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Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs

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Abstract

The discovery of higher-order epistatic interactions is an important task in the field of genome wide association studies which allows for the identification of complex interaction patterns between multiple genetic markers. Some existing bruteforce approaches explore the whole space of k-interactions in an exhaustive manner resulting in almost intractable execution times. Computational cost can be reduced drastically by restricting the search space with suitable preprocessing filters which prune unpromising candidates. Other approaches mitigate the execution time by employing massively parallel accelerators in order to benefit from the vast computational resources of these architectures. In this paper, we combine a novel preprocessing filter, namely SingleMI, with massively parallel computation on modern GPUs to further accelerate epistasis discovery. Our implementation improves both the runtime and accuracy when compared to a previous GPU counterpart that employs mutual information clustering for prefiltering. SingleMI is open source software and publicly available at: https://github.com/sleeepyjack/singlemi/.

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Authors and Affiliations

  1. Institut für Informatik, Johannes Gutenberg-Universität Mainz, Mainz, Germany

    Daniel Jünger, Christian Hundt & Bertil Schmidt

  2. Grupo de Arquitectura de Computadores, Universidade da Coruña, A Coruña, Spain

    Jorge González Domínguez

Authors
  1. Daniel Jünger
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  2. Christian Hundt
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  3. Jorge González Domínguez
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  4. Bertil Schmidt
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Correspondence to Jorge González Domínguez.

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Jünger, D., Hundt, C., Domínguez, J.G. et al. Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs. Cluster Comput 20, 1899–1908 (2017). https://doi.org/10.1007/s10586-017-0938-9

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  • DOI: https://doi.org/10.1007/s10586-017-0938-9

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