HCS Spring 2016 Seminar Series

Feb 24, 2016, 11:30am - 12:30pm
Deadline: 
Location: 
244 Kottman Hall (Columbus) video-linked to 121 Fisher Auditorium (Wooster)
Antonio Cabrera
Research Associate
Department of Horticulture and Crop Science
The Ohio State University 

“Utilizing genomic selection to accelerate the pace of developing FHB resistant wheat.”

Abstract: Fusarium Head Blight (FHB) is a major disease caused by F. graminearum, that infects wheat (Triticum aestivum L.) and other cereals. One important aspect for managing FHB in wheat is breeding for resistant varieties. However, evaluating FHB within a breeding program takes a large amount of resources. Marker assisted selection (MAS) has been effective for traits controlled by major genes, but most of the genes controlling FHB resistance are not effectively deployed by conventional MAS. Genomic selection (GS) is a new form of MAS and can facilitate breeding for complex traits by estimating all marker effects simultaneously and predicting the genomic estimated breeding values (GEBVs) that will be used as selection criteria. GS has the potential to increase the genetic gain per year by decreasing the time per cycle. The challenge remains now in implementing GS and identifying the model with the highest prediction accuracy for each trait. We evaluated the prediction accuracy of several GS models in a population of 640 soft winter wheat lines. The population was evaluated for incidence (INC), severity (SEV), index (IND), Fusarium damaged kernel (FDK), kernel damage index (ISK), and deoxynivalenol concentration (DON) in inoculated FHB nurseries in multiple environments and genotypic data was obtained through genotyping by sequencing (GBS). Ten-fold cross validation prediction abilities ranged from 0.45 (INC) to 0.57 (SEV). Similar prediction accuracies were obtained within clusters but were much lower when data from one cluster was used to predict another. Eliminating the top 10-15% less predictable individuals increased prediction accuracy by up to 58%. The results from this work will facilitate GS implementation and the identification of the best lines for selection and crossing for FHB resistance within this population