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.

Reveal the kernel dehydration mechanisms in maize based on proteomic and metabolomic analysis.

Background: Kernel dehydration is an important factor for the mechanized harvest in maize. Kernel moisture content (KMC) and kernel dehydration rate (KDR) are important indicators for kernel dehydration. Although quantitative trait loci and genes related to KMC have been identified, where most of them only focus on the KMC at harvest, these are still far from sufficient to explain all genetic variations, and the relevant regulatory mechanisms are still unclear.

Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development.

Background: Maize kernel filling, which is closely related to the process of double fertilization and is sensitive to a variety of environmental conditions, is an important component of maize yield determination. Silk is an important tissue of maize ears that can discriminate pollen and conduct pollination. Therefore, investigating the molecular mechanisms of kernel development and silk senescence will provide important information for improving the pollination rate to obtain high maize yields.

New insights into the response of maize to fluctuations in the light environment.

Light is the most important environmental cue signaling the transition from skotomorphogenesis to photomorphogenesis, thus affecting plant development and metabolic activity. How the light response mechanisms of maize seedlings respond to fluctuations in the light environment has not been well characterized to date. In this study, we built a gene coexpression network from a dynamic transcriptomic map of maize seedlings exposed to different light environments.