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

被引：0
作者：
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
Lude Franke
机构：
[1] Boston Children’s Hospital,Division of Endocrinology and Center for Basic and Translational Obesity Research
[2] Medical and Population Genetics Program,Department of Genetics
[3] Broad Institute of MIT and Harvard,Department of Genetics
[4] University of Groningen,Division of Genetics
[5] University Medical Centre Groningen,Department of Medical Sciences
[6] Harvard Medical School,Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics
[7] Brigham and Women’s Hospital,Department of Biostatistics and Center for Statistical Genetics
[8] Genetics of Complex Traits,Division of Rheumatology
[9] University of Exeter Medical School,undefined
[10] University of Exeter,undefined
[11] Queensland Brain Institute,undefined
[12] The University of Queensland,undefined
[13] The University of Queensland Diamantina Institute,undefined
[14] The Translation Research Institute,undefined
[15] Section on Growth and Development,undefined
[16] Program in Developmental Endocrinology and Genetics,undefined
[17] Eunice Kennedy Shriver National Institute of Child Health and Human Development,undefined
[18] National Institutes of Health,undefined
[19] Molecular Epidemiology and Science for Life Laboratory,undefined
[20] Uppsala University,undefined
[21] Estonian Genome Center,undefined
[22] University of Tartu,undefined
[23] University of Michigan,undefined
[24] University of Michigan,undefined
[25] Partners HealthCare Center for Personalized Genetic Medicine,undefined
[26] Immunology and Allergy,undefined
[27] Brigham and Women’s Hospital,undefined
[28] Faculty of Medical and Human Sciences,undefined
[29] University of Manchester,undefined
来源：
Nature Communications | / 6卷
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摘要：
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.
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