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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
20/06/2015 |
Actualizado : |
20/06/2015 |
Tipo de producción científica : |
Informes Agroclimáticos |
Autor : |
GIMENEZ, A.; CASTAÑO, J.; FUREST, J.; CAL, A.; TISCORNIA, G.; SCHIAVI, C. |
Afiliación : |
AGUSTIN EDUARDO GIMENEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE PEDRO CASTAÑO SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE MARIA FUREST CROCCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS IGNACIO SCHIAVI RAMPELBERG, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informe Agroclimático 2014 - Situación a Julio. |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2014. |
Páginas : |
4 p. |
Idioma : |
Español |
Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; 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; EVAPOTRANSPIRACION; 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.ainfo.inia.uy/digital/bitstream/item/4737/1/Inf.Agr.-julio-2014.pdf
http://www.inia.uy/Publicaciones/Paginas/Informe-agroclimatico-2014---Situacion-Julio.aspx
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Marc : |
LEADER 02091nam a2200805 a 4500 001 1052893 005 2015-06-20 008 2014 bl uuuu u0uu1 u #d 100 1 $aGIMENEZ, A. 245 $aInforme Agroclimático 2014 - Situación a Julio.$h[electronic resource] 260 $aMontevideo (Uruguay): INIA$c2014 300 $a4 p. 650 $aAGROCLIMATOLOGIA 650 $aCAMBIO CLIMATICO 650 $aCLIMA 650 $aCLIMATOLOGIA 650 $aESTACIONES METEOROLOGICAS 650 $aESTRES HIDRICO 650 $aEVAPORACION 650 $aEVAPOTRANSPIRACION 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 $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 AGROCLIMATICOS 653 $aGRAS 653 $aHELIOFANOGRAFO 653 $aINFORMACION SATELITAL 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 $aCASTAÑO, J. 700 1 $aFUREST, J. 700 1 $aCAL, A. 700 1 $aTISCORNIA, G. 700 1 $aSCHIAVI, C.
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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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