Location: Plant Science Research
Project Number: 6070-21220-017-002-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Apr 1, 2020
End Date: Mar 31, 2025
Objective:
Genotype-Environment interactions are differences in relative performance of varieties across environments. Such interactions can hinder genetic gains, or they can be exploited for environment-specific variety development. To capitalize on genotype-environment interactions, however, we need to understand how to integrate information on past performance of varieties across diverse environments with genotyping information and environmental weather and soil data. We are participating in the Genomes to Fields Project, which has collected data on thousands of corn hybrids across more than 100 environments to date and has weather and genotype data available. This funding will support data analysis. and development of new analytical methods to integrate this information to allow better prediction of corn hybrid performance in specific environments. In addition, experiments will be conducted to test if prediction models based on transcriptomics (gene expression) can be used to more efficiently select corn inbreds that produce optimal hybrids.
Approach:
The North Carolina State University research group will:
- Phenotypic, genotypic, and environmental data collection at NC location for Genomes 2 Field Project.
- Identifying and removing outlier data points likely due to error in raw trait, marker, and weather data sets.
- Develop methods to handle missing data in downstream analyses or to impute missing data with good accuracy.
- Check trait-environment combinations to ensure data have useful heritability and otherwise filter out data.
- Estimate hybrid-environment mean values and hybrid mean values across environments.
- Development and testing of genomic prediction models based on hybrid testing data for both wide-adaptation and environment-specific adaptation.
- Conduct experiments to test ability of early seedling gene expression to predict hybrid performance and select on gene expression profiles to test the value of such predictions in selection.
