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Activity Number: 29 - SPEED: An Ensemble of Advances in Genomics and Genetics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #327095
Title: A Hybrid Method of the Sequential Monte Carlo and the Edgeworth Expansion for Computation of Very Small P-Values in Permutation Tests
Author(s): James Jian Yang* and Anne YuhPey Buu
Companies: University of Michigan and University of Michigan
Keywords: Genome-wide association study; p-value; Edgeworth expansion

Permutation tests are very useful when parametric assumptions are violated or distributions of test statistics are mathematically intractable. The major advantage of permutation tests is that the procedure is so general that it is applicable to most test statistics. The computational expense is, however, unpractical in high-dimensional settings such as genomewide association studies (GWAS). This study provides a comprehensive review of existing methods that can compute very small $p$-values efficiently. A common issue with existing methods is that they can only be applied to a specific test statistic. To fill in the knowledge gap, we propose a hybrid method of the sequential Monte Carlo and the Edgeworth expansion approximation for a studentized statistic, which is applicable to a variety of test statistics. The simulation results show that the proposed method performs better than competing methods. Furthermore, applications of the proposed method are demonstrated by statistical analysis on the GWAS data from the Study of Addiction: Genetics and Environment (SAGE).

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

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