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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
15/07/2020 |
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Data da última atualização: |
15/07/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRAO, M. A. G.; FONSECA, A. F. A. da; CARBONETTO, P.; SLEPHENS, M.; FRANCO, A. A. |
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Afiliação: |
LUÍS FELIPE VENTORIM FERRÃO, ESALQ; ROMÁRIO GAVA FERRÃO, INCAPER; MARIA AMELIA GAVA FERRAO, CNPCa; AYMBIRÉ FRANCISCO ALMEIDA DA FONSECA, CNPCa; PETER CARBONETTO, UNIVERSIDADE DE CHICAGO; MATTHEW SLEPHENS, UNIVERSIDADE DE CHICAGO; ANTONIO AUGUSTO FRANCO, ESALQ. |
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Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, v. 122, p. 261-275, 2018. |
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DOI: |
https://doi.org/10.1038/s41437-018-0105-y |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. MenosGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our... Mostrar Tudo |
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Thesagro: |
Coffea Canephora; Seleção Genética; Seleção Genótipa. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214636/1/Accurate-genomic-prediction-of-Coffea-canephora-Incaper.pdf
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Marc: |
LEADER 02485naa a2200241 a 4500
001 2123866
005 2020-07-15
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1038/s41437-018-0105-y$2DOI
100 1 $aFERRÃO, L. F. V.
245 $aAccurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.$h[electronic resource]
260 $c2018
520 $aGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
650 $aCoffea Canephora
650 $aSeleção Genética
650 $aSeleção Genótipa
700 1 $aFERRÃO, R. G.
700 1 $aFERRAO, M. A. G.
700 1 $aFONSECA, A. F. A. da
700 1 $aCARBONETTO, P.
700 1 $aSLEPHENS, M.
700 1 $aFRANCO, A. A.
773 $tHeredity$gv. 122, p. 261-275, 2018.
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Embrapa Café (CNPCa) |
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