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Biological interpretation of genome-wide association studies using predicted gene functions

Author

Listed:
  • Tune H. Pers

    (Boston Children’s Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard)

  • Juha M. Karjalainen

    (University of Groningen, University Medical Centre Groningen)

  • Yingleong Chan

    (Boston Children’s Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard
    Harvard Medical School)

  • Harm-Jan Westra

    (Brigham and Women’s Hospital)

  • Andrew R. Wood

    (Genetics of Complex Traits, University of Exeter Medical School, University of Exeter)

  • Jian Yang

    (Queensland Brain Institute, The University of Queensland
    The University of Queensland Diamantina Institute, The Translation Research Institute)

  • Julian C. Lui

    (Section on Growth and Development, Program in Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health)

  • Sailaja Vedantam

    (Boston Children’s Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard)

  • Stefan Gustafsson

    (Molecular Epidemiology and Science for Life Laboratory, Uppsala University)

  • Tonu Esko

    (Boston Children’s Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard
    Estonian Genome Center, University of Tartu)

  • Tim Frayling

    (Genetics of Complex Traits, University of Exeter Medical School, University of Exeter)

  • Elizabeth K. Speliotes

    (University of Michigan)

  • Michael Boehnke

    (University of Michigan)

  • Soumya Raychaudhuri

    (Medical and Population Genetics Program, Broad Institute of MIT and Harvard
    Brigham and Women’s Hospital
    Partners HealthCare Center for Personalized Genetic Medicine
    Immunology and Allergy, Brigham and Women’s Hospital)

  • Rudolf S. N. Fehrmann

    (University of Groningen, University Medical Centre Groningen)

  • Joel N. Hirschhorn

    (Boston Children’s Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard
    Harvard Medical School)

  • Lude Franke

    (University of Groningen, University Medical Centre Groningen)

Abstract

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Suggested Citation

  • Tune H. Pers & Juha M. Karjalainen & Yingleong Chan & Harm-Jan Westra & Andrew R. Wood & Jian Yang & Julian C. Lui & Sailaja Vedantam & Stefan Gustafsson & Tonu Esko & Tim Frayling & Elizabeth K. Spel, 2015. "Biological interpretation of genome-wide association studies using predicted gene functions," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms6890
    DOI: 10.1038/ncomms6890
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