Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.

Abstract

Genomic selection can enhance the rate of genetic gain of cane and sucrose yield in sugarcane (Saccharum L.), an important industrial crop worldwide. We assessed the predictive ability (PA) for six traits, such as theoretical recoverable sugar (TRS), number of stalks (NS), stalk weight (SW), cane yield (CY), sugar yield (SY), and fiber content (Fiber) using 20,451 single nucleotide polymorphisms (SNPs) with 22 statistical models based on the genomic estimated breeding values of 567 genotypes within and across five stages of the Louisiana sugarcane breeding program. TRS and SW with high heritability showed higher PA compared to other traits, while NS had the lowest. Machine learning (ML) methods, such as random forest and support vector machine (SVM), outperformed others in predicting traits with low heritability. ML methods predicted TRS and SY with the highest accuracy in cross-stage predictions, while Bayesian models predicted NS and CY with the highest accuracy. Extended genomic best linear unbiased prediction models accounting for dominance and epistasis effects showed a slight improvement in PA for a few traits. When both NS and TRS, which can be available as early as stage 2, were considered in a multi-trait selection model, the PA for SY in stage 5 could increase up to 0.66 compared to 0.30 with a single-trait model. Marker density assessment suggested 9091 SNPs were sufficient for optimal PA of all traits. The study demonstrated the potential of using historical data to devise genomic prediction strategies for clonal selection early in sugarcane breeding programs.