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 fivefold 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 on average 0.20 across all traits and crop cycles for all tested models. Utilizing a trait-assisted GS model, we could effectively predict the fivefold cross-validated genomic estimated breeding value of ratoon crops using both SNPs and trait values from the plant cane crop. We found that the plateau of prediction accuracy could be achieved with 4000 to 5000 SNPs. 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 is possibly a new direction for improving sugar and yield-related traits in sugarcane.

Sugarcane (Saccharum L. spp. hybrids) growers depend on breeding programs for new, high-yielding cultivars that have resistance to abiotic and biotic stresses, so breeders continually seek out widely adapted, high yielding germplasm to be used as parents for their programs. Cultivars are sometimes used for this purpose, but their use may be minimized to prevent genetic diversity erosion. The purpose of this study was to determine the importance of cultivars as parents in three USA (one in Florida and two in Louisiana) sugarcane breeding programs by quantifying the percentage of cultivars that had these parental groupings based on published registrations and crossing records. The percentage of cultivars with at least one commercial parent for each program was 81.8%, 77.5%, and 64.3% for the Houma (Ho), Louisiana, Canal Point (CP), Florida and Louisiana State University (LSU) programs, respectively, but cultivars were recently used as parents in only 11.8% (Ho), 16.39% (CP), and 34.3% (LSU) of crosses. The results indicate that the CP and Ho programs should consider increasing the use of cultivars as parents in their breeding programs to increase the probability of selecting potential commercial genotypes, but this should be balanced with high diversity crosses to avoid the loss of diversity.

Sugarcane (Saccharum spp.) is an important perennial grass crop for both sugar and biofuel industries. The Louisiana sugarcane breeding program is focused on improving sugar yield by incrementally increasing genetic gain. With the advancement in genotyping and (highthroughput) phenotyping techniques, genomic selection is a promising marker-assisted breeding tool. In this study, we assessed ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program. Datasets (individual and pooled in various combinations) were constructed based on soil type (light—Commerce silty loam, heavy—Sharkey clay) and crop (plant cane, ratoon). A total of 3906 SNPs were used to predict the genomic estimated breeding values (GEBVs) of the clones for sucrose content and cane and sugar yield. Prediction accuracy was estimated by both Spearman’s rank correlation and Pearson’s correlation between phenotypic breeding values and GEBVs. All traits showed significant variation with moderate (42% for sucrose content) to high (85% for cane and sugar yield) heritability. Prediction accuracy based on rank correlation was high (0.47–0.80 for sucrose content; 0.61–0.69 for cane yield, and 0.56–0.72 for sugar yield) in all cross-effect prediction models where soil and crop types were considered as fixed effects. In general, Bayesian models demonstrated a higher correlation than rrBLUP. The Pearson’s correlation without soil and crop type as fixed effects was lower with no clear pattern among the models. The results demonstrate the potential implementation of genomic prediction in the Louisiana sugarcane variety development program.