We have provided new methodological insights into the analysis of gene expression variation. By employing pooling of divergent populations and conditional permutation schemes, we increased the sensitivity of our analysis, detecting smaller regulatory effects shared across populations. One can imagine a more sophisticated conditional permutation scheme that would permit pooling of any set of populations for which the population identities or relatedness metrics are known. We have also employed a non-parametric test, namely Spearman rank correlation, and demonstrated that it has enough power to be used in such studies. In addition, SRC has some advantages over linear regression due to the fact that, contrary to the linear regression where outliers can have a large impact on the p-values, SRC is not sensitive to them and therefore the nominal p-values can be used directly in methods that estimate FDR (example given in Figure S6).
