportunity to identify the genetic variants underlying complex human diseases. Successful utilization of this approach requires a large sample size to identify single nucleotide polymorphisms (SNPs) with subtle effects. Meta-analysis is a cost-efficient means to achieve large sample size by combining data from multiple independent GWAS; however, results from studies performed on different populations can be variable due to various reasons, including varied linkage equilibrium structures as well as gene-gene and gene-environment interactions. Nevertheless, one should expect effects of the SNP are more similar between similar populations than those between populations with quite different genetic and environmental backgrounds. Prior information on populations of GWAS is often not considered in current meta-analysis methods, rendering such analyses less optimal for the detecting association. This article describes a test that improves meta-analysis to incorporate variable heterogeneity among populations. The proposed method is remarkably simple in computation and hence can be performed in a rapid fashion in the setting of GWAS. Simulation results demonstrate the validity and higher power of the proposed method over conventional methods in the presence of heterogeneity. As a demonstration, we applied the test to real GWAS data to identify SNPs associated with circulating insulin-like growth factor I concentrations.
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Affiliation(s)
- Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
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14
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Menaa F. Stroke in sickle cell anemia patients: a need for multidisciplinary approaches. Atherosclerosis 2013; 229:496-503. [PMID: 23746538 DOI: 10.1016/j.atherosclerosis.2013.05.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2012] [Revised: 05/07/2013] [Accepted: 05/07/2013] [Indexed: 12/12/2022]
Abstract
Sickle cell anemia (SCA) is an autosomal recessive disorder, with Mendelian inheritance pattern, caused by a missense mutation in the β-polypeptide chain of the hemoglobin B. SCA preferentially affects populations in countries where malaria was/is present (e.g. Africa, USA, Brazil). Thereby, in USA, the incidence of SCA is relatively high, around 1/500, and the prevalence is about 1/1000. In Brazil, SCA represents a major public health problem with an incidence ranging from 1/2000 to 1/600 depending on the regions. Homozygotic patients present more severe medical conditions and reduced life expectancy than heterozygous individuals who generally are asymptomatic. Eventually, this life-threatening disease displays a complex etiology owing to heterogeneous phenotypes and clinical outcomes, subsequently affecting the management of the patients. One of the most critical complications associated with SCA is stroke, a leading neurologic cause of death and disability. About 24% of SCA patients have a stroke by the age of 45 and 11% by the age of 20. From the general population, twin and familial aggregation studies as well as genome-wide association studies (GWAS), mostly in pediatric populations with ischemic stroke, showed that the risk of stroke has a substantial genetic component. Nevertheless, to fully characterize genomic contributors of stroke and permit reliable personalized medicine, multidisciplinary studies incorporating knowledge from clinical medicine, epidemiology, genetics, and molecular biology, are required. In this manuscript, stroke in SCA patients is extensively reviewed with emphasis to the US and Brazilian populations. Recent advances in genomics analysis of stroke in SCA patients are highlighted.
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Affiliation(s)
- Farid Menaa
- Center of Hematology and Hemotherapy (Hemocentro), School of Medicine and Medical Sciences (FCM), University of Campinas (UNICAMP), São Paulo, Brazil.
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15
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Evangelou E, Ioannidis JPA. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 2013; 14:379-89. [PMID: 23657481 DOI: 10.1038/nrg3472] [Citation(s) in RCA: 382] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.
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Affiliation(s)
- Evangelos Evangelou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
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