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Biological interpretation of genome-wide association studies using predicted gene functions.
Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude.
Afiliação
  • Pers TH; 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA.
  • Karjalainen JM; Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen 9711, The Netherlands.
  • Chan Y; 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [3] Department of Genetics, Harvard M
  • Westra HJ; Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
  • Wood AR; Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK.
  • Yang J; 1] Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia [2] The University of Queensland Diamantina Institute, The Translation Research Institute, Brisbane, Queensland 4012, Australia.
  • Lui JC; Section on Growth and Development, Program in Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892, USA.
  • Vedantam S; 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA.
  • Gustafsson S; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala 75185, Sweden.
  • Esko T; 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [3] Estonian Genome Center, Universit
  • Frayling T; Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK.
  • Speliotes EK; Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
  • Boehnke M; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
  • Raychaudhuri S; 1] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [2] Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA [3] Partners HealthCare Center for Personalized Genetic Medicine, Boston, Massachusetts 02115, U
  • Fehrmann RS; Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen 9711, The Netherlands.
  • Hirschhorn JN; 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [3] Department of Genetics, Harvard M
  • Franke L; Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen 9711, The Netherlands.
Nat Commun ; 6: 5890, 2015 Jan 19.
Article em En | MEDLINE | ID: mdl-25597830
The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos