Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine

Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engel...

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Main Authors: Cappa, Eduardo Pablo, Chen, Charles, Klutsch, Jennifer G., Sebastian-Azcona, Jaime, Ratcliffe, Blaise, Wei, Xiaojing, Da Ros, Letitia, Ullan, Aziz, Liu, Yang, Bernowicz, Andy, Sadoway, Shane, Mansfield, Shawn D., Erbilgin, Nadir, Thomas, Barb R., El-Kassaby, Yousry A.
Format: Artículo
Language:Inglés
Published: BMC 2023
Subjects:
Online Access:http://hdl.handle.net/20.500.12123/14342
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7
https://doi.org/10.1186/s12864-022-08747-7
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author Cappa, Eduardo Pablo
Chen, Charles
Klutsch, Jennifer G.
Sebastian-Azcona, Jaime
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullan, Aziz
Liu, Yang
Bernowicz, Andy
Sadoway, Shane
Mansfield, Shawn D.
Erbilgin, Nadir
Thomas, Barb R.
El-Kassaby, Yousry A.
author_browse Bernowicz, Andy
Cappa, Eduardo Pablo
Chen, Charles
Da Ros, Letitia
El-Kassaby, Yousry A.
Erbilgin, Nadir
Klutsch, Jennifer G.
Liu, Yang
Mansfield, Shawn D.
Ratcliffe, Blaise
Sadoway, Shane
Sebastian-Azcona, Jaime
Thomas, Barb R.
Ullan, Aziz
Wei, Xiaojing
author_facet Cappa, Eduardo Pablo
Chen, Charles
Klutsch, Jennifer G.
Sebastian-Azcona, Jaime
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullan, Aziz
Liu, Yang
Bernowicz, Andy
Sadoway, Shane
Mansfield, Shawn D.
Erbilgin, Nadir
Thomas, Barb R.
El-Kassaby, Yousry A.
author_sort Cappa, Eduardo Pablo
collection INTA Digital
description Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values fromn the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias.
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institution Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina)
language Inglés
publishDate 2023
publishDateRange 2023
publishDateSort 2023
publisher BMC
publisherStr BMC
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spelling INTA143422024-03-21T13:46:54Z Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine Cappa, Eduardo Pablo Chen, Charles Klutsch, Jennifer G. Sebastian-Azcona, Jaime Ratcliffe, Blaise Wei, Xiaojing Da Ros, Letitia Ullan, Aziz Liu, Yang Bernowicz, Andy Sadoway, Shane Mansfield, Shawn D. Erbilgin, Nadir Thomas, Barb R. El-Kassaby, Yousry A. Quantitative Genetics Marker-assisted Selection Genome-wide Association Studies Parameters Genética Quantitativa Selección Asistida por Marcadores Estudios de Asociación del Genoma Completo Pinus contorta Parámetros Genomic Prediction Single and Multiple Trait Mixed Models Predicción Genómica Modelos Mixtos de Rasgos Unicos y Múltiples Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values fromn the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Instituto de Recursos Biológicos Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos Fil: Klutsch, Jennifer G. University of Alberta. Department of Renewable Resources; Canadá Fil: Sebastian-Azcona, Jaime. University of Alberta. Department of Renewable Resources; Canadá Fil: Ratchiffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá Fil: Wei, Xiaojing. University of Alberta; Department of Renewable Resources; Canada Fil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá Fil: Ullah, Aziz. University of Alberta. Department of Renewable Resources; Canadá Fil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá Fil: Benowicz, Andy. Alberta Agriculture and Forestry. Forest Stewardship and Trade Branch; Canadá Fil: Sadoway, Shane. Blue Ridge Lumber Inc.; Canadá Fil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá Fil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; Canadá Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canada Fil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá 2023-03-28T18:14:33Z 2023-03-28T18:14:33Z 2022-07-23 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/14342 https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7 1976-9571 2092-9293 https://doi.org/10.1186/s12864-022-08747-7 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf BMC BMC Genomics 23 : Article number: 536 (2022)
spellingShingle Quantitative Genetics
Marker-assisted Selection
Genome-wide Association Studies
Parameters
Genética Quantitativa
Selección Asistida por Marcadores
Estudios de Asociación del Genoma Completo
Pinus contorta
Parámetros
Genomic Prediction
Single and Multiple Trait Mixed Models
Predicción Genómica
Modelos Mixtos de Rasgos Unicos y Múltiples
Cappa, Eduardo Pablo
Chen, Charles
Klutsch, Jennifer G.
Sebastian-Azcona, Jaime
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullan, Aziz
Liu, Yang
Bernowicz, Andy
Sadoway, Shane
Mansfield, Shawn D.
Erbilgin, Nadir
Thomas, Barb R.
El-Kassaby, Yousry A.
Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title_full Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title_fullStr Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title_full_unstemmed Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title_short Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine
title_sort multiple trait analyses improved the accuracy of genomic prediction and the power of genome wide association of productivity and climate change adaptive traits in lodgepole pine
topic Quantitative Genetics
Marker-assisted Selection
Genome-wide Association Studies
Parameters
Genética Quantitativa
Selección Asistida por Marcadores
Estudios de Asociación del Genoma Completo
Pinus contorta
Parámetros
Genomic Prediction
Single and Multiple Trait Mixed Models
Predicción Genómica
Modelos Mixtos de Rasgos Unicos y Múltiples
url http://hdl.handle.net/20.500.12123/14342
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7
https://doi.org/10.1186/s12864-022-08747-7
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