Genomic predictions of breeding values in a cloned Eucalyptus globulus population in Chile

Durán, Ricardo; Isik, Fikret; Zapata-Valenzuela, Jaime; Balocchi, Claudio; Valenzuela, Sofía

doi:10.1007/s11295-017-1158-4

Cited by 37 publications

(26 citation statements)

References 63 publications

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“…In the past 2 years, a number of additional experimental GS studies have been reported (Cappa et al, 2017 , 2018 ; Duran et al, 2017 ; Lenz et al, 2017 ; Müller et al, 2017 ; Ratcliffe et al, 2017 ; Tan et al, 2017 , 2018 ; Thistlethwaite et al, 2017 ) (Table 1 ; Resende et al, 2017b ; Chen et al, 2018 ; De Moraes et al, 2018 ; El-Dien et al, 2018 ; Kainer et al, 2018 ; Suontama et al, 2018 ). Many of them in species of Eucalyptus for which public high-throughput genotyping platforms of DArT (Sansaloni et al, 2010 ) and SNPs (Silva-Junior et al, 2015 ) have been available.…”

Section: Gs: Advances and Challenges In Forest Treesmentioning

confidence: 99%

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

et al. 2018

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Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

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Section: Gs: Advances and Challenges In Forest Treesmentioning

confidence: 99%

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

et al. 2018

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“…The development of several genotyping platforms through high-density single nucleotide polymorphism (SNP) arrays, such as genotyping-by-sequencing (GBS) or SNP chips, has enabled the identification of quantitative trait loci (QTL) for different target traits in various plant species [3][4][5][6]. Silva-Junior et al [7], for instance, developed a genome-wide SNP chip for multiple species of Eucalyptus, which has been effective for genomic studies in a wide variety of economically important eucalypt species and their hybrids, including Eucalyptus grandis, Eucalyptus urophylla, Eucalyptus nitens and Eucalyptus globulus [8][9][10][11][12]. However, despite the versatility of this SNP array, it does not perform as well in terms of genome coverage or number of available SNPs for species which are more-distantly related to those for which the chip was developed [13].…”

Section: Introductionmentioning

confidence: 99%

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

Ballesta

Bush

Silva

et al. 2020

Plants

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High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available.

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“…Due to its advantages over traditional methods, GS is becoming increasingly prevalent in plant and tree breeding. Some recent examples include White spruce ( Beaulieu et al 2014 ; Ratcliffe et al 2017 ), Eucalyptus ( M. D. V. Resende et al 2012 ; Durán et al 2017 ; Tan et al 2017 ), and Loblolly pine ( M. F. R. Resende et al 2012 ; Zapata-Valenzuela et al 2013 ). For traits such as wood density, height, diameter and growth, GS has consistently matched or exceeded the accuracy and gain from pedigree BLUP.…”

mentioning

confidence: 99%

Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea

Kainer

Stone

Padovan

et al. 2018

G3 Genes|Genomes|Genetics

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Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in Eucalyptus polybractea, a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration (0.59 and 0.73, respectively), than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height (0.25 and 0.19, respectively). Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species.

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Genomic predictions of breeding values in a cloned Eucalyptus globulus population in Chile

Cited by 37 publications

References 63 publications

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea

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