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Publication Detail

Title: Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models.

Authors: Hu, Ting; Andrew, Angeline S; Karagas, Margaret R; Moore, Jason H

Published In Pac Symp Biocomput, (2013)

Abstract: The rapid development of sequencing technologies makes thousands to millions of genetic attributes available for testing associations with various biological traits. Searching this enormous high-dimensional data space imposes a great computational challenge in genome-wide association studies. We introduce a network-based approach to supervise the search for three-locus models of disease susceptibility. Such statistical epistasis networks (SEN) are built using strong pairwise epistatic interactions and provide a global interaction map to search for higher-order interactions by prioritizing genetic attributes clustered together in the networks. Applying this approach to a population-based bladder cancer dataset, we found a high susceptibility three-way model of genetic variations in DNA repair and immune regulation pathways, which holds great potential for studying the etiology of bladder cancer with further biological validations. We demonstrate that our SEN-supervised search is able to find a small subset of three-locus models with significantly high associations at a substantially reduced computational cost.

PubMed ID: 23424144 Exiting the NIEHS site

MeSH Terms: Algorithms; Computational Biology; Data Mining/statistics & numerical data; Databases, Nucleic Acid/statistics & numerical data; Epistasis, Genetic*; Gene Regulatory Networks; Genetic Predisposition to Disease; Genome-Wide Association Study/statistics & numerical data; Humans; Models, Genetic*; Models, Statistical; Polymorphism, Single Nucleotide; Software; Urinary Bladder Neoplasms/genetics

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