A powerful method for combining P-values in genomic studies

After genetic regions have been identified in genomewide association studies (GWAS), investigators often follow up with more targeted investigations of specific regions. These investigations typically are based on single nucleotide polymorphisms (SNPs) with dense coverage of a region. Methods are thus needed to test the hypothesis of any association in given genetic regions. Several approaches for combining P-values obtained from testing individual SNP hypothesis tests are available. We recently proposed a sequential procedure for testing the global null hypothesis of no association in a region. When this global null hypothesis is rejected, this method provides a list of significant hypotheses and has weak control of the family-wise error rate. In this paper, we devise a permutation-based version of the test that accounts for correlations of tests based on SNPs in the same genetic region. Based on simulated data, the method has correct control of the type I error rate and higher or comparable power to other tests.