Deep-coverage whole genome sequences and blood lipids among 16,324 individuals View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-08-23

AUTHORS

Pradeep Natarajan, Gina M. Peloso, Seyedeh Maryam Zekavat, May Montasser, Andrea Ganna, Mark Chaffin, Amit V. Khera, Wei Zhou, Jonathan M. Bloom, Jesse M. Engreitz, Jason Ernst, Jeffrey R. O’Connell, Sanni E. Ruotsalainen, Maris Alver, Ani Manichaikul, W. Craig Johnson, James A. Perry, Timothy Poterba, Cotton Seed, Ida L. Surakka, Tonu Esko, Samuli Ripatti, Veikko Salomaa, Adolfo Correa, Ramachandran S. Vasan, Manolis Kellis, Benjamin M. Neale, Eric S. Lander, Goncalo Abecasis, Braxton Mitchell, Stephen S. Rich, James G. Wilson, L. Adrienne Cupples, Jerome I. Rotter, Cristen J. Willer, Sekar Kathiresan, NHLBI TOPMed Lipids Working Group

ABSTRACT

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits—plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia. More... »

PAGES

3391

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  • Journal

    TITLE

    Nature Communications

    ISSUE

    1

    VOLUME

    9

    Author Affiliations

  • Broad Institute of Harvard & MIT, 02142, Cambridge, MA, USA
  • Department of Biostatistics, Boston University School of Public Health, 02118, Boston, MA, USA
  • Department of Computational Biology & Bioinformatics, Yale University, 06520, New Haven, CT, USA
  • School of Medicine, University of Maryland, 21201, Baltimore, MD, USA
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, 02114, Boston, MA, USA
  • Department of Computational Medicine and Bioinformatics, School of Public Health, University of Michigan, 48109, Ann Arbor, MI, USA
  • Society of Fellows, Harvard University, 02138, Cambridge, MA, USA
  • Department of Biological Chemistry, University of California, Los Angeles, 90095, Los Angeles, CA, USA
  • Institute for Molecular Medicine Finland, 00290, Helsinki, Finland
  • Estonian Genome Center, University of Tartu, 51010, Tartu, Estonia
  • Center for Public Health Genomics, University of Virginia, 22908, Charlottesville, VA, USA
  • Department of Biostatistics, University of Washington, 98195, Seattle, WA, USA
  • Department of Medicine, University of Mississippi Medical Center, 39216, Jackson, MS, USA
  • Framingham Heart Study, 01702, Framingham, MA, USA
  • Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology, 02139, Cambridge, MA, USA
  • Department of Biostatistics, School of Public Health, University of Michigan, 48109, Ann Arbor, MI, USA
  • Department of Physiology and Biophysics, University of Mississippi Medical Center, 39216, Jackson, MS, USA
  • Institute for Translational Genomics and Population Sciences, LABioMed and Departments of Pediatrics and Medicine, Harbor-UCLA Medical Center, 90502, Torrance, CA, USA
  • Departments of Human Genetics, Internal Medicine, and Computational Medicine and Bioinformatics, University of Michigan, 48109, Ann Arbor, MI, USA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-018-05747-8

    DOI

    http://dx.doi.org/10.1038/s41467-018-05747-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1106199477

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30140000


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