Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce.
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 (...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Springer
2025
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| Online Access: | 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 |
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| author | 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_browse | Benowicz, Andy Bhumireddy, Sudarshana Cappa, Eduardo Pablo Chen, Charles Da Ros, Letitia El - Kassaby, Yousry Erbilgin, Nadir Klutsch, Jennifer Liu, Yang Mansfieid, Shawn Ratcliffe, Blaise Sebastian - Azcona, Jaime Thomas, Barb Wei, Xiaojing |
| 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_sort | Cappa, Eduardo Pablo |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA21919 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA219192025-04-04T17:38:52Z 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 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 Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica 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 2025-04-04T16:44:06Z 2025-04-04T16:44:06Z 2025-03-24 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 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 Springer Heredity 134 (2). (Marzo 2025) |
| spellingShingle | Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica 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 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. |
| 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_short | 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 |
| topic | Genomes Genoma Picea glauca Genomic prediction White spruce Predicción genómica |
| 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 |
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