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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
03/10/2018 |
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Actualizado : |
24/02/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LADO, B.; VÁZQUEZ, D.; QUINCKE, M.; SILVA, P.; AGUILAR, I.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Universidad de la República (UdelaR)/ Facultad de Agronomía; DANIEL VÁZQUEZ PEYRONEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Universidad de la República (UdelaR)/ Facultad de Agronomía; Universidad de Wisconsin-Madison. |
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Título : |
Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article]. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Theoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS. |
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ISSN : |
0040-5752 |
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DOI : |
10.1007/s00122-018-3186-3 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 29 January 2018 / Accepted: 10 September 2018 / Published online: 19 September 2018.
Supplementary materials.
Acknowledgements: We express our appreciation for the effort of the technical personnel of INIA La Estanzuela from ?Laboratorio de calidad industrial de granos.? Support for doctoral work of BL was provided by Agencia Nacional de Investigación e Innovación (ANII), Uruguay, through Grant POS_NAC_2013_1_11261 and by Comisión Sectorial de Investigación Científica (CSIC), Uruguay, through grants in the program internships abroad. We would like to thank two anonymous reviewers for their comments that improved the manuscript.
Open Access
Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
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Contenido : |
KEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.
ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.
© 2018, The Author(s). MenosKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters.
ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped f... Presentar Todo |
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Palabras claves : |
ABILITY TESTING; FORECASTING; GENOMIC PREDICTIONS; PLANT BREEDING PROGRAMS; PLANTS (BOTANY); PLATAFORMA AGROALIMENTOS; QUALITY CONTROL; SOFTWARE TESTING. |
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Thesagro : |
GENES. |
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Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/11357/1/Lado2018-Article-ResourceAllocationOptimization.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/12863/1/122-2018-3186-MOESM1-ESM.pdf
https://link.springer.com/content/pdf/10.1007%2Fs00122-018-3186-3.pdf
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Marc : |
LEADER 03937naa a2200325 a 4500
001 1059141
005 2022-02-24
008 2018 bl uuuu u00u1 u #d
022 $a0040-5752
024 7 $a10.1007/s00122-018-3186-3$2DOI
100 1 $aLADO, B.
245 $aResource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. [Original article].$h[electronic resource]
260 $c2018
500 $aArticle history: Received: 29 January 2018 / Accepted: 10 September 2018 / Published online: 19 September 2018. Supplementary materials. Acknowledgements: We express our appreciation for the effort of the technical personnel of INIA La Estanzuela from ?Laboratorio de calidad industrial de granos.? Support for doctoral work of BL was provided by Agencia Nacional de Investigación e Innovación (ANII), Uruguay, through Grant POS_NAC_2013_1_11261 and by Comisión Sectorial de Investigación Científica (CSIC), Uruguay, through grants in the program internships abroad. We would like to thank two anonymous reviewers for their comments that improved the manuscript. Open Access Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
520 $aKEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. ABSTRACT: Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program. © 2018, The Author(s).
650 $aGENES
653 $aABILITY TESTING
653 $aFORECASTING
653 $aGENOMIC PREDICTIONS
653 $aPLANT BREEDING PROGRAMS
653 $aPLANTS (BOTANY)
653 $aPLATAFORMA AGROALIMENTOS
653 $aQUALITY CONTROL
653 $aSOFTWARE TESTING
700 1 $aVÁZQUEZ, D.
700 1 $aQUINCKE, M.
700 1 $aSILVA, P.
700 1 $aAGUILAR, I.
700 1 $aGUTIÉRREZ, L.
773 $tTheoretical and Applied Genetics, 1 December 2018, Volume 131, Issue 12, pp. 2719-2731. OPEN ACCESS.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/08/2022 |
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Actualizado : |
22/05/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
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Circulación / Nivel : |
Nacional - -- |
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Autor : |
CARRACELAS, B.; NAVAJAS, E.; VERA, B.; CIAPPESONI, G. |
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Afiliación : |
EMERITA BEATRIZ CARRACELAS MARQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BRENDA VERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
SNP arrays evaluation as tools in genetic improvement in Corriedale sheep in Uruguay. [Evaluación de paneles de SNP como herramientas en la mejora genética de ovinos Corriedale en Uruguay]. [Avaliação de painéis de SNP como ferramentas em melhoramento genético de ovinos Corriedale no Uruguai]. |
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Complemento del título : |
Animal production and pastures. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Agrociencia Uruguay, 2022, vol. 26, number 2, article e998. doi: https://doi.org/10.31285/AGRO.26.998 |
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ISSN : |
2730-5066 |
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DOI : |
10.31285/AGRO.26.998 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 09 Feb 2022; Accepted 30 May 2022; Published 19 Aug 2022. Editor: Mariana Carriquiry, Universidad de la República, Facultad de Agronomía, Montevideo, Uruguay. Correspondence: Beatriz Carracelas, bcarracelas@inia.org.uy -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (CC BY 4.0). |
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Contenido : |
ABSTRACT.- One control strategy for gastrointestinal nematodes (GIN) is genetic selection. This studys objective was to compare eggs per gram of feces (FEC) and fiber diameter (FD) estimated breeding values (EBV) and genomic EBV (GEBV) in Corriedale breed. Analysis included 19547 lambs with data, and 454, 711 and 383 genotypes from 170, 507 and 50K SNP chips, respectively. A univariate animal model was used for EBV and GEBV estimation, which included contemporary group, type of birth and dam age as fixed effects, and age at recording as covariate. Differential weights (?) were considered in the genomic relationship matrix (G), and the best fit models were identified using Akaikes Information Criterion (AIC), which were later used for GEBV and accuracies estimation. The use of only impacted on low density SNP chips. No differences were observed in mean accuracies for the whole population. However, in the genotyped subgroup accuracies increased by 2% with the 170 SNP chip (?=0.25), and by 5% (?=0.5) and 14% (?=0.75) with the 507 SNP chip. No differences were observed in FD EBV and GEBV mean accuracies. These results show that it is possible to increase GEBV accuracies despite the use of low-density chips -.-.-.-.-.-.-.-.-.-.-.-..-RESUMEN.- Una alternativa para el control de losnematodos gastrointestinales (NGI) es la selección genética. El objetivo de este trabajo fue comparar las precisiones de los valores de cría (EBV) y los EBV genómicos (GEBV) del recuento de huevos por gramo en heces (HPG) y diámetro de fibra (DF) en la raza Corriedale. El análisis incluyó 19547 corderos con datos fenotípicos y 454, 711 y 383 genotipados con paneles o chips de 170, 507 y 50K SNP, respectivamente. Los EBV y GEBV se estimaron con un modelo animal univariado que incluyó los efectos fijos:grupo contemporáneo, tipo de nacimiento y edad de la madre,y edad al registro como covariable. Se consideraron pesos diferenciales (?) en la matriz de relaciones genómicas, identificándose los modelos con mejor ajuste con el criterio de información de Akaike (AIC), que fueron utili-zados para la estimación de los GEBV y sus precisiones. El uso de ?solo impactó en el ajuste con paneles de baja densidad. No se encontraron diferencias en las precisiones promedio de la población total. En cambio, en el subgrupo de animales genotipados las precisiones aumentaron 2% con 170 SNP (?=0.25), y con 507 SNP 5% (?=0.5) y 14% (?=0.75). No hubo diferencias en precisiones de los EBV y los GEBV de DF. Los resultados muestran que es posible aumentar las precisiones de los GEBV aun con paneles de baja densidad. -.-.-.-.-.-.-.-.-.-.-.-..-RESUMO.- Uma alternativa para o controle de nematóidesgastrointestinais (NGI) é a seleção genética. O objetivo deste trabalho foi comparar as precisões dos valores genéticos estimados (EBV) e dos EBVs genômicos (GEBV) da contagem de ovos por grama de fezes (OPG) e diâmetro de fibra (DF) na raça Corriedale. Aanálise incluiu 19547 cordeiros com dados e 454, 711 e 383 genotipados de 170, 507 e 50K SNPs, respectivamente. Foram estimados os EBV e GEBV com um modelo animal univariado que incluiu efeitos fixos de grupo contemporâneo, tipo de nascimento e idade da mãe e idade no registro (covariável). Pesos diferenciais (?) foram considerados na matriz de relações genômicas, identificando os modelos com melhor ajuste via critério de informação de Akaike (AIC), os quais foram utilizados para estimar o GEBV e suas precisões. O uso de ?=0.75 somente impactou no ajuste com painéis de baixa densidade. Não foram encontradas diferenças na precisão média da população total. Em contraste, no subgrupo de animais genotipados as precisões aumentaram 2% com 170 SNPs (?=0.25), e com 507 SNPs 5% (?=0.5) e 14% (?=0.75). Não houve diferenças na precisão de EBV e GEBV de DF. Os resultados mostram que é possível aumentar a precisão de GEBVs mesmo que se utilizem painéis de baixa densidade. MenosABSTRACT.- One control strategy for gastrointestinal nematodes (GIN) is genetic selection. This studys objective was to compare eggs per gram of feces (FEC) and fiber diameter (FD) estimated breeding values (EBV) and genomic EBV (GEBV) in Corriedale breed. Analysis included 19547 lambs with data, and 454, 711 and 383 genotypes from 170, 507 and 50K SNP chips, respectively. A univariate animal model was used for EBV and GEBV estimation, which included contemporary group, type of birth and dam age as fixed effects, and age at recording as covariate. Differential weights (?) were considered in the genomic relationship matrix (G), and the best fit models were identified using Akaikes Information Criterion (AIC), which were later used for GEBV and accuracies estimation. The use of only impacted on low density SNP chips. No differences were observed in mean accuracies for the whole population. However, in the genotyped subgroup accuracies increased by 2% with the 170 SNP chip (?=0.25), and by 5% (?=0.5) and 14% (?=0.75) with the 507 SNP chip. No differences were observed in FD EBV and GEBV mean accuracies. These results show that it is possible to increase GEBV accuracies despite the use of low-density chips -.-.-.-.-.-.-.-.-.-.-.-..-RESUMEN.- Una alternativa para el control de losnematodos gastrointestinales (NGI) es la selección genética. El objetivo de este trabajo fue comparar las precisiones de los valores de cría (EBV) y los EBV genómicos (GEBV) del recuento... Presentar Todo |
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Palabras claves : |
Accuracy; FEC; GEBV; HPG; OPG; Precisão; Precisión. |
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Thesagro : |
CORRIEDALE. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16637/1/998-Article-Text-6221-1-10-20220819.pdf
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Marc : |
LEADER 05383naa a2200289 a 4500
001 1063543
005 2023-05-22
008 2022 bl uuuu u00u1 u #d
022 $a2730-5066
024 7 $a10.31285/AGRO.26.998$2DOI
100 1 $aCARRACELAS, B.
245 $aSNP arrays evaluation as tools in genetic improvement in Corriedale sheep in Uruguay. [Evaluación de paneles de SNP como herramientas en la mejora genética de ovinos Corriedale en Uruguay]. [Avaliação de painéis de SNP como ferramentas em melhoramento genético de ovinos Corriedale no Uruguai].$h[electronic resource]
260 $c2022
500 $aArticle history: Received 09 Feb 2022; Accepted 30 May 2022; Published 19 Aug 2022. Editor: Mariana Carriquiry, Universidad de la República, Facultad de Agronomía, Montevideo, Uruguay. Correspondence: Beatriz Carracelas, bcarracelas@inia.org.uy -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (CC BY 4.0).
520 $aABSTRACT.- One control strategy for gastrointestinal nematodes (GIN) is genetic selection. This studys objective was to compare eggs per gram of feces (FEC) and fiber diameter (FD) estimated breeding values (EBV) and genomic EBV (GEBV) in Corriedale breed. Analysis included 19547 lambs with data, and 454, 711 and 383 genotypes from 170, 507 and 50K SNP chips, respectively. A univariate animal model was used for EBV and GEBV estimation, which included contemporary group, type of birth and dam age as fixed effects, and age at recording as covariate. Differential weights (?) were considered in the genomic relationship matrix (G), and the best fit models were identified using Akaikes Information Criterion (AIC), which were later used for GEBV and accuracies estimation. The use of only impacted on low density SNP chips. No differences were observed in mean accuracies for the whole population. However, in the genotyped subgroup accuracies increased by 2% with the 170 SNP chip (?=0.25), and by 5% (?=0.5) and 14% (?=0.75) with the 507 SNP chip. No differences were observed in FD EBV and GEBV mean accuracies. These results show that it is possible to increase GEBV accuracies despite the use of low-density chips -.-.-.-.-.-.-.-.-.-.-.-..-RESUMEN.- Una alternativa para el control de losnematodos gastrointestinales (NGI) es la selección genética. El objetivo de este trabajo fue comparar las precisiones de los valores de cría (EBV) y los EBV genómicos (GEBV) del recuento de huevos por gramo en heces (HPG) y diámetro de fibra (DF) en la raza Corriedale. El análisis incluyó 19547 corderos con datos fenotípicos y 454, 711 y 383 genotipados con paneles o chips de 170, 507 y 50K SNP, respectivamente. Los EBV y GEBV se estimaron con un modelo animal univariado que incluyó los efectos fijos:grupo contemporáneo, tipo de nacimiento y edad de la madre,y edad al registro como covariable. Se consideraron pesos diferenciales (?) en la matriz de relaciones genómicas, identificándose los modelos con mejor ajuste con el criterio de información de Akaike (AIC), que fueron utili-zados para la estimación de los GEBV y sus precisiones. El uso de ?solo impactó en el ajuste con paneles de baja densidad. No se encontraron diferencias en las precisiones promedio de la población total. En cambio, en el subgrupo de animales genotipados las precisiones aumentaron 2% con 170 SNP (?=0.25), y con 507 SNP 5% (?=0.5) y 14% (?=0.75). No hubo diferencias en precisiones de los EBV y los GEBV de DF. Los resultados muestran que es posible aumentar las precisiones de los GEBV aun con paneles de baja densidad. -.-.-.-.-.-.-.-.-.-.-.-..-RESUMO.- Uma alternativa para o controle de nematóidesgastrointestinais (NGI) é a seleção genética. O objetivo deste trabalho foi comparar as precisões dos valores genéticos estimados (EBV) e dos EBVs genômicos (GEBV) da contagem de ovos por grama de fezes (OPG) e diâmetro de fibra (DF) na raça Corriedale. Aanálise incluiu 19547 cordeiros com dados e 454, 711 e 383 genotipados de 170, 507 e 50K SNPs, respectivamente. Foram estimados os EBV e GEBV com um modelo animal univariado que incluiu efeitos fixos de grupo contemporâneo, tipo de nascimento e idade da mãe e idade no registro (covariável). Pesos diferenciais (?) foram considerados na matriz de relações genômicas, identificando os modelos com melhor ajuste via critério de informação de Akaike (AIC), os quais foram utilizados para estimar o GEBV e suas precisões. O uso de ?=0.75 somente impactou no ajuste com painéis de baixa densidade. Não foram encontradas diferenças na precisão média da população total. Em contraste, no subgrupo de animais genotipados as precisões aumentaram 2% com 170 SNPs (?=0.25), e com 507 SNPs 5% (?=0.5) e 14% (?=0.75). Não houve diferenças na precisão de EBV e GEBV de DF. Os resultados mostram que é possível aumentar a precisão de GEBVs mesmo que se utilizem painéis de baixa densidade.
650 $aCORRIEDALE
653 $aAccuracy
653 $aFEC
653 $aGEBV
653 $aHPG
653 $aOPG
653 $aPrecisão
653 $aPrecisión
700 1 $aNAVAJAS, E.
700 1 $aVERA, B.
700 1 $aCIAPPESONI, G.
773 $tAgrociencia Uruguay, 2022, vol. 26, number 2, article e998. doi: https://doi.org/10.31285/AGRO.26.998
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