data integration workflow for search of disease driving genes and genetic variants数据集成工作流有哪些信誉好的足球投注网站疾病的基因和基因变异开车.pdfVIP

Data Integration Workflow for Search of Disease Driving Genes and Genetic Variants 1 2 3 4 Sirkku Karinen , Tuomas Heikkinen , Heli Nevanlinna , Sampsa Hautaniemi * 1 Sirkku Karinen Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland, 2 Tuomas Heikkinen Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland, 3 Heli Nevanlinna Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland, 4 Sampsa Hautaniemi Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland Abstract Comprehensive characterization of a gene’s impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association app