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
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.
Nat Commun. 2015 Jan 19;6:5890. 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.
从基因关联中获取生物学见解的主要挑战在于确定哪些基因和通路能够解释这些关联。在此,我们展示了DEPICT,这是一种综合工具,它利用预测的基因功能来系统地对关联位点上最可能的因果基因进行优先级排序,突出富集的通路,并识别关联位点的基因高表达的组织/细胞类型。DEPICT并不局限于具有既定功能的基因,还能对许多表型的相关基因集进行优先级排序。