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Machine Learning for Detecting Gene-Gene Interactions

A Review

  • Biomedical Genomics and Proteomics
  • Published:
Applied Bioinformatics

Abstract

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grants AI059694, LM009012, AI057661, AI064625, HL65234, RR018787, ES007373 and HD047447. This work was also supported by generous funds from the Vanderbilt Program in Biomathematics and the Norris-Cotton Cancer Center at Dartmouth Medical School.

The authors have no conflicts of interest that are directly relevant to the content of this review.

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Authors and Affiliations

  1. Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University Medical School, Nashville, Tennessee, USA

    Brett A. McKinney, David M. Reif & Marylyn D. Ritchie

  2. Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, New Hampshire, USA

    Brett A. McKinney, David M. Reif &  Jason H. Moore

  3. Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA

    Jason H. Moore

  4. Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire, USA

    Jason H. Moore

  5. Department of Computer Science, University of New Hampshire, Durham, New Hampshire, USA

    Jason H. Moore

  6. Department of Computer Science, University of Vermont, Burlington, Vermont, USA

    Jason H. Moore

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  1. Brett A. McKinney
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  2. David M. Reif
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  3. Marylyn D. Ritchie
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  4. Jason H. Moore
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Correspondence to Jason H. Moore.

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McKinney, B.A., Reif, D.M., Ritchie, M.D. et al. Machine Learning for Detecting Gene-Gene Interactions. Appl-Bioinformatics 5, 77–88 (2006). https://doi.org/10.2165/00822942-200605020-00002

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