ArticleBioData mining2015
gammaMAXT: a fast multiple-testing correction algorithm.
Article in BioData mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
- Empowering individual trait prediction using interactions for precision medicine.BMC bioinformatics · 2021Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.BioData mining · 2019Article
- Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease.Neurobiology of aging · 2018Article
- Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data).BMC bioinformatics · 2018Article
- The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.Annals of translational medicine · 2018Article
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
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5 authors.
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Abstract
backgroundThe purpose of the MaxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements in terms of computing time and memory of this procedure are proportional to the number of investigated hypotheses. The memory issue has been solved in 2013 by Van Lishout's implementation of MaxT, which makes the memory usage independent from the size of the dataset. This algorithm is implemented in MBMDR-3.0.3, a software that is able to identify genetic interactions, for a variety of SNP-SNP based epistasis models effectively. On the other hand, that implementation turned out to be less suitable for genome-wide interaction analysis studies, due to the prohibitive computational burden.
resultsIn this work we introduce gammaMAXT, a novel implementation of the maxT algorithm for multiple testing correction. The algorithm was implemented in software MBMDR-4.2.2, as part of the MB-MDR framework to screen for SNP-SNP, SNP-environment or SNP-SNP-environment interactions at a genome-wide level. We show that, in the absence of interaction effects, test-statistics produced by the MB-MDR methodology follow a mixture distribution with a point mass at zero and a shifted gamma distribution for the top 10 % of the strictly positive values. We show that the gammaMAXT algorithm has a power comparable to MaxT and maintains FWER, but requires less computational resources and time. We analyze a dataset composed of 10(6) SNPs and 1000 individuals within one day on a 256-core computer cluster. The same analysis would take about 10(4) times longer with MBMDR-3.0.3.
conclusionsThese results are promising for future GWAIs. However, the proposed gammaMAXT algorithm offers a general significance assessment and multiple testing approach, applicable to any context that requires performing hundreds of thousands of tests. It offers new perspectives for fast and efficient permutation-based significance assessment in large-scale (integrated) omics studies.
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