Computational genetic mapping testing for robustness- To assess the robustness of HBCGM using different subsets of input strain data, we developed software that could automatically perform a re-sampling analysis. For each iteration, a number (3, 4, 5 or 6) was randomly selected to determine the number of strains that would be randomly excluded from the 23 total available strains. This number of randomly selected strains was then excluded from mapping analysis. The haplotypes identified with p < 0.01 were tabulated. This process was repeated 100 times. If a re-sampled strain panel was identical to one that had already been re-sampled, this iteration was disregarded, and another subset was randomly chosen using the same procedure. To measure the overall association of a gene with the phenotype among the 100 random re-sampled subsets, we defined a score based on p values obtained, which was calculated as: , where pi was the resulting p value for that gene in the ith re-sampled experiment (if the p value was bigger than the cutoff and therefore censored in the result, pi was set to 0.01,
