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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
03/01/2018 |
Data da última atualização: |
11/01/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. |
Afiliação: |
M. Nascimento, UFV; F. F. e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; C. D. Cruz, UFV; A. C. C. Nascimento, UFV; J. M. S. Viana, UFV; C. F. Azevedo, UFV; L. M. A. Barroso, UFV. |
Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
Páginas: |
12 p. |
Idioma: |
Inglês |
Conteúdo: |
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. MenosGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved be... Mostrar Tudo |
Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
Thesagro: |
Estatística. |
Thesaurus Nal: |
Marker-assisted selection; Simulation models; Statistics. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
LEADER 02634naa a2200313 a 4500 001 2084109 005 2018-01-11 008 2017 bl uuuu u00u1 u #d 100 1 $aNASCIMENTO, M. 245 $aRegularized quantile regression applied to genome-enabled prediction of quantitative traits.$h[electronic resource] 260 $c2017 300 $a12 p. 520 $aGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. 650 $aMarker-assisted selection 650 $aSimulation models 650 $aStatistics 650 $aEstatística 653 $aGenomic selection 653 $aRegularized regression 653 $aSeleção genômica 653 $aSNP effects 700 1 $aSILVA, F. F. e 700 1 $aRESENDE, M. D. V. de 700 1 $aCRUZ, C. D. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aVIANA, J. M. S. 700 1 $aAZEVEDO, C. F. 700 1 $aBARROSO, L. M. A. 773 $tGenetics and Molecular Research$gv. 16, n. 1, gmr16019538, 2017.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
Biblioteca(s): |
Embrapa Suínos e Aves. |
Data corrente: |
31/10/2022 |
Data da última atualização: |
31/10/2022 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
DAL PIZZOL, M. S.; MARCELINO, D. E. P.; IBELLI, A. M. G.; NEIS, F. T.; SALMÓRIA, L. A.; PEIXOTO, J. de O.; LEDUR, M. C. |
Afiliação: |
MARIANE SPUDEIT DAL PIZZOL, Universidade do Estado de Santa Catarina/Chapecó; DÉBORA ESTER PETRY MARCELINO, Faculdade Concórdia; ADRIANA MERCIA GUARATINI IBELLI, CNPSA; FERNANDA TONELLO NEIS, IFC/Concórdia; LETÍCIA ALVES SALMÓRIA, Universidade Estadual do Centro Oeste do Paraná/Guarapuava; JANE DE OLIVEIRA PEIXOTO, CNPSA; MONICA CORREA LEDUR, CNPSA. |
Título: |
Perfil da expressão dos genes PERP2, LEPR e ANGPLT5 em frangos de corte de 21 dias normais e afetados com necrose da cabeça do fêmur. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL ON-LINE, 14., 2021. Anais... Chapecó: UDESC: Concórdia: Embrapa Suínos e Aves, 2022. |
Idioma: |
Português |
Palavras-Chave: |
Bone integrity; Integridade óssea; Locomotor problems; Ossificação; Ossification; Problemas locomotores. |
Thesagro: |
Avicultura; Genética. |
Thesaurus NAL: |
Genetics; Poultry. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1147915/1/final9765.pdf
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Marc: |
LEADER 00976nam a2200289 a 4500 001 2147915 005 2022-10-31 008 2022 bl uuuu u00u1 u #d 100 1 $aDAL PIZZOL, M. S. 245 $aPerfil da expressão dos genes PERP2, LEPR e ANGPLT5 em frangos de corte de 21 dias normais e afetados com necrose da cabeça do fêmur.$h[electronic resource] 260 $aIn: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL ON-LINE, 14., 2021. Anais... Chapecó: UDESC: Concórdia: Embrapa Suínos e Aves$c2022 650 $aGenetics 650 $aPoultry 650 $aAvicultura 650 $aGenética 653 $aBone integrity 653 $aIntegridade óssea 653 $aLocomotor problems 653 $aOssificação 653 $aOssification 653 $aProblemas locomotores 700 1 $aMARCELINO, D. E. P. 700 1 $aIBELLI, A. M. G. 700 1 $aNEIS, F. T. 700 1 $aSALMÓRIA, L. A. 700 1 $aPEIXOTO, J. de O. 700 1 $aLEDUR, M. C.
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