Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.

Autores: Cappa, Eduardo Pablo; Chen, Charles; Klutsch, Jennifer; Sebastian - Azcona, Jaime; Ratcliffe, Blaise; Wei, Xiaojing; Da Ros, Letitia; Liu, Yang; Bhumireddy, Sudarshana; Benowicz, Andy; Mansfieid, Shawn; Erbilgin, Nadir; Thomas, Barb; El - Kassaby, Yousry
Año de publicación: 2025
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: Exploring the relationship between phenotype, genotype, and environment is essential in quantitative genetics. Considering the complex genetic architecture of economically important traits, integrating genotype-by-environment interactions in a genome wide association (GWAS) and genomic prediction (GP) framework is imperative. This integration is crucial for identifying robust markers with stability across diverse environments and improving the predictive accuracy of individuals’ performance within specific target environments. We conducted a multi-environment GWAS and GP analysis for 30 productivity, defense, and climate adaptability traits on 1540 white spruce trees from Alberta, Canada, genotyped for 467,224 SNPs and growing across three environments. We identified 563 significant associations (p-value < 1.07 ×10−05) across the studied traits and environments, with 105 SNPs showing overlapping associations in two or three environments. Wood density, myrcene, total monoterpenes, α-pinene, and catechin exhibited the highest overlap (>50%) across environments. Gas exchange traits, including intercellular CO2 concentration and intrinsic water use efficiency, showed the highest number of significant associations (>38%) but less stability (<1.2%) across environments. Predictive ability (PA) varied significantly (0.03–0.41) across environments for 20 traits, with stable carbon isotope ratio having the highest average PA (0.36) and gas exchange traits the lowest (0.07). Only two traits showed differences in prediction bias (PB) across environments, with 80% of site-trait PB falling within a narrow range (0.90 to 1.10). Integrating multi-environment GWAS and GP analyses proved useful in identifying site-specific markers, understanding environmental impacts on PA and PB, and ultimately providing indirect insights into the environmental factors that influenced this white spruce breeding program.
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. Natural Resources Canada. Canadian Forest Service. Northern Forestry Center; Canadá
Fil: Sebastian - Azcona, Jaime. Instituto de Recursos Naturales y Agrobiología de Sevilla. Irrigation and Crop Ecophysiology Group; España
Fil: Sebastian - Azcona, Jaime. University of Alberta, Department of Renewable Resources, Edmonton, Canadá
Fil: Ratcliffe, 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; Canadá
Fil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá
Fil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fil: Bhumireddy, Sudarshana. University of Alberta. Department of Biological Sciences. Biological Sciences Building; Canadá
Fil: Bhumireddy, Sudarshana.University of Saskatchewan, Department of Chemistry; Canadá
Fil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; Canadá
Fil: Mansfieid, Shawn. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Science; Canadá
Fil: Mansfieid, Shawn. University of British Columbia. Faculty of Science. Department of Botany; Canadá
Fil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; Canadá
Fil: Thomas, Barb. University of Alberta. Department of Renewable Resources; Canadá
Fil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fuente: Heredity 134 (2). (Marzo 2025)
Materia: Genomes
Genoma
Picea glauca
Genomic prediction
White spruce
Predicción genómica
Nivel de accesibilidad: acceso abierto
Condiciones de uso: http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Institución: Instituto Nacional de Tecnología Agropecuaria
OAI Identificador: oai:localhost:20.500.12123/21919

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spelling	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.Cappa, Eduardo PabloChen, CharlesKlutsch, JenniferSebastian - Azcona, JaimeRatcliffe, BlaiseWei, XiaojingDa Ros, LetitiaLiu, YangBhumireddy, SudarshanaBenowicz, AndyMansfieid, ShawnErbilgin, NadirThomas, BarbEl - Kassaby, YousryGenomesGenomaPicea glaucaGenomic predictionWhite sprucePredicción genómicaExploring the relationship between phenotype, genotype, and environment is essential in quantitative genetics. Considering the complex genetic architecture of economically important traits, integrating genotype-by-environment interactions in a genome wide association (GWAS) and genomic prediction (GP) framework is imperative. This integration is crucial for identifying robust markers with stability across diverse environments and improving the predictive accuracy of individuals’ performance within specific target environments. We conducted a multi-environment GWAS and GP analysis for 30 productivity, defense, and climate adaptability traits on 1540 white spruce trees from Alberta, Canada, genotyped for 467,224 SNPs and growing across three environments. We identified 563 significant associations (p-value < 1.07 ×10−05) across the studied traits and environments, with 105 SNPs showing overlapping associations in two or three environments. Wood density, myrcene, total monoterpenes, α-pinene, and catechin exhibited the highest overlap (>50%) across environments. Gas exchange traits, including intercellular CO2 concentration and intrinsic water use efficiency, showed the highest number of significant associations (>38%) but less stability (<1.2%) across environments. Predictive ability (PA) varied significantly (0.03–0.41) across environments for 20 traits, with stable carbon isotope ratio having the highest average PA (0.36) and gas exchange traits the lowest (0.07). Only two traits showed differences in prediction bias (PB) across environments, with 80% of site-trait PB falling within a narrow range (0.90 to 1.10). Integrating multi-environment GWAS and GP analyses proved useful in identifying site-specific markers, understanding environmental impacts on PA and PB, and ultimately providing indirect insights into the environmental factors that influenced this white spruce breeding program.Fil: 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. Natural Resources Canada. Canadian Forest Service. Northern Forestry Center; CanadáFil: Sebastian - Azcona, Jaime. Instituto de Recursos Naturales y Agrobiología de Sevilla. Irrigation and Crop Ecophysiology Group; EspañaFil: Sebastian - Azcona, Jaime. University of Alberta, Department of Renewable Resources, Edmonton, CanadáFil: Ratcliffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Wei, Xiaojing. University of Alberta. Department of Renewable Resources; CanadáFil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; CanadáFil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáFil: Bhumireddy, Sudarshana. University of Alberta. Department of Biological Sciences. Biological Sciences Building; CanadáFil: Bhumireddy, Sudarshana.University of Saskatchewan, Department of Chemistry; CanadáFil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; CanadáFil: Mansfieid, Shawn. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Science; CanadáFil: Mansfieid, Shawn. University of British Columbia. Faculty of Science. Department of Botany; CanadáFil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; CanadáFil: Thomas, Barb. University of Alberta. Department of Renewable Resources; CanadáFil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáSpringer2025-04-04T16:44:06Z2025-04-04T16:44:06Z2025-03-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21919https://www.nature.com/articles/s41437-025-00747-z1365-25400018-067Xhttps://doi.org/10.1038/s41437-025-00757-xHeredity 134 (2). (Marzo 2025)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)2025-10-23T11:19:28Zoai:localhost:20.500.12123/21919instacron: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 11:19:28.669INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
title	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
spellingShingle	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce. Cappa, Eduardo Pablo Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica
title_short	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
title_full	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
title_fullStr	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
title_full_unstemmed	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
title_sort	Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
dc.creator.none.fl_str_mv	Cappa, Eduardo Pablo Chen, Charles Klutsch, Jennifer Sebastian - Azcona, Jaime Ratcliffe, Blaise Wei, Xiaojing Da Ros, Letitia Liu, Yang Bhumireddy, Sudarshana Benowicz, Andy Mansfieid, Shawn Erbilgin, Nadir Thomas, Barb El - Kassaby, Yousry
author	Cappa, Eduardo Pablo
author_facet	Cappa, Eduardo Pablo Chen, Charles Klutsch, Jennifer Sebastian - Azcona, Jaime Ratcliffe, Blaise Wei, Xiaojing Da Ros, Letitia Liu, Yang Bhumireddy, Sudarshana Benowicz, Andy Mansfieid, Shawn Erbilgin, Nadir Thomas, Barb El - Kassaby, Yousry
author_role	author
author2	Chen, Charles Klutsch, Jennifer Sebastian - Azcona, Jaime Ratcliffe, Blaise Wei, Xiaojing Da Ros, Letitia Liu, Yang Bhumireddy, Sudarshana Benowicz, Andy Mansfieid, Shawn Erbilgin, Nadir Thomas, Barb El - Kassaby, Yousry
author2_role	author author author author author author author author author author author author author
dc.subject.none.fl_str_mv	Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica
topic	Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica
dc.description.none.fl_txt_mv	Exploring the relationship between phenotype, genotype, and environment is essential in quantitative genetics. Considering the complex genetic architecture of economically important traits, integrating genotype-by-environment interactions in a genome wide association (GWAS) and genomic prediction (GP) framework is imperative. This integration is crucial for identifying robust markers with stability across diverse environments and improving the predictive accuracy of individuals’ performance within specific target environments. We conducted a multi-environment GWAS and GP analysis for 30 productivity, defense, and climate adaptability traits on 1540 white spruce trees from Alberta, Canada, genotyped for 467,224 SNPs and growing across three environments. We identified 563 significant associations (p-value < 1.07 ×10−05) across the studied traits and environments, with 105 SNPs showing overlapping associations in two or three environments. Wood density, myrcene, total monoterpenes, α-pinene, and catechin exhibited the highest overlap (>50%) across environments. Gas exchange traits, including intercellular CO2 concentration and intrinsic water use efficiency, showed the highest number of significant associations (>38%) but less stability (<1.2%) across environments. Predictive ability (PA) varied significantly (0.03–0.41) across environments for 20 traits, with stable carbon isotope ratio having the highest average PA (0.36) and gas exchange traits the lowest (0.07). Only two traits showed differences in prediction bias (PB) across environments, with 80% of site-trait PB falling within a narrow range (0.90 to 1.10). Integrating multi-environment GWAS and GP analyses proved useful in identifying site-specific markers, understanding environmental impacts on PA and PB, and ultimately providing indirect insights into the environmental factors that influenced this white spruce breeding program. 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. Natural Resources Canada. Canadian Forest Service. Northern Forestry Center; Canadá Fil: Sebastian - Azcona, Jaime. Instituto de Recursos Naturales y Agrobiología de Sevilla. Irrigation and Crop Ecophysiology Group; España Fil: Sebastian - Azcona, Jaime. University of Alberta, Department of Renewable Resources, Edmonton, Canadá Fil: Ratcliffe, 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; Canadá Fil: Da Ros, Letitia. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá Fil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá Fil: Bhumireddy, Sudarshana. University of Alberta. Department of Biological Sciences. Biological Sciences Building; Canadá Fil: Bhumireddy, Sudarshana.University of Saskatchewan, Department of Chemistry; Canadá Fil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; Canadá Fil: Mansfieid, Shawn. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Science; Canadá Fil: Mansfieid, Shawn. University of British Columbia. Faculty of Science. Department of Botany; Canadá Fil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; Canadá Fil: Thomas, Barb. University of Alberta. Department of Renewable Resources; Canadá Fil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
description	Exploring the relationship between phenotype, genotype, and environment is essential in quantitative genetics. Considering the complex genetic architecture of economically important traits, integrating genotype-by-environment interactions in a genome wide association (GWAS) and genomic prediction (GP) framework is imperative. This integration is crucial for identifying robust markers with stability across diverse environments and improving the predictive accuracy of individuals’ performance within specific target environments. We conducted a multi-environment GWAS and GP analysis for 30 productivity, defense, and climate adaptability traits on 1540 white spruce trees from Alberta, Canada, genotyped for 467,224 SNPs and growing across three environments. We identified 563 significant associations (p-value < 1.07 ×10−05) across the studied traits and environments, with 105 SNPs showing overlapping associations in two or three environments. Wood density, myrcene, total monoterpenes, α-pinene, and catechin exhibited the highest overlap (>50%) across environments. Gas exchange traits, including intercellular CO2 concentration and intrinsic water use efficiency, showed the highest number of significant associations (>38%) but less stability (<1.2%) across environments. Predictive ability (PA) varied significantly (0.03–0.41) across environments for 20 traits, with stable carbon isotope ratio having the highest average PA (0.36) and gas exchange traits the lowest (0.07). Only two traits showed differences in prediction bias (PB) across environments, with 80% of site-trait PB falling within a narrow range (0.90 to 1.10). Integrating multi-environment GWAS and GP analyses proved useful in identifying site-specific markers, understanding environmental impacts on PA and PB, and ultimately providing indirect insights into the environmental factors that influenced this white spruce breeding program.
publishDate	2025
dc.date.none.fl_str_mv	2025-04-04T16:44:06Z 2025-04-04T16:44:06Z 2025-03-24
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/20.500.12123/21919 https://www.nature.com/articles/s41437-025-00747-z 1365-2540 0018-067X https://doi.org/10.1038/s41437-025-00757-x
url	http://hdl.handle.net/20.500.12123/21919 https://www.nature.com/articles/s41437-025-00747-z https://doi.org/10.1038/s41437-025-00757-x
identifier_str_mv	1365-2540 0018-067X
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)
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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	Springer
publisher.none.fl_str_mv	Springer
dc.source.none.fl_str_mv	Heredity 134 (2). (Marzo 2025) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str	INTA Digital (INTA)
collection	INTA Digital (INTA)
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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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Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.

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