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A guide to genome-wide association analysis and post-analytic interrogation.
Reed, Eric; Nunez, Sara; Kulp, David; Qian, Jing; Reilly, Muredach P; Foulkes, Andrea S.
Afiliación
  • Reed E; Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, U.S.A.
  • Nunez S; Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, U.S.A.
  • Kulp D; Department of Computer Science, University of Massachusetts, Amherst, MA, U.S.A.
  • Qian J; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, U.S.A.
  • Reilly MP; Department of Medicine, University of Pennsylvania, Philadelphia, PA, U.S.A.
  • Foulkes AS; Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, U.S.A.
Stat Med ; 34(28): 3769-92, 2015 Dec 10.
Article en En | MEDLINE | ID: mdl-26343929
This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome-wide association analysis. Approaches to post-analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open-source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome-wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Estudio de Asociación del Genoma Completo Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Estudio de Asociación del Genoma Completo Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos