Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-10-27

AUTHORS

Jennifer A Brody, Alanna C Morrison, Joshua C Bis, Jeffrey R O'Connell, Michael R Brown, Jennifer E Huffman, Darren C Ames, Andrew Carroll, Matthew P Conomos, Stacey Gabriel, Richard A Gibbs, Stephanie M Gogarten, Namrata Gupta, Cashell E Jaquish, Andrew D Johnson, Joshua P Lewis, Xiaoming Liu, Alisa K Manning, George J Papanicolaou, Achilleas N Pitsillides, Kenneth M Rice, William Salerno, Colleen M Sitlani, Nicholas L Smith, Susan R Heckbert, Cathy C Laurie, Braxton D Mitchell, Ramachandran S Vasan, Stephen S Rich, Jerome I Rotter, James G Wilson, Eric Boerwinkle, Bruce M Psaty, L Adrienne Cupples

ABSTRACT

The increasing volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created the cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multicenter WGS analyses, including data-sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3,996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for translating WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations. More... »

PAGES

1560-1563

Journal

TITLE

Nature Genetics

ISSUE

11

VOLUME

49

Author Affiliations

  • Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
  • Department of Epidemiology, Human Genetics Center, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
  • Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland, Baltimore, Maryland, USA
  • Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, Massachusetts, USA
  • DNAnexus, Inc., Mountain View, California, USA
  • Department of Biostatistics, University of Washington, Seattle, Washington, USA
  • Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
  • Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
  • Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
  • Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
  • Department of Epidemiology, University of Washington, Seattle, Washington, USA
  • Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland, USA
  • Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
  • Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
  • Departments of Pediatrics and Medicine, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor -UCLA Medical Center, Torrance, California, USA
  • Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, USA
  • Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, Washington, USA
  • Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3968

    DOI

    http://dx.doi.org/10.1038/ng.3968

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092405015

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29074945


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