• 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA.
• Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen 9711, The Netherlands.
• 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [3] Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
• Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
• Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK.
• 1] Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia [2] The University of Queensland Diamantina Institute, The Translation Research Institute, Brisbane, Queensland 4012, Australia.
• 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, Bethesda, Maryland 20892, USA.
• Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala 75185, Sweden.
• 1] Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts 02115, USA [2] Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA [3] Estonian Genome Center, University of Tartu, Tartu 51010, Estonia.
• Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.

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.