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

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. 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

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

“…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]. GS was adopted in tree breeding in the last decade and different methods to estimate prediction efficiencies or accuracies of the cross-validated genomic predictions models have been implemented [2,86].…”

Genetic control of tracheid properties in Norway spruce wood

“…The locations of the annual rings were identified, as well as of their compartments of earlywood (EW), transitionwood (TW) and latewood (LW), using the “20–80 density” definition 45 , established for use in different types of studies 46 – 48 . Averages for all rings and their compartments were calculated for the traits and organised to be ready for use in continued genetic evaluations, such as the work on solid wood traits 10 , on tracheid traits 49 and for wood traits 22 , genomic selection 50 and influences of age and weather 51 . The traits addressed in the current work are listed in Table 1 .…”

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

“…Sequence capture genotyping may be a useful alternative to DNA chips in GS of forest trees. It was used for genotyping Picea abies [54][55][56] and Pinus radiata [67], and was the only method applied in all GS studies on Douglas-fir [19,63,72]. A comparison of two genotyping methods-sequence capture followed by next generation sequencing (NGS) versus EucHIP60K.br-showed their equivalence in terms of genomic prediction of the traits of interest in eucalyptus [44].…”

“…Unlike phenotypic mass selection based on an ancestral relationship matrix (matrix A), genomic prediction relies on a marker-based relationship matrix (matrix G), which provides a more accurate assessment of genetic similarity [56]. Generally, the studies showed the superiority of marker-based models over pedigree-based models.…”

“…The degree of relatedness in a training population affects the prediction accuracy and is an important factor in the relationships between training and test populations, as was demonstrated in a number of tree studies. In these studies, the progeny of full-and/or half-sibs within one generation was usually divided into training and test populations [56]. This improved the accuracy of GEBV prediction by increasing the likelihood of the same chromosome segments being present in both populations [111].…”