Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions

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.

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

“…Trait data must be in Linkage Disequilibrium-LD or genetic auto-correlation (e.g., Kelleher et al, 2012), with the molecular markers or with the samples' genetic co-ancestry. GP utility has been demonstrated ( Table 1) in model forest tree species such as Eucalyptus Suontama et al, 2019), and conifers as Pinus Li et al, 2019) and Douglas-fir (Thistlethwaite et al, 2017(Thistlethwaite et al, , 2019b, but also in non-model perennial crops such as coffee (Sousa et al, 2018), rubber (Cros et al, 2019;Souza et al, 2019) and oil palm (Cros et al, 2015). GP may even fit epigenetics (Roudbar et al, 2020), as well as multi-trait genomic models as was recently confirmed in Norway spruce for growth, wood quality and weevil resistance traits (Lenz et al, 2020).…”

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

“…A GS study on Norway spruce planted on two sites in northern Sweden showed that for all four traits, the accuracies of within-site training and selection were always higher than those of cross-site training and selection [54]. A recent study on rubber tree demonstrated the superiority of multi-environment GS models over single-environment ones [53].…”

“…The results in interior spruce were more modest than in pine species: efficiency increased by 6-33% and the breeding cycle reduced by 25% [14]. A twofold reduction in the breeding cycle was achieved in eucalyptus [22,46] and even threefold reduction in some Picea species [58,64] and rubber tree [53]. At the same time, GS relies on expensive genotypic analysis, and this must be taken into account when comparing breeding methods in terms of efficiency.…”

“…The authors suggested that the higher accuracy of GBS was mainly due to the large increase in the number of markers. GBS was used in GS of both coniferous-Picea engelmannii × P. glauca [14,15], Pinus sylvestris [68]-and deciduous species-rubber tree [53], Castanea dentata [28].…”

“…Drought in field conditions is hardly predictable, and even more so is its duration and intensity. In a study on rubber tree [53], plants were cultivated on sites with different water availability conditions, but such sites would differ not only in the amount of precipitation, but also in temperature, soil composition, and many other factors. Meanwhile, drought tolerance is becoming an increasingly valuable trait for forest trees due to ongoing global climate change.…”