IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0140758.html
   My bibliography  Save this article

Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation

Author

Listed:
  • Damien C Croteau-Chonka
  • Angela J Rogers
  • Towfique Raj
  • Michael J McGeachie
  • Weiliang Qiu
  • John P Ziniti
  • Benjamin J Stubbs
  • Liming Liang
  • Fernando D Martinez
  • Robert C Strunk
  • Robert F Lemanske Jr
  • Andrew H Liu
  • Barbara E Stranger
  • Vincent J Carey
  • Benjamin A Raby

Abstract

Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate

Suggested Citation

  • Damien C Croteau-Chonka & Angela J Rogers & Towfique Raj & Michael J McGeachie & Weiliang Qiu & John P Ziniti & Benjamin J Stubbs & Liming Liang & Fernando D Martinez & Robert C Strunk & Robert F Lema, 2015. "Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    • Handle: RePEc:plo:pone00:0140758
    DOI: 10.1371/journal.pone.0140758
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140758
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0140758&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0140758?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. John Platig & Peter J Castaldi & Dawn DeMeo & John Quackenbush, 2016. "Bipartite Community Structure of eQTLs," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-17, September.

    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:pone00:0140758. 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.