Multi‐trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce

Autor: Martin Perron, Jean Beaulieu, Jean Bousquet, Simon Nadeau, Patrick Lenz, Marie-Josée Mottet, Nathalie Isabel
Přispěvatelé: Canadian Wood Fibre Centre, Natural Resources Canada (NRCan), Université Laval [Québec] (ULaval), Direction de la recherche forestière, Ministère des Ressources naturelles du Québec, Laurentian Forestry Centre
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Evolutionary Applications, Vol 13, Iss 1, Pp 76-94 (2020)
Evolutionary Applications
Evolutionary Applications, Blackwell, 2019, Forest genomics: Advancing climate adaptation, forest health, productivity and conservation, 13 (1), pp.76-94. ⟨10.1111/eva.12823⟩
ISSN: 1752-4571
1752-4563
Popis: International audience; Plantation-grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single- and multi-trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate-to-high heritability and low genotype-by-environment interactions. Weevil resistance was genetically positively correlated with tree height, height-to-diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi-trait models performed similarly as single-trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi-trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high-quality, weevil-resistant Norway spruce reforestation stock with high accuracy achieved from single-trait or multi-trait GS.
Databáze: OpenAIRE
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