Vegetative indices data boost genomic selection models in predicting oat grain yield

Authors: OLIVEIRA, GUILHERME - South Dakota State University; BAZZER, SUMANDEEP - South Dakota State University; MAIMAITIJIANG, MAITINIYAZI - South Dakota State University; Nandety, Raja Sekhar; CHANG, JIYUL - South Dakota State University; Fiedler, Jason; CAFEE, MELANIE - South Dakota State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/10/2024
Publication Date: 7/21/2024
Citation: Oliveira, G., Bazzer, S., Maimaitijiang, M., Nandety, R., Chang, J., Fiedler, J.D., Cafee, M. 2024. Vegetative indices data boost genomic selection models in predicting oat grain yield. Meeting Abstract.

Interpretive Summary:

Technical Abstract: Traditional genomic selection (GS) methods have been improved by integrating information about major genes into these models. However, for complex traits like grain yield (GYL), this approach may be limited due to the absence of large-effect loci directly affecting this trait. Drawing on insights from phenomics research, the implementation of vegetative indices data has improved crop yield modeling. This suggests that incorporating major genes information affecting these variables might be a viable strategy for enhancing GS models to improve GYL prediction. Therefore, the study aimed to collect vegetative indices using a sensor mounted on an unmanned aerial vehicle (UAV) in oat breeding populations and identify genomic regions associated with these vegetative indices, incorporating them as fixed effects in linear and additive genomic models and evaluating their capability to increase oat GYL predictions accuracies. The UAV flights were conducted in four distinct environments. In each environment, five vegetative indices associated with biomass and chlorophyll levels, which are linked to grain yield (GYL), were collected. Data was gathered from four separate drone flights in each environment, resulting in 20 vegetative indices variables (4 flights x 5 indices). Additionally, the average values of these vegetative indices from the four flights were calculated, providing 5 more variables. In total, including the GYL, 26 variables were collected in each environment (20 individual vegetative index variables, 5 average vegetative index variables, and GYL). Conducting a genome-wide association study (GWAS), fifty-seven molecular markers across twenty of the twenty-one oat chromosomes were identified in association with some of the analyzed vegetative indices. In addition, the broad heritability of the vegetative indices ranged from values close to 0 (no significant associations) to 0.89. These results indicate the presence of genomic variability for the traits related to these vegetative indices in the oat breeding populations under investigation with a complex genetic architecture. Using only the most significant markers (lowest p-values), prediction accuracies for grain yield increased by 7.5-10% in multi-environmental scenarios. These results support the use of vegetative indices for improving GS accuracy for oat grain yield and highlight the benefit of a joint application of phenomics and genomics.