Skip to main content
ARS Home » Southeast Area » Canal Point, Florida » Sugarcane Field Station » Research » Publications at this Location » Publication #399066
Research Project: Development of High-Yielding, Stress Tolerant Sugarcane Cultivars Using Agronomic, Genetic, and Molecular Approaches

Location: Sugarcane Field Station

Title: Enhancing prediction accuracy by incorporating known locus (Bru1) as fixed effect for brown rust resistance in sugarcane

Author
item Islam, Md
item QIN, LIFANG - Guangxi University
item MCCORD, PER - Washington State University
item Sood, Sushma

Submitted to: International Society of Sugar Cane Technologists Proceedings
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/26/2022
Publication Date: 2/19/2023
Citation: Islam, M.S., Qin, L., Mccord, P.H., Sood, S.G. 2023. Enhancing prediction accuracy by incorporating known locus (Bru1) as fixed effect for brown rust resistance in sugarcane. International Society of Sugar Cane Technologists Proceedings. 31:443-446.

Interpretive Summary: Brown rust (BR) is one of the detrimental diseases and could cause up to 50% of yield loss in sugarcane. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. A field trial was conducted over two years to screen 432 sugarcane clones for BR resistance. The genotypic data of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using five-fold cross-validation, we found GS prediction accuracies for BR ranged from 0.28 to 0.43 across two crop cycles and combined cycles. The prediction ability was further improved (~2% ) by incorporating a known major gene (Bru1) for resistance to BR as a fixed effect in the GS model. Results indicated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for BR resistance sugarcane breeding.

Technical Abstract: Brown rust (Puccinia melanocephala H. & P. Sydow) could cause up to 50% of yield loss in sugarcane, and breeding for host-plant resistance is the most economical, efficient, and environmentally friendly approach to control this disease. However, as breeding progress is limited, genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. For that, a field trial was conducted over two crop cycles to screen 432 sugarcane clones for brown rust (BR) resistance using the whorl-inoculation method. The genotypic data of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using five-fold cross-validation, we found GS prediction accuracies for BR ranged from 0.28 to 0.43 across two crop cycles and combined cycles. The prediction ability was further improved (~2% across crop cycles) by incorporating a known major gene (Bru1) for resistance to BR as a fixed effect in the GS model. The performance of nonparametric GS models outpaced the parametric models, signifying that non-additive genetic effects could contribute to genomic sources underlying BR resistance. Results indicated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for BR resistance sugarcane breeding.