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
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...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
BMC
2022
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| Materias: | |
| Acceso en línea: | 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 |
| _version_ | 1855485108413267968 |
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| author | 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_browse | Azcona, Jaime Sebastián Benowicz, 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 Thomas, Barb R. Ullah, Aziz Wei, Xiaojing |
| 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_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 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. |
| format | Artículo |
| id | INTA13117 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | BMC |
| publisherStr | BMC |
| record_format | dspace |
| spelling | INTA131172022-10-14T11:33:29Z 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 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. 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 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 2022-10-14T11:19:29Z 2022-10-14T11:19:29Z 2022-07-23 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/13117 https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7 1471-2164 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 : 536 (2022) |
| spellingShingle | 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 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. 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 |
| 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_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_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 |
| 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 |
| 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 |
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