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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
07/10/2014 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
VITEZICA, Z.G.; AGUILAR, I.; MISZTAL, I.; LEGARRA, A. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Bias in genomic predictions for populations under selection. |
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Fecha de publicación : |
2011 |
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Fuente / Imprenta : |
Genetics Research, 2011, v.93 no.5, p.357-366. |
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ISSN : |
0016-6723 |
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DOI : |
10.1017/S001667231100022X |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 27 January 2011 / Revised 27 March 2011 / Published online 18 July 2011. |
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Contenido : |
ABSTRACT.
Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.
© 2011 Cambridge University Press. |
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Thesagro : |
DETECCIÓN DE QTLS; MARCADORES GENÉTICOS; MEJORAMIENTO GENÉTICO ANIMAL. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 01990naa a2200229 a 4500
001 1050957
005 2019-10-09
008 2011 bl uuuu u00u1 u #d
022 $a0016-6723
024 7 $a10.1017/S001667231100022X$2DOI
100 1 $aVITEZICA, Z.G.
245 $aBias in genomic predictions for populations under selection.$h[electronic resource]
260 $c2011
500 $aArticle history: Received 27 January 2011 / Revised 27 March 2011 / Published online 18 July 2011.
520 $aABSTRACT. Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically. © 2011 Cambridge University Press.
650 $aDETECCIÓN DE QTLS
650 $aMARCADORES GENÉTICOS
650 $aMEJORAMIENTO GENÉTICO ANIMAL
700 1 $aAGUILAR, I.
700 1 $aMISZTAL, I.
700 1 $aLEGARRA, A.
773 $tGenetics Research, 2011$gv.93 no.5, p.357-366.
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INIA Las Brujas (LB) |
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| Registros recuperados : 81 | |
| 14. |  | LEGARRA, A.; AGUILAR, I.; MISZTAL, I. Single step methods with a view towards poultry breeding. Volume Species Breeding: Poultry, 324. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.324. Acknowledgements: This work has been financed by X-Gen and GenSSeq actions from SelGen metaprogram (INRA).| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 17. | | MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. Acceleration of computations in AI REML for single-step GBLUP models. Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), 703. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| 19. | | BERMANN, M.; MISZTAL, I.; LOURENCO, D.; AGUILAR, I.; LEGARRA, A. Definition of reliabilities for models with metafounders. [289] Part 17 - Challenges - improving genomic prediction. In: Proceedings of the World Congress on Genetics Applied to Livestock Production (WCGALP), 12., Rotterdam, the Netherlands, 3-8 July 2022. doi: https://doi.org/10.3920/978-90-8686-940-4_289 1217-1220. Article history: Published online: February 9, 2023. -- Corresponding author: A. Legarra, email: andres.legarra@inrae.fr -- Acknowledgment: This work received financing from European Unions' Horizon 2020 Research & Innovation Programme,...| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 81 | |
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