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

dc.contributor.authorPers, TH
dc.contributor.authorKarjalainen, JM
dc.contributor.authorChan, Y
dc.contributor.authorWestra, H-J
dc.contributor.authorWood, AR
dc.contributor.authorYang, J
dc.contributor.authorLui, JC
dc.contributor.authorVedantam, S
dc.contributor.authorGustafsson, S
dc.contributor.authorEsko, T
dc.contributor.authorFrayling, T
dc.contributor.authorSpeliotes, EK
dc.contributor.authorGenetic Investigation of ANthropometric Traits (GIANT) Consortium
dc.contributor.authorBoehnke, M
dc.contributor.authorRaychaudhuri, S
dc.contributor.authorFehrmann, RSN
dc.contributor.authorHirschhorn, JN
dc.contributor.authorFranke, L
dc.date.accessioned2017-09-13T15:28:05Z
dc.date.issued2015-01-19
dc.description.abstractThe 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.en_GB
dc.description.sponsorshipT.H.P. was supported by The Danish Council for Independent Research Medical Sciences (FSS) The Alfred Benzon Foundation. J.C.L. was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH. L.F. was financially supported by grants from the Netherlands Organization for Scientific Research (NWO-VENI grant 916-10135 and NWO VIDI grant 917-14374) and a Horizon Breakthrough grant from the Netherlands Genomics Initiative (grant 92519031). The research leading to these results has received funding from the European Community’s Health Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 259867. We thank the DGI Consortium for making raw genotype and phenotype data available, and the Global Lipids Genetics Consortium and the International Inflammatory Bowel Disease Genetics Consortium for making summary statistics available. We thank Drs. Ayellet V. Segrè, Elizabeth J. Rossin, Jeffrey Baron, Kasper Lage and Pascal Timshel for helpful comments and discussions. This work was supported by The National Institute of Diabetes and Digestive and Kidney Diseases [2R01DK075787 to J.N.H.].en_GB
dc.identifier.citationVol. 6, article 5890en_GB
dc.identifier.doi10.1038/ncomms6890
dc.identifier.urihttp://hdl.handle.net/10871/29318
dc.language.isoenen_GB
dc.publisherSpringer Natureen_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/25597830en_GB
dc.subjectGenome-Wide Association Studyen_GB
dc.subjectSoftwareen_GB
dc.titleBiological interpretation of genome-wide association studies using predicted gene functionsen_GB
dc.typeArticleen_GB
dc.date.available2017-09-13T15:28:05Z
exeter.place-of-publicationEnglanden_GB
dc.descriptionThis is the final version of the article. Available from Springer Nature via the DOI in this record.en_GB
dc.identifier.journalNature Communicationsen_GB


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