Because seven markers were analyzed, a nominally significant result for a specific marker may not be statistically significant after accounting for the inflation of type 1 error due to multiple comparisons. Consequently, we performed 10,000 permutations to assess the effects of multiple comparisons. To generate each permuted data set, we permuted the disease status among the cases and controls and analyzed associations between the permutated trait values and seven markers individually. That is, each permuted data set retains the same number of affected and normal individuals but any potential association between disease status and genotypes is removed through the random assignment of disease status through permutation. A p-value for each of the seven markers was calculated from each permuted data set, with 10,000 permutations, resulting in a 10,000 × 7 matrix in which each row corresponded to the seven p-values from each permutation and each column represents the association between the permuted traits and a specific marker. This matrix was used to adjust the alpha level for multiple comparisons. For example, to calculate the overall p-value for the region, we
