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ARS Home » Southeast Area » Canal Point, Florida » Sugarcane Field Station » Research » Publications at this Location » Publication #392954

Research Project: Development of High-Yielding, Stress Tolerant Sugarcane Cultivars Using Agronomic, Genetic, and Molecular Approaches

Location: Sugarcane Field Station

Title: Evaluation of Genomic Selection Model of Different Yield and Sugar Component Traits in Sacharum Spp. Hybrid

item Islam, Md
item MCCORD, PER - Washington State University
item Read, Quentin
item QIN, LIFANG - Guangxi University
item LIPKA, ALEXANDER - University Of Illinois
item Sood, Sushma
item Todd, James
item OLATOYE, MERCUS - University Of Illinois

Submitted to: American Society of Sugar Cane Technologists
Publication Type: Abstract Only
Publication Acceptance Date: 3/31/2022
Publication Date: 7/16/2022
Citation: Islam, M.S., Mccord, P.H., Read, Q.D., Qin, L., Lipka, A.E., Sood, S.G., Todd, J.R., Olatoye, M.O. 2022. Evaluation of Genomic Selection Model of Different Yield and Sugar Component Traits in Sacharum Spp. Hybrid. American Society of Sugar Cane Technologists. 30-31.

Interpretive Summary: N/A

Technical Abstract: Genomic selection (GS) has been proven to improve the selection process in breeding programs. The objectives of the study were to experimentally evaluate seven different GS methods for highly polyploid and complex sugarcane hybrids and to determine future breeding approaches to be implemented in the USDA ARS, Canal Point (CP) sugarcane breeding program. While prediction accuracy differed by trait and by harvest crop year, there were only small differences in prediction accuracy among the different models. Prediction accuracy has been improved by accounting for different gene effects (additive and dominance). The number of molecular markers and training population size are also important factors for predicting the genomic estimated breeding value (GEBV). We found that in this population of hybrids, acceptable prediction accuracy could be achieved with 3000 to 5000 SNP markers, and that prediction accuracy did not decline with decreasing size of training population until it was reduced below 50% of the original number of hybrids. In addition, we used datasets from different harvesting crop cycles (plant cane, first ratoon and second ratoon) to perform true validations, in which a model is trained on a given cycle and applied on a test population from a separate cycle. We will propose a methodology for implementing GS in the selection process of the CP sugarcane breeding program