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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 : |
08/03/2022 |
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Actualizado : |
02/12/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
RODRÍGUEZ NEIRA, J.D.; PERIPOLLI, E.; DE NEGREIROS M.P.M.; ESPIGOLAN, R.; LÓPEZ-CORREA R.; AGUILAR, I.; LOBO R.B.; BALDI, F. |
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Afiliación : |
JUAN DIEGO RODRIGUEZ NEIRA, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil; ELISA PERIPOLLI, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil; MARIA PAULA MARINHO DE NEGREIROS, Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil; RAFAEL ESPIGOLAN, Departamento de Medicina Veterinária, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (Usp), Pirassununga, 13535-900, Brazil; RODRIGO LÓPEZ-CORREA, Departamento de Genética y Mejoramiento Animal, Facultad de Veterinaria, Universidad de La República, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAYSILDO B. LOBO, Associação Nacional de Criadores e Pesquisadores (ANCP), Ribeirão Preto, Brazil; FERNANDO BALDI, Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, 14884-900, Brazil. |
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Título : |
Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Journal of Applied Genetics, 2022, Volume 63, Issue 2, pages 389-400. doi: https://doi.org/10.1007/s13353-022-00685-0 |
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ISSN : |
1234-1983 |
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DOI : |
10.1007/s13353-022-00685-0 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 26 September 2021; Revised 25 January 2022; Accepted 2 February 2022.
Corresponding author: Rodriguez Neira, J.D.; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, Brazil; email:juan.diego@unesp.br -- This study was supported in conjunction by Programa Estudantes Convênio de Pós-Graduação da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PECPG-CAPES, call no. 32/2017); the National Association of Breeders and Researchers (ANCP), the Programa Escala de Estudiantes de Pós-Graduação of Asociación de Universidades GRUPO MONTEVIDEO (PEEPg/AUGM-2019); the Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias (FCAV/Unesp); the Universidad de la Republica, Facultad de Veterinaria (UdelaR), Departamento de Genética y Mejoramiento Animal; and the Instituto Nacional de Investigación Agropecuaria of Uruguay (INIA). |
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Contenido : |
ABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.
© 2022, The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences. MenosABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10... Presentar Todo |
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Palabras claves : |
Accuracy; Beef cattle; Genomic selection; Inflation; Minor allele frequency; SNP arrays. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
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022 $a1234-1983
024 7 $a10.1007/s13353-022-00685-0$2DOI
100 1 $aRODRÍGUEZ NEIRA, J.D.
245 $aPrediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 26 September 2021; Revised 25 January 2022; Accepted 2 February 2022. Corresponding author: Rodriguez Neira, J.D.; Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Universidade Estadual Paulista (Unesp), Jaboticabal, Brazil; email:juan.diego@unesp.br -- This study was supported in conjunction by Programa Estudantes Convênio de Pós-Graduação da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PECPG-CAPES, call no. 32/2017); the National Association of Breeders and Researchers (ANCP), the Programa Escala de Estudiantes de Pós-Graduação of Asociación de Universidades GRUPO MONTEVIDEO (PEEPg/AUGM-2019); the Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias (FCAV/Unesp); the Universidad de la Republica, Facultad de Veterinaria (UdelaR), Departamento de Genética y Mejoramiento Animal; and the Instituto Nacional de Investigación Agropecuaria of Uruguay (INIA).
520 $aABSTRACT. - This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection. © 2022, The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.
653 $aAccuracy
653 $aBeef cattle
653 $aGenomic selection
653 $aInflation
653 $aMinor allele frequency
653 $aSNP arrays
700 1 $aPERIPOLLI, E.
700 1 $aDE NEGREIROS M.P.M.
700 1 $aESPIGOLAN, R.
700 1 $aLÓPEZ-CORREA R.
700 1 $aAGUILAR, I.
700 1 $aLOBO R.B.
700 1 $aBALDI, F.
773 $tJournal of Applied Genetics, 2022, Volume 63, Issue 2, pages 389-400. doi: https://doi.org/10.1007/s13353-022-00685-0
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| 1. |  | ZANINE, A.M.; RODRIGUEZ MOTTA, J.P.; FERREIRA, D.J.; DE SOUZA, A.L.; RIBEIRO, M.D.; GERON, L.J.V.; FAJARDO, M.; SPRUNK, M.; PINHO, R.M.A. Milk performance and grazing behaviour of dairy cows in response to pasture allowance. Animal Production Science, 2019, Volume 59, Issue 4, Pages 749-756. Article history: Submitted: 26 July 2017 /Accepted: 15 February 2018 / Published online: 14 May 2018.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
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