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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
21/02/2014 |
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Actualizado : |
24/06/2021 |
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Tipo de producción científica : |
Documentos |
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Autor : |
CARBALLO, S.; PAGANI, C.; FEIPPE, A. |
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Afiliación : |
SERGIO JOSE CARBALLO SILVA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTINA PAGANI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA ALICIA FEIPPE FERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Implementación de un sistema HACCP para el manejo poscosecha de lechuga mantecosa. Un modelo para la industria hortícola uruguaya. |
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Fecha de publicación : |
2000 |
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Fuente / Imprenta : |
In: INIA Las Brujas; Programa Horticultura. Presentación de resultados experimentales en lechuga: reunión de divulgación, 2000 Trabajos presentados. Las Brujas, Canelones (Uruguay): INIA, 2000. |
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Páginas : |
p. 39-44. |
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Serie : |
(INIA Serie Actividades de Difusión ; 222) |
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Idioma : |
Español |
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Thesagro : |
LACTUCA SATIVA; LECHUGAS; POSTCOSECHA; SEGURIDAD ALIMENTARIA. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/301/1/18429200808111120.pdf;ad222#page=41
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Marc : |
LEADER 00796naa a2200205 a 4500
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100 1 $aCARBALLO, S.
245 $aImplementación de un sistema HACCP para el manejo poscosecha de lechuga mantecosa. Un modelo para la industria hortícola uruguaya.
260 $c2000
300 $ap. 39-44.
490 $a(INIA Serie Actividades de Difusión ; 222)
650 $aLACTUCA SATIVA
650 $aLECHUGAS
650 $aPOSTCOSECHA
650 $aSEGURIDAD ALIMENTARIA
700 1 $aPAGANI, C.
700 1 $aFEIPPE, A.
773 $tIn: INIA Las Brujas; Programa Horticultura. Presentación de resultados experimentales en lechuga: reunión de divulgación, 2000 Trabajos presentados. Las Brujas, Canelones (Uruguay): INIA, 2000.
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Registro original : |
INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
11/12/2018 |
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Actualizado : |
18/06/2019 |
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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 : |
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. |
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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. |
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Título : |
Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. |
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ISSN : |
1932-6203 |
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DOI : |
10.1371/journal.pone.0181752 |
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Idioma : |
Inglés |
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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. |
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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 |
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Palabras claves : |
CATTLE; COMPUTER SIMULATION; GENETIC VARIABILITY; GENETICS; GENOMICS; INHERITANCE PATTERNS; PEDIGREE. |
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Asunto categoría : |
L01 Ganadería |
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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 : |
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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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