Norway spruce is one of the most important economical important forest species in northern Europe. There is a long tradition of genetic improvement of this species in Sweden, Finland and Norway, with the selection of trees for management and propagation (plus-trees) starting since the 1940s. Since the selection of this material from the natural populations, improvement of the stock of this material has consisted in testing the offspring of managed crosses in different environments. After these assessments, the most suitable individuals (e.g faster growth rate, more resistant to frost damage and better wood quality) are used to establish the next cycle of propagation. This strategy has been fruitful, and it is expected that the gain of the harvested wood will increase. However, this strategy is time consuming (25 years) and requires considerable economic investment to maintain the testing sites. The idea of this project is to improve this strategy of breeding Norway spruce, by taking advantage of the most recent information that is currently available from the genome (DNA sequences) of this species. We plan to use two technological advances to 1) assess how much of variation (how healthy is the bred material) in respect to the natural population where it originally came from, and 2) to accelerate the step of selection, by using a predictive strategy that forecast the future performance of a tree but based only on their genomic information (thus reducing the time for testing). These two implementations become more important as the breeding and management of this species advance in future generations. We need to improve the strategy to select the most competitive individuals to increase economic gain, but at the same time we need to avoid making the stock of trees used in forestry susceptible negative effects associated with a reduction of genetic diversity.
In the reporting period 2021 the following milestones have been reached:
a) Curated genotypes with the 50K SNP array. We obtained the genotypes of 2,536 samples (including plus-trees + offspring) with a total of 42,529 single nucleotide polymorphisms (SNPs) of high quality (after filtering for quality). This set of curated genotypes is ready to use in combination with the phenotypic data in the development of the predictive model for these to breeding populations. This curated matrix of genotypes is being currently incorporated into the PFLOR data base of Skogfrøverket for its future implementation in the predictive model
b) Curated genotypes with the exome capture. We obtained the genotypes of 366 individuals (representing the natural range from Fennoscandia, plus-trees, and highly selected clones from the breeding program in Norway). The curated genotypes (vcf file with SNPs) consist of 1,485,801 SNPs, of which 327,827 are of high quality (after filtering from quality and missing data per sample). This data set will be used to determine the levels of genetic diversity maintained in the breeding population and how much is lost as three different levels of selection are used in this breeding population. We are currently processing the data and we expect to have the first estimates of the level of diversity at the end of 2021.
The project is in line with the project plan for the first milestone, but we expect to complete the other milestone (Validated predictive model for GS) during 2022.
Breeding of conifer trees posses several challenges associated with their particular biology. These species posses long life cycles and thus investments are made several years before the eventual utilization of genetically improved material. Thus, this is susceptible to changes in the economic objectives of the products, market demands, management policies and ecological conditions. This is the particular case of breeding of Picea abies in Norway and other northern countries. Currently, there are emerging technologies to aid breeding programs of conifer trees with a variety of new genotyping tools and genomic information. Although genomic selection (GS) has been implemented on some domesticated animals and crops, this has not been operationally implemented on conifer trees. One of the reasons is that there is still a need to develop new knowledge on how these technologies could be implemented. In addition, the specific conditions of breeding of P. abies in Norway need further research. Thus, although there are nowadays available technology and analytical methods to implement GS and metrics of quality (genetic diversity) into the breeding programs of trees, specific R&D is required for their further operational implementation. This proposal is therefore focused on addressing the specific challenges for implementing GS and genetic diversity assessment into the breeding cycle of P.abies under the Norwegian conditions. Therefore, the overall goal of this proposal is to implement two new innovative steps into the breeding cycle of Picea abies with the purpose of reducing the cost of producing improved genetic material, as well as certifying the seedlots obtained. These two new implementations will increase the competitiveness of Skogfrøverket by providing improved at a lower cost and the necessary information to manage a sustainable genetic diversity on the breeding program of this species