Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

Journal: Genome medicine
Published Date: Oct 14, 2021

Abstract

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.

Authors

  • Francisco M De La Vega
    Fabric Genomics Inc., Oakland, CA, USA.
  • Shimul Chowdhury
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
  • Barry Moore
    Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.
  • Erwin Frise
    Fabric Genomics Inc., Oakland, CA, USA.
  • Jeanette McCarthy
    Fabric Genomics Inc., Oakland, CA, USA.
  • Edgar Javier Hernandez
    Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.
  • Terence Wong
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
  • Kiely James
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
  • Lucia Guidugli
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
  • Pankaj B Agrawal
    Division of Genetics and Genomics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Casie A Genetti
    Division of Genetics and Genomics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Catherine A Brownstein
    Division of Genetics and Genomics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Alan H Beggs
    Division of Genetics and Genomics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Britt-Sabina Löscher
    Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel & University Hospital Schleswig-Holstein, Kiel, Germany.
  • Andre Franke
    Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
  • Braden Boone
    HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
  • Shawn E Levy
    HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
  • Katrin Õunap
    Department of Clinical Genetics, United Laboratories, Tartu University Hospital, Tartu, Estonia.
  • Sander Pajusalu
    Department of Clinical Genetics, United Laboratories, Tartu University Hospital, Tartu, Estonia.
  • Matt Huentelman
    Center for Rare Childhood Disorders, Translational Genomics Research Institute, Phoenix, AZ, USA.
  • Keri Ramsey
    Center for Rare Childhood Disorders, Translational Genomics Research Institute, Phoenix, AZ, USA.
  • Marcus Naymik
    Center for Rare Childhood Disorders, Translational Genomics Research Institute, Phoenix, AZ, USA.
  • Vinodh Narayanan
    Center for Rare Childhood Disorders, Translational Genomics Research Institute, Phoenix, AZ, USA.
  • Narayanan Veeraraghavan
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
  • Paul Billings
    Fabric Genomics Inc., Oakland, CA, USA.
  • Martin G Reese
    Fabric Genomics Inc., Oakland, CA, USA. mreese@fabricgenomics.com.
  • Mark Yandell
    Eccles Institute of Human Genetics (M.Y.), University of Utah, Salt Lake City.
  • Stephen F Kingsmore
    Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.

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