Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel.

ABSTRACT

The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.