Genomic prediction and doubled haploids transformed hybrid maize breeding by increasing selection intensity and speeding up generation cycles. However, genomic prediction, which traditionally uses SNP markers, falls short in accurately modeling hybrid performance in specific environments. As a response to environmental stresses, plants dynamically modulate gene expression. We hypothesize that integrating gene expression modulation into genomic prediction will increase the accuracy of these models across environments. To test this hypothesis, we will evaluate gene expression modulation of maize inbreds and their hybrids responding to temperature (heat and cold) stress. Leveraging both genotypic and phenotypic data from the Genomes2Fields collaborative Genotype×Environment project, we aim to discern the optimal way to integrate this information into genomic prediction models and assess its influence on prediction accuracy. Stochastic simulations will then be used to effectively find operational strategies to obtain and use gene expression information in breeding programs. We envision that the results of this project will shed light on the contribution of gene expression modulation to plasticity and environmental responsiveness and will be relevant to other crops. Ultimately, we will explore genomic selection methodologies that allow breeders to improve hybrid yield and agronomic performance more efficiently in specific environments to respond quickly to new stresses. This project will help guarantee adequate agricultural production despite a growing world population, pressure on natural resources, and climate change.
Collaborators: Martin Bohn, Alex Lipka, Sam Fernandes
Funding Agency: USDA-NIFA
Project Timeline: 2024-2027
