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Optimized permutation testing for information theoretic measures of multi-gene interactions.

James M Kunert-Graf1, Nikita A Sakhanenko2, David J Galas2

  • 1Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA. jkunert@pnri.org.

BMC Bioinformatics
|April 8, 2021
PubMed
Summary
This summary is machine-generated.

Permutation testing for genome-wide association studies (GWAS) is now faster. Our new method accelerates multi-locus interaction analysis by directly transforming count tables, improving computational efficiency for large datasets.

Keywords:
Information theoryMulti-locus GWASMultivariable interactionsPermutation testing

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Permutation testing is the gold standard for multi-test significance analysis due to its exactness and minimal assumptions.
  • The naive approach of re-running the full analysis pipeline for each permutation is computationally intensive, especially for multi-locus genome-wide association studies (GWAS).
  • The combinatorial explosion of potential interactions in multi-locus GWAS makes naive permutation testing intractable.

Purpose of the Study:

  • To develop a computationally tractable approach for permutation testing in multi-locus GWAS.
  • To specifically address SNP-SNP-phenotype interactions using multivariable measures.
  • To overcome the computational bottleneck in permutation testing for large-scale genetic datasets.

Main Methods:

  • Developed an approach for permutation testing in multi-locus GWAS focusing on SNP-SNP-phenotype interactions.
  • Utilized multivariable measures computable from frequency count tables, including Information Theory-based measures.
  • Eliminated the need to reconstruct count tables at each permutation iteration by directly transforming them.

Main Results:

  • Identified the construction of count tables as the primary computational bottleneck in permutation testing for GWAS.
  • Achieved a speed-up factor exceeding 10^3 compared to the naive approach by directly transforming count tables.
  • Demonstrated that the developed approach is insensitive to the number of samples, making it suitable for large datasets.

Conclusions:

  • The developed approach significantly enhances the computational tractability of permutation testing for large-scale genotype-phenotype interaction studies.
  • The method is well-suited for modern large-scale datasets with hundreds of thousands of individuals.
  • Freely available code is provided to facilitate the application of this approach and replication of study findings.