New insights into genomic selection through population-based non-parametric prediction methods.

New insights into genomic selection through population-based non-parametric prediction methods.

Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.

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Main Authors: LIMA L. P., AZEVEDO, C. F., RESENDE, M. D. V. de, SILVA, F. F. e, SUELA, M. M., NASCIMENTO, M., VIANA, J. M. S.
Other Authors: Leísa Pires Lima, Universidade Federal de Viçosa; Camila Ferreira Azevedo, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Matheus Massariol Suela, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; José Marcelo Soriano Viana, Universidade Federal de Viçosa.
Format: Artigo de periódico biblioteca
Language:Ingles
English
Published: 2019-07-05
Subjects:Genomic prediction, Genetic gain, Asian rice, Oryza Sativa, Arroz, Selection index,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110406
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spelling dig-alice-doc-11104062020-06-10T04:06:37Z New insights into genomic selection through population-based non-parametric prediction methods. LIMA L. P. AZEVEDO, C. F. RESENDE, M. D. V. de SILVA, F. F. e SUELA, M. M. NASCIMENTO, M. VIANA, J. M. S. Leísa Pires Lima, Universidade Federal de Viçosa; Camila Ferreira Azevedo, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Matheus Massariol Suela, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; José Marcelo Soriano Viana, Universidade Federal de Viçosa. Genomic prediction Genetic gain Asian rice Oryza Sativa Arroz Selection index Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability. 2020-06-10T04:06:30Z 2020-06-10T04:06:30Z 2019-07-05 2019 Artigo de periódico Scientia Agricicola, v. 76, n. 4, p. 290-298, July/Aug. 2019. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110406 10.1590/1678-992x-2017-0351 Ingles en openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language Ingles
English
topic Genomic prediction
Genetic gain
Asian rice
Oryza Sativa
Arroz
Selection index
Genomic prediction
Genetic gain
Asian rice
Oryza Sativa
Arroz
Selection index
spellingShingle Genomic prediction
Genetic gain
Asian rice
Oryza Sativa
Arroz
Selection index
Genomic prediction
Genetic gain
Asian rice
Oryza Sativa
Arroz
Selection index
LIMA L. P.
AZEVEDO, C. F.
RESENDE, M. D. V. de
SILVA, F. F. e
SUELA, M. M.
NASCIMENTO, M.
VIANA, J. M. S.
New insights into genomic selection through population-based non-parametric prediction methods.
description Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.
author2 Leísa Pires Lima, Universidade Federal de Viçosa; Camila Ferreira Azevedo, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Matheus Massariol Suela, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; José Marcelo Soriano Viana, Universidade Federal de Viçosa.
author_facet Leísa Pires Lima, Universidade Federal de Viçosa; Camila Ferreira Azevedo, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Matheus Massariol Suela, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; José Marcelo Soriano Viana, Universidade Federal de Viçosa.
LIMA L. P.
AZEVEDO, C. F.
RESENDE, M. D. V. de
SILVA, F. F. e
SUELA, M. M.
NASCIMENTO, M.
VIANA, J. M. S.
format Artigo de periódico
topic_facet Genomic prediction
Genetic gain
Asian rice
Oryza Sativa
Arroz
Selection index
author LIMA L. P.
AZEVEDO, C. F.
RESENDE, M. D. V. de
SILVA, F. F. e
SUELA, M. M.
NASCIMENTO, M.
VIANA, J. M. S.
author_sort LIMA L. P.
title New insights into genomic selection through population-based non-parametric prediction methods.
title_short New insights into genomic selection through population-based non-parametric prediction methods.
title_full New insights into genomic selection through population-based non-parametric prediction methods.
title_fullStr New insights into genomic selection through population-based non-parametric prediction methods.
title_full_unstemmed New insights into genomic selection through population-based non-parametric prediction methods.
title_sort new insights into genomic selection through population-based non-parametric prediction methods.
publishDate 2019-07-05
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110406
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AT resendemdvde newinsightsintogenomicselectionthroughpopulationbasednonparametricpredictionmethods
AT silvaffe newinsightsintogenomicselectionthroughpopulationbasednonparametricpredictionmethods
AT suelamm newinsightsintogenomicselectionthroughpopulationbasednonparametricpredictionmethods
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