Advancing crop development is essential to meet the growing demand for agricultural products, particularly for combating stressors and improving nutritional value. Here, we present a quantitative trait loci (QTL) analysis on tar spot of maize, a genome wide association study (GWAS) on phenolic compound accumulation in maize kernels, and Fourier transform-infrared (FT-IR) spectroscopy used to model the phenolic compounds previously found in kernels. This research maps the genetic resistance to maize tar spot disease using a structured QTL analysis on a Stiff-Stalk MAGIC population. The use of the structured population with multi-location field trails resulted in multiple significant QTL that can be used for candidate gene extraction and the future breeding of resistant maize varieties. Additionally, this research focuses on the phenolic compound profiles of maize kernels to detect the natural range of accumulation and to understand the genetic architecture of the compounds and their pathways. GWAS highlighted 170 significant SNPs and 390 potential candidate genes that contribute to phenolic compound accumulation in maize kernel tissues. Lastly, this research uses FT-IR spectroscopy to predict the phenolic compound accumulation in maize kernels without the costly analytical chemistry. Random forest and partial least squares regression modeling types and numerous spectral preprocessing techniques were tested for their accuracy to model these phenotypes. These studies contribute to crop improvement by providing tools that can be used in future plant breeding programs that promote disease resistance, increased nutritional elements, and rapid phenotyping to create more efficient pipelines.
Read
