Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production

Cros, David; Mbo-Nkoulou, Luther; Bell, Joseph Martin; Oum, Jean; Masson, Aurélien; Soumahoro, Mouman; Tran, Dinh Minh; Achour, Zeineb; Guen, Vincent Le; Clément-Demange, André

doi:10.1016/j.indcrop.2019.111464

Cited by 38 publications

(71 citation statements)

References 55 publications

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“…Resende et al (2012a, 2012b) demonstrated encouraging prospects of this new method, and the potential for GS in conifers, pines, and eucalypts has since been confirmed (Zapata-Valenzuela et al, 2013; Lima, 2014; El-Dien et al, 2015; Ratcliffe et al, 2015; Bartholome et al, 2016; Isik et al, 2016), supporting further the potential for GS to accelerate the breeding of forest trees. In the case of rubber trees, a recent study explored GS in a breeding program (Cros et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…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).…”

Section: Predictive Breeding Promises Boosting Forest Tree Genetic Immentioning

confidence: 81%

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

2020

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Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

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“…GP may offer a more thoughtful picture of complex traits (e.g., thermal adaptation), presumably regulated by many low-effect loci ( Pritchard et al, 2010 ). GP has so far informed predictions of single adaptive traits in populations with known pedigrees ( Saint Pierre et al, 2012 ; Cros et al, 2019 ) and hybrid origins ( Technow et al, 2014 ; Tan et al, 2017 ), as well as multi-trait inferences across diverse unrelated populations ( Crossa et al, 2007 , 2016 ; Resende et al, 2012 ; Suontama et al, 2019 ) under genotype by environment interactions (GxE) ( Montesinos-Lopez et al, 2018 ; Crossa et al, 2019 ) facing polygenic climate adaptation ( Isabel et al, 2020 ). GP of thermal adaptive traits across multiple generations and populations may be incipient ( Table 1 ), yet it harbors a promising potential, as was demonstrated by reversely predicting unobserved thermal phenology in 1900-year-old ancient corn ( Swarts et al, 2017 ), and as we prospect in the last section of this mini-review.…”

Section: From Deeper Genomic Signatures Of Selection To Mid-term Predmentioning

confidence: 99%

Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past

2020

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Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations’ responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder’s equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.

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Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production

Cited by 38 publications

References 55 publications

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

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

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

Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past

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