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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
21/05/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
TORRES, D.; BENNADJI, Z.; NIKICHUK, N.; SCOZ, R.; BALMELLI, G. |
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Afiliación : |
DIEGO GABRIEL TORRES DINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ZOHRA BENNADJI SOUALHIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NATALIA ISABEL NIKICHUK BELL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO JAVIER SCOZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO DANIEL BALMELLI HERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Trazabilidad molecular como herramienta para asegurar la productividad esperada en plantaciones clonales. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
ln: INIA Tacuarembó. Programa Nacional de Producción Forestal. Jornada Técnica, 11 julio, 2012, Tacuarembó, Uruguay. Biotecnología forestal. Tacuarembó (Uruguay): INIA, 2012. |
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Páginas : |
p. 3 |
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Serie : |
(INIA Serie Actividades de Difusión ; 684) |
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ISSN : |
1688-9258 |
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Idioma : |
Español |
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Notas : |
INIA Tacuarembó |
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Contenido : |
Las características biológicas del género Eucalyptus hace que sea posible la clonación de genotipos superiores, tanto por macro como por micropropagación, generando así clones de élite. Dichos clones son el producto final de la toma de decisiones de los mejoradores y son multiplicados por miles en viveros para ser posteriormente cultivados en plantaciones comerciales, logrando así una productividad diferencial. La correcta identificación de clones es la aplicación de los marcadores moleculares más demandada por los mejoradores del género Eucalyptus a nivel mundial. |
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Thesagro : |
BIOTECNOLOGIA; FORESTACIÓN. |
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Asunto categoría : |
K10 Producción forestal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8021/1/SAD-684-3.pdf
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Marc : |
LEADER 01400naa a2200241 a 4500
001 1026148
005 2018-05-21
008 2012 bl uuuu u00u1 u #d
022 $a1688-9258
100 1 $aTORRES, D.
245 $aTrazabilidad molecular como herramienta para asegurar la productividad esperada en plantaciones clonales.
260 $c2012
300 $ap. 3
490 $a(INIA Serie Actividades de Difusión ; 684)
500 $aINIA Tacuarembó
520 $aLas características biológicas del género Eucalyptus hace que sea posible la clonación de genotipos superiores, tanto por macro como por micropropagación, generando así clones de élite. Dichos clones son el producto final de la toma de decisiones de los mejoradores y son multiplicados por miles en viveros para ser posteriormente cultivados en plantaciones comerciales, logrando así una productividad diferencial. La correcta identificación de clones es la aplicación de los marcadores moleculares más demandada por los mejoradores del género Eucalyptus a nivel mundial.
650 $aBIOTECNOLOGIA
650 $aFORESTACIÓN
700 1 $aBENNADJI, Z.
700 1 $aNIKICHUK, N.
700 1 $aSCOZ, R.
700 1 $aBALMELLI, G.
773 $tln: INIA Tacuarembó. Programa Nacional de Producción Forestal. Jornada Técnica, 11 julio, 2012, Tacuarembó, Uruguay. Biotecnología forestal. Tacuarembó (Uruguay): INIA, 2012.
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INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
10/09/2014 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; LAWLOR, T.J.; FORNI, S.; WELLER, J.I. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Are evaluations on young genotyped animals benefiting from the past generations?. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Journal of Dairy Science, 2014, v.97, no.6, p.3930-3942. OPEN ACCESS |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2013-7769 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received November 26, 2013. // Accepted February 11, 2014. OPEN ACCESS |
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Contenido : |
ABSTRACT.
Data sets of US Holsteins, Israeli Holsteins, and pigs from PIC (a Genus company, Hendersonville, TN) were used to evaluate the effect of different numbers of generations on ability to predict genomic breeding values of young genotyped animals. The influence of including only 2 generations of ancestors (A2) or all ancestors (Af) was also investigated. A total of 34,506 US Holsteins, 1,305 Israeli Holsteins, and 5,236 pigs were genotyped. The evaluations were computed by traditional BLUP and single-step genomic BLUP, and computing performance was assessed for the latter method. For the 2 Holstein data sets, coefficients of determination (R2) and regression (?) of deregressed evaluations from a full data set with records up to 2011 on estimated breeding values and genomic estimated breeding values from the truncated data sets were computed. The thresholds for data deletion were set by intervals of 5 yr, based on the average generation interval in dairy cattle. For the PIC data set, correlations between corrected phenotypes and estimated or genomic estimated breeding values were used to evaluate predictive ability on young animals born in 2010 and 2011. The reduced data set contained data up to 2009, and the thresholds were set based on an average generation interval of 3 yr. The number of generations that could be deleted without a reduction in accuracy depended on data structure and trait. For US Holsteins, removing 3 and 4 generations of data did not reduce accuracy of evaluations for final score in Af and A2 scenarios, respectively. For Israeli Holsteins, the accuracies for milk, fat, and protein yields were the highest when only phenotypes recorded in 2000 and later were included and full pedigrees were applied. Of the 135 Israeli bulls with genotypes (validation set) and daughter records only in the complete data set, 38 and 97 were sons of Israeli and foreign bulls, respectively. Although more phenotypic data increased the prediction accuracy for sons of Israeli bulls, the reverse was true for sons of foreign bulls. Also, more phenotypic data caused large inflation of genomic estimated breeding values for sons of foreign bulls, whereas the opposite was true with the deletion of all but the most recent phenotypic data. Results for protein and fat percentage were different from those for milk, fat, and protein yields; however, relatively, the changes in coefficients of determination and regression were smaller for percentage traits. For PIC data set, removing data from up to 5 generations did not erode predictive ability for genotyped animals for the 2 reproductive traits used in validation. Given the data used in this study, truncating old data reduces computation requirements but does not decrease the accuracy. For small populations that include local and imported animals, truncation may be beneficial for one group of animals and detrimental to another group. MenosABSTRACT.
Data sets of US Holsteins, Israeli Holsteins, and pigs from PIC (a Genus company, Hendersonville, TN) were used to evaluate the effect of different numbers of generations on ability to predict genomic breeding values of young genotyped animals. The influence of including only 2 generations of ancestors (A2) or all ancestors (Af) was also investigated. A total of 34,506 US Holsteins, 1,305 Israeli Holsteins, and 5,236 pigs were genotyped. The evaluations were computed by traditional BLUP and single-step genomic BLUP, and computing performance was assessed for the latter method. For the 2 Holstein data sets, coefficients of determination (R2) and regression (?) of deregressed evaluations from a full data set with records up to 2011 on estimated breeding values and genomic estimated breeding values from the truncated data sets were computed. The thresholds for data deletion were set by intervals of 5 yr, based on the average generation interval in dairy cattle. For the PIC data set, correlations between corrected phenotypes and estimated or genomic estimated breeding values were used to evaluate predictive ability on young animals born in 2010 and 2011. The reduced data set contained data up to 2009, and the thresholds were set based on an average generation interval of 3 yr. The number of generations that could be deleted without a reduction in accuracy depended on data structure and trait. For US Holsteins, removing 3 and 4 generations of data did not reduce accura... Presentar Todo |
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Palabras claves : |
DAIRY CATTLE; GENOMIC SELECTION; PEDIGREE DEPTH; SINGLE-STEP GENOMIC BLUP. |
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Thesagro : |
BLUP; GANADO DE LECHE; SELECCIÓN GENÓMICA. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3064/1/Aguilar-I.-2014-Jr.Dairy-Sci.-v.976-p.3930-3942.pdf
https://www.journalofdairyscience.org/article/S0022-0302(14)00225-2/pdf
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Marc : |
LEADER 03893naa a2200313 a 4500
001 1050114
005 2019-10-09
008 2014 bl uuuu u00u1 u #d
022 $a0022-0302
024 7 $a10.3168/jds.2013-7769$2DOI
100 1 $aLOURENCO, D.A.L.
245 $aAre evaluations on young genotyped animals benefiting from the past generations?.$h[electronic resource]
260 $c2014
500 $aArticle history: Received November 26, 2013. // Accepted February 11, 2014. OPEN ACCESS
520 $aABSTRACT. Data sets of US Holsteins, Israeli Holsteins, and pigs from PIC (a Genus company, Hendersonville, TN) were used to evaluate the effect of different numbers of generations on ability to predict genomic breeding values of young genotyped animals. The influence of including only 2 generations of ancestors (A2) or all ancestors (Af) was also investigated. A total of 34,506 US Holsteins, 1,305 Israeli Holsteins, and 5,236 pigs were genotyped. The evaluations were computed by traditional BLUP and single-step genomic BLUP, and computing performance was assessed for the latter method. For the 2 Holstein data sets, coefficients of determination (R2) and regression (?) of deregressed evaluations from a full data set with records up to 2011 on estimated breeding values and genomic estimated breeding values from the truncated data sets were computed. The thresholds for data deletion were set by intervals of 5 yr, based on the average generation interval in dairy cattle. For the PIC data set, correlations between corrected phenotypes and estimated or genomic estimated breeding values were used to evaluate predictive ability on young animals born in 2010 and 2011. The reduced data set contained data up to 2009, and the thresholds were set based on an average generation interval of 3 yr. The number of generations that could be deleted without a reduction in accuracy depended on data structure and trait. For US Holsteins, removing 3 and 4 generations of data did not reduce accuracy of evaluations for final score in Af and A2 scenarios, respectively. For Israeli Holsteins, the accuracies for milk, fat, and protein yields were the highest when only phenotypes recorded in 2000 and later were included and full pedigrees were applied. Of the 135 Israeli bulls with genotypes (validation set) and daughter records only in the complete data set, 38 and 97 were sons of Israeli and foreign bulls, respectively. Although more phenotypic data increased the prediction accuracy for sons of Israeli bulls, the reverse was true for sons of foreign bulls. Also, more phenotypic data caused large inflation of genomic estimated breeding values for sons of foreign bulls, whereas the opposite was true with the deletion of all but the most recent phenotypic data. Results for protein and fat percentage were different from those for milk, fat, and protein yields; however, relatively, the changes in coefficients of determination and regression were smaller for percentage traits. For PIC data set, removing data from up to 5 generations did not erode predictive ability for genotyped animals for the 2 reproductive traits used in validation. Given the data used in this study, truncating old data reduces computation requirements but does not decrease the accuracy. For small populations that include local and imported animals, truncation may be beneficial for one group of animals and detrimental to another group.
650 $aBLUP
650 $aGANADO DE LECHE
650 $aSELECCIÓN GENÓMICA
653 $aDAIRY CATTLE
653 $aGENOMIC SELECTION
653 $aPEDIGREE DEPTH
653 $aSINGLE-STEP GENOMIC BLUP
700 1 $aMISZTAL, I.
700 1 $aTSURUTA, S.
700 1 $aAGUILAR, I.
700 1 $aLAWLOR, T.J.
700 1 $aFORNI, S.
700 1 $aWELLER, J.I.
773 $tJournal of Dairy Science, 2014$gv.97, no.6, p.3930-3942. OPEN ACCESS
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