Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions - Souza, Livia M.; Francisco, Felipe R.; Gonçalves, Paulo S.; Scaloppi Junior, Erivaldo J.; Le Guen, Vincent; Fritsche-Neto, Roberto; Souza, Anete P. (Frontiers in Plant Science, 2019)
Several genomic prediction models incorporating genotype x environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions decrease selection accuracy and limit genetic gains in plant breeding. Two genomic data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods (a linear kernel (genomic best linear unbiased predictor, GBLUP) (GB) and a nonlinear kernel (Gaussian kernel, GK)) and the prediction accuracies (PAs) of four genomic prediction models: (1) a single-environment, main genotypic effect model (SM); (2) a multienvironment, main genotypic effect model (MM); (3) a multienvironment, single variance G×E deviation model (MDs); and (4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of GS in 435 rubber tree individuals at two sites and genotyped the individuals with genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development with a broad-sense heritability (H^2) of 0.59. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to rubber tree data showed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK), suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. With the incorporation of GS, a 5-fold increase in response to selection was obtained for SC with multienvironment GS (MM, MDe or MDS) compared to a conventional genetic breeding model (CBM). Furthermore, GS resulted in a more balanced selection response in SC, and if used in conjunction with traditional genetic breeding programs, it contributed to a reduction in selection time. Given the rapid advances in genotyping methods and their declining costs, balanced against the overall costs of managing large progeny trials and potential increases in gains per unit time, we hope that GS can be implemented in rubber tree breeding programs.