Quantitative genetics and genomics converge to accelerate forest tree breeding
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and marke...
| Main Authors: | , , , , , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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2019
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2018.01693/full http://hdl.handle.net/20.500.12123/4605 https://doi.org/10.3389/fpls.2018.01693 |
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| author | Grattapaglia, Dario Silva Junior, Orzenil B. Resende, Rafael T. Cappa, Eduardo Pablo Müller, Bárbara S. F. Tan, Biyue Isik, Fikret Ratcliffe, Blaise El-Kassaby, Yousry A. |
| author_browse | Cappa, Eduardo Pablo El-Kassaby, Yousry A. Grattapaglia, Dario Isik, Fikret Müller, Bárbara S. F. Ratcliffe, Blaise Resende, Rafael T. Silva Junior, Orzenil B. Tan, Biyue |
| author_facet | Grattapaglia, Dario Silva Junior, Orzenil B. Resende, Rafael T. Cappa, Eduardo Pablo Müller, Bárbara S. F. Tan, Biyue Isik, Fikret Ratcliffe, Blaise El-Kassaby, Yousry A. |
| author_sort | Grattapaglia, Dario |
| collection | INTA Digital |
| description | Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests
and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest
trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters’ estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding. |
| format | info:ar-repo/semantics/artículo |
| id | INTA4605 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | INTA46052019-03-14T13:13:06Z Quantitative genetics and genomics converge to accelerate forest tree breeding Grattapaglia, Dario Silva Junior, Orzenil B. Resende, Rafael T. Cappa, Eduardo Pablo Müller, Bárbara S. F. Tan, Biyue Isik, Fikret Ratcliffe, Blaise El-Kassaby, Yousry A. Quantitative Genetics Quantitative Trait Loci Forest Trees Genética Cuantitativa Loci de Rasgos Cuantitativos Árboles Forestales Genomic Selection Tree Breeding Whole-genome Regression Single Nucleotide Polymorphisms Marker Assisted Selection Realized Genomic Relationship Selección Genómica Cría de Arboles Regresión de Todo el Genoma Polimorfismos de un Sólo Nucleótido Selección Asistida por Marcador Relación Genómica Realizada Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters’ estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding. Fil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasília. Programa de Ciências Genômicas e Biotecnologia; Brasil. Universidade de Brasília. Departamento de Biologia Celular; Brasil. North Carolina State University. Department of Forestry and Environmental Resources; Estados Unidos Fil: Silva-Junior, Orzenil B. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasília. Programa de Ciências Genômicas e Biotecnologia; Brasil Fil: Resende, Rafael T. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina Fil: Müller, Bárbara S. F. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade de Brasília. Departamento de Biologia Celular; Brasil Fil: Tan, Biyue. Stora Enso AB. Biomaterials Division; Suecia Fil: Isik, Fikret. North Carolina State University. Department of Forestry and Environmental Resources; Estados Unidos Fil: Rateliffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá Fil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá 2019-03-14T12:58:47Z 2019-03-14T12:58:47Z 2018-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.frontiersin.org/articles/10.3389/fpls.2018.01693/full http://hdl.handle.net/20.500.12123/4605 https://doi.org/10.3389/fpls.2018.01693 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 Frontiers in Plant Science 9 : 1693 (November 2018) |
| spellingShingle | Quantitative Genetics Quantitative Trait Loci Forest Trees Genética Cuantitativa Loci de Rasgos Cuantitativos Árboles Forestales Genomic Selection Tree Breeding Whole-genome Regression Single Nucleotide Polymorphisms Marker Assisted Selection Realized Genomic Relationship Selección Genómica Cría de Arboles Regresión de Todo el Genoma Polimorfismos de un Sólo Nucleótido Selección Asistida por Marcador Relación Genómica Realizada Grattapaglia, Dario Silva Junior, Orzenil B. Resende, Rafael T. Cappa, Eduardo Pablo Müller, Bárbara S. F. Tan, Biyue Isik, Fikret Ratcliffe, Blaise El-Kassaby, Yousry A. Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title | Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title_full | Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title_fullStr | Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title_full_unstemmed | Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title_short | Quantitative genetics and genomics converge to accelerate forest tree breeding |
| title_sort | quantitative genetics and genomics converge to accelerate forest tree breeding |
| topic | Quantitative Genetics Quantitative Trait Loci Forest Trees Genética Cuantitativa Loci de Rasgos Cuantitativos Árboles Forestales Genomic Selection Tree Breeding Whole-genome Regression Single Nucleotide Polymorphisms Marker Assisted Selection Realized Genomic Relationship Selección Genómica Cría de Arboles Regresión de Todo el Genoma Polimorfismos de un Sólo Nucleótido Selección Asistida por Marcador Relación Genómica Realizada |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2018.01693/full http://hdl.handle.net/20.500.12123/4605 https://doi.org/10.3389/fpls.2018.01693 |
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