Your browser doesn't support javascript.
loading
A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Auer, Paul L; Bielak, Lawrence F; Bis, Joshua C; Blackwell, Thomas W; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Conomos, Matthew P; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I; Göring, Harald H H; Guo, Xiuqing; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Lin, Bridget M; Manichaikul, Ani; Manning, Alisa K; Martin, Lisa W; Mathias, Rasika A; Meigs, James B; 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.
Afiliación
  • Li Z; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. li@hsph.harvard.edu.
  • Li X; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA. li@hsph.harvard.edu.
  • 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.
  • Selvaraj MS; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Arapoglou T; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
  • Quick C; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Liu Y; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Chen H; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Sun R; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Dey R; School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
  • Arnett DK; 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.
  • Auer PL; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 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.
  • Blackwell TW; Dean's Office, University of Kentucky, College of Public Health, Lexington, KY, USA.
  • Blangero J; Division of Biostatistics, Institute for Health & Equity and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 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 Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Cade BE; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Conomos MP; 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.
  • Franceschini N; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
  • Freedman BI; Department of Biostatistics, University of Washington, Seattle, WA, 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.
  • Kalyani RR; Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA.
  • Kooperberg C; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Kral BG; 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.
  • Lange LA; Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
  • Lin BM; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
  • Manichaikul A; Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Manning AK; 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.
  • Mathias RA; GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Meigs JB; 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 Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
  • Morrison AC; Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
  • Naseri T; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • O'Connell JR; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Palmer ND; Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Peyser PA; Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA.
  • Psaty BM; Division in Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA.
  • Raffield LM; GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Nat Methods ; 19(12): 1599-1611, 2022 12.
Article en En | MEDLINE | ID: mdl-36303018
ABSTRACT
Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genoma / Estudio de Asociación del Genoma Completo Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genoma / Estudio de Asociación del Genoma Completo Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos