Abstract
Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare 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 genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via 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 in conjunction with a broad-sense heritability (H 2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs.
Highlights
Rubber tree (Hevea brasiliensis) breeding programs are generally characterized by breeding cycles of 25–30 years and include the crosses, evaluation, and selection of field progeny as well as the propagation of selected superior materials (Gonçalves et al, 2006)
Using the genotyped single-nucleotide polymorphism (SNP), we assessed the population structure via principal component analysis (PCA), and the results indicated that the 411 genotypes fell into two major clusters (Supplementary Figure 3), which mainly contained hybrids derived from the PR255 × PB217 cross and hybrids derived from the GT1 × RRIM701 and GT1 × PB235 crosses
Incorporating and improving the genomic prediction accuracy (PA) of rubber trees are a challenge for the successful application of Genomic selection (GS) in breeding programs
Summary
Rubber tree (Hevea brasiliensis) breeding programs are generally characterized by breeding cycles of 25–30 years and include the crosses, evaluation, and selection of field progeny as well as the propagation of selected superior materials (Gonçalves et al, 2006). Rubber tree breeding programs are complex and costly because the large size of these trees necessitates experiments over large tracts of land, and progeny tests are expensive to establish, manage over many years, and evaluate via measurements. Priyadarshan (2017) proposed two strategies: 1) truncating the breeding steps that follow conventional means and 2) incorporating genomics into breeding programs to identify high-yielding genotypes in half-sib, full-sib, and polycross seedlings during the juvenile stage to minimize both space and time The time taken to derive a Hevea through breeding must be substantially reduced. Priyadarshan (2017) proposed two strategies: 1) truncating the breeding steps that follow conventional means and 2) incorporating genomics into breeding programs to identify high-yielding genotypes in half-sib, full-sib, and polycross seedlings during the juvenile stage to minimize both space and time
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