IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0018636.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018636
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0018636&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0018636?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bálint Mészáros & István Simon & Zsuzsanna Dosztányi, 2009. "Prediction of Protein Binding Regions in Disordered Proteins," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    2. Jae Kyoung Kim & Trachette L Jackson, 2013. "Mechanisms That Enhance Sustainability of p53 Pulses," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    3. Wanwan Tang & Xuebing Wu & Rui Jiang & Yanda Li, 2009. "Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy," PLOS Genetics, Public Library of Science, vol. 5(5), pages 1-18, May.
    4. Jungnam Joo & Minjung Kwak & Gang Zheng, 2010. "Improving Power for Testing Genetic Association in Case–Control Studies by Reducing the Alternative Space," Biometrics, The International Biometric Society, vol. 66(1), pages 266-276, March.
    5. Piotrowska, Monika J. & Bartłomiejczyk, Agnieszka & Bodnar, Marek, 2018. "Mathematical analysis of a generalised p53-Mdm2 protein gene expression model," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 26-44.
    6. Jeffrey V Wong & Bochong Li & Lingchong You, 2012. "Tension and Robustness in Multitasking Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-12, April.
    7. Wassim Abou-Jaoudé & Madalena Chaves & Jean-Luc Gouzé, 2011. "A Theoretical Exploration of Birhythmicity in the p53-Mdm2 Network," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
    8. Minjung Kwak & Jungnam Joo & Gang Zheng, 2009. "A Robust Test for Two-Stage Design in Genome-Wide Association Studies," Biometrics, The International Biometric Society, vol. 65(4), pages 1288-1295, December.
    9. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    10. Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    11. Yong-Jun Shin & Ali H Sayed & Xiling Shen, 2012. "Adaptive Models for Gene Networks," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-6, February.
    12. Ikeda, N., 2007. "Network formed by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 701-713.
    13. H. Zhang & G. Zheng & Z. Li, 2006. "Statistical Analysis for Haplotype-Based Matched Case–Control Studies," Biometrics, The International Biometric Society, vol. 62(4), pages 1124-1131, December.
    14. Shruti Koulgi & Archana Achalere & Uddhavesh Sonavane & Rajendra Joshi, 2015. "Investigating DNA Binding and Conformational Variation in Temperature Sensitive p53 Cancer Mutants Using QM-MM Simulations," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-22, November.
    15. Ning Jiang & Minghui Wang & Tianye Jia & Lin Wang & Lindsey Leach & Christine Hackett & David Marshall & Zewei Luo, 2011. "A Robust Statistical Method for Association-Based eQTL Analysis," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    16. Kari E. North & Lisa J. Martin, 2008. "The Importance of Gene—Environment Interaction," Sociological Methods & Research, , vol. 37(2), pages 164-200, November.
    17. Xiaofeng Zhu & Richard S Cooper, 2007. "Admixture Mapping Provides Evidence of Association of the VNN1 Gene with Hypertension," PLOS ONE, Public Library of Science, vol. 2(11), pages 1-10, November.
    18. Jaya M. Satagopan & E. S. Venkatraman & Colin B. Begg, 2004. "Two-Stage Designs for Gene–Disease Association Studies with Sample Size Constraints," Biometrics, The International Biometric Society, vol. 60(3), pages 589-597, September.
    19. Li, Zhaohai & Zhang, Hong & Zheng, Gang & Gastwirth, Joseph L. & Gail, Mitchell H., 2009. "Excess false positive rate caused by population stratification and disease rate heterogeneity in case-control association studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1767-1781, March.
    20. Ding Xiao & Weiss Scott & Raby Benjamin & Lange Christoph & Laird Nan M, 2009. "Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-17, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0018636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.