Abstract
Defining the extent of epistasis—the nonindependence of the effects of mutations—is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and the analysis beyond just pairwise effects can be problematic. Here, we demonstrate the application of a particular mathematical formalism, the weighted Walsh-Hadamard transform, which unifies a number of different definitions of epistasis. We provide a computational implementation of such analysis using a computer-generated higher-order mutational dataset. We discuss general considerations regarding the null hypothesis for independent mutational effects, which then allows a quantitative identification of epistasis in an experimental dataset.
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Acknowledgments
I thank Michael A. Stiffler and DerZen Fan for critical reading of the manuscript.
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Poelwijk, F.J. (2019). Context-Dependent Mutation Effects in Proteins. In: Sikosek, T. (eds) Computational Methods in Protein Evolution. Methods in Molecular Biology, vol 1851. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8736-8_7
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DOI: https://doi.org/10.1007/978-1-4939-8736-8_7
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