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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
16/01/2020 |
Actualizado : |
16/01/2020 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
NAVARRO, M.; PERAZA, P.; NAVAJAS, E. |
Afiliación : |
M. NAVARRO; PABLO PERAZA DOS SANTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Metagenomic approach to assist genetic improvement programs in reducing methane emissions and improving feed efficiency from beef cattle. [Abstract] |
Complemento del título : |
Botecnología Animal. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
In: REDBIO; INIA (Instituto Nacional de Investigación Agropecuaria); REDBIO Argentina. X Encuentro Latinoamericano y del Caribe de Biotecnología Agropecuaria y XI Simposio Redbio Argentina. Libro de Resúmenes. Montevideo 12 - 15 Noviembre 2019. Montevideo (UY): INIA, 2019. p. 166. |
Serie : |
(INIA Serie Técnica; 253) |
ISBN : |
e-ISBN 978-9974-38-437-8 |
ISSN : |
1688-9266 |
DOI : |
http://doi.org/10.35676/INIA/ST.253 |
Idioma : |
Inglés |
Contenido : |
Beef meat is one of the products with the highest intensity of greenhouse gas emissions in livestock production chains. Emissions are mainly in the form of methane (CH4) produced by enteric fermentation. This process is carried out by the symbiotic ruminal microbiota and causes a loss of energy for the host which may decrease the feed efficiency (FE). |
Palabras claves : |
EMISIÓN DE GASES DE EFECTO INVERNADERO. |
Thesagro : |
BOVINOS PARA CARNE. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14027/1/st-253-p166.pdf
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Marc : |
LEADER 01287nam a2200193 a 4500 001 1060633 005 2020-01-16 008 2019 bl uuuu u01u1 u #d 022 $a1688-9266 024 7 $ahttp://doi.org/10.35676/INIA/ST.253$2DOI 100 1 $aNAVARRO, M. 245 $aMetagenomic approach to assist genetic improvement programs in reducing methane emissions and improving feed efficiency from beef cattle. [Abstract]$h[electronic resource] 260 $aIn: REDBIO; INIA (Instituto Nacional de Investigación Agropecuaria); REDBIO Argentina. X Encuentro Latinoamericano y del Caribe de Biotecnología Agropecuaria y XI Simposio Redbio Argentina. Libro de Resúmenes. Montevideo 12 - 15 Noviembre 2019. Montevideo (UY): INIA, 2019. p. 166.$c2019 490 $a(INIA Serie Técnica; 253) 520 $aBeef meat is one of the products with the highest intensity of greenhouse gas emissions in livestock production chains. Emissions are mainly in the form of methane (CH4) produced by enteric fermentation. This process is carried out by the symbiotic ruminal microbiota and causes a loss of energy for the host which may decrease the feed efficiency (FE). 650 $aBOVINOS PARA CARNE 653 $aEMISIÓN DE GASES DE EFECTO INVERNADERO 700 1 $aPERAZA, P. 700 1 $aNAVAJAS, E.
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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. |
Fecha actual : |
29/09/2014 |
Actualizado : |
15/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
B - 2 |
Autor : |
WANG, H.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; MUIR, W.M. |
Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
Título : |
Genome-wide association mapping including phenotypes from relatives without genotypes. |
Fecha de publicación : |
2012 |
Fuente / Imprenta : |
Genetics Research, 2012, v.94, no.2, p.73-83. OPEN ACCESS. |
ISSN : |
0016-6723 |
DOI : |
10.1017/S0016672312000274 |
Idioma : |
Inglés |
Notas : |
Article history: Received 19 September 2011 / Revised 8 December 2011 and 9 March 2012. / Accepted 13 March 2012. |
Contenido : |
ABSTRACT.
A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 05. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 089 was obtained by ssGBLUP after one iteration, which was 001 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 082 and 074 for ssGBLUP and CGWAS with m=8, and 083 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.
© Cambridge University Press 2012. MenosABSTRACT.
A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 05. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 089 was obtained by ssGBLUP after one iteration, which was 001 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 082 and 074 for ssGBLUP and CGWAS with m=8, an... Presentar Todo |
Thesagro : |
ANIMALES; CRIA; FENOTIPOS; GENOTIPO; MARCADORES GENÉTICOS; MEJORAMIENTO GENÉTICO ANIMAL. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3348/1/Aguilar-I.-2012.-Genet.Res.Camb.-v.942-p.73-83.pdf
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Marc : |
LEADER 02696naa a2200277 a 4500 001 1050706 005 2019-10-15 008 2012 bl uuuu u00u1 u #d 022 $a0016-6723 024 7 $a10.1017/S0016672312000274$2DOI 100 1 $aWANG, H. 245 $aGenome-wide association mapping including phenotypes from relatives without genotypes.$h[electronic resource] 260 $c2012 500 $aArticle history: Received 19 September 2011 / Revised 8 December 2011 and 9 March 2012. / Accepted 13 March 2012. 520 $aABSTRACT. A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 05. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 089 was obtained by ssGBLUP after one iteration, which was 001 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 082 and 074 for ssGBLUP and CGWAS with m=8, and 083 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data. © Cambridge University Press 2012. 650 $aANIMALES 650 $aCRIA 650 $aFENOTIPOS 650 $aGENOTIPO 650 $aMARCADORES GENÉTICOS 650 $aMEJORAMIENTO GENÉTICO ANIMAL 700 1 $aMISZTAL, I. 700 1 $aAGUILAR, I. 700 1 $aLEGARRA, A. 700 1 $aMUIR, W.M. 773 $tGenetics Research, 2012$gv.94, no.2, p.73-83. OPEN ACCESS.
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