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

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.