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Abstract
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 [ver mas...]
dc.contributor.authorCappa, Eduardo Pablo
dc.contributor.authorKlutsch, Jennifer G.
dc.contributor.authorSebastian-Azcona, Jaime
dc.contributor.authorRatchiffe, Blaise
dc.contributor.authorXiaojing, Wei
dc.contributor.authorDa Ros, Letitia
dc.contributor.authorYang, Liu
dc.contributor.authorChen, Charles
dc.contributor.authorBenowicz, Andy
dc.contributor.authorSadoway, Shane
dc.contributor.authorMansfield, Shawn D.
dc.contributor.authorErbilgin, Nadir
dc.contributor.authorThomas, Barb R.
dc.contributor.authorEl-Kassaby, Yousry A.
dc.date.accessioned2022-04-29T16:31:59Z
dc.date.available2022-04-29T16:31:59Z
dc.date.issued2022-03
dc.identifier.issn1932-6203
dc.identifier.otherhttps://doi.org/10.1371/journal.pone.0264549
dc.identifier.urihttp://hdl.handle.net/20.500.12123/11776
dc.identifier.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0264549
dc.description.abstractTree 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.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherPlos ONEes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.sourcePLoS ONE 17 (3) : e0264549. (March 2022)es_AR
dc.subjectGenómicaes_AR
dc.subjectGenomicseng
dc.subjectFitomejoramientoes_AR
dc.subjectPlant Breedingeng
dc.subjectPicea glaucaes_AR
dc.subjectClimaes_AR
dc.subjectClimateeng
dc.subjectCambio Climáticoes_AR
dc.subjectClimate Changeeng
dc.subjectProductividades_AR
dc.subjectProductivityeng
dc.subject.otherAbeto Blancoes_AR
dc.subject.otherWhite Spruceeng
dc.titleIntegrating genomic information and productivity and climate-adaptability traits into a regional white spruce breeding programes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenInstituto de Recursos Biológicoses_AR
dc.description.filFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentinaes_AR
dc.description.filFil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Klutsch, Jenifer G. University of Alberta; Department of Renewable Resources; Canadaes_AR
dc.description.filFil: Sebastian-Azcona, Jaime. University of Alberta; Department of Renewable Resources; Canadaes_AR
dc.description.filFil: Ratchiffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadáes_AR
dc.description.filFil: Xiaojing, Wei. University of Alberta; Department of Renewable Resources; Canadaes_AR
dc.description.filFil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadáes_AR
dc.description.filFil: Yang, Liu. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadáes_AR
dc.description.filFil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidoses_AR
dc.description.filFil: Benowicz, Andy. Alberta Agriculture and Forestry. Forest Stewardship and Trade Branch; Canadáes_AR
dc.description.filFil: Sadoway, Shane. Blue Ridge Lumber Inc.; Canadáes_AR
dc.description.filFil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadáes_AR
dc.description.filFil: Erbilgin, Nadir. University of Alberta; Department of Renewable Resources; Canadaes_AR
dc.description.filFil: Thomas, Barb R. University of Alberta; Department of Renewable Resources; Canadaes_AR
dc.description.filFil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadáes_AR
dc.subtypecientifico


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