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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 : |
07/11/2018 |
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Actualizado : |
07/11/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
CHIAIA, H.L.J.; PERIPOLLI, E.; DE OLIVEIRA SILVA, R.M.; FEITOSA, F.L.B.; DE LEMOS, M.V.A.; BERTON, M.P.; OLIVIERI, B.F.; ESPIGOLAN, R.; TONUSSI, R.L.; GORDO, D.G.M.; DE ALBUQUERQUE, L.G.; DE OLIVEIRA, H.N.; FERRINHO, A.M.; MUELLER, L.F.; KLUSKA, S.; TONHATI, H.; PEREIRA, A.S.C.; AGUILAR, I.; BALDI, F. |
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Afiliación : |
HERMENEGILDO LUCAS JUSTINO CHIAIA, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; ELISA PERIPOLLI, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; RAFAEL MEDEIROS DE OLIVEIRA SILVA, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; FABIELE LOISE BRAGA FEITOSA, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; MARCOS VINÍCIUS ANTUNES DE LEMOS, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; MARIANA PIATTO BERTON, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; BIANCA FERREIRA OLIVIERI, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; RAFAEL ESPIGOLAN, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; RAFAEL LARA TONUSSI, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; DANIEL GUSTAVO MANSAN GORDO, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; LUCIA GALVÃO DE ALBUQUERQUE, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; HENRIQUE NUNES DE OLIVEIRA, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; ADRIELLE MATHIAS FERRINHO, Faculdade de Medicina Veterinária e Zootecnia, USP, Brazil.; LENISE FREITAS MUELLER, Faculdade de Zootecnia e Engenharia de Alimentos, USP, Brazil.; SABRINA KLUSKA, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; HUMBERTO TONHATI, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil.; ANGÉLICA SIMONE CRAVO PEREIRA, Faculdade de Medicina Veterinária e Zootecnia, USP, Brazil.; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BALDI, Faculdade de Ciências Agrárias e Veterinárias, UNESP, Brazil. |
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Título : |
Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Journal of Applied Genetics, 1 November 2018, volume 59, Issue 4, pages 493-501. |
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DOI : |
10.1007/s13353-018-0470-5 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 15 May 2018 // Revised: 28 August 2018 // Accepted: 17 September 2018. |
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Contenido : |
ABSTRACT.
The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a ?bottleneck effect? and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.
© 2018, Institute of Plant Genetics, Polish Academy of Sciences, Poznan. MenosABSTRACT.
The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a ?bottleneck effect? and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype a... Presentar Todo |
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Palabras claves : |
BOS INDICUS; GENOMIC PREDICTION; LIPID PROFILE; SINGLE-STEP; SNP-BLUP. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04880naa a2200421 a 4500
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005 2018-11-07
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1007/s13353-018-0470-5$2DOI
100 1 $aCHIAIA, H.L.J.
245 $aGenomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes.$h[electronic resource]
260 $c2018
500 $aArticle history: Received: 15 May 2018 // Revised: 28 August 2018 // Accepted: 17 September 2018.
520 $aABSTRACT. The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a ?bottleneck effect? and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile. © 2018, Institute of Plant Genetics, Polish Academy of Sciences, Poznan.
653 $aBOS INDICUS
653 $aGENOMIC PREDICTION
653 $aLIPID PROFILE
653 $aSINGLE-STEP
653 $aSNP-BLUP
700 1 $aPERIPOLLI, E.
700 1 $aDE OLIVEIRA SILVA, R.M.
700 1 $aFEITOSA, F.L.B.
700 1 $aDE LEMOS, M.V.A.
700 1 $aBERTON, M.P.
700 1 $aOLIVIERI, B.F.
700 1 $aESPIGOLAN, R.
700 1 $aTONUSSI, R.L.
700 1 $aGORDO, D.G.M.
700 1 $aDE ALBUQUERQUE, L.G.
700 1 $aDE OLIVEIRA, H.N.
700 1 $aFERRINHO, A.M.
700 1 $aMUELLER, L.F.
700 1 $aKLUSKA, S.
700 1 $aTONHATI, H.
700 1 $aPEREIRA, A.S.C.
700 1 $aAGUILAR, I.
700 1 $aBALDI, F.
773 $tJournal of Applied Genetics, 1 November 2018, volume 59, Issue 4, pages 493-501.
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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 : |
10/07/2019 |
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Actualizado : |
10/07/2019 |
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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 : |
BENÍTEZ-GALEANO, M.J.; VALLET, T.; CARRAU, L.; HERNÁNDEZ-RODRÍGUEZ, L.; BERTALMIO, A.; RIVAS, F.; RUBIO, L.; MAESO, D.; VIGNUZZI, M.; MORATORIO, G.; COLINA, R. |
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Afiliación : |
MARÍA JOSÉ BENÍTEZ-GALEANO, Laboratory of Molecular Virology, University of the Republic, Salto, Uruguay.; THOMAS VALLET, Institut Pasteur, Viral Populations and Pathogenesis, Centre National de la Recherche Scientifique, Paris, France; LUCÍA CARRAU, Institut Pasteur, Viral Populations and Pathogenesis, Centre National de la Recherche Scientifique, Paris, France; LESTER HERNÁNDEZ RODRÍGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; ANA MARIA BERTALMIO CASARIEGO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS FERNANDO RIVAS GRELA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LETICIA PAOLA RUBIO CATTANI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DIEGO CESAR MAESO TOZZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCO VIGNUZZI, Institut Pasteur, Viral Populations and Pathogenesis, Centre National de la Recherche Scientifique, Paris, France; GONZALO MORATORIO, Institut Pasteur, Viral Populations and Pathogenesis, Centre National de la Recherche Scientifique, Paris, France; RODNEY COLINA, Laboratory of Molecular Virology, University of the Republic, Salto, Uruguay. |
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Título : |
Complete genome sequence of a novel recombinant Citrus tristeza virus, a resistance-breaking isolate from Uruguay. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Genome Announcements, 1 May 2018, Volume 6, Issue 22, Article number e00442-18. OPEN ACCESS. |
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ISSN : |
2169-8287 |
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DOI : |
10.1128/genomeA.00442-18 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 19 April 2018 / Accepted 24 April / 2018 Published 31 May 2018.
Accession number(s). The genomic sequence for isolate CTV DSST-17 was deposited in GenBank under accession number MH186146. |
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Contenido : |
ABSTRACT.
We report here the complete genome sequence of a Citrus tristeza virus (CTV) from Uruguay, sequenced by using Illumina and Sanger sequencing technology. This CTV DSST-17 genome clustered within genotype resistance breaking (RB) and presents two recombination events.
© 2018 Benítez-Galeano et al. |
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Thesagro : |
CITRUS. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13013/1/Genome-Announcements.-2018.-10.1128genomeA.00442-18.pdf
https://mra.asm.org/content/6/22/e00442-18
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Marc : |
LEADER 01386naa a2200289 a 4500
001 1059941
005 2019-07-10
008 2018 bl uuuu u00u1 u #d
022 $a2169-8287
024 7 $a10.1128/genomeA.00442-18$2DOI
100 1 $aBENÍTEZ-GALEANO, M.J.
245 $aComplete genome sequence of a novel recombinant Citrus tristeza virus, a resistance-breaking isolate from Uruguay.$h[electronic resource]
260 $c2018
500 $aArticle history: Received 19 April 2018 / Accepted 24 April / 2018 Published 31 May 2018. Accession number(s). The genomic sequence for isolate CTV DSST-17 was deposited in GenBank under accession number MH186146.
520 $aABSTRACT. We report here the complete genome sequence of a Citrus tristeza virus (CTV) from Uruguay, sequenced by using Illumina and Sanger sequencing technology. This CTV DSST-17 genome clustered within genotype resistance breaking (RB) and presents two recombination events. © 2018 Benítez-Galeano et al.
650 $aCITRUS
700 1 $aVALLET, T.
700 1 $aCARRAU, L.
700 1 $aHERNÁNDEZ-RODRÍGUEZ, L.
700 1 $aBERTALMIO, A.
700 1 $aRIVAS, F.
700 1 $aRUBIO, L.
700 1 $aMAESO, D.
700 1 $aVIGNUZZI, M.
700 1 $aMORATORIO, G.
700 1 $aCOLINA, R.
773 $tGenome Announcements, 1 May 2018, Volume 6, Issue 22, Article number e00442-18. OPEN ACCESS.
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