Source: NORTH CAROLINA STATE UNIV submitted to NRP
GENOMES TO FIELDS GENOMIC PREDICTION RESEARCH
Sponsoring Institution
Agricultural Research Service/USDA
Project Status
ACTIVE
Funding Source
Reporting Frequency
Annual
Accession No.
0433477
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Sep 1, 2017
Project End Date
Aug 30, 2022
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
(N/A)
Non Technical Summary
(N/A)
Animal Health Component
10%
Research Effort Categories
Basic
90%
Applied
10%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20115101080100%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1510 - Corn;

Field Of Science
1080 - Genetics;
Goals / Objectives
Focused on maize, one of the most valuable crop species worldwide, the Genomes to Fields (G2F) Initiative focuses on leveraging and building on the publicly-funded maize genome sequencing projects to advance our understanding of the role of existent genetic variation to increase plantsâ¿¿ resilience to environmental influences. The Genotype by Environment (GXE) project is a multi-investigator project that is part of G2F. Since 2014, this project has evaluated more than 1,500 unique maize hybrids across more than 77 environments for a total of more than 45,000 experimental plots. This funding will contribute to continue this collaborative project into the future including support for germplasm development, data collection, high-throughput phenotyping deployment, and data analysis focused on improving environment-specific hybrid performance predictions.
Project Methods
The North Carolina State University research group will have activities within the following areas to support the GXE project within G2F: - Development of germplasm resources - Production and distribution of inbred and hybrid seed to cooperators for trials - Phenotypic, genotypic, and environmental data collection - Development and testing of genomic prediction models based on hybrid testing data