%0 Journal Article
%T Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes
%A Hongying Dai
%A Madhusudan Bhandary
%A Mara L Becker
%A Steve J Leeder
%A Roger Gaedigk
%A Alison A Motsinger-Reif
%J BioData Mining
%D 2012
%I BioMed Central
%R 10.1186/1756-0381-5-3
%X We suggest incorporating a global test of p-values to filtration procedures to identify the optimal number of genes/SNPs for further MDR analysis and demonstrate this approach using a ReliefF filter technique. We compare the performance of different global testing procedures in this context, including the Kolmogorov-Smirnov test, the inverse chi-square test, the inverse normal test, the logit test, the Wilcoxon test and Tippett¡¯s test. Additionally we demonstrate the approach on a real data application with a candidate gene study of drug response in Juvenile Idiopathic Arthritis.Extensive simulation of correlated p-values show that the inverse chi-square test is the most appropriate approach to be incorporated with the screening approach to determine the optimal number of SNPs for the final MDR analysis. The Kolmogorov-Smirnov test has high inflation of Type I errors when p-values are highly correlated or when p-values peak near the center of histogram. Tippett¡¯s test has very low power when the effect size of GxG interactions is small.The proposed global tests can serve as a screening approach prior to individual tests to prevent false discovery. Strong power in small sample sizes and well controlled Type I error in absence of GxG interactions make global tests highly recommended in epistasis studies.
%K P-value
%K Global tests
%K ReliefF
%K Multifactor dimensionality reduction
%U http://www.biodatamining.org/content/5/1/3/abstract