gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

Min P test-a gene region-level testing procedure for SNP data based on resampling

Meeting Abstract

Search Medline for

  • Stefanie Hieke - University Medical Center Freiburg, Freiburg

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds055

doi: 10.3205/11gmds055, urn:nbn:de:0183-11gmds0551

Published: September 20, 2011

© 2011 Hieke.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Introduction: 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.

Method: The gene region-level summary, as the min P test, assesses the statistical significance of the smallest p-trend within each gene region and, therefore, considers a reduced number of tests. The min P test is a permutation-based method that can be based on several univariate tests per SNP. In permutation resampling, the observed variable (case/control status) is randomly re-assigned without replacement to "pseudo case/control status". A test statistic is then recomputed using the "pseudo" data and compared to the marginal test statistic in the original data set. This procedure is repeated B times. The inference is based on the permutation distribution of the minimum of the ordered p-values from the marginal test of each SNP. The gene region-level summary is mostly compatible with univariate statistical tests per SNP conducted seperately over multiple loci.

Results: Combining the p-values from tests in a permutations-based approach prevents an increase of the false-positive rates, as correlations of SNPs are automatically taken into account. We developed an R package that brings together three different kinds of tests that are scattered over several R packages, and automatically selects the most appropriate one for the design at hand. The implementation in the minPtest package integrates two different parallel computing packages, thus optimally leveraging available resources for speedy results. The package provides a function to simulate SNP data with known structure, allowing the user to explore different scenarios and settings. The gene region-level summary min P test, implemented in the minPtest package, is illustrated by means of a data example.

Conclusion: The minPtest package provides a useful and feasible implementation of a gene region-level summary, using the min P test, controlling the false-positive rate and having higher power.


Chen BE, et al. Resampling-based multiple hypothesis testing procedures for genetic case-control association studies. Genetic Epidemiology. 2006;30:495-507.
R Development Core Team. R: A Language and Environment for Statistical Computing. 2010. ISBN 3-900051-07-0. Available from: External link
Westfall PH, et al. Multiple tests for genetic effects in association studies. Methods Mol Biol. 2002;184:143-168.
Westfall PH, Young SS. Resampling-Based Multiple Testing: Example and Methods for p-Value Adjustment. New York: Wiley; 1993.