Location: Plant, Soil and Nutrition Research
Project Number: 8062-21000-043-002-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 16, 2016
End Date: Sep 15, 2021
The proposed work is a joint effort to develop genomic and bioinformatic approaches to dissect agronomic traits in maize, maize’s wild relatives, and other crops. The goal is to apply machine learning, advanced statistical, and genomic approaches to identify functional variants. 1. Genomically and agronomically characterize germplasm under a range of abiotic conditions. 2. Develop computational approaches for relating genotype and phenotype to identify variants contributing to adaptation and heterosis. 3. Deploy the data storage and analysis approaches in easy to use software for the entire community.
1. Maize and its wild relative Tripsacum will be evaluated for performance under a range of abiotic conditions in the field and greenhouse. 2. Germplasm will be genotyped and profiled using whole genome resequencing, repeat genotyping (repGen), and 3’ RNAseq. 3. Data mining and machine learning approaches will be used to complement the statistical genetic approaches used to identify functional variants. 4. The software will be deployed in the open source TASSEL software package and as software plugins to the GOBII (Genomic & Open-source Breeding Informatics Initiative) data warehouse.