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IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors.
Amariuta, Tiffany; Luo, Yang; Gazal, Steven; Davenport, Emma E; van de Geijn, Bryce; Ishigaki, Kazuyoshi; Westra, Harm-Jan; Teslovich, Nikola; Okada, Yukinori; Yamamoto, Kazuhiko; Price, Alkes L; Raychaudhuri, Soumya.
Afiliação
  • Amariuta T; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
  • Luo Y; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
  • Gazal S; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
  • Davenport EE; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
  • van de Geijn B; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
  • Ishigaki K; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
  • Westra HJ; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
  • Teslovich N; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Okada Y; Osaka University Graduate School of Medicine, Osaka, Japan; Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan.
  • Yamamoto K; RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
  • Price AL; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. Elect
  • Raychaudhuri S; Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambri
Am J Hum Genet ; 104(5): 879-895, 2019 05 02.
Article em En | MEDLINE | ID: mdl-31006511
ABSTRACT
Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures where specific transcription factors (TFs) are bound. To link these two features, we introduce IMPACT, a genome annotation strategy that identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT distinguishes between bound and unbound TF motif sites with high accuracy (average AUPRC 0.81, SE 0.07; across 8 tested TFs) and outperforms state-of-the-art TF binding prediction methods, MocapG, MocapS, and Virtual ChIP-seq. Second, in eight tested cell types, RNA polymerase II IMPACT annotations capture more cis-eQTL variation than sequence-based annotations, such as promoters and TSS windows (25% average increase in enrichment). Third, integration with rheumatoid arthritis (RA) summary statistics from European (N = 38,242) and East Asian (N = 22,515) populations revealed that the top 5% of CD4+ Treg IMPACT regulatory elements capture 85.7% of RA h2, the most comprehensive explanation for RA h2 to date. In comparison, the average RA h2 captured by compared CD4+ T histone marks is 42.3% and by CD4+ T specifically expressed gene sets is 36.4%. Lastly, we find that IMPACT may be used in many different cell types to identify complex trait associated regulatory elements.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Artrite Reumatoide / Fatores de Transcrição / Genoma Humano / Sequências Reguladoras de Ácido Nucleico / Anotação de Sequência Molecular / Epigenômica / Epigenoma Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Am J Hum Genet Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Artrite Reumatoide / Fatores de Transcrição / Genoma Humano / Sequências Reguladoras de Ácido Nucleico / Anotação de Sequência Molecular / Epigenômica / Epigenoma Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Am J Hum Genet Ano de publicação: 2019 Tipo de documento: Article