Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study.
This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
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Format: | Artigo de periódico biblioteca |
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2022-01-26
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Subjects: | Regressão Linear, Seleção Genótipa, Genomics, Plant selection guides, Plant breeding, |
Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139325 https://doi.org/10.1371/journal.pone.0243666 |
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dig-alice-doc-11393252022-01-26T15:00:34Z Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. OLIVEIRA, G. F. NASCIMENTO, A. C. C. NASCIMENTO, M. SANT'ANNA, I. de C. ROMERO, J. V. AZEVEDO, C. F. BHERING, L. L. CAIXETA, E. T. GABRIELA FRANÇA OLIVEIRA, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT'ANNA, IAC; JUAN VICENTE ROMERO, AGROSAVIA; CAMILA FERREIRA AZEVEDO, UFV; LEONARDO LOPES BHERING, UFV; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios. 2022-01-26T15:00:24Z 2022-01-26T15:00:24Z 2022-01-26 2021 Artigo de periódico Plos One, v. 16, n. 1, e0243666, 2021. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139325 https://doi.org/10.1371/journal.pone.0243666 Ingles en openAccess |
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Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding |
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Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding OLIVEIRA, G. F. NASCIMENTO, A. C. C. NASCIMENTO, M. SANT'ANNA, I. de C. ROMERO, J. V. AZEVEDO, C. F. BHERING, L. L. CAIXETA, E. T. Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
description |
This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios. |
author2 |
GABRIELA FRANÇA OLIVEIRA, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT'ANNA, IAC; JUAN VICENTE ROMERO, AGROSAVIA; CAMILA FERREIRA AZEVEDO, UFV; LEONARDO LOPES BHERING, UFV; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. |
author_facet |
GABRIELA FRANÇA OLIVEIRA, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT'ANNA, IAC; JUAN VICENTE ROMERO, AGROSAVIA; CAMILA FERREIRA AZEVEDO, UFV; LEONARDO LOPES BHERING, UFV; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. OLIVEIRA, G. F. NASCIMENTO, A. C. C. NASCIMENTO, M. SANT'ANNA, I. de C. ROMERO, J. V. AZEVEDO, C. F. BHERING, L. L. CAIXETA, E. T. |
format |
Artigo de periódico |
topic_facet |
Regressão Linear Seleção Genótipa Genomics Plant selection guides Plant breeding |
author |
OLIVEIRA, G. F. NASCIMENTO, A. C. C. NASCIMENTO, M. SANT'ANNA, I. de C. ROMERO, J. V. AZEVEDO, C. F. BHERING, L. L. CAIXETA, E. T. |
author_sort |
OLIVEIRA, G. F. |
title |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
title_short |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
title_full |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
title_fullStr |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
title_full_unstemmed |
Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
title_sort |
quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study. |
publishDate |
2022-01-26 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1139325 https://doi.org/10.1371/journal.pone.0243666 |
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