Identifying differentially expressed genes. To assess whether a gene in a given sample was differentially expressed, we used a previously described and validated error model for testing whether the mean log ratio of the intensity measures between the experiment and reference channels was significantly different from zero [42,43,48]. Based on this error model we obtained p-values for each of the individual gene expression measures in each sample as previously described [33]. We then computed the standard deviation of –log10 of the p-value for each gene expression measure over all samples profiled for a given tissue, and then rank ordered all of the genes profiled in each tissue based on this standard deviation value (rank ordered in descending order). Genes that fall at the top of this rank ordered list can be considered as the most differentially expressed or variable genes in the study. We have previously shown that this type of ordering approach well captures the most active genes in a set of samples [33]. For demonstrating the number of genome-wide significant eQTLs and eSNPs as a function of differential
