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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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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 : |
12/06/2024 |
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Actualizado : |
12/06/2024 |
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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 : |
FARIÑA, S.; MORENO, O.V.; CANDIOTI, F.; VILLANUEVA, C.; LEDEZMA, W.S.; MOSCOSO, C.J.; CAJARVILLE, C.; CHARLÓN, V.; ABAUNZA, L.U.; VITERI, A.G.; CHIRIFE, S.; HERRERA, D.; STIRLING, S. |
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Afiliación : |
SANTIAGO FARIÑA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ORCID: https://orcid.org/0000-0003-2386-7136; OSIRIS VIGIL MORENO, Instituto de Innovación Agropecuaria de Panamá (IDIAP), Ciudad del Saber, Clayton, Cuidad de Panamá, Panama; FRANCISCO CANDIOTI, Salto Agro S.S., Bgdier. E. López 1822, Gdor. Candioti, Santa Fe, Argentina; CRISTÓBAL VILLANUEVA, Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), Turrialba, Costa Rica; WILLIAM SÁNCHEZ LEDEZMA, Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria (INTA), Costa Rica; CRISTIAN J. MOSCOSO, Instituto de Investigaciones Agropecuarias, INIA Remehue, P.O. Box 24-0, Osorno, Chile; CECILIA CAJARVILLE, Facultad de Veterinaria, Universidad de la República (UdelaR), Ruta 1, km 42.5, San José, Uruguay; VERÓNICA CHARLÓN, Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Rafaela, Francia 459, Rafaela, Santa Fe, Argentina; LUIS URBINA ABAUNZA, Instituto Nicaragüense de Tecnología Agropecuaria (INTA), Managua, Nicaragua; ANTONIO GUACAPIÑA VITERI, Instituto Nacional de Investigaciones Agropecuarias (INIAP), Estación Experimental Santa Catalina, Panamericana Sur km. 1, Mejía, Ecuador; SILVIA CHIRIFE, Instituto Paraguayo de Tecnología Agraria (IPTA), Oficina Central, Ruta 2, Km 10,5, San Lorenzo, Paraguay; DOMICIANO HERRERA, Instituto de Innovación Agropecuaria de Panamá (IDIAP), Ciudad del Saber, Clayton, Cuidad de Panamá, Panama; MARÍA SOFÍA STIRLING SANTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ORCID: https://orcid.org/0000-0001-6053-0386. |
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Título : |
Milk production systems in Latin America and the Caribbean: Biophysical, socio-economic, and environmental performance. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Agricultural Systems, 2024, Volume 218, 03987. https://doi.org/10.1016/j.agsy.2024.103987 |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2024.103987 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 2 December 2023, Revised 8 March 2024, Accepted 30 April 2024, Available online 7 May 2024, Version of Record 7 May 2024. -- Correspondence: Fariña, S.; Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 50 km 11, Colonia, Uruguay; email:santiagofarina1@gmail.com -- Funding: This research was funded by FONTAGRO through the project "Sustainable Intensification of Dairy" with grant FTG/RF-15940-RG. |
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Contenido : |
ABSTRACT.- CONTEXT: The Latin America-Caribbean (LAC) region has the potential to pursue a sustainable expansion of livestock production and capitalise on the growing global demand for dairy and beef products. However, it is necessary to understand the predominant production systems and their biophysical, economic, social, and environmental performances. OBJECTIVE: This study aimed to compare the milk production systems of nine countries of the region according to their major biophysical, socio-economic, and environmental performance indicators. METHODS: A panel of experts from different research organisations of each of the 9 countries defined the modal milk production system based on national reports and statistics. Each system was modelled by means of a whole-farm model, and 22 performance indicators were estimated as a result. The countries were classified in groups by cluster and principal component analyses and the sources of similarities and differences among them were described using the information provided by the 22 indicators evaluated. © 2023 |
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Palabras claves : |
Dairy system; Decent work and economic growth - Goal 8; LAC region; Partnership for the goals - Goal 17; Performance indicators; SISTEMA LECHERO - INIA; Sustainability; Sustainable Development Goals (SDGs). |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 02736naa a2200397 a 4500
001 1064642
005 2024-06-12
008 2024 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2024.103987$2DOI
100 1 $aFARIÑA, S.
245 $aMilk production systems in Latin America and the Caribbean$bBiophysical, socio-economic, and environmental performance.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 2 December 2023, Revised 8 March 2024, Accepted 30 April 2024, Available online 7 May 2024, Version of Record 7 May 2024. -- Correspondence: Fariña, S.; Instituto Nacional de Investigación Agropecuaria (INIA), Ruta 50 km 11, Colonia, Uruguay; email:santiagofarina1@gmail.com -- Funding: This research was funded by FONTAGRO through the project "Sustainable Intensification of Dairy" with grant FTG/RF-15940-RG.
520 $aABSTRACT.- CONTEXT: The Latin America-Caribbean (LAC) region has the potential to pursue a sustainable expansion of livestock production and capitalise on the growing global demand for dairy and beef products. However, it is necessary to understand the predominant production systems and their biophysical, economic, social, and environmental performances. OBJECTIVE: This study aimed to compare the milk production systems of nine countries of the region according to their major biophysical, socio-economic, and environmental performance indicators. METHODS: A panel of experts from different research organisations of each of the 9 countries defined the modal milk production system based on national reports and statistics. Each system was modelled by means of a whole-farm model, and 22 performance indicators were estimated as a result. The countries were classified in groups by cluster and principal component analyses and the sources of similarities and differences among them were described using the information provided by the 22 indicators evaluated. © 2023
653 $aDairy system
653 $aDecent work and economic growth - Goal 8
653 $aLAC region
653 $aPartnership for the goals - Goal 17
653 $aPerformance indicators
653 $aSISTEMA LECHERO - INIA
653 $aSustainability
653 $aSustainable Development Goals (SDGs)
700 1 $aMORENO, O.V.
700 1 $aCANDIOTI, F.
700 1 $aVILLANUEVA, C.
700 1 $aLEDEZMA, W.S.
700 1 $aMOSCOSO, C.J.
700 1 $aCAJARVILLE, C.
700 1 $aCHARLÓN, V.
700 1 $aABAUNZA, L.U.
700 1 $aVITERI, A.G.
700 1 $aCHIRIFE, S.
700 1 $aHERRERA, D.
700 1 $aSTIRLING, S.
773 $tAgricultural Systems, 2024, Volume 218, 03987. https://doi.org/10.1016/j.agsy.2024.103987
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