Genetic analysis of wood quality traits in Norway spruce open-pollinated progenies and their parent plus trees at clonal archives and the evaluation of phenotypic selection of plus trees

Zhou, Linghua; Chen, Zhiqiang; Lundqvist, Sven‐Olof; Olsson, Lars E.; Grahn, Thomas; Karlsson, Bo; Wu, Harry X.; García‐Gil, Maria Rosario

doi:10.1139/cjfr-2018-0117

Cited by 8 publications

(10 citation statements)

References 54 publications

(62 reference statements)

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“…Two-way analysis of variance (F test) was used to test the significance of growth differences in clones for all phenotypic traits. The clonal repeatability was estimated following [40] to determine the proportion of the total phenotypic variance that was due to the genotypes of clones by…”

Section: Discussionmentioning

confidence: 99%

“…Two-way analysis of variance (F test) was used to test the significance of growth differences in clones for all phenotypic traits. The clonal repeatability was estimated following [ 40 ] to determine the proportion of the total phenotypic variance that was due to the genotypes of clones by

. Using clonal variances, the broad-sense genetic correlation was computed according to Liang et al [ 9 ] by dividing trait covariance values by the square root of the sum of trait variances.…”

Section: Methodsmentioning

confidence: 99%

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Phenotypic Variability and Genetic Diversity in a Pinus koraiensis Clonal Trial in Northeastern China

Kaviriri

Zhang

et al. 2020

Genes

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Combining phenotypic and genetic characteristics in a genetic variation study is of paramount importance to effectively orient the selection of producers’ elite trees in a seed orchard. In total, 28 phenotypic characteristics and 16 microsatellite loci were used to analyze the clonal genetic variation, to characterize the genetic diversity, and to refine the genetic classifications of 110 Pinus koraiensis clones grown in the Naozhi orchard in northeastern China. All clones were significantly different in most traits. Most of the phenotypic characteristics showed great genetic variation among clones, while the genotypic differentiation was weak between the selection sites of clones. The SSR markers showed a relatively high level of genetic diversity (Na = 4.67 ± 0.43, Ne = 2.916 ± 0.18, I = 1.15 ± 0.07, Ho = 0.69 ± 0.04, He = 0.62 ± 0.02, and mean polymorphic information content (PIC) of 0.574), with higher heterozygosity as an indication of a lower probability of inbreeding events in the orchard. Despite weak correlation coefficients between dissimilarity matrices (r(A/B), range equal to 0.022, p-value < 0.001), the genetic and phenotypic classifications congruently subdivided all the clones into three major groups. The patterns of phenotypic trait variations and genetic diversity are valuable to effectively select materials in breeding programs of P. koraiensis.

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Section: Discussionmentioning

confidence: 99%

“…Two-way analysis of variance (F test) was used to test the significance of growth differences in clones for all phenotypic traits. The clonal repeatability was estimated following [ 40 ] to determine the proportion of the total phenotypic variance that was due to the genotypes of clones by

. Using clonal variances, the broad-sense genetic correlation was computed according to Liang et al [ 9 ] by dividing trait covariance values by the square root of the sum of trait variances.…”

Section: Methodsmentioning

confidence: 99%

Phenotypic Variability and Genetic Diversity in a Pinus koraiensis Clonal Trial in Northeastern China

Kaviriri

Zhang

et al. 2020

Genes

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“…The phenotypic data was previously described in Zhou et al, 2019 [38]. Increment cores of 12 mm diameter from pith to bark were collected from the progenies in 2011 and in 2012.…”

Section: Phenotypic Datamentioning

confidence: 99%

Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce

et al. 2020

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Background: Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle, but the efforts to develop routines for operational breeding are so far limited. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce (Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. Results: GS predictive abilities were comparable with those based on pedigree-based prediction. Marker-based PAs were generally 25-30% higher for traits density, MFA and MOE measured with SilviScan than for their respective standing tree-based method which measured with Pilodyn and Hitman. Prediction accuracy (PC) of the standing tree-based methods were similar or even higher than increment core-based method. 78-95% of the maximal PAs of density, MFA and MOE obtained from coring to the pith at high age were reached by using data possible to obtain by drilling 3-5 rings towards the pith at tree age 10-12. Conclusions: This study indicates standing tree-based measurements is a cost-effective alternative method for GS. PA of GS methods were comparable with those pedigree-based prediction. The highest PAs were reached with at least 80-90% of the dataset used as training set. Selection for trait density could be conducted at an earlier age than for MFA and MOE. Operational breeding can also be optimized by training the model at an earlier age or using 3 to 5 outermost rings at tree age 10 to 12 years, thereby shortening the cycle and reducing the impact on the tree. Background Norway spruce is one of the most important conifer species in Europe in relation to economic and ecological aspects [1]. Breeding of Norway spruce started in the 1940s with phenotypic selection of plus-trees, first in natural populations and later in even-aged plantations [2]. Norway spruce breeding cycle is approximately 25-30 years long, of which the production of seeds and the evaluation of the trees take roughly one-half of that time [3]. Genomic prediction using genome-wide dense markers or genomic selection (GS) was first introduced by Meuwissen [4]. The method modelling the effect of large numbers of DNA markers covering the entire genome and subsequently predict the genomic value of individuals that have been genotyped, but not phenotyped. As compared to the phenotypic mass selection based on a pedigree-based relationship matrix (A matrix), genomic prediction relies on constructing a marker-based relationship matrix (G matrix). The superiority of the G

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“…The phenotypic data was previously described in 32 . The samples collected from the progenies in 2011 and in 2012 were taken as increment cores of 12 mm diameter from pith to bark.…”

Section: Phenotypic Datamentioning

confidence: 99%

The effect of tree (and cambium) age on genomic prediction for solid wood properties in Norway spruce

Zhou¹,

Chen²,

Olsson

et al. 2020

Preprint

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Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce ( Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. GS predictive abilities (PA) were comparable with those based on pedigree-based selection. The highest PAs were reached with at least 80-90% of the dataset used as training set. Use of different statistical methods had no significant impact on the estimated PAs. We also compared the abilities to predict density, MFA and MOE of 19 year old trees with use of models trained on data from coring at different ages and to different depths into the stem. The comparison indicated that close to the maximal PAs can be reached at age 10-12 by drilling only half way (ringwise) towards the pith, thereby reducing the impact on the tree.

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Genetic analysis of wood quality traits in Norway spruce open-pollinated progenies and their parent plus trees at clonal archives and the evaluation of phenotypic selection of plus trees

Cited by 8 publications

References 54 publications

Phenotypic Variability and Genetic Diversity in a Pinus koraiensis Clonal Trial in Northeastern China

Phenotypic Variability and Genetic Diversity in a Pinus koraiensis Clonal Trial in Northeastern China

Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce

The effect of tree (and cambium) age on genomic prediction for solid wood properties in Norway spruce

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