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minPtest: a resampling based gene region-level testing procedure for genetic case-control studies

Author

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  • Stefanie Hieke
  • Harald Binder
  • Alexandra Nieters
  • Martin Schumacher

Abstract

Current technologies generate a huge number of single nucleotide polymorphism (SNP) genotype measurements in case-control studies. The resulting multiple testing problem can be ameliorated by considering candidate gene regions. The minPtest R package provides the first widely accessible implementation of a gene region-level summary for each candidate gene using the min $$P$$ test. The latter is a permutation-based method that can be based on different univariate tests per SNP. The package brings together three different kinds of tests which were scattered over several R packages, and automatically selects the most appropriate one for the study design at hand. The implementation of the minPtest integrates two different parallel computing packages, thus optimally leveraging available resources for speedy results. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Stefanie Hieke & Harald Binder & Alexandra Nieters & Martin Schumacher, 2014. "minPtest: a resampling based gene region-level testing procedure for genetic case-control studies," Computational Statistics, Springer, vol. 29(1), pages 51-63, February.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:51-63
    DOI: 10.1007/s00180-012-0391-4
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    1. W. Sauerbrei & P. Royston, 1999. "Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 71-94.
    2. Patrick Royston & Gareth Ambler, 1999. "Multivariable fractional polynomials," Stata Technical Bulletin, StataCorp LP, vol. 8(43).
    3. Manuel Eugster & Jochen Knaus & Christine Porzelius & Markus Schmidberger & Esmeralda Vicedo, 2011. "Hands-on tutorial for parallel computing with R," Computational Statistics, Springer, vol. 26(2), pages 219-239, June.
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    1. Harald Binder & Hans Kestler & Matthias Schmid, 2014. "Proceedings of Reisensburg 2011," Computational Statistics, Springer, vol. 29(1), pages 1-2, February.

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