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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
11/12/2018 |
Actualizado : |
18/06/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
TONUSSI, R. L.; SILVA, R. M. D. O.; MAGALHÃES, A.F.B.; ESPIGOLAN, R.; PERIPOLLI, E.; OLIVIERI, B. F.; FEITOSA, F. L. B.; LEMOS, M. V. A.; BERTON, M. P.; CHIAIA, H. L. J.; PEREIRA, A. S. C.; LÔBO, R. B.; BEZERRA, L. A. F.; MAGNABOSCO, C. D. U.; LOURENÇO, D.A.L.; AGUILAR, I.; BALDI, F. |
Afiliación : |
RAFAEL LARA TONUSSI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; RAFAEL MEDEIROS DE OLIVEIRA SILVA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; FABRÍCIA BRAGA MAGALHÃES, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; RAFAEL ESPIGOLAN, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazi; ELISA PERIPOLLI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; BIANCA FERREIRA OLIVIERI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; FABIELI LOISE BRAGA FEITOSA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; MARCOS VINICÍUS ANTUNES LEMOS, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; MARIANA PIATTO BERTON, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; HERMENEGILDO LUCAS JUSTINO CHIAIA, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil; ANGELICA SIMONE CRAVO PEREIRA, Department of Nutrition and Animal Production, Faculty of Animal Science and Food Engineering, Pirassununga, Brazil; RAYSILDO BARBOSA LÔBO, National Association of Breeders and Researchers (ANCP), Ribeirão Preto, Brazil; LUIZ ANTÔNIO FRAMARTINO BEZERRA, Department of Genetic, Medical School of Ribeirão Preto, Ribeirão Preto, Brazil; CLÁUDIO DE ULHOA MAGNABOSCO, Brazilian Agricultural Research Corporation (EMBRAPA), Distrito Federal, Brazil; DANIELA ANDRESSA LINO LOURENÇO, Department of Animal and Dairy Science, University of Georgia, Athens, Georgia, United States of America; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BALDI, Department of Animal Science, School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo, Brazil. |
Título : |
Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. |
ISSN : |
1932-6203 |
DOI : |
10.1371/journal.pone.0181752 |
Idioma : |
Inglés |
Notas : |
Article history: Received September 22, 2016 // Accepted July 6, 2017 // Published September 28, 2017.
Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and in Figshare.
Funding: This work was funded by the Sao Paulo Research Foundation (FAPESP), 2013/25910-0, Mr Rafael Lara Tonussi, and Sao Paulo Research Foundation (FAPESP), 2011/21241-0, PhD Fernando Bald. |
Contenido : |
ABSTRACT.
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty.
© 2017 Tonussi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. MenosABSTRACT.
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the popula... Presentar Todo |
Palabras claves : |
CATTLE; COMPUTER SIMULATION; GENETIC VARIABILITY; GENETICS; GENOMICS; INHERITANCE PATTERNS; PEDIGREE. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12157/1/journal.pone.0181752.pdf
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0181752&type=printable
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181752#sec009
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Marc : |
LEADER 04899naa a2200433 a 4500 001 1059368 005 2019-06-18 008 2017 bl uuuu u00u1 u #d 022 $a1932-6203 024 7 $a10.1371/journal.pone.0181752$2DOI 100 1 $aTONUSSI, R. L. 245 $aApplication of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article).$h[electronic resource] 260 $c2017 500 $aArticle history: Received September 22, 2016 // Accepted July 6, 2017 // Published September 28, 2017. Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and in Figshare. Funding: This work was funded by the Sao Paulo Research Foundation (FAPESP), 2013/25910-0, Mr Rafael Lara Tonussi, and Sao Paulo Research Foundation (FAPESP), 2011/21241-0, PhD Fernando Bald. 520 $aABSTRACT. The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty. © 2017 Tonussi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 653 $aCATTLE 653 $aCOMPUTER SIMULATION 653 $aGENETIC VARIABILITY 653 $aGENETICS 653 $aGENOMICS 653 $aINHERITANCE PATTERNS 653 $aPEDIGREE 700 1 $aSILVA, R. M. D. O. 700 1 $aMAGALHÃES, A.F.B. 700 1 $aESPIGOLAN, R. 700 1 $aPERIPOLLI, E. 700 1 $aOLIVIERI, B. F. 700 1 $aFEITOSA, F. L. B. 700 1 $aLEMOS, M. V. A. 700 1 $aBERTON, M. P. 700 1 $aCHIAIA, H. L. J. 700 1 $aPEREIRA, A. S. C. 700 1 $aLÔBO, R. B. 700 1 $aBEZERRA, L. A. F. 700 1 $aMAGNABOSCO, C. D. U. 700 1 $aLOURENÇO, D.A.L. 700 1 $aAGUILAR, I. 700 1 $aBALDI, F. 773 $tPLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS.
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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
13/04/2020 |
Actualizado : |
13/04/2020 |
Tipo de producción científica : |
Informes Agroclimáticos |
Autor : |
INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); GRAS |
Afiliación : |
UNIDAD DE AGROCLIMA Y SISTEMAS DE INFORMACIÓN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informe agroclimático 2020- Situación a Febrero. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2020. |
Serie : |
(Informe Agroclimático; Año 15, No.2) |
Idioma : |
Español |
Notas : |
Equipo de trabajo INIA-GRAS (Unidad de Agtech y sistemas de Información): Adrián Cal, Guadalupe Tiscornia, Carlos Schiavi, Gabriel García. |
Contenido : |
Contenido. Síntesis de la Situación Agroclimática de Febrero -- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Mar-Abr-May elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Previsión de estrés calórico en bovinos. Se encuentra disponible en la web del GRAS dentro del ítem "Alertas y herramientas". Acceso directo es: http://www.inia.uy/gras/Alertas-y-herramientas/Prevision-ITH-Vacunos |
Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; AGTECH; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICAS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INFORME AGROCLIMÁTICO 2020; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
Asunto categoría : |
P40 Meteorología y climatología |
URL : |
http://www.inia.uy/Publicaciones/Documentos%20compartidos/Informe%20agroclimatico%20INIA-GRAS%20Febrero%20de%202020.pdf
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Marc : |
LEADER 02911nam a2200793 a 4500 001 1061019 005 2020-04-13 008 2020 bl uuuu u0uu1 u #d 100 1 $aINIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA) 245 $aInforme agroclimático 2020- Situación a Febrero.$h[electronic resource] 260 $aMontevideo (Uruguay): INIA$c2020 490 $a(Informe Agroclimático; Año 15, No.2) 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 Febrero -- Perspectivas Climáticas Trimestrales elaboradas por el IRI de la Universidad de Columbia -- Índice de Vegetación (IVDN) -- Precipitaciones -- Porcentaje de Agua Disponible (PAD) -- Índice de Bienestar Hídrico (IBH) -- Agua No Retenida (ANR) -- Perspectivas Climáticas Mar-Abr-May elaboradas por el IRI de la Universidad de Columbia. Destacamos para este mes: Previsión de estrés calórico en bovinos. Se encuentra disponible en la web del GRAS dentro del ítem "Alertas y herramientas". Acceso directo es: http://www.inia.uy/gras/Alertas-y-herramientas/Prevision-ITH-Vacunos 650 $aAGROCLIMATOLOGIA 650 $aCAMBIO CLIMATICO 650 $aCLIMA 650 $aCLIMATOLOGIA 650 $aESTACIONES METEOROLOGICAS 650 $aESTRES HIDRICO 650 $aEVAPORACION 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 $aSUELO 650 $aTEMPERATURA 650 $aTERMOMETROS 653 $aAGROCLIMA 653 $aAGROCLIMATOLOGÍA 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 $aESTRÉS HÍDRICO 653 $aGRAFICAS AGROCLIMATICAS 653 $aGRAS 653 $aHELIOFANOGRAFO 653 $aINFORMACION SATELITAL 653 $aINFORME AGROCLIMÁTICO 2020 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 $aSIMETRICO 653 $aTANQUE A 653 $aTERMOCUPLAS 653 $aTERMOHIDROGRAFO 653 $aVARIABLES AGROCLIMATICAS 653 $aVELETA 700 1 $aGRAS
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