Comparing the genomes of two species offers a robust approach to unveil potentially overlooked genes that wield substantial influence on observed agronomic traits. Extensive phenotypic data from maize and sorghum were previously utilized in genome-wide association studies. This project aims to harness the potential of comparative genomics to strengthen confidence in these marker-trait associations and to suggest previously unexplored relationships. To achieve this goal, insights from studies on the genetically diverse Sorghum Association Panel and the maize Wisconsin Diversity Panel were leveraged to classify candidate genes as either shared between the two species or unique to one or the other. Candidate orthologous genes found in both species, associated with shared phenotypes, enhance the reliability of the associations within each species. Additionally, genes unique to each species provide parameters to inform future predictive models. Finally, given the ancient tetraploidy of maize and the biased loss of genes over time, the candidate genes identified in sorghum provide valuable information for understanding the orthologs found in the maize subgenomes.

Enhancing photosynthesis for increased sorghum grain yield has become a key focus in sorghum breeding efforts. Phenotyping, involving the measurement of various morpho-physiological and physical traits associated with photosynthesis and grain yield, is a time-intensive process. However, the potential of non-invasive leaf-level hyperspectral imaging to swiftly detect plant performance, optimizing photosynthesis and grain yield, is promising. This study aimed to evaluate the feasibility of utilizing hyperspectral reflectance in the 350–950 nm range for the rapid estimation of these traits in intact sorghum leaves. Multiple machine learning regression algorithms were developed using leaf-level hyperspectral reflectance data from nearly 400 sorghum accessions within an association panel. The best-performing prediction models were then considered as potential methods for constructing a prediction model targeting multiple other physiological and yield traits in sorghum accessions. The results indicate that this approach enables the early detection of leaf photosynthetic and yield traits through leaf-level hyperspectral reflectance without the need for a full-range, high-cost leaf spectrometer.