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Am J Hum Genet. 2019 May 2;104(5):879-895. doi: 10.1016/j.ajhg.2019.03.012. Epub 2019 Apr 18.

IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors.

Author information

1
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, Cambridge, MA 02142, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
2
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, Cambridge, MA 02142, USA.
3
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.
4
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, Cambridge, MA 02142, USA; Faculty of Medical Sciences, University of Groningen, Groningen, the Netherlands.
5
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
6
Osaka University Graduate School of Medicine, Osaka, Japan; Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan.
7
RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
8
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. Electronic address: aprice@hsph.harvard.edu.
9
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, Cambridge, MA 02142, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA; Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. Electronic address: soumya@broadinstitute.org.

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.

KEYWORDS:

CD4(+); T cells; arthritis; autoimmune; binding; epigenomics; heritability; polygenic; rheumatoid; transcription

PMID:
31006511
PMCID:
PMC6506796
[Available on 2019-11-02]
DOI:
10.1016/j.ajhg.2019.03.012
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