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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
10/04/2001 |
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Data da última atualização: |
02/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PAGLIARINI, M. S.; FREITAS, P. M. de; BATISTA, L. A. R. |
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Afiliação: |
MARIA SUELY PAGLIARINI, UEM; PATRICIA MATIAS DE FREITAS, UEM; LUIZ ALBERTO ROCHA BATISTA, CPPSE. |
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Título: |
Chromosome stickiness in meiosis of a brazilian Paspalum accession. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
Cytologia, v.65, p.289-293, 2000. |
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Idioma: |
Inglês |
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Conteúdo: |
Citogenetic studies carried out over a period of 2 consecutive years on a still unidentified Brazilian accession of Paspalum (BRA-014176) revealed a meiotic mutation causing chromose stickiness in microsporogenis. In all inflorescences collected in both years, meiosis was normal until diakinesis. Chromosome stikiness became evident from metaphase I, whwn the cromosome appeared as a dense cluster. Chromosome bridges of different thickeness were observed from anaphase I to the microspore stage. In the microspores, the necleus became pynotic and in the pollen grain the chromatin suffered degeration causing pollen sterility. |
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Palavras-Chave: |
Chromosone stickiness; Mutant. |
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Thesaurus Nal: |
microsporogenesis; Paspalum. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 01167naa a2200193 a 4500
001 1045373
005 2022-05-02
008 2000 bl uuuu u00u1 u #d
100 1 $aPAGLIARINI, M. S.
245 $aChromosome stickiness in meiosis of a brazilian Paspalum accession.$h[electronic resource]
260 $c2000
520 $aCitogenetic studies carried out over a period of 2 consecutive years on a still unidentified Brazilian accession of Paspalum (BRA-014176) revealed a meiotic mutation causing chromose stickiness in microsporogenis. In all inflorescences collected in both years, meiosis was normal until diakinesis. Chromosome stikiness became evident from metaphase I, whwn the cromosome appeared as a dense cluster. Chromosome bridges of different thickeness were observed from anaphase I to the microspore stage. In the microspores, the necleus became pynotic and in the pollen grain the chromatin suffered degeration causing pollen sterility.
650 $amicrosporogenesis
650 $aPaspalum
653 $aChromosone stickiness
653 $aMutant
700 1 $aFREITAS, P. M. de
700 1 $aBATISTA, L. A. R.
773 $tCytologia$gv.65, p.289-293, 2000.
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Embrapa Pecuária Sudeste (CPPSE) |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
18/01/2017 |
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Data da última atualização: |
18/01/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
GLÓRIA, L. S.; CRUZ, C. D.; VIEIRA, R. A. M.; RESENDE, M. D. V. de; LOPES, P. S.; SIQUEIRA, O. H. G. B. D. de; SILVA, F. F. e. |
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Afiliação: |
Leonardo Siqueira Glória, UFV; Cosme Damião Cruz, UFV; Ricardo Augusto Mendonça Vieira, Universidade Estadual do Norte Fluminense; MARCOS DEON VILELA DE RESENDE, CNPF; Paulo Sávio Lopes, UFV; Otávio H. G. B. Dias de Siqueira, UFV; Fabyano Fonseca e Silva, UFV. |
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Título: |
Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Livestock Science, v. 191, p. 91-96, Sept. 2016. |
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DOI: |
http://dx.doi.org/10.1016/j.livsci.2016.07.015 |
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Idioma: |
Inglês |
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Conteúdo: |
Recently, there is an increasing interest on semi- and non-parametric methods for genome-enabled prediction, among which the Bayesian regularized artificial neural networks (BRANN) stand. We aimed to evaluate the predictive performance of BRANN and to exploit SNP effects and heritability estimates using two different approaches (relative importance-RI, and relative contribution-RC). Additionally, we aimed also to compare BRANN with the traditional RR-BLUP and BLASSO by using simulated datasets. The simplest BRANN (net1), RR-BLUP and BLASSO methods outperformed other more parameterized BRANN (net2, net3, ? net6) in terms of predictive ability. For both simulated traits (Y1 and Y2) the net1 provided the best h2 estimates (0.33 for both, being the true h2=0.35), whereas RR-BLUP (0.18 and 0.22 for Y1 and Y2, respectively) and BLASSO (0.20 and 0.26 for Y1 and Y2, respectively) underestimated h2. The marker effects estimated from net1 (using RI and RC approaches) and RR-BLUP were similar, but the shrinkage strength was remarkable for BLASSO on both traits. For Y1, the correlation between the true fifty QTL effects and the effects estimated for the SNPs located in the same QTL positions were 0.61, 0.60, 0.60 and 0.55, for RI, RC, RR-BLUP and BLASSO; and for Y2, these correlations were 0.81, 0.81, 0.81 and 0.71, respectively. In summary, we believe that estimates of SNP effects are promising quantitative tools to bring discussions on chromosome regions contributing most effectively to the phenotype expression when using ANN for genomic predictions. MenosRecently, there is an increasing interest on semi- and non-parametric methods for genome-enabled prediction, among which the Bayesian regularized artificial neural networks (BRANN) stand. We aimed to evaluate the predictive performance of BRANN and to exploit SNP effects and heritability estimates using two different approaches (relative importance-RI, and relative contribution-RC). Additionally, we aimed also to compare BRANN with the traditional RR-BLUP and BLASSO by using simulated datasets. The simplest BRANN (net1), RR-BLUP and BLASSO methods outperformed other more parameterized BRANN (net2, net3, ? net6) in terms of predictive ability. For both simulated traits (Y1 and Y2) the net1 provided the best h2 estimates (0.33 for both, being the true h2=0.35), whereas RR-BLUP (0.18 and 0.22 for Y1 and Y2, respectively) and BLASSO (0.20 and 0.26 for Y1 and Y2, respectively) underestimated h2. The marker effects estimated from net1 (using RI and RC approaches) and RR-BLUP were similar, but the shrinkage strength was remarkable for BLASSO on both traits. For Y1, the correlation between the true fifty QTL effects and the effects estimated for the SNPs located in the same QTL positions were 0.61, 0.60, 0.60 and 0.55, for RI, RC, RR-BLUP and BLASSO; and for Y2, these correlations were 0.81, 0.81, 0.81 and 0.71, respectively. In summary, we believe that estimates of SNP effects are promising quantitative tools to bring discussions on chromosome regions contributing most effectively ... Mostrar Tudo |
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Palavras-Chave: |
Genetic parameters; QTL; Redes neurais. |
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Thesagro: |
Parâmetro Genético. |
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Thesaurus NAL: |
Marker-assisted selection; Neural networks. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02443naa a2200277 a 4500
001 2061138
005 2017-01-18
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1016/j.livsci.2016.07.015$2DOI
100 1 $aGLÓRIA, L. S.
245 $aAccessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks.$h[electronic resource]
260 $c2016
520 $aRecently, there is an increasing interest on semi- and non-parametric methods for genome-enabled prediction, among which the Bayesian regularized artificial neural networks (BRANN) stand. We aimed to evaluate the predictive performance of BRANN and to exploit SNP effects and heritability estimates using two different approaches (relative importance-RI, and relative contribution-RC). Additionally, we aimed also to compare BRANN with the traditional RR-BLUP and BLASSO by using simulated datasets. The simplest BRANN (net1), RR-BLUP and BLASSO methods outperformed other more parameterized BRANN (net2, net3, ? net6) in terms of predictive ability. For both simulated traits (Y1 and Y2) the net1 provided the best h2 estimates (0.33 for both, being the true h2=0.35), whereas RR-BLUP (0.18 and 0.22 for Y1 and Y2, respectively) and BLASSO (0.20 and 0.26 for Y1 and Y2, respectively) underestimated h2. The marker effects estimated from net1 (using RI and RC approaches) and RR-BLUP were similar, but the shrinkage strength was remarkable for BLASSO on both traits. For Y1, the correlation between the true fifty QTL effects and the effects estimated for the SNPs located in the same QTL positions were 0.61, 0.60, 0.60 and 0.55, for RI, RC, RR-BLUP and BLASSO; and for Y2, these correlations were 0.81, 0.81, 0.81 and 0.71, respectively. In summary, we believe that estimates of SNP effects are promising quantitative tools to bring discussions on chromosome regions contributing most effectively to the phenotype expression when using ANN for genomic predictions.
650 $aMarker-assisted selection
650 $aNeural networks
650 $aParâmetro Genético
653 $aGenetic parameters
653 $aQTL
653 $aRedes neurais
700 1 $aCRUZ, C. D.
700 1 $aVIEIRA, R. A. M.
700 1 $aRESENDE, M. D. V. de
700 1 $aLOPES, P. S.
700 1 $aSIQUEIRA, O. H. G. B. D. de
700 1 $aSILVA, F. F. e
773 $tLivestock Science$gv. 191, p. 91-96, Sept. 2016.
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