Location: Sugarcane ResearchTitle: A genome-wide association study and genomic prediction for fiber and sucrose contents in sugarcane
|XIONG, HAIZHENG - University Of Arkansas|
|CHEN, YILIN - University Of Arkansas|
|SHI, AINONG - University Of Arkansas|
Submitted to: Proceedings American Society of Horticultural Sciences
Publication Type: Abstract Only
Publication Acceptance Date: 5/6/2022
Publication Date: 7/31/2022
Citation: Xiong, H., Chen, Y., Shi, A., Pan, Y.-B. 2022. A genome-wide association study and genomic prediction for fiber and sucrose contents in sugarcane [abstract]. 2022 ASHS annual conference July 30–August 3, Chicago, IL. Proceedings American Society of Horticultural Sciences. Article 37890. https://ashs.confex.com/ashs/2022/meetingapp.cgi/Paper/37890.
Technical Abstract: Sugarcane (Saccharum spp. hybrids) is an economically important crop for both the sugar and biofuel industries. Fiber and sucrose contents are two critical quantitative traits in sugarcane breeding that require multiple-year and multiple-location evaluations. Molecular marker-assisted selection could significantly reduce the time and cost in developing new sugarcane varieties. The objectives of this study were to conduct a genome-wide association study to identify DNA markers associated with fiber and sucrose contents and to perform genomic prediction (GP) for the two traits. Fiber and sucrose data were collected from the plantcane and first-ratoon crops of 221 self-pollinated progenies of LCP 85-384, the most popular Louisiana sugarcane cultivar between 1999 and 2007. A total of 1,051 polymorphic DNA markers were used to identify significant markers using Mixed Linear Model, Generalized Linear Model, and Fixed and Random Model Circulating Probability Unification. The results showed that 10 markers were associated with fiber and 11 markers were associated with sucrose contents. These trait-associated markers were applied to GP by ridge regression best linear unbiased predictor and Bayesian Bayes A, Bayes B, Bayes LASSO, and Bayes ridge regression models implemented in the BGLR package of the R program. The accuracy of GP varied from 15% to 25% for fiber content and 13% to 18% for sucrose content. Upon validation, these markers can be applied to marker-assisted selection and genomic selection of fiber and sucrose contents in sugarcane breeding.