Gene expression data sets have recently been exploited to study genetic factors that modulate complex traits. However, it has been challenging to establish a direct link between variation in patterns of gene expression and variation in higher order traits such as neuropharmacological responses and patterns of behavior. Here we illustrate an approach that combines gene expression data with new bioinformatics resources to discover genes that potentially modulate behavior. We have exploited three complementary genetic models to obtain convergent evidence that differential expression of a subset of genes and molecular pathways influences ethanol-induced conditioned taste aversion (CTA). As a first step, cDNA microarrays were used to compare gene expression profiles of two null mutant mouse lines with difference in ethanol-induced aversion. Mice lacking a functional copy of G protein-gated potassium channel subunit 2 (Girk2) show a decrease in the aversive effects of ethanol, whereas preproenkephalin (Penk) null mutant mice show the opposite response. We hypothesize that these behavioral differences are generated in part by alterations in expression downstream of the null alleles. We then exploited the WebQTL databases to examine the genetic covariance between mRNA expression levels and measurements of ethanol-induced CTA in BXD recombinant inbred (RI) strains. Finally, we identified a subset of genes and functional groups associated with ethanol-induced CTA in both null mutant lines and BXD RI strains. Collectively, these approaches highlight the phosphatidylinositol signaling pathway and identify several genes including protein kinase C beta isoform and preproenkephalin in regulation of ethanol- induced conditioned taste aversion. Our results point to the increasing potential of the convergent approach and biological databases to investigate genetic mechanisms of complex traits.
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