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Eur J Hum Genet. 2014 Mar;22(3):414-8. doi: 10.1038/ejhg.2013.144. Epub 2013 Jul 10.

Correction for multiple testing in a gene region.

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  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
  • 21] Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA [2] Bioinformatics Program, Boston University, Boston, MA, USA.
  • 31] Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA [2] Department of Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA.
  • 4Department of Neurology, Boston University School of Medicine, Boston, MA, USA.

Abstract

Several methods to correct for multiple testing within a gene region have been proposed. These methods are useful for candidate gene studies, and to fine map gene-regions from GWAs. The Bonferroni correction and permutation are common adjustments, but are overly conservative and computationally intensive, respectively. Other options include calculating the effective number of independent single-nucleotide polymorphisms (SNPs) or using theoretical approximations. Here, we compare a theoretical approximation based on extreme tail theory with four methods for calculating the effective number of independent SNPs. We evaluate the type-I error rates of these methods using single SNP association tests over 10 gene regions simulated using 1000 Genomes data. Overall, we find that the effective number of independent SNP method by Gao et al, as well as extreme tail theory produce type-I error rates at the or close to the chosen significance level. The type-I error rates for the other effective number of independent SNP methods vary by gene region characteristics. We find Gao et al and extreme tail theory to be efficient alternatives to more computationally intensive approaches to control for multiple testing in gene regions.

PMID:
23838599
PMCID:
PMC3925272
DOI:
10.1038/ejhg.2013.144
[PubMed - indexed for MEDLINE]
Free PMC Article
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