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A General Model for Multilocus Epistatic Interactions in Case-Control Studies

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  • Zhong Wang
  • Tian Liu
  • Zhenwu Lin
  • John Hegarty
  • Walter A Koltun
  • Rongling Wu

Abstract

Background: Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes. Methodology: We develop a general model for testing high-order epistatic interactions for a complex disease in a case-control study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components. Conclusions: Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis.

Suggested Citation

  • Zhong Wang & Tian Liu & Zhenwu Lin & John Hegarty & Walter A Koltun & Rongling Wu, 2010. "A General Model for Multilocus Epistatic Interactions in Case-Control Studies," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-9, August.
    • Handle: RePEc:plo:pone00:0011384
    DOI: 10.1371/journal.pone.0011384
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    References listed on IDEAS

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    1. Todd L Edwards & Stephen D Turner & Eric S Torstenson & Scott M Dudek & Eden R Martin & Marylyn D Ritchie, 2010. "A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-8, February.
    2. Nunkesser, Robin & Bernholt, Thorsten & Schwender, Holger & Ickstadt, Katja & Wegener, Ing, 2007. "Detecting high-order interactions of single nucleotide polymorphisms using genetic programming," Technical Reports 2007,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Gang Fang & Majda Haznadar & Wen Wang & Haoyu Yu & Michael Steinbach & Timothy R Church & William S Oetting & Brian Van Ness & Vipin Kumar, 2012. "High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.

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