Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce. Zhou, L., Chen, Z., Olsson, L., Grahn, T., Karlsson, B., Wu, H. X., Lundqvist, S., & García-Gil, M. R. BMC Genomics, 21(1):323, December, 2020.
Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce [link]Paper  doi  abstract   bibtex   
Abstract 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.
@article{zhou_effect_2020,
	title = {Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of {Norway} spruce},
	volume = {21},
	issn = {1471-2164},
	url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6737-3},
	doi = {10.1186/s12864-020-6737-3},
	abstract = {Abstract
            
              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.},
	language = {en},
	number = {1},
	urldate = {2021-06-07},
	journal = {BMC Genomics},
	author = {Zhou, Linghua and Chen, Zhiqiang and Olsson, Lars and Grahn, Thomas and Karlsson, Bo and Wu, Harry X. and Lundqvist, Sven-Olof and García-Gil, María Rosario},
	month = dec,
	year = {2020},
	pages = {323},
}

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