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Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.
Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Selvaraj, Margaret Sunitha; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Bielak, Lawrence F; Bis, Joshua C; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Göring, Harald H H; Guo, Xiuqing; Haessler, Jeffrey; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Manichaikul, Ani; Martin, Lisa W; McGarvey, Stephen T; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Sitlani, Colleen M; Smith, Jennifer A; Taylor, Kent D.
Afiliação
  • Li X; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Quick C; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Zhou H; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Gaynor SM; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Liu Y; School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
  • Chen H; Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Selvaraj MS; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Sun R; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
  • Dey R; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Arnett DK; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Bielak LF; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Bis JC; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Blangero J; University of Kentucky, College of Public Health, Lexington, KY, USA.
  • Boerwinkle E; Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
  • Bowden DW; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
  • Brody JA; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Cade BE; Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Correa A; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
  • Cupples LA; Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Curran JE; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
  • de Vries PS; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Duggirala R; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
  • Freedman BI; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
  • Göring HHH; Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
  • Guo X; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
  • Haessler J; Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA.
  • Kalyani RR; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Kooperberg C; Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Kral BG; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Lange LA; Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Manichaikul A; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Martin LW; 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.
  • McGarvey ST; Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Mitchell BD; GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Montasser ME; Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Morrison AC; GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Naseri T; Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
  • O'Connell JR; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • Palmer ND; Division of Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA.
  • Peyser PA; Department of Epidemiology, International Health Institute, Department of Anthropology, Brown University, Providence, RI, USA.
  • Psaty BM; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Raffield LM; Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA.
  • Redline S; Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Reiner AP; Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Reupena MS; Ministry of Health, Government of Samoa, Apia, Samoa.
  • Rice KM; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Rich SS; Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Sitlani CM; Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
  • Smith JA; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
  • Taylor KD; Departments of Epidemiology, University of Washington, Seattle, WA, USA.
Nat Genet ; 55(1): 154-164, 2023 01.
Article em En | MEDLINE | ID: mdl-36564505
ABSTRACT
Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla / Lipídeos Tipo de estudo: Risk_factors_studies / Systematic_reviews Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla / Lipídeos Tipo de estudo: Risk_factors_studies / Systematic_reviews Idioma: En Ano de publicação: 2023 Tipo de documento: Article