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Epistasis Analysis Using ReliefF

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Epistasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1253))

Abstract

Here we introduce the ReliefF machine learning algorithm and some of its extensions for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few examples of published studies of complex human diseases that have used ReliefF.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grants AI59694, EY022300, GM103534, GM103506, LM009012, LM010098, and LM011360.

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Authors and Affiliations

  1. Department of Community and Family Medicine, Geisel School of Medicine, DHMC, One Medical Center Drive, HB 7937, Lebanon, NH, 03756, USA

    Jason H. Moore

  2. Department of Genetics, Geisel School of Medicine, DHMC, Lebanon, NH, USA

    Jason H. Moore

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  1. Jason H. Moore
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Correspondence to Jason H. Moore .

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Editors and Affiliations

  1. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Jason H. Moore

  2. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Scott M. Williams

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Moore, J.H. (2015). Epistasis Analysis Using ReliefF. In: Moore, J., Williams, S. (eds) Epistasis. Methods in Molecular Biology, vol 1253. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2155-3_17

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  • DOI: https://doi.org/10.1007/978-1-4939-2155-3_17

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2154-6

  • Online ISBN: 978-1-4939-2155-3

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