Mining genic resources regulating nitrogen-use efficiency based on integrative biological analyses and their breeding applications in maize and other crops.

Nitrogen (N) is an essential factor for limiting crop yields, and cultivation of crops with low nitrogen-use efficiency (NUE) exhibits increasing environmental and ecological risks. Hence, it is crucial to mine valuable NUE improvement genes, which is very important to develop and breed new crop varieties with high NUE in sustainable agriculture system. Quantitative trait locus (QTL) and genome-wide association study (GWAS) analysis are the most common methods for dissecting genetic variations underlying complex traits. In addition, with the advancement of biotechnology, multi-omics technologies can be used to accelerate the process of exploring genetic variations. In this study, we integrate the substantial data of QTLs, quantitative trait nucleotides (QTNs) from GWAS, and multi-omics data including transcriptome, proteome, and metabolome and further analyze their interactions to predict some NUE-related candidate genes. We also provide the genic resources for NUE improvement among maize, rice, wheat, and sorghum by homologous alignment and collinearity analysis. Furthermore, we propose to utilize the knowledge gained from classical cases to provide the frameworks for improving NUE and breeding N-efficient varieties through integrated genomics, systems biology, and modern breeding technologies.

The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.).

Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.