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| Registros recuperados : 6 | |
| 5. |  | REYNO, R.; SILVEIRA, F.; STEINHORST, D.; MARTINEZ, Y.; DE SOUZA, G.; OLIVARO, C.; MEDEROS, A. Avances en los estudios de cuantificación y caracterización de taninos condensados en leguminosas del género Lotus. Pasturas. Revista INIA Uruguay, Setiembre 2022, no.70, p.47-51. (Revista INIA; 70).| Biblioteca(s): INIA Las Brujas. |
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| 6. | | LAGOMARSINO, X.; LUZARDO, S.; MONTOSSI, F.; MEDEROS, A.; BOTTERO, S.; MARTÍNEZ, Y.; CARRACELAS, E.; ZAMIT, W.; BENTANCURT, M. Suplementación infrecuente con afrechillo de arroz a terneros pastoreando praderas mejoradas: ¿Es necesario suplementar todos los dias? ln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011. p. 27-30 (INIA Serie Actividades de Difusión ; 657)| Biblioteca(s): INIA Tacuarembó. |
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| Registros recuperados : 6 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
24/07/2020 |
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Actualizado : |
24/07/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
LOURENCO, D.; LEGARRA, A.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; MISZTAL, I. |
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Afiliación : |
DANIELA LOURENCO, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA; ANDRÉS LEGARRA, Institut National de la Recherche Agronomique, UMR1388 GenPhySE, 31326 Castanet Tolosan, France; SHOGO TSURUTA, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA; YUTAKA MASUDA, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IGNACY MISZTAL, Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA. |
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Título : |
Single-step genomic evaluations from theory to practice: using snp chips and sequence data in blupf90. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Genes, July 2020. Volume 11, Issue 7, Article number 790, Pages 1-32. Open Access. Doi: https://doi.org/10.3390/genes11070790 |
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ISSN : |
2073-4425 |
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DOI : |
10.3390/genes11070790 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 19 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 14 July 2020.
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data):
https://www.mdpi.com/journal/genes/special_issues/Genomic_Prediction |
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Contenido : |
ABSTRACT.
Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data. |
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Palabras claves : |
Genome-wide association; Genomic prediction; Genomic selection; SINGLE-STEP GENOMIC BLUP. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
https://www.mdpi.com/2073-4425/11/7/790/pdf
https://www.mdpi.com/2073-4425/11/7/790/review_report
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Marc : |
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100 1 $aLOURENCO, D.
245 $aSingle-step genomic evaluations from theory to practice$busing snp chips and sequence data in blupf90.$h[electronic resource]
260 $c2020
500 $aArticle history: Received: 19 June 2020 / Revised: 3 July 2020 / Accepted: 6 July 2020 / Published: 14 July 2020. (This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data): https://www.mdpi.com/journal/genes/special_issues/Genomic_Prediction
520 $aABSTRACT. Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.
653 $aGenome-wide association
653 $aGenomic prediction
653 $aGenomic selection
653 $aSINGLE-STEP GENOMIC BLUP
700 1 $aLEGARRA, A.
700 1 $aTSURUTA, S.
700 1 $aMASUDA, Y.
700 1 $aAGUILAR, I.
700 1 $aMISZTAL, I.
773 $tGenes, July 2020. Volume 11, Issue 7, Article number 790, Pages 1-32. Open Access. Doi: https://doi.org/10.3390/genes11070790
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