However, in the present study, creQTL appeared to identify interactions that were supported by recent work in the literature. A very compelling benefit of creQTL over trans-eQTL is greater computational efficiency and a reduced multiple testing burden, as there was an approximate 25-fold reduction in the number of statistical tests. By organizing genes into co-regulated clusters prior to statistical testing, the problem becomes more computationally feasible and the resulting output more biologically interpretable. The identification of more significant hits after adjustment was likely not simply due to a lighter multiple testing penalty; cis-eQTL required 1,468,327 tests, far less than the 19,323,486 tests for creQTL, and produced no statistically significant results even at the more liberal q-value cutoff of 0.05. Overall, these results suggest that individual SNP-gene interactions are more difficult to detect (as in eQTL) when compared to relative changes in the expression levels of tightly co-regulated genes, in agreement with previous work in yeast [9]. creQTL may be better-suited to the identification of the strongest trans drivers of gene expression because it better explains the data; i.e., genes do
