A Multifaceted Approach to Understanding and Improving Nitrogen Utilization Efficiency in Maize.

Increased nitrogen use efficiency (NUE) has been an important target for past maize improvement, with higher NUE enhancing economic and environmental sustainability of maize cropping systems. Extensive field testing, combined with transcriptomic and functional genomic analysis, has identified a complex genetic architecture controlling N utilization. A multi-faceted approach utilizing genome selection, candidate gene validation and targeted manipulation is being used to understand this complexity and how it can be leveraged to improve maize NUE. Among many quantitative trait loci (QTL), nine robust intervals were determined to account for 5-15% of variation for multiple N utilization traits across years. Allelic combinations associated with enhanced N utilization at these intervals were identified in the IBM population. Hybrid field testing of those IBM lines with favorable ideotypes for high NUE QTL alleles indicate that tester selection has a strong impact on NUE traits. Candidate genes for these nine regions were identified, including a high affinity nitrate transporter, NRT1.1B, whose rice homolog has been shown to contribute to NUE variation. Community resources, including the use of near-isogenic lines, are being used for QTL verification. Additional investigation of single nucleotide polymorphism (SNP) markers in more than 400 maize genes enriched for regulatory functions and control of N utilization showed divergent selection among the major germplasm groups for temperate maize production, indicating that heterozygosity is a preferred breeding goal to maximize N utilization and yield. Experiments directed at understanding N-responsive regulatory programs across developmental profiles were implemented in situ and in vitro. The resulting transcriptomic analysis, metabolite profiling, and network analysis identified factors involved in gene expression at a systems level including the N responsive bZip and dof transcription factor families. Current development of a transgenic platform for maize will allow for efficient verification of these transcription factors as well as other candidate genes.