Performing Department
(N/A)
Non Technical Summary
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 genotypic and phenotypic data previously generated from the Genomes2Fields collaborative Genotype×Environment project, we will determine how incorporating this information into genomic prediction models impacts 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.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
(N/A)
Developmental
30%
Goals / Objectives
The changing agricultural landscape necessitates breeding crops that can thrive in diverse environments to ensure a sustainable food, feed, and fiber supply. While genomic prediction has revolutionized crop and animal breeding, current methods struggle to predict genotype performance across different environments (genotype-by-environment interaction), partly due to the limitations of genomic markers like SNPs. Understanding how gene expression varies in response to the environment (transcriptome variation) is crucial, as it influences an individual's performance within a specific environment. Previous research suggests that transcript abundance can predict performance as well as or better than SNP markers. Building on this, we will incorporate transcriptome profiles' variation in response to the environment, essential for improving genotype performance prediction across environments. The specific objectives are:Objective 1 - Assess variation in gene expression modulation across different environmental conditions.Objective 2- Determine the usefulness of information from transcriptional modulation and gene co-expression networks for genomic prediction.Objective 3 - Investigate the advantages and challenges of utilizing gene expression modulation information in maize breeding programs.
Project Methods
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 genotypic and phenotypic data previously generated from the Genomes2Fields collaborative Genotype×Environment project, we will determine how incorporating this information into genomic prediction models impacts prediction accuracy. Stochastic simulations will then be used to effectively find operational strategies to obtain and use gene expression information in breeding programs.