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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
03/01/2018 |
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Data da última atualização: |
11/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
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Páginas: |
12 p. |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
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Thesagro: |
Estatística. |
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Thesaurus Nal: |
Marker-assisted selection; Simulation models; Statistics. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
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001 2084109
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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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Embrapa Florestas (CNPF) |
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| 1. |  | 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. Regularized quantile regression applied to genome-enabled prediction of quantitative traits. Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. 12 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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