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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 market...

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Autores principales: Grattapaglia, Dario, Silva-Junior, Orzenil B., Resende, Rafael T., Cappa, Eduardo P., Müller, Bárbara S. F., Tan, Biyue, Isik, Fikret, Ratcliffe, Blaise, El-Kassaby, Yousry A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262028/
https://www.ncbi.nlm.nih.gov/pubmed/30524463
http://dx.doi.org/10.3389/fpls.2018.01693
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author Grattapaglia, Dario
Silva-Junior, Orzenil B.
Resende, Rafael T.
Cappa, Eduardo P.
Müller, Bárbara S. F.
Tan, Biyue
Isik, Fikret
Ratcliffe, Blaise
El-Kassaby, Yousry A.
author_facet Grattapaglia, Dario
Silva-Junior, Orzenil B.
Resende, Rafael T.
Cappa, Eduardo P.
Müller, Bárbara S. F.
Tan, Biyue
Isik, Fikret
Ratcliffe, Blaise
El-Kassaby, Yousry A.
author_sort Grattapaglia, Dario
collection PubMed
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.
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spelling pubmed-62620282018-12-06 Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding Grattapaglia, Dario Silva-Junior, Orzenil B. Resende, Rafael T. Cappa, Eduardo P. Müller, Bárbara S. F. Tan, Biyue Isik, Fikret Ratcliffe, Blaise El-Kassaby, Yousry A. Front Plant Sci Plant Science 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. Frontiers Media S.A. 2018-11-22 /pmc/articles/PMC6262028/ /pubmed/30524463 http://dx.doi.org/10.3389/fpls.2018.01693 Text en Copyright © 2018 Grattapaglia, Silva-Junior, Resende, Cappa, Müller, Tan, Isik, Ratcliffe and El-Kassaby. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Grattapaglia, Dario
Silva-Junior, Orzenil B.
Resende, Rafael T.
Cappa, Eduardo P.
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 Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262028/
https://www.ncbi.nlm.nih.gov/pubmed/30524463
http://dx.doi.org/10.3389/fpls.2018.01693
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