Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions
Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare...
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| Format: | Article |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.01353/full |
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| author | Livia M. Souza Felipe R. Francisco Paulo S. Gonçalves Erivaldo J. Scaloppi Junior Vincent Le Guen Roberto Fritsche-Neto Anete P. Souza Anete P. Souza |
| author_facet | Livia M. Souza Felipe R. Francisco Paulo S. Gonçalves Erivaldo J. Scaloppi Junior Vincent Le Guen Roberto Fritsche-Neto Anete P. Souza Anete P. Souza |
| author_sort | Livia M. Souza |
| collection | DOAJ |
| description | Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs. |
| first_indexed | 2024-12-11T11:08:49Z |
| format | Article |
| id | doaj.art-326e6f70ec8e4cafa31e8657aaeab164 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-11T11:08:49Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-326e6f70ec8e4cafa31e8657aaeab1642022-12-22T01:09:37ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-10-011010.3389/fpls.2019.01353465080Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E InteractionsLivia M. Souza0Felipe R. Francisco1Paulo S. Gonçalves2Erivaldo J. Scaloppi Junior3Vincent Le Guen4Roberto Fritsche-Neto5Anete P. Souza6Anete P. Souza7Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, BrazilMolecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, BrazilCenter of Rubber Tree and Agroforestry Systems, Agronomic Institute (IAC), Votuporanga, BrazilCenter of Rubber Tree and Agroforestry Systems, Agronomic Institute (IAC), Votuporanga, BrazilCentre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR AGAP, Montpellier, FranceDepartamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz” Universidade de São Paulo (ESALQ/USP), Piracicaba, BrazilMolecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, BrazilDepartment of Plant Biology, Biology Institute, University of Campinas (UNICAMP), Campinas, BrazilSeveral genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs.https://www.frontiersin.org/article/10.3389/fpls.2019.01353/fullHevea brasiliensisbreedingmultienvironmentsingle nucleotidegenotyping |
| spellingShingle | Livia M. Souza Felipe R. Francisco Paulo S. Gonçalves Erivaldo J. Scaloppi Junior Vincent Le Guen Roberto Fritsche-Neto Anete P. Souza Anete P. Souza Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions Frontiers in Plant Science Hevea brasiliensis breeding multienvironment single nucleotide genotyping |
| title | Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions |
| title_full | Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions |
| title_fullStr | Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions |
| title_full_unstemmed | Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions |
| title_short | Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions |
| title_sort | genomic selection in rubber tree breeding a comparison of models and methods for managing g e interactions |
| topic | Hevea brasiliensis breeding multienvironment single nucleotide genotyping |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.01353/full |
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