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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
26/01/2018 |
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Data da última atualização: |
26/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; BORBA, T. C. O.; AGUIAR, J. T.; NEVES, P. C. F.; MORAIS, O. P. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; TEREZA CRISTINA DE OLIVEIRA BORBA, CNPAF; JORDENE T. AGUIAR; PERICLES DE CARVALHO FERREIRA NEVES, CNPAF; ORLANDO PEIXOTO DE MORAIS, CNPAF. |
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Título: |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 4, gmr16039849, Dec. 2017. |
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ISSN: |
1676-5680 |
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DOI: |
10.4238/gmr16039849 |
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Idioma: |
Inglês |
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Conteúdo: |
In genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop. MenosIn genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the ge... Mostrar Tudo |
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Palavras-Chave: |
GBLUP models; Genetic architecture; Predictive accuracy; Variance components. |
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Thesagro: |
Arroz; Melhoramento genético vegetal; Oryza sativa; Seleção recorrente. |
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Thesaurus Nal: |
Plant breeding; quantitative traits; Rice. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/171723/1/CNPAF-2017-gmr-opmj.pdf
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Marc: |
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001 2086472
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008 2017 bl uuuu u00u1 u #d
022 $a1676-5680
024 7 $a10.4238/gmr16039849$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aRelevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding$h[electronic resource]
260 $c2017
520 $aIn genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop.
650 $aPlant breeding
650 $aquantitative traits
650 $aRice
650 $aArroz
650 $aMelhoramento genético vegetal
650 $aOryza sativa
650 $aSeleção recorrente
653 $aGBLUP models
653 $aGenetic architecture
653 $aPredictive accuracy
653 $aVariance components
700 1 $aDUARTE, J. B.
700 1 $aBRESEGHELLO, F.
700 1 $aCOELHO, A. S. G.
700 1 $aBORBA, T. C. O.
700 1 $aAGUIAR, J. T.
700 1 $aNEVES, P. C. F.
700 1 $aMORAIS, O. P.
773 $tGenetics and Molecular Research$gv. 16, n. 4, gmr16039849, Dec. 2017.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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