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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
10/09/2014 |
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Actualizado : |
15/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
WANG, H.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; FERNANDO, R.L.; VITEZICA, Z.; OKIMOTO, R.; WING, T.; HAWKEN, R.; MUIR, W.M. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. |
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Fecha de publicación : |
2014 |
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Fuente / Imprenta : |
Frontiers in Genetics, 2014, v.5, p.1-10. OPEN ACCESS. |
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ISSN : |
1664-8021 |
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DOI : |
10.3389/fgene.2014.00134 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 03 March 2014 // Paper pending published 04 April 2014 // Accepted 25 April 2014 // Published online: 20 May 2014. |
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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 |
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Palabras claves : |
ASSOCIATION MAPPING; BayesB; BODY WEIGHT; BROILER CHICKEN; CARNE DE AVES; GENOME-WIDE ASSOCIATION; SsGWAS. |
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Thesagro : |
ANIMALES DE CARNE; PESO CORPORAL; POLLO. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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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 : |
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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
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653 $aBROILER CHICKEN
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700 1 $aAGUILAR, I.
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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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