Learning genetic epistasis using bayesian network scoring criteria

Xia Jiang, Richard E Neapolitan, M. Michael Barmada, Shyam Visweswaran

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    The advent of high-throughput genotyping technology has brought the promise of identifying genetic variations that underlie common diseases such as hypertension, diabetes mellitus, cancer and Alzheimer’s disease. However, our knowledge of the genetic architecture of common diseases remains limited; this is in part due to the complex relationship between the genotype and the phenotype. One likely reason for this complex relationship arises from gene-gene and gene-environment interactions. So an important challenge in the analysis of high-throughput genetic data is the development of computational and statistical methods to identify genegene interactions. In this paper we apply Bayesian network scoring criteria to identifying gene-gene interactions from genome-wide association study (GWAS) data.

    Original languageEnglish (US)
    Title of host publicationBioinformatics
    Subtitle of host publicationThe Impact of Accurate Quantification on Proteomic and Genetic Analysis and Research
    PublisherApple Academic Press
    Pages235-264
    Number of pages30
    ISBN (Electronic)9781482246629
    ISBN (Print)9781771880190
    DOIs
    StatePublished - Jan 1 2014

    ASJC Scopus subject areas

    • Mathematics(all)
    • Biochemistry, Genetics and Molecular Biology(all)

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