LenzNadeauMottetEtAl2020

Reference

Lenz, P.R.N., Nadeau, S., Mottet, M.-J., Perron, M., Isabel, N., Beaulieu, J., Bousquet, J. (2020) Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evolutionary Applications, 13(1):76-94. (Scopus )

Abstract

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. © 2019 Her Majesty the Queen in Right of Canada. Environmental DNA published by John Wiley & Sons Ltd.

EndNote Format

You can import this reference in EndNote.

BibTeX-CSV Format

You can import this reference in BibTeX-CSV format.

BibTeX Format

You can copy the BibTeX entry of this reference below, orimport it directly in a software like JabRef .

@ARTICLE { LenzNadeauMottetEtAl2020,
    AUTHOR = { Lenz, P.R.N. and Nadeau, S. and Mottet, M.-J. and Perron, M. and Isabel, N. and Beaulieu, J. and Bousquet, J. },
    TITLE = { Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce },
    JOURNAL = { Evolutionary Applications },
    YEAR = { 2020 },
    VOLUME = { 13 },
    NUMBER = { 1 },
    PAGES = { 76-94 },
    NOTE = { cited By 3 },
    ABSTRACT = { 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. © 2019 Her Majesty the Queen in Right of Canada. Environmental DNA published by John Wiley & Sons Ltd. },
    AFFILIATION = { Canadian Wood Fibre Centre, Natural Resources Canada, Québec, QC, Canada; Canada Research Chair in Forest Genomics, Institute of Integrative Biology and Systems, Centre for Forest Research, Université Laval, Québec, QC, Canada; Ministère des Forêts, de la Faune et des Parcs, Gouvernement du Québec, Direction de la recherche forestière, Québec, QC, Canada; Laurentian Forestry Centre, Natural Resources Canada, Québec, QC, Canada },
    AUTHOR_KEYWORDS = { breeding; conifers; index selection; insect resistance; multi-trait genomic selection; Norway spruce; white pine weevil; wood quality },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/eva.12823 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077144305&doi=10.1111%2feva.12823&partnerID=40&md5=0483b77cbe5d5b338fb37e44e0756f4c },
}

********************************************************** *************************** FRQNT ************************ **********************************************************

Un regroupement stratégique du

********************************************************** *********************** Infolettre *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ***************** Pub - Congrès Mycelium ****************** **********************************************************

Reporté en 2021

********************************************************** ***************** Pub - IWTT ****************** **********************************************************

Reporté en 2021

**********************************************************

***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...