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Biblioteca (s) : |
INIA Las Brujas; INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
26/06/2019 |
Tipo de producción científica : |
Documentos |
Autor : |
BUSSONI, A.; ANDREONI, I. |
Título : |
Los desafíos del mejoramiento genético de especies forestales de rápido crecimiento de cara al cambio global: análisis económicos |
Fecha de publicación : |
2011 |
Fuente / Imprenta : |
ln: INIA Tacuarembó. Programa Nacional de Producción Forestal. Seminario técnico, 31 de agosto, Tacuarembó, 2011. Mejoramiento especies forestales de rápido crecimiento. Tacuarembó (Uruguay): INIA, 2011. |
Páginas : |
p. 33-53 |
Serie : |
(INIA Serie Actividades de Difusión; 653) |
Idioma : |
Español |
Thesagro : |
FORESTACION; MEJORAMIENTO GENETICO. |
Asunto categoría : |
-- K10 Producción forestal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12919/1/SAD653P33-53.pdf
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Marc : |
LEADER 00727naa a2200169 a 4500 001 1012432 005 2019-06-26 008 2011 bl uuuu u00u1 u #d 100 1 $aBUSSONI, A. 245 $aLos desafíos del mejoramiento genético de especies forestales de rápido crecimiento de cara al cambio global$banálisis económicos 260 $c2011 300 $ap. 33-53 490 $a(INIA Serie Actividades de Difusión; 653) 650 $aFORESTACION 650 $aMEJORAMIENTO GENETICO 700 1 $aANDREONI, I. 773 $tln: INIA Tacuarembó. Programa Nacional de Producción Forestal. Seminario técnico, 31 de agosto, Tacuarembó, 2011. Mejoramiento especies forestales de rápido crecimiento. Tacuarembó (Uruguay): INIA, 2011.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
15/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
B - 2 |
Autor : |
FRAGOMENI, B.O.; MISZTAL, I.; LOURENCO, D.L.; AGUILAR, I.; OKIMOTO, R.; MUIR, W.M. |
Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Changes in variance explained by top SNP windows over generations for three traits in broiler chicken |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, no.Oct., Article number 332. OPEN ACCESS. |
ISSN : |
1664-8021 |
DOI : |
10.3389/fgene.2014.00332 |
Idioma : |
Inglés |
Notas : |
Article history: Published 01 October 2014. |
Contenido : |
ABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir. MenosABSTRACT.
The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.
© 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and... Presentar Todo |
Palabras claves : |
Gene identification; Genome-wide association study; Genomic selection; QTL; SsGBLUP. |
Thesagro : |
MEJORAMIENTO GENETICO ANIMAL; POLLO DE ENGORDE. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5195/1/Aguilar-I.-2014.-Frontiers-in-Genetics.pdf
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Marc : |
LEADER 02443naa a2200301 a 4500 001 1053891 005 2019-10-09 008 2014 bl uuuu u00u1 u #d 022 $a1664-8021 024 7 $a10.3389/fgene.2014.00332$2DOI 100 1 $aFRAGOMENI, B.O. 245 $aChanges in variance explained by top SNP windows over generations for three traits in broiler chicken$h[electronic resource] 260 $c2014 500 $aArticle history: Published 01 October 2014. 520 $aABSTRACT. The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated. © 2014 Fragomeni, Misztal, Lourenco, Aguilar, Okimoto and Muir. 650 $aMEJORAMIENTO GENETICO ANIMAL 650 $aPOLLO DE ENGORDE 653 $aGene identification 653 $aGenome-wide association study 653 $aGenomic selection 653 $aQTL 653 $aSsGBLUP 700 1 $aMISZTAL, I. 700 1 $aLOURENCO, D.L. 700 1 $aAGUILAR, I. 700 1 $aOKIMOTO, R. 700 1 $aMUIR, W.M. 773 $tFrontiers in Genetics, 2014$gv.5, no.Oct., Article number 332. OPEN ACCESS.
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