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Related Concept Videos

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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gammaMAXT: a fast multiple-testing correction algorithm.

François Van Lishout1, Francesco Gadaleta1, Jason H Moore2

  • 1Systems and Modeling Unit, Montefiore Institute, University of Liège, Allée de la découverte 10, Liège, 4000 Belgium ; Bioinformatics and Modeling, GIGA-R, Avenue de l'Hôpital 1, Sart-Tilman, 4000 Belgium.

Biodata Mining
|November 24, 2015
PubMed
Summary
This summary is machine-generated.

gammaMAXT offers a faster, more efficient alternative for controlling the family-wise error rate (FWER) in simultaneous hypothesis testing. This new algorithm significantly reduces computational burden for genome-wide interaction studies.

Keywords:
3-order interactionsAlgorithmicGamma distributionGenome-wide interaction studiesMaxTMultiple testingSNP-environment interactions

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • The MaxT algorithm controls family-wise error rate (FWER) in simultaneous hypothesis testing but has high computational demands.
  • Previous implementations of MaxT, like in MBMDR-3.0.3, were memory-efficient but computationally prohibitive for genome-wide studies.
  • Identifying genetic interactions requires efficient multiple testing correction methods, especially for large-scale genomic data.

Purpose of the Study:

  • Introduce gammaMAXT, a novel, computationally efficient implementation of the MaxT algorithm.
  • Adapt the MaxT algorithm for genome-wide interaction analysis, including SNP-SNP, SNP-environment, and SNP-SNP-environment interactions.
  • Provide a general significance assessment and multiple testing approach for large-scale omics studies.

Main Methods:

  • Implemented gammaMAXT within the MB-MDR (v4.2.2) framework for genome-wide screening.
  • Analyzed the distribution of test statistics under MB-MDR methodology, identifying a mixture distribution with a point mass at zero and a shifted gamma distribution.
  • Evaluated gammaMAXT's performance against the standard MaxT algorithm in terms of power, FWER control, and computational efficiency.

Main Results:

  • gammaMAXT demonstrates comparable statistical power to MaxT while maintaining FWER control.
  • The new algorithm significantly reduces computational resources and time required for analysis.
  • A genome-wide analysis of 10^6 SNPs and 1000 individuals was completed in one day using gammaMAXT on a 256-core cluster, a task estimated to take 10^4 times longer with MBMDR-3.0.3.

Conclusions:

  • gammaMAXT provides a computationally efficient solution for genome-wide interaction studies (GWAIs).
  • The algorithm offers a generalizable approach for significance testing in any context involving numerous hypotheses.
  • gammaMAXT enables fast and efficient permutation-based significance assessment in large-scale omics research.