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

• Published in: Nature communications Vol. 6; no. 1; p. 5890
• Main Authors: Pers, Tune H., Karjalainen, Juha M., Chan, Yingleong, Westra, Harm-Jan, Wood, Andrew R., Yang, Jian, Lui, Julian C., Vedantam, Sailaja, Gustafsson, Stefan, Esko, Tonu, Frayling, Tim, Speliotes, Elizabeth K., Boehnke, Michael, Raychaudhuri, Soumya, Fehrmann, Rudolf S. N., Hirschhorn, Joel N., Franke, Lude
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.01.2015; Nature Publishing Group
• Subjects: 38/39; 631/114/2398; 631/208/191; 631/208/205/2138; Endocrinology; Gene expression; Genetics; Genome-Wide Association Study - methods; Genomes; Hospitals; Humanities and Social Sciences; Medicin och hälsovetenskap; multidisciplinary; Proteins; Science; Science (multidisciplinary); Software; Queensland Australia; United Kingdom > UK; United States > US; Massachusetts; Australia; Ann Arbor Michigan; Michigan
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms6890

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. Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.