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

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. Speliotes, Michael Boehnke, Soumya Raychaudhuri, Rudolf S. N. Fehrmann, Joel N. Hirschhorn () and Lude Franke ()
Additional contact information
Tune H. Pers: Boston Children’s Hospital
Juha M. Karjalainen: University of Groningen, University Medical Centre Groningen
Yingleong Chan: Boston Children’s Hospital
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
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
Stefan Gustafsson: Molecular Epidemiology and Science for Life Laboratory, Uppsala University
Tonu Esko: Boston Children’s Hospital
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
Rudolf S. N. Fehrmann: University of Groningen, University Medical Centre Groningen
Joel N. Hirschhorn: Boston Children’s Hospital
Lude Franke: University of Groningen, University Medical Centre Groningen

Nature Communications, 2015, vol. 6, issue 1, 1-9

Abstract: 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.

Date: 2015
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DOI: 10.1038/ncomms6890

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