#Norway spruce tree seedlings trial#

Without clonally replicated experiments, however, it is difficult to explain how total genetic variance is distributed among the three components and make a sound judgment in optimal deployment strategy.

Currently, many tree species based on genomic data has discovered significant epistatic genetic variance based on the interaction between QTL pairs, such as additive × additive, additive × dominance, and dominance × dominance interaction effects (Muñoz et al. In contrast, another model proposed by Wu ( 1996) was based on the assumption that epistasis for a quantitative trait is limited to interactions between pairs of quantitative trait loci (QTL) only. The model assumes that the epistatic genetic effect is mainly from numerous high-order interactions between loci. First, Foster and Shaw ( 1988) estimated the epistatic genetic variance from a clonally replicated experiment using the expected covariance among relatives. Traditionally, two methods have been used to partition the genetic variance in forestry without using inbreeding methods.

#Norway spruce tree seedlings trial#

The main reasons are the lack of well-designed experiments with a full-sib family structure in a clonally replicated trial and limited number of families and clones within a family tested. In forestry, however, quantitative geneticists have commonly ignored the interaction between alleles (dominance) at a locus and the interactions alleles between loci (epistasis) in the analysis of progeny trials (Foster and Shaw 1988). The total genetic variance in a biological trait can be partitioned into additive, dominance, and epistatic variances (Falconer and Mackay 1996). Clonal mean selection and vegetative deployment are the most effective. This study highlights that testing of 30–40 clones per family would maximize the realized genetic gain for different clonal selection scenarios, either selecting the best ten or 20 clones without any co-ancestry restrictions or selecting the best single clone from each of the best ten or 20 families (e.g., co-ancestry restriction). Selecting the top 1% of tested clones (clonal forestry) would have 48.4% and 134.6% more gain than the gain predicted for the seedling deployment of selected full-sib families and half-sib family (family forestry), respectively, at the same selection intensity. The genetic gain predicted for clonal deployment was greater than any of the other four deployment strategies. ResultsĪdditive and non-additive genetic variances were equally important for growth traits while non-additive variance was small or not significant for wood quality trait. Wood quality and growth traits were measured in all clonal trials and additive and non-additive genetic variances are partitioned.

Aimsĭevelop the optimal clonal deployment strategy of Norway spruce in Sweden.

Genetic parameters and genetic gains for wood quality and growth traits were estimated in six large clonal progeny trials. Karst) and clonal deployment will have at least 50% more or double genetic gain than the seedling deployment. There is considerable genetic gain of tree volume from clonal deployment in Norway spruce ( Picea abies (L.) H.