IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0066545.html
   My bibliography  Save this article

A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

Author

Listed:
  • Jiang Gui
  • Jason H Moore
  • Scott M Williams
  • Peter Andrews
  • Hans L Hillege
  • Pim van der Harst
  • Gerjan Navis
  • Wiek H Van Gilst
  • Folkert W Asselbergs
  • Diane Gilbert-Diamond

Abstract

We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR’s constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR’s testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.

Suggested Citation

  • Jiang Gui & Jason H Moore & Scott M Williams & Peter Andrews & Hans L Hillege & Pim van der Harst & Gerjan Navis & Wiek H Van Gilst & Folkert W Asselbergs & Diane Gilbert-Diamond, 2013. "A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-7, June.
    • Handle: RePEc:plo:pone00:0066545
    DOI: 10.1371/journal.pone.0066545
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0066545
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0066545&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0066545?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
    ---><---

    Citations

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


    Cited by:

    1. Yongkang Kim & Taesung Park, 2015. "Robust Gene-Gene Interaction Analysis in Genome Wide Association Studies," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-17, August.
    2. Roland Moore & Kristin Ashby & Tsung-Jen Liao & Minjun Chen, 2021. "Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury," IJERPH, MDPI, vol. 18(20), pages 1-12, October.

    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:pone00:0066545. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.