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Activity Number: 269
Type: Contributed
Date/Time: Monday, August 1, 2016 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #321612
Title: More Efficient and Robust Permutation-Based Methods for Controlling the False Discovery Rate
Author(s): Divya Nair* and Christopher Corcoran and Pralay Senchaudhuri and Alexandre Buer and William Welbourn, Jr.
Companies: and Utah State University and Cytel and Cytel and Clinipace Worldwide
Keywords: MaxT/minP multiple testing procedure ; bootstrap modification to maxT/minP procedure ; parallelized algorithm ; case-control GWAS study
Abstract:

The significant memory required for permutation-based maxT and minP procedures makes them very difficult or often impossible to apply in practice to moderate or large datasets. This is problematic, given the nature of modern analytics and data mining. We propose a parallelized algorithm that reduces computational time by orders of magnitude. We illustrate with an analysis of 600,000 markers in a case-control study of genome-wide association for over 2000 subjects, for which permutation-based minP requires several years of computing time using the genetic analysis software package PLINK. Our approach yielded results in less than two hours. In addition, we propose a bootstrapping modification to maxT/minP that improves their statistical performance when analyzing binomials with very small outcome probabilities.


Authors who are presenting talks have a * after their name.

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