Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices
Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position...
| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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Nature
2021
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/10732 https://www.nature.com/articles/s41437-021-00450-9 https://doi.org/10.1038/s41437-021-00450-9 |
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| author | Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo |
| author_browse | Acuña, Cintia Vanesa Aguirre, Natalia Cristina Cappa, Eduardo Pablo Cisneros, Esteban F. Garcia, Martin Nahuel Harrand, Leonel Jurcic, Esteban Javier Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Martinez, Maria Carolina Munilla, Sebastian Oberschelp, Gustavo Pedro Javier Palazzini, Dino Pathauer, Pablo Santiago Rivas, Juan Gabriel Villalba, Pamela Victoria |
| author_facet | Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo |
| author_sort | Jurcic, Esteban Javier |
| collection | INTA Digital |
| description | Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield.
In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018).
The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019). |
| format | Artículo |
| id | INTA10732 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Nature |
| publisherStr | Nature |
| record_format | dspace |
| spelling | INTA107322021-11-10T12:02:09Z Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP Eucalyptus L’Hér. (Myrtaceae) is the most valuable and globally planted forest tree genus. Fast growth, adaptability to a broad diversity of tropical and subtropical regions, combined with versatile wood properties for energy, solid products, pulp, and paper have warranted their outstanding position in current world forestry (de Lima et al. 2019). Eucalyptus dunnii Maiden (hereafter E. dunnii) has become increasingly used in commercial afforestation due to its combined good performance for growth, stem straightness, and frost tolerance, together with suitable wood density and pulp yield. In a broad sense, genomic selection (GS) is a family of statistical methods developed for predicting the breeding values of nonphenotyped individuals with the assistance of a large number of molecular markers widespread distributed throughout the genome (Meuwissen et al. 2001). These methods exploit cosegregation between markers and quantitative trait loci (QTL) in linkage disequilibrium (LD). In forest trees, GS is of particular benefit due to the extended breeding cycles caused by delayed reproductive maturity and the need for early selection of traits that express late in life (Mphahlele et al. 2020). In this context, GS has a potentially substantial impact on the rate of genetic gain by increasing the intensity and accuracy of selection and, particularly, by shortening the generational interval (Grattapaglia et al. 2018). The genomic best linear unbiased prediction (GBLUP) is one of the most commonly GS methods. It is basically a variant of the standard BLUP method (hereafter ABLUP, cf. Henderson 1984), where the pedigree-based numerator relationship matrix (Amatrix) is replaced by a genomic relationship matrix (G-matrix, e.g., Habier et al. 2013). Many empirical studies with forest tree species have shown that GBLUP is a very promising approach for tree breeding (e.g., Mphahlele et al. 2020; Resende et al. 2017; Lenz et al. 2019). However, to our knowledge, only two of them have directly investigated the efficiency of genomic prediction using only genotyped trees in E. dunnii through GBLUP (Naidoo et al. 2018; Jones et al. 2019). Fil: Jurcic, Esteban J. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Villalba, Pamela Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Pathauer, Pablo Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina Fil: Palazzini, Dino A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Oberschelp, Gustavo Pedro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina Fil: Harrand, Leonel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concordia; Argentina Fil: Garcia, Martin Nahuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Aguirre, Natalia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Acuña, Cintia Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Martinez, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rivas, Juan Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cisneros, Esteban F. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; Argentina Fil: Lopez, Juan Adolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bella Vista; Argentina Fil: Marcucci Poltri, Susana Noemi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina 2021-11-10T11:31:46Z 2021-11-10T11:31:46Z 2021-06-18 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/10732 https://www.nature.com/articles/s41437-021-00450-9 1365-2540 0018-067X https://doi.org/10.1038/s41437-021-00450-9 eng info:eu-repograntAgreement/INTA/PNFOR-1104062/AR./Mejoramiento genético de especies forestales introducidas para usos de alto valor. info:eu-repograntAgreement/INTA/PNFOR-1104064/AR./Aplicación de herramientas moleculares para el uso y la conservación de la diversidad genética forestal. info:eu-repo/semantics/restrictedAccess application/pdf Nature Heredity 127 (2) : 176-189 (2021) |
| spellingShingle | Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP Jurcic, Esteban Javier Villalba, Pamela Victoria Pathauer, Pablo Santiago Palazzini, Dino Oberschelp, Gustavo Pedro Javier Harrand, Leonel Garcia, Martin Nahuel Aguirre, Natalia Cristina Acuña, Cintia Vanesa Martinez, Maria Carolina Rivas, Juan Gabriel Cisneros, Esteban F. Lopez, Juan Adolfo Marcucci Poltri, Susana Noemi Munilla, Sebastian Cappa, Eduardo Pablo Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title | Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_full | Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_fullStr | Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_full_unstemmed | Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_short | Single - step genomic prediction of Eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| title_sort | single step genomic prediction of eucalyptus dunnii using different identity by descent and identity by state relation ship matrices |
| topic | Eucalyptus Genética Genetics Eucalyptus dunnii genomic prediction predicción genómica ssGBLUP |
| url | http://hdl.handle.net/20.500.12123/10732 https://www.nature.com/articles/s41437-021-00450-9 https://doi.org/10.1038/s41437-021-00450-9 |
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