Location: Sugarcane Field StationTitle: Genomic Selection: An Emerging Breeding Tool for Accelerating Genetic Gain in Sugarcane
|MCCORD, PER - Washington State University|
|OLATOYE, MARCUS - University Of Illinois|
|QIN, LIFANG - Guangxi University|
|LIPKA, ALEXANDER - University Of Illinois|
Submitted to: Sugar Journal
Publication Type: Abstract Only
Publication Acceptance Date: 4/16/2020
Publication Date: 6/11/2020
Citation: Islam, M.S., Mccord, P.H., Olatoye, M.O., Qin, L., Sood, S.G., Lipka, A.E. 2020. Genomic Selection: An Emerging Breeding Tool for Accelerating Genetic Gain in Sugarcane. Sugar Journal. 83(1):15.
Interpretive Summary: N/A
Technical Abstract: Although the total sugarcane production has increased worldwide, the rate of growth is lower compared to other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in animal breeding as well as in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 422 Saccharum spp. hybrid clones in Canal Point, FL following augmented row column design with two replications. Two rust diseases (Brown and Orange rust) were screened artificially using the whorl inoculation method in the field condition for two years (2017 and 2018). The genotypic data was generated through exome capture sequencing technologies. After filtering, a set of 16,267 single nucleotide polymorphic markers were employed to analyze the five-fold cross-validation GS prediction using multiple GS models. The results revealed that the five-fold cross-validation GS prediction accuracy of genomic estimated breeding value for brown and orange rust varies from 0.17 to 0.28 and 0.16 to 0.29, respectively across two years and combined years. GS can potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding.