Tree improvement programs are long-term and resource-demanding endeavors consisting of repeated cycles of breeding, testing, and selection and suffer from protracted testing phases. Phenotypic selection is commonly practiced and often requires trees reaching certain age and/or size resulting in slow accumulation of genetic gain. Open-pollinated (OP) family testing is the simplest and most economical means for screening, evaluating, and ranking large number of candidate parent trees but suffers from inflated additive genetic variance and heritability estimates. This dissertation investigates genomic selection (GS) and its applicability to forestry in selection and progeny testing evaluation. To address these two applications, I studied yield and wood traits from two white spruce populations, genotyped using Genotyping-by-Sequencing and SNPs array. I investigated the applicability of GS using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR)) algorithms and validated the derived predictive models in space across three progeny testing sites in interior British Columbia. Moreover, using principal component analysis (PCA), I fitted a multi-traits GS predictive model to address the inter-correlation among the studied attributes. Additionally, the Genomic Best Linear Unbiased Predictor (GBLUP) was used in genetic variance decomposition framework to unravel additive from non-additive genetic variances and I compared the results to that from the traditional pedigree-based (ABLUP) analysis. Differences between the RR-BLUP and GRR predictive models’ accuracies were observed indicating that the studied attributes’ genetic architecture is complex. Validating the GS’s predictive models in space clearly confirmed multi- to single-site superiority as they account for the genotype x environment interaction, commonly observed in forestry evaluation trials. When PCA scores used as multi-trait representatives, GS prediction models produced surprising results where the concurrent selection of negatively correlated traits such as wood density and growth is possible. The genetic variance decomposition indicated that the genomic-based approach outperformed that of the pedigree-based with the successfully separation of additive from non-additive genetic effects. This approach was demonstrated in a single- and extended to multi-site scenario, propelling OP testing to the forefront of forest trees genetic evaluation. In general, the effectiveness of GS was clearly demonstrated as an alternative selection and evaluation method.