SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Ballesta, Paulina; Maldonado, Carlos; Pérez‐Rodríguez, Paulino; Mora, Freddy

doi:10.3390/plants8090331

Cited by 36 publications

(35 citation statements)

References 83 publications

(126 reference statements)

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“…Our estimated prediction accuracies for growth traits of S. platyclados were similar to those obtained for E. benthamii (DBH 0.153-0.162, height 0.019-0.025) and C. japonica (DBH 0.033-0.209, height 0.026-0.114) populations in the North and South Kanto region of Japan. However, it was slightly lower than those reported on E. pellita (DBH 0.427-0.438, height 0.339-0.341), C. japonica (DBH 0.505, height 0.432) populations in the Kyushu region of Japan, Pinus taeda (DBH 0.46, for height 0.38) and E. globulus (DBH 0.17-0.45, height 0.21-0.44) [22,23,33,79]. We obtained similar prediction accuracies for wood density to reported values for loblolly pine (0.112-0.226 and 0.20-0.23, respectively), but much lower accuracy for wood stiffness (0.075-0.155 and 0.39-0.42, respectively) [23].…”

Section: Genomic Prediction Accuracies Of Bayesian and Machine Learnimentioning

confidence: 55%

“…However, too few markers may have been used in the cited studies (384 and 288 SNPs, respectively) to obtain accurate LD estimates. Recent studies have detected more extensive LD, e.g., up to 10-12 kb in E. globulus [79], 16-34 kb in poplar (P. trichocarpa) [80] and 65-110 kb in C. japonica [81]. A high proportion of SNPs in non-coding regions of the genome may also mask the true extent of LD, as they provide lower estimates of recombination rates between loci than SNPs in coding regions [81].…”

Section: Detection Of Significant Markers By Gwasmentioning

confidence: 99%

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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Sawitri

Tani

Na’iem

et al. 2020

Forests

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Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.

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Section: Genomic Prediction Accuracies Of Bayesian and Machine Learnimentioning

confidence: 55%

Section: Detection Of Significant Markers By Gwasmentioning

confidence: 99%

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Sawitri

Tani

Na’iem

et al. 2020

Forests

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“…The execution of GS in animal and crop breeding programs, such as dairy cattle, oat, maize and wheat, increased genetic gains [44,73]. Implementation of GS in tree breeding is underway with recent publications in eucalypts [61,[74][75][76][77], white spruce [78][79][80], black spruce (Picea mariana [Mill.] BSP) [60], interior spruce [39,70], Norway spruce [68,81,82], loblolly pine [58,83,84], lodgepole pine (Pinus contorta Douglas) [85] and maritime pine [66,67].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

et al. 2020

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Background Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. Conclusions Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.

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“…Several studies have explored the potential of the selection based on genomic tools in forest species [33][34][35][36], including E. cladocalyx [12,37,38]. According to the results, GSq models outperformed traditional GS models in terms of predictive ability when at least ten significant marker-trait associations were included in GSq.…”

Section: Comparison Between Genomic Prediction Modelsmentioning

confidence: 99%

“…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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SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Cited by 36 publications

References 83 publications

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

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

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