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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

Wenan Chen1, Xi Gao2, Jiexun Wang1, Chuanyu Sun1, Wen Wan1, Degui Zhi3, Nianjun Liu3, Xiangning Chen4 and Guimin Gao1*

Author Affiliations

1 Department of Biostatistics, Virginia Commonwealth University School of Medicine, 830 East Main Street, One Capitol Square, 7th Floor, Richmond, VA 23298-0032, USA

2 Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Room E4225, PO Box 843019, Richmond, VA 23284-3019, USA

3 Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA

4 Departments of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA 23298-0003, USA

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BMC Proceedings 2011, 5(Suppl 9):S86  doi:10.1186/1753-6561-5-S9-S86

Published: 29 November 2011

Abstract

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive.