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Registros recuperados : 3 | |
1. | | LOURENCO, D.A.L.; FRAGOMENI, B.O.; TSURUTA, S.; AGUILAR, I.; ZUMBACH, B.; HAWKEN, R.J.; LEGARRA, A.; MISZTAL, I. Accuracy of estimated breeding values with genomic information on males, females, or both: An example on broiler chicken. Genetics Selection Evolution, 2015, v. 242, p. 47-56. OPEN ACCESS. Article history: Received: 14 October 2014 / Accepted: 22 June 2015 / Published: 02 July 2015.Biblioteca(s): INIA Las Brujas. |
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2. | | WANG, H.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; FERNANDO, R.L.; VITEZICA, Z.; OKIMOTO, R.; WING, T.; HAWKEN, R.; MUIR, W.M. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers in Genetics, 2014, v.5, p.1-10. OPEN ACCESS. Article history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014.Biblioteca(s): INIA Las Brujas. |
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3. | | MISZTAL, I.; WANG, H.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.; FRAGOMENI, B. O.; ZHANG, X.; MUIR, W. M.; CHENG, H. H.; OKIMOTO, R.; WING, T.; HAWKEN, R. R.; ZUMBACH, B.; FERNANDO, R. GWAS using ssGBLUP. Volume Species Breeding: Poultry, 325. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325.Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 3 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
10/09/2014 |
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.; FERNANDO, R.L.; VITEZICA, Z.; OKIMOTO, R.; WING, T.; HAWKEN, R.; 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 in a single-step (ssGWAS) for 6-week body weight in broiler chickens. |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, p.1-10. OPEN ACCESS. |
ISSN : |
1664-8021 |
DOI : |
10.3389/fgene.2014.00134 |
Idioma : |
Inglés |
Notas : |
Article history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014. |
Contenido : |
ABSTRACT.
The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.
© 2014 Wang, Misztal, Aguilar, Legarra, Fernando, Vitezica, Okimoto, Wing, Hawken and Muir. MenosABSTRACT.
The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation att... Presentar Todo |
Palabras claves : |
ASSOCIATION MAPPING; BayesB; BODY WEIGHT; BROILER CHICKEN; CARNE DE AVES; GENOME-WIDE ASSOCIATION; SsGWAS. |
Thesagro : |
ANIMALES DE CARNE; PESO CORPORAL; POLLO. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3067/1/Aguilar-I.-2014-Frontiers-in-Genetics-v.5p.1-10.pdf
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Marc : |
LEADER 03333naa a2200385 a 4500 001 1050116 005 2019-10-15 008 2014 bl uuuu u00u1 u #d 022 $a1664-8021 024 7 $a10.3389/fgene.2014.00134$2DOI 100 1 $aWANG, H. 245 $aGenome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.$h[electronic resource] 260 $c2014 500 $aArticle history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014. 520 $aABSTRACT. The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined. © 2014 Wang, Misztal, Aguilar, Legarra, Fernando, Vitezica, Okimoto, Wing, Hawken and Muir. 650 $aANIMALES DE CARNE 650 $aPESO CORPORAL 650 $aPOLLO 653 $aASSOCIATION MAPPING 653 $aBayesB 653 $aBODY WEIGHT 653 $aBROILER CHICKEN 653 $aCARNE DE AVES 653 $aGENOME-WIDE ASSOCIATION 653 $aSsGWAS 700 1 $aMISZTAL, I. 700 1 $aAGUILAR, I. 700 1 $aLEGARRA, A. 700 1 $aFERNANDO, R.L. 700 1 $aVITEZICA, Z. 700 1 $aOKIMOTO, R. 700 1 $aWING, T. 700 1 $aHAWKEN, R. 700 1 $aMUIR, W.M. 773 $tFrontiers in Genetics, 2014$gv.5, p.1-10. OPEN ACCESS.
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