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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
29/09/2014 |
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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.; 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. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
Genetics Research, 2012, v.94, no.2, p.73-83. OPEN ACCESS. |
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ISSN : |
0016-6723 |
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DOI : |
10.1017/S0016672312000274 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 19 September 2011 / Revised 8 December 2011 and 9 March 2012. / Accepted 13 March 2012. |
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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 |
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Thesagro : |
ANIMALES; CRIA; FENOTIPOS; GENOTIPO; MARCADORES GENÉTICOS; MEJORAMIENTO GENÉTICO ANIMAL. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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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 : |
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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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Registro original : |
INIA Las Brujas (LB) |
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| Registros recuperados : 7 | |
| 1. |  | FRAGOMENI, B.O.; MISZTAL, I.; LOURENCO, D.L.; AGUILAR, I.; OKIMOTO, R.; MUIR, W.M. Changes in variance explained by top SNP windows over generations for three traits in broiler chicken Frontiers in Genetics, 2014, v.5, no.Oct., Article number 332. OPEN ACCESS. Article history: Published 01 October 2014.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : B - 2 |
| Biblioteca(s): INIA Las Brujas. |
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| 2. | | CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; LEGARRA, A.; MUIR, W.M. Effect of different genomic relationship matrices on accuracy and scale. Journal of Animal Science, 2011, v.89, no.9, p.2673-2679. Article history: Received September 29, 2010. / Accepted March 21, 2011.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
| Biblioteca(s): INIA Las Brujas. |
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| 4. | | CHEN, C. Y.; MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; MEUWISSEN, T.; AGGREY, S. E.; MUIR, W. M. Genome wide marker assisted selection in chicken: making the most of all data, pedigree, phenotypic, and genomic in a simple one step procedure. Volume Genetic improvement programmes: Selection using molecular information - Lecture Sessions, 0288. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0288. Acknowledgements: The authors thank Cobb-Vantress for access to data for this study. This study was partially funded by the Holstein Association, Smithfield Premium Genetics, and by AFRI grants 2009-65205-05665 and 2010-65205-20366 from...| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 5. | | 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.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : B - 2 |
| Biblioteca(s): INIA Las Brujas. |
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| 6. | | CHEN, C.Y.; MISZTAL, I.; AGUILAR, I.; TSURUTA, S.; MEUWISSEN, T.H.E.; AGGREY, S.E.; WING, T.; MUIR, W.M. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens. Journal of Animal Science, 2011, v.89, no.1, p.23-28. Article history: Received April 9, 2010 / Accepted September 22, 2010.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 1 |
| Biblioteca(s): INIA Las Brujas. |
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| 7. | | 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.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 7 | |
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