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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
15/10/2014 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; MEUWISSEN, T.H.E.; AGGREY, S.E.; WING, T.; MUIR, W.M. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Journal of Animal Science, 2011, v.89, no.1, p.23-28. |
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ISSN : |
0021-8812 |
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DOI : |
10.2527/jas.2010-3071 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received April 9, 2010 / Accepted September 22, 2010. |
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Contenido : |
ABSTRACT.
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/ FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.
©2011 American Society of Animal Science. MenosABSTRACT.
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and... Presentar Todo |
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Thesagro : |
MARCADORES GENÉTICOS; POLLO. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 02840naa a2200265 a 4500
001 1051115
005 2019-10-15
008 2011 bl uuuu u00u1 u #d
022 $a0021-8812
024 7 $a10.2527/jas.2010-3071$2DOI
100 1 $aCHEN, C.Y.
245 $aGenome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step$bAn example using broiler chickens.$h[electronic resource]
260 $c2011
500 $aArticle history: Received April 9, 2010 / Accepted September 22, 2010.
520 $aABSTRACT. Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/ FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based. ©2011 American Society of Animal Science.
650 $aMARCADORES GENÉTICOS
650 $aPOLLO
700 1 $aMISZTAL, I.
700 1 $aAGUILAR, I.
700 1 $aTSURUTA, S.
700 1 $aMEUWISSEN, T.H.E.
700 1 $aAGGREY, S.E.
700 1 $aWING, T.
700 1 $aMUIR, W.M.
773 $tJournal of Animal Science, 2011$gv.89, no.1, p.23-28.
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Registro original : |
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 : |
04/11/2022 |
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Actualizado : |
04/11/2022 |
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Tipo de producción científica : |
Informes Agroclimáticos |
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Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); GRAS |
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Afiliación : |
UNIDAD DE AGROCLIMA Y SISTEMAS DE INFORMACIÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Informe agroclimático 2022- Situación a Octubre. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Montevideo (UY): INIA, 2022. |
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Páginas : |
6 p. |
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Serie : |
(Informe Agroclimático; Año 17, No.10) |
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Idioma : |
Español |
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Notas : |
Equipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García. |
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Contenido : |
Contenido. Síntesis de la Situación Agroclimática de Octubre-- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia (Más información puede encontrarse en el sitio del IRI: http://www.iri.columbia.edu) -- Índice de Vegetación (NDVI) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Agua No Retenida (ANR) -- Índice de Bienestar Hídrico (IBH) -- Perspectivas Climáticas Nov-Dic-Ene elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Pronóstico de DON en trigo. Mapas nacionales mostrando la estimación de cantidad de DON en trigo a cosecha. Link directo: http://www.inia.uy/gras/Alertas-y-herramientas/Pron%C3%B3stico-DON-para-trigo- |
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Palabras claves : |
AGTECH; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; GRAFICAS AGROCLIMATICAS; GRAS; INFORMACION SATELITAL; INFORME AGROCLIMÁTICO 2022; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SISTEMAS DE INFORMACIÓN Y TRANSFORMACIÓN DIGITAL - INIA; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
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Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SISTEMAS DE INFORMACION GEOGRAFICOS; TEMPERATURA; TERMOMETROS. |
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Asunto categoría : |
P40 Meteorología y climatología |
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URL : |
http://www.inia.uy/Publicaciones/Documentos%20compartidos/Informe%20agroclimatico%20INIA-GRAS%20Octubre%20de%202022.pdf
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Marc : |
LEADER 02960nam a2200757 a 4500
001 1063702
005 2022-11-04
008 2022 bl uuuu u0uu1 u #d
100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA)
245 $aInforme agroclimático 2022- Situación a Octubre.$h[electronic resource]
260 $aMontevideo (UY): INIA$c2022
300 $a6 p.
490 $a(Informe Agroclimático; Año 17, No.10)
500 $aEquipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García.
520 $aContenido. Síntesis de la Situación Agroclimática de Octubre-- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia (Más información puede encontrarse en el sitio del IRI: http://www.iri.columbia.edu) -- Índice de Vegetación (NDVI) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Agua No Retenida (ANR) -- Índice de Bienestar Hídrico (IBH) -- Perspectivas Climáticas Nov-Dic-Ene elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Pronóstico de DON en trigo. Mapas nacionales mostrando la estimación de cantidad de DON en trigo a cosecha. Link directo: http://www.inia.uy/gras/Alertas-y-herramientas/Pron%C3%B3stico-DON-para-trigo-
650 $aAGROCLIMATOLOGIA
650 $aCAMBIO CLIMATICO
650 $aCLIMA
650 $aCLIMATOLOGIA
650 $aESTACIONES METEOROLOGICAS
650 $aESTRES HIDRICO
650 $aEVAPOTRANSPIRACION
650 $aHUMEDAD
650 $aHUMEDAD RELATIVA
650 $aLLUVIA
650 $aMETEOROLOGIA
650 $aPERSPECTIVAS
650 $aPLUVIOMETROS
650 $aPRONOSTICO DEL TIEMPO
650 $aSENSORES
650 $aSISTEMAS
650 $aSISTEMAS DE INFORMACION
650 $aSISTEMAS DE INFORMACION GEOGRAFICOS
650 $aTEMPERATURA
650 $aTERMOMETROS
653 $aAGTECH
653 $aBOLETIN AGROCLIMÁTICO
653 $aCARACTERIZACIÓN AGROCLIMÁTICA
653 $aDIRECCION VIENTO
653 $aESTACIONES AGROMETEOROLOGICAS
653 $aESTACIONES AUTOMATICAS
653 $aESTACIONES INIA
653 $aESTADO DEL TIEMPO
653 $aGRAFICAS AGROCLIMATICAS
653 $aGRAS
653 $aINFORMACION SATELITAL
653 $aINFORME AGROCLIMÁTICO 2022
653 $aINUNDACIONES
653 $aLLUVIAS DIARIAS
653 $aMAXIMA
653 $aMEDIA
653 $aMINIMA
653 $aPANEL SOLAR
653 $aPERSPECTIVAS CLIMATICAS
653 $aPLUVIOMETRO
653 $aPRECIPITACION NACIONAL
653 $aPREVENCION HELADAS
653 $aPRONOSTICO
653 $aSENSOR
653 $aSISTEMAS DE INFORMACIÓN Y TRANSFORMACIÓN DIGITAL - INIA
653 $aTANQUE A
653 $aTERMOCUPLAS
653 $aTERMOHIDROGRAFO
653 $aVARIABLES AGROCLIMATICAS
653 $aVELETA
700 1 $aGRAS
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