Location: Sugarcane Field StationTitle: Accuracy of genomic prediction of yield and sugar traits in Saccharum spp. hybrids
|MCCCORD, PER - Washington State University|
|QIN, LIFANG - Guangxi University|
|LIPKA, ALEXANDER - University Of Illinois|
Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2022
Publication Date: 9/10/2022
Citation: Islam, M.S., McCord, P.H., Read, Q.D., Qin, L., Lipka, A.E., Sood, S.G., Todd, J.R., Olatoye, O.M. 2022. Accuracy of genomic prediction of yield and sugar traits in Saccharum spp. hybrids. Agriculture. 12:1436. https://doi.org/10.3390/agriculture12091436
Interpretive Summary: The genetic gain for key sugar content and yield traits has plateaued in sugarcane in the past decade. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Eleven sugar and yield related traits were evaluated. After filtering, a set of 10,435 single nucleotide polymorphic markers were used to assess the prediction accuracy of seven GS models. Using five-fold cross-validation, we observed GS prediction accuracies ranged from 0.11 to 0.37 across traits and crop cycles. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that non-additive genetic effects could contribute genomic sources underlying traits. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding.
Technical Abstract: Genomic selection (GS) has been demonstrated to enhance the selection process in breeding programs. The objectives of this study were to experimentally evaluate different GS methods in sugarcane hybrids and to determine the prospect of GS in future breeding approaches. Using sugar and yield-related trait data from 432 sugarcane clones and 10,435 single nucleotide polymorphisms (SNPs), a study was conducted using seven different GS models. While five-fold cross-validated prediction accuracy differed by trait and by crop cycle, there were only small differences in prediction accuracy among the different models. Prediction accuracy was modestly improved by accounting for different gene effects (additive and dominance). Utilizing a trait-assisted GS model, we could effectively predict the five-fold cross-validated genomic estimated breeding value of ratoon crops using both SNPs and trait values from the plant cane crop. We found that an acceptable level of prediction accuracy could be achieved with 4,000 to 5,000 SNPs. A prediction accuracy did not decline with decreasing size of the training population until it was reduced below 60% (259) to 80% (346) of the original number of clones. Our findings suggest that GS could open a new avenue for improving sugar and yield-related traits in sugarcane.