Multiple-trait analyses improved the accurary 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.; Azcona, Jaime Sebastián; Ratcliffe, Blaise; Wei, Xiaojing; Da Ros, Letitia; Ullah, Aziz; Liu, Yang; Benowicz, 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 from 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. Results: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos
Fil: Klutsch, Jenifer G. University of Alberta. Department of Renewable Resources; Canada
Fil: Azcona, Jaime Sebastián. University of Alberta. Department of Renewable Resources; Canadá. Instituto de Recursos Naturales y Agrobiología de Sevilla; España
Fil: Rateliffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fil: Wei, Xiaojimg. University of Alberta. Department of Renewable Resources; Canadá
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 : 536 (2022)
Materia
Parámetros Genéticos
Genómica
Cambio Climático
Pinus
Genetic Parameters
Genomics
Climate Change
Quantitative Genetic Parameters
Genomic Prediction
Parámetros Genéticos Cuantitativos
Predicción Genómica
Genome Wide Association Analysis
Análisis de Asociación del Genoma Completo
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:20.500.12123/13117
Acceder
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oai_identifier_str oai:localhost:20.500.12123/13117
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Multiple-trait analyses improved the accurary 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.Azcona, Jaime SebastiánRatcliffe, BlaiseWei, XiaojingDa Ros, LetitiaUllah, AzizLiu, YangBenowicz, AndySadoway, ShaneMansfield, Shawn D.Erbilgin, NadirThomas, Barb R.El-Kassaby, Yousry A.Parámetros GenéticosGenómicaCambio ClimáticoPinusGenetic ParametersGenomicsClimate ChangeQuantitative Genetic ParametersGenomic PredictionParámetros Genéticos CuantitativosPredicción GenómicaGenome Wide Association AnalysisAnálisis de Asociación del Genoma CompletoGenomic 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 from 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. Results: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados UnidosFil: Klutsch, Jenifer G. University of Alberta. Department of Renewable Resources; CanadaFil: Azcona, Jaime Sebastián. University of Alberta. Department of Renewable Resources; Canadá. Instituto de Recursos Naturales y Agrobiología de Sevilla; EspañaFil: Rateliffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Wei, Xiaojimg. University of Alberta. Department of Renewable Resources; CanadáFil: 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; CanadáBMC2022-10-14T11:19:29Z2022-10-14T11:19:29Z2022-07-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/13117https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-71471-2164https://doi.org/10.1186/s12864-022-08747-7BMC Genomics 23 : 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)2025-10-23T11:18:08Zoai:localhost:20.500.12123/13117instacron: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 11:18:09.268INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Multiple-trait analyses improved the accurary 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 accurary 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 accurary of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine
Cappa, Eduardo Pablo
Parámetros Genéticos
Genómica
Cambio Climático
Pinus
Genetic Parameters
Genomics
Climate Change
Quantitative Genetic Parameters
Genomic Prediction
Parámetros Genéticos Cuantitativos
Predicción Genómica
Genome Wide Association Analysis
Análisis de Asociación del Genoma Completo
title_short Multiple-trait analyses improved the accurary 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 accurary 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 accurary 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 accurary 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 accurary 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.
Azcona, Jaime Sebastián
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullah, Aziz
Liu, Yang
Benowicz, 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.
Azcona, Jaime Sebastián
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullah, Aziz
Liu, Yang
Benowicz, Andy
Sadoway, Shane
Mansfield, Shawn D.
Erbilgin, Nadir
Thomas, Barb R.
El-Kassaby, Yousry A.
author_role author
author2 Chen, Charles
Klutsch, Jennifer G.
Azcona, Jaime Sebastián
Ratcliffe, Blaise
Wei, Xiaojing
Da Ros, Letitia
Ullah, Aziz
Liu, Yang
Benowicz, 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 Parámetros Genéticos
Genómica
Cambio Climático
Pinus
Genetic Parameters
Genomics
Climate Change
Quantitative Genetic Parameters
Genomic Prediction
Parámetros Genéticos Cuantitativos
Predicción Genómica
Genome Wide Association Analysis
Análisis de Asociación del Genoma Completo
topic Parámetros Genéticos
Genómica
Cambio Climático
Pinus
Genetic Parameters
Genomics
Climate Change
Quantitative Genetic Parameters
Genomic Prediction
Parámetros Genéticos Cuantitativos
Predicción Genómica
Genome Wide Association Analysis
Análisis de Asociación del Genoma Completo
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 from 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. Results: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos
Fil: Klutsch, Jenifer G. University of Alberta. Department of Renewable Resources; Canada
Fil: Azcona, Jaime Sebastián. University of Alberta. Department of Renewable Resources; Canadá. Instituto de Recursos Naturales y Agrobiología de Sevilla; España
Fil: Rateliffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fil: Wei, Xiaojimg. University of Alberta. Department of Renewable Resources; Canadá
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 from 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. Results: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-14T11:19:29Z
2022-10-14T11:19:29Z
2022-07-23
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/20.500.12123/13117
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7
1471-2164
https://doi.org/10.1186/s12864-022-08747-7
url http://hdl.handle.net/20.500.12123/13117
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7
https://doi.org/10.1186/s12864-022-08747-7
identifier_str_mv 1471-2164
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 : 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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