Student: Adam Klingberg
The material produced by the current breeding program outperforms unimproved material by 25 % (Mullin and Lee 2013). Through the establishment of advanced seed orchards, this gain is subsequently deployed to the forest through improved seed and seedlings, produced from grafted seed orchards that requires 10-15 years for seed production. Tree breeding is a cyclic process involving the recruitment of new breeding populations by selecting the best trees from progeny trials, conducting mating among the elite trees, and implementing new progeny tests. Today, operational tree breeding relies on field testing and quantitative genetic theory; the selection of the best trees is based on breeding values calculated from phenotypic characteristics and data observed in genetic field trials. Phenotypes are usually measured after trees reach an informative age (6-15 years for conifers). In advanced tree-breeding programs, with phenotypic data from several generations, multiple sites, and multiple traits, sophisticated statistical methods such as Best Linear Unbiased Prediction (BLUP) are utilized for breeding value prediction with increased accuracy (Gilmour et al. 2015). Currently, each new breeding cycle (generation) increases the productivity by about 10 % and the time for completing a breeding cycle is 25 years. Incorporating genomic information as part of the genetic evaluation has the potential to increase genetic gain through the greater accuracy of genomic relationship assessment compared with conventional pedigree, and by shortening the time to complete a breeding cycle and thus increasing genetic gain per unit time.
The material produced by the current breeding program outperforms unimproved material by 25 % (Mullin and Lee 2013). Through the establishment of advanced seed orchards, this gain is subsequently deployed to the forest through improved seed and seedlings, produced from grafted seed orchards that requires 10-15 years for seed production. Tree breeding is a cyclic process involving the recruitment of new breeding populations by selecting the best trees from progeny trials, conducting mating among the elite trees, and implementing new progeny tests. Today, operational tree breeding relies on field testing and quantitative genetic theory; the selection of the best trees is based on breeding values calculated from phenotypic characteristics and data observed in genetic field trials. Phenotypes are usually measured after trees reach an informative age (6-15 years for conifers). In advanced tree-breeding programs, with phenotypic data from several generations, multiple sites, and multiple traits, sophisticated statistical methods such as Best Linear Unbiased Prediction (BLUP) are utilized for breeding value prediction with increased accuracy (Gilmour et al. 2015). Currently, each new breeding cycle (generation) increases the productivity by about 10 % and the time for completing a breeding cycle is 25 years. Incorporating genomic information as part of the genetic evaluation has the potential to increase genetic gain through the greater accuracy of genomic relationship assessment compared with conventional pedigree, and by shortening the time to complete a breeding cycle and thus increasing genetic gain per unit time.
The tree milestones include: (1) Apply new statistical and computation science to increase accuracy of breeding value prediction, heritability, and genetic gain in breeding programs. (2) Develop a novel tree genetic evaluation method by integrating genomic and phenotypic data in a single-step approach. And (3) Apply genomic selection for clonal forestry in Norway spruce.
Main supervisor: Harry Wu (SLU, Umeå)
Co supervisors/Industrial partner: Mari Suontama (Skogforsk), Torgny Persson (Skogforsk, Henrik Hallingbäck (Skogforsk)
Main supervisor: Harry Wu (SLU, Umeå)
Co supervisors/Industrial partner: Mari Suontama (Skogforsk), Torgny Persson (Skogforsk, Henrik Hallingbäck (Skogforsk)