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- Genetical genomics: use all data

	Figure 1 Approach proposed in this paper. Schematic representation of the analysis of genetical genomics experiment, the blue circles represent the markers tested and the yellow circles, each of the transcript levels analysed, an arrow signifies that the effect has been included in the model for the transcript. The left cartoon (a) represents the current strategy for eQTL searching: it consists of including the most significant marker in the model when testing each transcript independently. Several arrows pointing to a transcript means that the transcript is affected by several QTL, while many arrows starting in a single marker represents an eQTL hotspot (H). The right cartoon (b) presents the strategy proposed here, which suggests that external expression levels can be included as covariates in the model for the expression level studied (the arrows that start and end at the cDNA circles). Including cDNAs in the model can dramatically affect the final eQTL map, some positions may be shifted, some previous eQTL may disappear or some new appear. The bottom line of this approach is that all markers and all expression levels are potential regressors to be considered. The optimum model could be chosen using some of the available criteria, like AIC, BIC, DIC or AUC among others. (Click image to enlarge)
	Figure 2 Comparison of eQTL profiles. P-value Profiles with model 1 (red dots) or 2 (blue line) for two genes, Prdx2 (top) and Lin7c (bottom). P-values are in log10 scale. Model 1 considers only the marker in the model, whereas model 2 also includes the most associated transcript level. (Click image to enlarge)
	Figure 3 AUCs for gene expression levels. Comparison between AUC for 67 gene expression levels considering the best 50 predictive variables chosen among all markers and cDNA levels (red solid squares), the best 50 variables chosen among all markers (green solid triangles) and considering the best 50 variables chosen among all transcript levels (blue open circles). All three AUCs for each expression level are in the same abscissa's position, genes were ranked according to AUC using all variables. It can be seen that using only markers results in consistently lower AUC, whereas there are no large differences between using all variables or only transcript levels. For some genes (23 out of 67), AUCs using only cDNAs were slightly better than using all variables, this occurred because the RFE algorithm [10] may not completely remove redundant information from all variables and thus does not always guarantee the absolute maximum. The 67 genes shown were chosen within those with most significant QTLs in Chesler et al. (2005). Thus, one should expect that markers are better predictors, and consequently higher AUC, for these genes than for a random gene. Note that an AUC of 50% means than the criterion is no better than a random ordering. (Click image to enlarge)

 

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