Date of Award:

5-2025

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Plants, Soils, and Climate

Committee Chair(s)

Amita Kaundal

Committee

Amita Kaundal

Committee

Norah Saarman

Committee

Paul Johnson

Committee

Rajandeep Sekhon

Committee

Steve Larson

Committee

Margaret Krause

Abstract

Salinity is a serious threat to food security, leading to reduced growth and development in land plants. Corn (Zea mays) is moderately sensitive to soil salinity, and high salinity reduces its growth and development, resulting in lower grain and biomass yield. Rapid development in DNA sequencing technologies provides excellent opportunities to develop suitable plant breeding methods to improve them. The objectives of the research were to find genes putatively linked with salinity stress-related traits in corn, predict salinity tolerance using DNA information alone, explore Artificial Intelligence methods to enhance prediction-accuracy, and investigate whether corn plants can memorize repeated exposure to salinity stress. A collection of 282 different types of corn lines was examined under greenhouse conditions to find novel genes. The study yielded 19 DNA signatures linked to plant height, shoot-fresh weight, and shoot-dry weight traits. A total of 83 candidate genes were near the 32 significant DNA signatures. Various statistical models were tested using different marker densities to predict salinity tolerance using genome-wide markers alone. Bayesian models performed better than the GBLUP at higher marker densities for SL_STI, RL_STI, and RW_STI. The genetic contribution towards the observed variation of traits in the population ranged from 31 to 54%. We tested two AI methods, Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN) for predicting salinity tolerance. MLP performed better than the traditional rrBLUP for SL_STI and RL_STI traits. A gene expression assay was conducted on leaf and root tissues to explore salinity stress memory in corn seedlings. We identified 669 genes showing memory response and identified transcription factors (master regulators) among them. The gene-modules identified through a co-expression network analysis showed highly significant correlations with the physiological traits. We validated the expression patterns of selected memory response genes through RT-qPCR. The research improves our understanding of the genetic makeup of salinity tolerance in corn seedlings. It generates information about the expected level of accuracies for DNA-based prediction of salinity tolerance in a diverse set of corn lines.

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