gammaMAXT: a fast multiple-testing correction algorithm.BACKGROUND
 In this work we introduce gammaMAXT, a novel implementation of the maxT algorithm for multiple testing correction.  We show that the gammaMAXT algorithm has a power comparable to MaxT and maintains FWER, but requires less computational resources and time.  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. 
gammaMAXT
The purpose of the MaxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing.  In this work we introduce gammaMAXT, a novel implementation of the maxT algorithm for multiple testing correction.  We show that the gammaMAXT algorithm has a power comparable to MaxT and maintains FWER, but requires less computational resources and time.  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. 
MaxT algorithm
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.  The same analysis would take about 10(4) times longer with MBMDR-3.0.3.



MBMDR-3.0.3
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. 
MBMDR-4.2.2
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. 
MB-MDR framework