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Avoiding the high Bonferroni penalty in genome‐wide association studies
Author(s) -
Gao Xiaoyi,
Becker Lewis C.,
Becker Diane M.,
Starmer Joshua D.,
Province Michael A.
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20430
Subject(s) - bonferroni correction , permutation (music) , multiple comparisons problem , genome wide association study , random permutation , genetic association , computer science , genotyping , statistical hypothesis testing , population , algorithm , mathematics , single nucleotide polymorphism , statistics , genetics , biology , combinatorics , medicine , genotype , gene , symmetric group , physics , environmental health , acoustics
A major challenge in genome‐wide association studies (GWASs) is to derive the multiple testing threshold when hypothesis tests are conducted using a large number of single nucleotide polymorphisms. Permutation tests are considered the gold standard in multiple testing adjustment in genetic association studies. However, it is computationally intensive, especially for GWASs, and can be impractical if a large number of random shuffles are used to ensure accuracy. Many researchers have developed approximation algorithms to relieve the computing burden imposed by permutation. One particularly attractive alternative to permutation is to calculate the effective number of independent tests, M eff , which has been shown to be promising in genetic association studies. In this study, we compare recently developed M eff methods and validate them by the permutation test with 10,000 random shuffles using two real GWAS data sets: an Illumina 1M BeadChip and an Affymetrix GeneChip ® Human Mapping 500K Array Set. Our results show that the simpleM method produces the best approximation of the permutation threshold, and it does so in the shortest amount of time. We also show that M eff is indeed valid on a genome‐wide scale in these data sets based on statistical theory and significance tests. The significance thresholds derived can provide practical guidelines for other studies using similar population samples and genotyping platforms. Genet. Epidemiol . 34:100–105, 2010. © 2009 Wiley‐Liss, Inc.