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22 páginas.- 4 figuras.- 4 tablas.- 111 referencias.- Supporting information :S1 Fig. Annual variation in average basal area increment (BAI) of the open-pollinated white spruce families for the period 1995–2016 at each of the three test sites. The red dashed line represents the year of the drought event and the green shadowed area represents the pre-drought period considered to calculate the Resistance index. S2 Fig. Density distribution for the studied traits in white spruce in each of the three test sites. Logarithmic transformations were applied to MFA and all monoterpene compounds to improve data normality. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S3 Fig. Pedigree and genomic relationships. Distribution of the number of pairwise additive relationships (excluding the diagonal elements) from the pedigree (after pedigree correction, left) and genomic (right) relationship matrices. Note that y-axis (Frequency) were cut at 40,000 (A-matrix, out of 2,343,490) and at 10,000 (G-matrix, out of 1,555,212) in order to more clearly visualize the differences between relationship matrices-matrix) and genomic-based (G-matrix) relationship matrices in each of the three white spruce sites. Abbreviations used for the sites are described in the Table 1. S5 Fig. Scatter plot between estimated genetic correlation between pairs of sites from the pedigree- (A-matrix) and genomic-based (G-matrix) relationship matrices in each of the 15 assessed traits in white spruce. Abbreviations used for the traits are described in the text. S1 Table. Estimated genetic correlations (and approximate standard errors) between the different traits from the multiple-trait analysis using the pedigree- (A-matrix, above diagonal) and genomic-based (G-matrix, below diagonal) relationship matrices for white spruce in each of the three test sites. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S2 Table. Estimated genetic correlations (and approximate standard errors) between the different sites from the multiple-site analysis using the pedigree- (A-matrix, above diagonal) and genomic-based (G-matrix, below diagonal) relationship matrices for white spruce in each of the three test sites. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S1 Text. Chemical analysis., Tree improvement programs often focus on improving productivity-related traits; however, under present climate change scenarios, climate change-related (adaptive) traits should also be incorporated into such programs. Therefore, quantifying the genetic variation and correlations among productivity and adaptability traits, and the importance of genotype by environment interactions, including defense compounds involved in biotic and abiotic resistance, is essential for selecting parents for the production of resilient and sustainable forests. Here, we estimated quantitative genetic parameters for 15 growth, wood quality, drought resilience, and monoterpene traits for Picea glauca (Moench) Voss (white spruce). We sampled 1,540 trees from three open-pollinated progeny trials, genotyped with 467,224 SNP markers using genotyping-by-sequencing (GBS). We used the pedigree and SNP information to calculate, respectively, the average numerator and genomic relationship matrices, and univariate and multivariate individual-tree models to obtain estimates of (co)variance components. With few site-specific exceptions, all traits examined were under genetic control. Overall, higher heritability estimates were derived from the genomic- than their counterpart pedigree-based relationship matrix. Selection for height, generally, improved diameter and water use efficiency, but decreased wood density, microfibril angle, and drought resistance. Genome-based correlations between traits reaffirmed the pedigree-based correlations for most trait pairs. High and positive genetic correlations between sites were observed (average 0.68), except for those pairs involving the highest elevation, warmer, and moister site, specifically for growth and microfibril angle. These results illustrate the advantage of using genomic information jointly with productivity and adaptability traits, and defense compounds to enhance tree breeding selection for changing climate., This work was funded by Genome Canada (https://www.genomecanada.ca/) RESFOR ID 10207, grants 16R75036 to YAE, RES0034654 to NE, and RES0031330 to BRT; Genome Alberta (https://genomealberta.ca/) RESFOR ID: LRF, grants RES0034664 to NE, 16R10106 to SDM, and RES0034657 to BRT; University of Alberta / Faculty ALES / Dept RR (https://www.ualberta.ca/index.html) grant RES0034569 to BRT; Alberta Innovates –BioSolutions (https://albertainnovates.ca/) grants RES0035327 to NE, 16R75221 to SDM, and RES0028979 to BRT; Genome BC (https://www. genomebc.ca/) grants 16R75421 to YAE and 16R75546 to SDM; Forest Resource Improvement Association of Alberta (FRIAA, https://friaa.ab.ca/) grants RES0037021 and RES0036845 to BRT; National Science Foundation (NSF, https://www.nsf.gov/) grants MRI-1531128, ACI-1548562, and ACI-1445606 to CC; The Extreme Science and Engineering Discovery (XSEDE, https://xras.xsede. org/public/requests/29304-XSEDE-MCB180177) grant MCB180177 to CC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip