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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
20/11/2017 |
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Data da última atualização: |
20/11/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MÜLLER, B. S. F.; NEVES, L. G.; ALMEIDA FILHO, J. E. de; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. R.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; KIRST, M.; GRATTAPAGLIA, D. |
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Afiliação: |
Bárbara S. F. Müller, UnB; Leandro G. Neves, RAPiD Genomics LLC; Janeo E. de Almeida Filho, University of Florida; Márcio F. R. Resende Junior, RAPiD Genomics LLC; Patricio R. Muñoz, University of Florida; PAULO EDUARDO TELLES DOS SANTOS, CNPF; ESTEFANO PALUDZYSZYN FILHO, CNPF; Matias Kirst, University of Florida; DARIO GRATTAPAGLIA, Cenargen. |
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Título: |
Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
BMC Genomics, v. 18, article 524, 2017. 17 p. |
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DOI: |
10.1186/s12864-017-3920-2 |
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Idioma: |
Inglês
Português |
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Conteúdo: |
Background: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Results: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000?10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. Conclusions: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees. MenosBackground: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Results: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000?10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range h... Mostrar Tudo |
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Palavras-Chave: |
Espécie exótica; Genomic selection; GWAS; Seleção genômica; SNP genotyping. |
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Thesagro: |
Eucalipto; Melhoramento genético vegetal. |
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Thesaurus Nal: |
Eucalyptus benthamii; Eucalyptus pellita; genetic relationships; marker-assisted selection; plant breeding. |
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Categoria do assunto: |
--
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/166929/1/2017-PauloE-BMCG-Genomic-prediction.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/181027/1/s12864-017-3920-2.pdf
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Marc: |
LEADER 03672naa a2200373 a 4500
001 2080081
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008 2017 bl uuuu u00u1 u #d
024 7 $a10.1186/s12864-017-3920-2$2DOI
100 1 $aMÜLLER, B. S. F.
245 $aGenomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.$h[electronic resource]
260 $c2017
520 $aBackground: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Results: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000?10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. Conclusions: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.
650 $aEucalyptus benthamii
650 $aEucalyptus pellita
650 $agenetic relationships
650 $amarker-assisted selection
650 $aplant breeding
650 $aEucalipto
650 $aMelhoramento genético vegetal
653 $aEspécie exótica
653 $aGenomic selection
653 $aGWAS
653 $aSeleção genômica
653 $aSNP genotyping
700 1 $aNEVES, L. G.
700 1 $aALMEIDA FILHO, J. E. de
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aMUÑOZ, P. R.
700 1 $aSANTOS, P. E. T. dos
700 1 $aPALUDZYSZYN FILHO, E.
700 1 $aKIRST, M.
700 1 $aGRATTAPAGLIA, D.
773 $tBMC Genomics$gv. 18, article 524, 2017. 17 p.
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Registro original: |
Embrapa Florestas (CNPF) |
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| 1. |  | MÜLLER, B. S. F.; NEVES, L. G.; ALMEIDA FILHO, J. E. de; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. R.; SANTOS, P. E. T. dos; PALUDZYSZYN FILHO, E.; KIRST, M.; GRATTAPAGLIA, D. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics, v. 18, article 524, 2017. 17 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas; Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 1 | |
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