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Data Integration Workflow for Search of Disease Driving Genes and Genetic Variants

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  • Sirkku Karinen
  • Tuomas Heikkinen
  • Heli Nevanlinna
  • Sampsa Hautaniemi

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 approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/

Suggested Citation

  • Sirkku Karinen & Tuomas Heikkinen & Heli Nevanlinna & Sampsa Hautaniemi, 2011. "Data Integration Workflow for Search of Disease Driving Genes and Genetic Variants," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-8, April.
    • Handle: RePEc:plo:pone00:0018636
    DOI: 10.1371/journal.pone.0018636
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    1. Neil J. Risch, 2000. "Searching for genetic determinants in the new millennium," Nature, Nature, vol. 405(6788), pages 847-856, June.
    2. Bert Vogelstein & David Lane & Arnold J. Levine, 2000. "Surfing the p53 network," Nature, Nature, vol. 408(6810), pages 307-310, November.
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    1. Li Wang & Yun Xiao & Yanyan Ping & Jing Li & Hongying Zhao & Feng Li & Jing Hu & Hongyi Zhang & Yulan Deng & Jiawei Tian & Xia Li, 2014. "Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.

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