Genetical genomics: use all data

It is shown that power can be increased with this strategy. We also show, using classical statistical and support vector machines techniques in a reanalysis of public data, that the external transcripts, i.e., transcripts other than the one being analysed, explain on average much more variability than the markers themselves. The presence of eQTL hotspots is reassessed in the light of these results.Model choice is a critical yet neglected issue in genetical genomics studies. Although we are far from having a general strategy for model choice in this area, we can at least propose that any transcript level is scanned not only for the markers genotyped but also for the rest of gene expression levels. Some sort of stepwise regression strategy can be used to select the final model.Genetical genomics is currently a very active area of research, promising to improve dramatically our knowledge on the genetic architecture of complex traits, including disease susceptibility. Its goal is to identify the polymorphisms responsible for the variation in gene expression levels and thus to improve our understanding of how gene networks are organised in an organism. Thus far, genetical genomics experiments have been analysed considering each expression level one at a time and using fairly simple statistical models, correcting only, e.g., by sex. As a consequence, the results are a collection of successive quantitative trait loci analysis (eQTL in the terminology introduced by Schadt et al. [1,2]), where each gene expression level is analysed independently. It is surprising that much effort has been dedicated to issues like data normalization [3] or computing efficiency [4] whereas modelling the trait itself (i.e., the expression level) has been severely neglected.Based on two rather reasonable assertions (i) a good modelling strategy should consider all available variables as potential effects in the model, and (ii) gene expressions are highly interconnected, we suggest that an eQTL m