Improving floral initiation in potted &lt;i&gt;Picea abies&lt;/i&gt; by supplemental light treatment

Almqvist, Curt

doi:10.14214/sf.7772

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…In our study, the nature of epistatic variation was only type additive × additive for all traits which can be an indication of more information required to discern between the epistatic components. Epistatic variance falling into one component only has been observed earlier as well (Munoz et al, 2014;El-Dien et al, 2016, 2018.…”

Section: Variance Components and Heritabilitymentioning

confidence: 63%

“…Congruent with those studies the same pattern was noticed, and a higher response was observed for GS models compared with traditional phenotypic selection, increasing proportionally to the ratio of individuals selected reaching the maximum when the best 50 individuals were selected. The reduction of 50% in the breeding cycle length assumed for Scots pine in the present study will only be possible by combining it with greenhouse flowering stimulation that will aid to produce female flowers at earlier ages (Almqvist, 2018; -Rodríguez et al, 2020), than the current flowering age for the species between 15 and 18 years of age (Matyas et al, 2004). The GBLUP epistatic model outperformed the PBLUP and GBLUP additive and dominance models, in genomic response estimations for almost all traits.…”

Section: Expected Response Of Gs and Genetic Gainsmentioning

confidence: 99%

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Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

et al. 2021

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Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

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Section: Variance Components and Heritabilitymentioning

confidence: 63%

Section: Expected Response Of Gs and Genetic Gainsmentioning

confidence: 99%

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

et al. 2021

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“…The first approach assumed that the cycle could be reduced to 18 years, shortening the progeny test time but considering that female flowering starts at 15–18 years in Scots pine [ 24 ]. The second approach presupposed that earlier flowering greenhouse stimulation [ 72 ] would produce female flowering at an earlier age in Scots pine, thus the breeding cycle could be reduced to about 11 years. In addition and for both approaches a reduction in the progeny test time was also assumed.…”

Section: Resultsmentioning

confidence: 99%

“…The results of the current study exhibited that a reduction of the cycle length by 50% increased the percentage GS efficiency to double for almost all traits, regardless the selection strategy (Table 3 ). Such reduction in the breeding cycle length of Scots pine could only be possible by shortening field-testing periods aided by the use genomic prediction at young ages, and that female flowering can start at earlier ages after greenhouse flowering stimulation [ 72 ]. Moreover, if cycles could even be shortened more than 50%, higher percentages of increase in GS efficiency could be reached which in the case of this study were almost triple than traditional pedigree-based selection (Table 3 ).…”

Section: Discussionmentioning

confidence: 99%

“…In order to estimate R S R G S : P S , two GS approaches were assumed to reduce the breeding cycle, by shortening the period of field progeny testing needed for phenotypic evaluations. In addition, for the first approach the cycle length was reduced to 18 years considering that female flowering starts at 15–18 years in Scots pine [ 24 ], whereas the second approach assumed that flowering greenhouse stimulation [ 72 ] would produce female flowering at around 11 years.…”

Section: Methodsmentioning

confidence: 99%

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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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Genomic Selection in Scots (Pinus Sylvestris) and Radiata (Pinus Radiata) Pines

Calleja-Rodriguez

Klápště

Dungey

et al. 2022

Compendium of Plant Genomes

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Improving floral initiation in potted <i>Picea abies</i> by supplemental light treatment

Cited by 4 publications

References 9 publications

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

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

Genomic Selection in Scots (Pinus Sylvestris) and Radiata (Pinus Radiata) Pines

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