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MCPerm: A Monte Carlo Permutation Method for Accurately Correcting the Multiple Testing in a Meta-Analysis of Genetic Association Studies

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Listed:
  • Yongshuai Jiang
  • Lanying Zhang
  • Fanwu Kong
  • Mingming Zhang
  • Hongchao Lv
  • Guiyou Liu
  • Mingzhi Liao
  • Rennan Feng
  • Jin Li
  • Ruijie Zhang

Abstract

Traditional permutation (TradPerm) tests are usually considered the gold standard for multiple testing corrections. However, they can be difficult to complete for the meta-analyses of genetic association studies based on multiple single nucleotide polymorphism loci as they depend on individual-level genotype and phenotype data to perform random shuffles, which are not easy to obtain. Most meta-analyses have therefore been performed using summary statistics from previously published studies. To carry out a permutation using only genotype counts without changing the size of the TradPerm P-value, we developed a Monte Carlo permutation (MCPerm) method. First, for each study included in the meta-analysis, we used a two-step hypergeometric distribution to generate a random number of genotypes in cases and controls. We then carried out a meta-analysis using these random genotype data. Finally, we obtained the corrected permutation P-value of the meta-analysis by repeating the entire process N times. We used five real datasets and five simulation datasets to evaluate the MCPerm method and our results showed the following: (1) MCPerm requires only the summary statistics of the genotype, without the need for individual-level data; (2) Genotype counts generated by our two-step hypergeometric distributions had the same distributions as genotype counts generated by shuffling; (3) MCPerm had almost exactly the same permutation P-values as TradPerm (r = 0.999; P

Suggested Citation

  • Yongshuai Jiang & Lanying Zhang & Fanwu Kong & Mingming Zhang & Hongchao Lv & Guiyou Liu & Mingzhi Liao & Rennan Feng & Jin Li & Ruijie Zhang, 2014. "MCPerm: A Monte Carlo Permutation Method for Accurately Correcting the Multiple Testing in a Meta-Analysis of Genetic Association Studies," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
    • Handle: RePEc:plo:pone00:0089212
    DOI: 10.1371/journal.pone.0089212
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    1. Christina M Lill & Johannes T Roehr & Matthew B McQueen & Fotini K Kavvoura & Sachin Bagade & Brit-Maren M Schjeide & Leif M Schjeide & Esther Meissner & Ute Zauft & Nicole C Allen & Tian Liu & Marcel, 2012. "Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database," PLOS Genetics, Public Library of Science, vol. 8(3), pages 1-10, March.
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    1. Vicente García-Navas & Timothée Bonnet & Dominique Waldvogel & Glauco Camenisch & Erik Postma, 2016. "Consequences of natal philopatry for reproductive success and mate choice in an Alpine rodent," Behavioral Ecology, International Society for Behavioral Ecology, vol. 27(4), pages 1158-1166.

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