Detecting epistasis in the presence of linkage disequilibrium: A focused comparison

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Abstract

We present results from a comparison of three epistasis-detection tools using large-scale simulated genetic data: SNPHarvester, SNPRuler and Ambience. The tools were chosen based on their merits to be representative of the state of the art of epistasis detection. We design and conduct experiments to test the performance of the methods in detecting interacting loci or their proxies in linkage disequilibrium (LD) tagged regions, in datasets containing simulated 2,3 and 4-way epistatic interactions. The results show that SNPHarvester is the fastest while Ambience is the most robust. Moreover, SNPRuler provides the best power, specially with higher-level interactions, but cannot scale-up to larger datasets.
Original languageEnglish
Title of host publicationThe International IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
PublisherIEEE
Pages96-103
Number of pages8
DOIs
Publication statusPublished - 2013

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