Quantitative Genetics, Genomics, and Phenomics in Maize and Sorghum

Authors

• Nikee Shrestha, University of Nebraska-LincolnFollow

First Advisor

James C. Schnable

Committee Members

Jinliang Yang, Reka Howard, Yufeng Ge, Kaustav Majumder

Date of this Version

5-2026

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College at the University of Nebraska in partial fulfilment of requirements for the degree of Doctor of Philosophy

Major: Complex Biosystems (Integrated Plant Biology)

Under the supervision of Professor James C. Schnable

Lincoln, Nebraska, May 2026

Comments

Copyright 2026, Nikee Shrestha. Used by permission

Abstract

Generating the best crop varieties and hybrids requires identifying genes associated with the phenotype of interest and screening thousands of varieties across multiple locations. Quantitative genetics, along with comparative genomics, phenomics, and machine learning approaches, can help us identify target genes associated with the phenotype of interest to develop the right genotypes. Satellite platforms offer logistical and technical advantages in scalability and accessibility and could facilitate plot-level predictions, especially with steadily improving spatial resolution, thereby empowering the large-scale screening of thousands of genotypes across diverse locations. In the first chapter, I evaluated over 20,000 plot-level images of over 80 hybrid maize varieties grown across the US corn belt under various management practices collected from (near- simultaneous) satellite and drone (synonym UAVs, UASs) flights integrated with ground truth yield measurement. Of the six baseline models examined, models employing data collected from satellite images often matched the performance of models employing drone images for both within and cross-environment yield prediction. Non-photochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. Plants with altered NPQ kinetics have achieved increases in yield in multiple crops. In the second chapter, I used genome-wide association studies along with a comparative genomics approach to identify genes associated with NPQ kinetics in both maize and sorghum. The function of the identified gene was validated via a loss-of-function mutant in arabidopsis revealing the conserved function of the gene in both monocot and dicot plants. Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. In the third chapter, I used a pretrained computer vision model to quantify seed color in sorghum (Sorghum bicolor) using a dataset of >1,500 images. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes—colored metabolites in sorghum seeds—and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. In the last chapter, I developed a simple classifier model that uses sequence and evolutionary features that can be generated for any species with an annotated reference genome assembly to accurately distinguish phenotype-associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of loss-of-function mutations. A model trained solely on genes from maize (Zea mays) identified and prioritized rice (Oryza sativa) and arabidopsis (Arabidopsis thaliana) genes that were highly enriched in genes with experimentally validated links to phenotypes in both evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes displayed patterns consistent with known biological properties of phenotype-associated genes. The data, approaches, and conclusions presented in this dissertation provide valuable knowledge to guide genotype selection, causal gene identification, and crop improvement in the future.

Advisor: James C. Schnable