We test the nonrandomness of clustering of nominally-significant SNPs using Monte Carlo simulations. We can also use these approaches to identify the nonrandomness of clustering within genes. For each simulation trial, a random set of SNPs from the database that contains the results from these studies is subjected to the same analytic procedures that had been used for the actual data analysis. The number of trials for which the results from the randomly-selected set of SNPs match or exceeded the results actually observed from the SNPs identified in the current study is tabulated. Empirical p values are calculated by dividing the number of trials for which the observed results are matched or exceeded by the total number of Monte Carlo simulation trials performed. This method examines the properties of the actual SNPs contained in each dataset. It is therefore relatively robust despite the uneven distributions of SNP markers across the genome, differences in linkage disequilibrium across the genome in different samples and the different SNPs genotyped using different assays.
