Genomic approaches have accelerated the study of the quantitative genetics that underlie phenotypic variation. These approaches associate genome-scale analyses such as transcript profiling with targeted phenotypes such as measurements of specific metabolites. Additionally, these approaches can help identify uncharacterized networks or pathways. However, little is known about the genomic architecture underlying data sets such as metabolomics or the potential of such data sets to reveal networks. To describe the genetic regulation of variation in the Arabidopsis thaliana metabolome and test our ability to integrate unknown metabolites into biochemical networks, we conducted a replicated metabolomic analysis on 210 lines of an Arabidopsis population that was previously used for targeted metabolite quantitative trait locus (QTL) and global expression QTL analysis. Metabolic traits were less heritable than the average transcript trait, suggesting that there are differences in the power to detect QTLs between transcript and metabolite traits. We used statistical analysis to identify a large number of metabolite QTLs with moderate phenotypic effects and found frequent epistatic interactions controlling a majority of the variation. The distribution of metabolite QTLs across the genome included 11 QTL clusters; 8 of these clusters were associated in an epistatic network that regulated plant central metabolism. We also generated two de novo biochemical network models from the available data, one of unknown function and the other associated with central plant metabolism.