A fast algorithm for learning epistatic genomic relationships

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

    Research output: Contribution to journalArticlepeer-review

    24 Scopus citations

    Abstract

    Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. Therefore, they do not scale to handling the high-dimensional data found in genome-wide association studies (GWAS). We represent genome-phenome interactions using a Bayesian network rule, which is a specialized Bayesian network. We develop an efficient search algorithm to learn from data a high scoring rule that may contain two or more interacting genes. Our experimental results using synthetic data indicate that this algorithm detects interacting genes as well as a Bayesian network combinatorial method, and it is much faster. Our results also indicate that the algorithm can successfully learn genome-phenome relationships using a real GWAS dataset.

    Original languageEnglish (US)
    Pages (from-to)341-345
    Number of pages5
    JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
    Volume2010
    StatePublished - 2010

    ASJC Scopus subject areas

    • Medicine(all)

    Fingerprint

    Dive into the research topics of 'A fast algorithm for learning epistatic genomic relationships'. Together they form a unique fingerprint.

    Cite this