Abstract
Multiple testing is a problem in genome-wide or region-wide association studies. In this report, we consider a study design given by the Genetic Analysis Workshop 15 (GAW15) Problem 3 - nuclear families (parents with their affected children) and unrelated controls. Based on this design, we propose three two-stage approaches to deal with the problem of multiple testing. The tests in the first stage, statistically independent of the association test used in the second stage, are used to screen or select single-nucleotide polymorphisms (SNPs). Then, in the second stage, a family-based association test is performed on a much smaller set of selected SNPs. Thus, the problem of multiple testing is much less severe. Our simulation studies and application to the dense SNP data of chromosome 6 in the GAW15 Problem 3 show that the two-stage methods are more powerful than the one-stage method (using the family-based association test only).
Highlights
Genome-wide or region-wide association is a promising approach to mapping complex disease genes [1,2]
Our simulation and the Genetic Analysis Workshop 15 (GAW15) data analysis results show that the three proposed two-stage methods have correct type I error rates and, in most cases, are more powerful than the pedigree disequilibrium test (PDT)
We evaluate the statement of the independence between the tests in the first stage and the PDT in the second stage
Summary
Genome-wide or region-wide association is a promising approach to mapping complex disease genes [1,2]. The success of genome-wide or region-wide association studies will depend on whether the information gain of increased number of single-nucleotide polymorphisms (SNPs) will be diluted by the multiple-comparison problem [3]. When tens or hundreds of thousands of SNPs are tested for association, the p-values need to be adjusted for controlling type I error rates. The first sample is used to screen and select SNPs for association tests. In mapping quantitative trait loci using family data, Van Steen et al [3] proposed an interesting approach that performs the SNP screening and association test using the same sample
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