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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
26/11/2015 |
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Actualizado : |
29/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
LOURENCO, D. A. L.; TSURUTA, S.; FRAGOMENI, B. O.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; BERTRAND, J. K.; AMEN, T. S.; WANG. L.; MOSER, D. W.; MISZTAL, I. |
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Afiliación : |
D. A. L. LOURENCO, Universidad de Georgia (UG); SHOGO TSURUTA, Universidad de Georgia (UG); B. O. FRAGOMENI, Universidad de Georgia (UG); Y. MASUDA, Universidad de Georgia (UG); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; A. LEGARRA, INRA (Institut National de la Recherche Agronomique); J. K. BERTRAND, Universidad de Georgia (UG); T. S. AMEN, Angus Genetics Inc.; L. WANG, Angus Genetics Inc.; D. W. MOSER, Angus Genetics Inc.; IGNACY MISZTAL, Universidad de Georgia (UG). |
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Título : |
Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*) |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Journal of Animal Science, 2015, v. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS. |
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DOI : |
10.2527/jas.2014-8836 |
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Idioma : |
Inglés |
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Notas : |
(*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers. |
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Contenido : |
ABSTRACT.
Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals,
which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an
index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in
the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.
© 2015 American Society of Animal Science. All rights reserved MenosABSTRACT.
Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals,
which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an
index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29,... Presentar Todo |
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Palabras claves : |
BEEF CATTLE; GENETIC RESURSION; INDIRECT PREDICTION. |
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Thesagro : |
GANADO DE CARNE; GENOMIC SELECTION; MEJORAMIENTO GENETICO ANIMAL. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5303/1/Lourenco-et-al-2015-JAS.pdf
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Marc : |
LEADER 04125naa a2200337 a 4500
001 1054005
005 2020-01-29
008 2015 bl uuuu u00u1 u #d
024 7 $a10.2527/jas.2014-8836$2DOI
100 1 $aLOURENCO, D. A. L.
245 $aGenetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*)$h[electronic resource]
260 $c2015
500 $a(*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers.
520 $aABSTRACT. Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability. © 2015 American Society of Animal Science. All rights reserved
650 $aGANADO DE CARNE
650 $aGENOMIC SELECTION
650 $aMEJORAMIENTO GENETICO ANIMAL
653 $aBEEF CATTLE
653 $aGENETIC RESURSION
653 $aINDIRECT PREDICTION
700 1 $aTSURUTA, S.
700 1 $aFRAGOMENI, B. O.
700 1 $aMASUDA, Y.
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aBERTRAND, J. K.
700 1 $aAMEN, T. S.
700 1 $aWANG. L.
700 1 $aMOSER, D. W.
700 1 $aMISZTAL, I.
773 $tJournal of Animal Science, 2015$gv. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
16/07/2018 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
MONTOSSI, F.; DE BARBIERI, I.; CIAPPESONI, G.; DE MATTOS, D.; MEDEROS, A.; LUZARDO, S.; SOARES DE LIMA, J.M.; DE LOS CAMPOS, G.; NOLLA, M.; SAN JULIAN, R.; GRATTAROLA, M.; PÉREZ JONES, J.; DONAGARAY, F.; FROS, A. |
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Afiliación : |
FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL DE MATTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AMERICA ESTHER MEDEROS SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO SAN JULIAN SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
El Proyecto Merino Fino riende cuentas: los productos logrados en los primeros ocho años (1998-2006). |
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Fecha de publicación : |
2007 |
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Fuente / Imprenta : |
El País Agropecuario, 2007, v. 13, no. 151, p. 30-33 |
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Idioma : |
Español |
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Contenido : |
El Proyecto Merino Fino del Uruguay ha cumplido satisfactoriamente con los objetivos trazados desde un principio. El esfuerzo conjunto y coordinado de productores
(ARU y SCMAU) Ysus instituciones (SUL e INlA) dio sus primeros frutos: se generó un cúmulo de información productiva y científica sin precedentes en el país, y un modelo asociativo y participativo de mejoramiento genético totalmente innovador. Los materiales genéticos finos y superfrno e multiplican a través de la cabaña
nacional, y se han generado señales internas de mercado para valorizar y promover el desarrollo de la producción e industrialización de lana finas y superfinas en el
Uruguay. |
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Palabras claves : |
ANIMAL PRODUCTION; SHEEP. |
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Thesagro : |
GANADERIA; LANA. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10823/1/151.pdf
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Marc : |
LEADER 01549naa a2200325 a 4500
001 1028570
005 2018-07-16
008 2007 bl uuuu u00u1 u #d
100 1 $aMONTOSSI, F.
245 $aEl Proyecto Merino Fino riende cuentas$blos productos logrados en los primeros ocho años (1998-2006).
260 $c2007
520 $aEl Proyecto Merino Fino del Uruguay ha cumplido satisfactoriamente con los objetivos trazados desde un principio. El esfuerzo conjunto y coordinado de productores (ARU y SCMAU) Ysus instituciones (SUL e INlA) dio sus primeros frutos: se generó un cúmulo de información productiva y científica sin precedentes en el país, y un modelo asociativo y participativo de mejoramiento genético totalmente innovador. Los materiales genéticos finos y superfrno e multiplican a través de la cabaña nacional, y se han generado señales internas de mercado para valorizar y promover el desarrollo de la producción e industrialización de lana finas y superfinas en el Uruguay.
650 $aGANADERIA
650 $aLANA
653 $aANIMAL PRODUCTION
653 $aSHEEP
700 1 $aDE BARBIERI, I.
700 1 $aCIAPPESONI, G.
700 1 $aDE MATTOS, D.
700 1 $aMEDEROS, A.
700 1 $aLUZARDO, S.
700 1 $aSOARES DE LIMA, J.M.
700 1 $aDE LOS CAMPOS, G.
700 1 $aNOLLA, M.
700 1 $aSAN JULIAN, R.
700 1 $aGRATTAROLA, M.
700 1 $aPÉREZ JONES, J.
700 1 $aDONAGARAY, F.
700 1 $aFROS, A.
773 $tEl País Agropecuario, 2007$gv. 13, no. 151, p. 30-33
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INIA Tacuarembó (TBO) |
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