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...

Autores
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.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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
Fuente
BMC Genomics 23 : Article number: 536 (2022)
Materia
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
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/14342
Acceder
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oai_identifier_str oai:localhost:20.500.12123/14342
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling 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 pineCappa, Eduardo PabloChen, CharlesKlutsch, Jennifer G.Sebastian-Azcona, JaimeRatcliffe, BlaiseWei, XiaojingDa Ros, LetitiaUllan, AzizLiu, YangBernowicz, AndySadoway, ShaneMansfield, Shawn D.Erbilgin, NadirThomas, Barb R.El-Kassaby, Yousry A.Quantitative GeneticsMarker-assisted SelectionGenome-wide Association StudiesParametersGenética QuantitativaSelección Asistida por MarcadoresEstudios de Asociación del Genoma CompletoPinus contortaParámetrosGenomic PredictionSingle and Multiple Trait Mixed ModelsPredicción GenómicaModelos Mixtos de Rasgos Unicos y MúltiplesGenomic 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 BiológicosFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados UnidosFil: Klutsch, Jennifer G. University of Alberta. Department of Renewable Resources; CanadáFil: Sebastian-Azcona, Jaime. University of Alberta. Department of Renewable Resources; CanadáFil: Ratchiffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Wei, Xiaojing. University of Alberta; Department of Renewable Resources; CanadaFil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Ullah, Aziz. University of Alberta. Department of Renewable Resources; CanadáFil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Benowicz, Andy. Alberta Agriculture and Forestry. Forest Stewardship and Trade Branch; CanadáFil: Sadoway, Shane. Blue Ridge Lumber Inc.; CanadáFil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; CanadáFil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; CanadaFil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáBMC2023-03-28T18:14:33Z2023-03-28T18:14:33Z2022-07-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/14342https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-71976-95712092-9293https://doi.org/10.1186/s12864-022-08747-7BMC Genomics 23 : Article number: 536 (2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:18:19Zoai:localhost:20.500.12123/14342instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:18:20.079INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv 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
spellingShingle 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
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
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_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_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
dc.creator.none.fl_str_mv 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 Cappa, Eduardo Pablo
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_role author
author2 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.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv 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
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
dc.description.none.fl_txt_mv 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
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.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-23
2023-03-28T18:14:33Z
2023-03-28T18:14:33Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 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
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
identifier_str_mv 1976-9571
2092-9293
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv BMC Genomics 23 : Article number: 536 (2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.982451

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