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Optimized Permutation Testing for Information Theoretic Measures of Multi-gene Interactions by Kunert-Graf, James M. (author);Sakhanenko, Nikita A. (author);Galas, David J. (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Optimized Permutation Testing for Information Theoretic Measures of Multi-gene Interactions about?

## Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. ## Results In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 10^3^ for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to

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Author

Kunert-Graf, James M. (author);Sakhanenko, Nikita A. (author);Galas, David J. (author)

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)