Metabolic quantitative trait locus (QTL) studies have allowed us to better understand the genetic architecture underlying naturally occurring plant metabolic variance. Here, we use two recombinant inbred line (RIL) populations to dissect the genetic architecture of natural variation of 155 metabolites measured in the mature maize (Zea mays) kernel. Overall, linkage mapping identified 882 metabolic QTLs in both RIL populations across two environments, with an average of 2.1 QTLs per metabolite. A large number of metabolic QTLs (more than 65%) were identified with moderate effects (r 2 = 2.1%-10%), while a small portion (less than 35%) showed major effects (r 2 . 10%). Epistatic interactions between these identified loci were detected for more than 30% of metabolites (with the proportion of phenotypic variance ranging from 1.6% to 37.8%), implying that genetic epistasis is not negligible in determining metabolic variation. In total, 57 QTLs were validated by our previous genomewide association study on the same metabolites that provided clues for exploring the underlying genes. A gene regulatory network associated with the flavonoid metabolic pathway was constructed based on the transcriptional variations of 28,769 genes in kernels (15 d after pollination) of 368 maize inbred lines. A large number of genes (34 of 58) in this network overlapped with previously defined genes controlled by maize PERICARP COLOR1, while three of them were identified here within QTL intervals for multiple flavonoids. The deeply characterized RIL populations, elucidation of metabolic phenotypes, and identification of candidate genes lay the foundation for maize quality improvement.Knowledge concerning plant metabolism is important for crop improvement as well as in exploring the potential for enhancing the accumulation of high-value products by metabolic engineering strategies. Recent studies that utilized a wide range of techniques, including biochemistry, informatics, genetics, and genomics, have boosted our understanding of the genetics of plant metabolism (Luo, 2015). Metabolic quantitative trait locus (QTL) studies that combined different techniques have aided us to better understand the genetic architecture underlying naturally occurring phenotypic variance and promise to better facilitate future plantbreeding strategies (Fernie and Schauer, 2009).Maize (Zea mays) kernels make a very large contribution to the diets of humans and animals. The chemical composition and impact of genetic variation on the metabolic diversity of maize kernels have been widely studied (Chander et al., 2008c;Li et al., 2013;Wen et al., 2014Wen et al., , 2015. The most important storage chemical components in mature maize kernels are starch (70%-75% of dry matter), protein (8%-10% of dry matter), and oil (4%-5% of dry matter), and the underlying genes and related pathways of these have been well studied during the past two decades (Moose et al., 2004;Fernie and Schauer, 2009;Li et al., 2013;Luo, 2015). Although only accumulating to low levels in the maize k...