Genomic selection in Douglas-fir

Conventional tree breeding productivity (especially in conifers) is primarily constrained by late expression of commercially important traits, and late onset of sexual maturity. These characteristics conjointly correspond to lengthy testing phases prior to selection, which in turn restrains the accumulation of genetic gain. GS has the potential to increase genetic gain per unit time by allowing for the prediction and selection of traits at an earlier age. This dissertation investigates some facets of GS, specifically in relation to Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)), and ‘real-world’ applications. Expressly: to compare pedigree based ABLUP and two GS methods; assess GS prediction accuracy over spatial and temporal deviations; validate the use of exome capture as a cost-effective genotyping platform for use in GS; use of GS to predict breeding values in the next generation; investigate the effect of relatedness on GS prediction accuracy; and to assess the number of makers required for GS in conifers. Chapter 2 utilizes exome capture as a genotyping platform to assess height and wood density GS prediction accuracies across space and time. A cross-generational GS analysis was performed in Chapter 3 using a progeny generation as an independent validation set. Chapter 4 investigates the effect of marker density on GS predictive accuracy in Douglas-fir and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). The overriding conclusion, is that while some of the GS models’ prediction accuracies were high, the main driving force was the tracking of relatedness rather than LD. However with regard to the ability of the available markers to track pedigree, exome capture was found to be very competent. Knowing this, the following trends were observed: GS models performed similarly and were comparable to ABLUP; genotype x environment interactions are an important consideration for GS spatial analyses; height at 12 years was deemed an acceptable age at which accurate predictions can be made concerning future height and wood density; moderate to high cross-generational GS prediction accuracies were obtained, but were influenced by the relationship between training and validation sets; and increasing marker number increases GS prediction accuracy, for Douglas-fir and Interior spruce.Forestry, Faculty ofForest and Conservation Sciences, Department ofGraduat