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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
11/12/2018 |
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Actualizado : |
11/12/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
ZHANG, X.; LOURENCO, D.; AGUILAR, I.; LEGARRA, A.; MISZTAL, I. |
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Afiliación : |
XINYUE ZHANG, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States; DANIELA LOURENCO, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRÉS LEGARRA, INRA (Institut National de la Recherche Agronomique); IGNACY MISZTAL, Animal and Dairy Science, Animal Breeding and Genetics, University of Georgia, United States. |
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Título : |
Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Frontiers in Genetics, 19 August 2016, Volume 7, Issue AUG, Article number 151. OPEN ACCESS |
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ISSN : |
1664-8021 |
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DOI : |
10.3389/fgene.2016.00151 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 15 May 2016 // Accepted 04 August 2016 // Published 19 August 2016.
Specialty section:
This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics. |
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Contenido : |
ABSTRACT.
Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects.
© 2016 Zhang, Lourenco, Aguilar, Legarra and Misztal. MenosABSTRACT.
Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssG... Presentar Todo |
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Palabras claves : |
BayesB; BayesC; GENOME-WIDE ASSOCIATION; SNP WINDOW; WssGBLUP. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12161/1/fgene-07-00151.pdf
https://www.frontiersin.org/articles/10.3389/fgene.2016.00151/full
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Marc : |
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260 $c2016
500 $aArticle history: Received 15 May 2016 // Accepted 04 August 2016 // Published 19 August 2016. Specialty section: This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics.
520 $aABSTRACT. Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u2) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects. © 2016 Zhang, Lourenco, Aguilar, Legarra and Misztal.
653 $aBayesB
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700 1 $aLOURENCO, D.
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700 1 $aLEGARRA, A.
700 1 $aMISZTAL, I.
773 $tFrontiers in Genetics, 19 August 2016, Volume 7, Issue AUG, Article number 151. OPEN ACCESS
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INIA Las Brujas (LB) |
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| Registros recuperados : 132 | |
| 61. |  | BANCHERO, G.; QUINTANS, G.; MARTIN, G.B.; MILTON, J.T.B.; LINDSAY, D.R. Nutrition and colostrum production in sheep. 2. Metabolic and hormonal responses to different energy sources in the final stages of pregnancy. Reproduction, Fertility and Development, 2004, v. 16, no. 6, p. 645-653. Article history: Submitted: 7 October 2003//Accepted: 3 June 2004//Published: 16 August 2004.
DOI: https://doi.org/10.1071/RD03092| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : A - 2 |
| Biblioteca(s): INIA Treinta y Tres. |
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| 62. | | ZORRILLA DE SAN MARTÍN. G.; JORGE, J.; ROEL, A.; PARFITT, J.; GIGENA, M.; GIGENA, F. Intensificación sostenible mediante rotaciones arroz-soja-pasturas/ganadería regadas por aspersión en lomadas del este de Uruguay. Resumen zafras 2019-2020 y 2020-2021 y conclusiones. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B. (Eds.) Arroz 2021. Montevideo (UY): INIA, 2022. p. 29-32. (INIA Serie Técnica; 262)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 66. | | SOARES DE LIMA, J.M.; FERNANDEZ, E.; FERRARO, B.; ZORRILLA DE SAN MARTÍN, G.; LANFRANCO, B. Riego por aspersión en rotaciones arroz-soja-pasturas. In: Terra, J. A.; Martínez, S.; Saravia, H.; Mesones, B. (Eds.) Arroz 2021. Montevideo (UY): INIA, 2022. p. 33-36. (INIA Serie Técnica; 262)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 68. | | DEAMBROSI, E.; ZORRILLA DE SAN MARTÍN, G.; LAUZ, M.; BLANCO, P.H.; TERRA, J.A. Rompiendo el techo de rendimiento del cultivo de arroz. Proyecto ANII ALI_1_2012_1_3507 (INIA, GMA-COOPAR, ACA) Zafra 2015-2016. ln: JORNADA ANUAL ARROZ, 2016, INIA TREINTA Y TRES, TREINTA Y TRES, UY. Arroz: resultados experimentales 2015-2016. Treinta y Tres, (Uruguay): INIA, 2016. Cap. 2, p. 1-4. (INIA, Serie Actividades de Difusión; 765) Acceso a publicación y presentación oral.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 69. | | DEAMBROSI, E.; ZORRILLA DE SAN MARTÍN, G.; LAUZ, M.; BLANCO, P.H.; TERRA, J.A. Rompiendo el techo de rendimiento del cultivo de arroz. Proyecto ANII ALI_1_2012_1_3507 (INIA, GMA-COOPAR, ACA) Zafra 2016-2017 - Trabajos de validación. In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. p. 89-93. (INIA Serie Técnica; 233)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
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| 71. | | VIÑOLES, C.; GONZÁLEZ BULNES, A.; MARTIN, G.B.; SALES ZLATAR, F.; SALE, S. Sheep an goats. ln: DesCôteaux, L.; Gnemmi, G.; Colloton, J., eds. Practical atlas of ruminant and camelid reproductive ultrasonography. Ames, IA: Wiley, 2010. p. 181-210 Capítulo 11.| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Tacuarembó. |
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| 72. | | BONILLA, O.; ZORRILLA DE SAN MARTÍN, G.; DEAMBROSI, E.; ROVIRA, P.J.; BERMÚDEZ, R. Sistema intensivo de arroz con ganadería vacuna y ovina (Unidad de Producción Arroz - Ganadería - UPAG). [Resumen]. ln: Conferencia Internacional de Arroz de Clima Templado, 3., 2003, Punta del Este, Uruguay Resúmenes. Montevideo (Uruguay): ACA; INIA; GMA; FLAR, 2003. p. 100. "Instituto Nacional de Investigación Agropecuaria, Uruguay (INIA); Asociación de Cultivadores de Arroz (ACA); Gremial de Molinos Arroceros (GMA); Fondo Latinoamericano de Arroz de Riego (FLAR)"| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 76. | | CARRACELAS, G.; GUILPART, N.; GRASSINI, P.; ZORRILLA DE SAN MARTÍN, G.; CASSMAN, K. Yield gap analysis of irrigated rice in Uruguay and comparison with other rice producing countries. [Resumen]. ln: Congresso Brasileiro de Arroz Irrigado, 11., 13-16 agosto, Camboriú, Brasil, 2019. 4 p.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Tacuarembó. |
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| 77. | | CARRACELAS, G.; GRASSINI, P.; GUILPART, N.; CASSMAN, K.; ZORRILLA DE SAN MARTÍN, G. Yield gap analysis and prognosis of yield increase of irrigated rice in Uruguay. [Abstract]. In: Rice Technical Working Group, 37, 2018, Proceedings. Long Beach, California (USA): Rice Technical Working Group, 2018. p. 131-132.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 78. | | CARRACELAS, G.; GUILPART, N.; GRASSINI, P.; CASSMAN, K.G.; ZORRILLA DE SAN MARTÍN, G. Yield gaps and yield increase of irrigated rice in Uruguay. [Presentación oral]. In: Rice Technical Working Group, 37, 2018, Proceedings. Long Beach, California (USA): Rice Technical Working Group, 2018.| Tipo: Presentaciones Orales |
| Biblioteca(s): INIA Tacuarembó. |
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| 79. | | CARRACELAS, G.; GUILPART, N.; CASSMAN, K.; GRASSINI, P.; ZORRILLA DE SAN MARTÍN, G. Yield potential and Yield gaps of irrigated rice in Uruguay and other rice producing countries. In: International Temperate Rice Conference, 6-9 de marzo, Griffith, NSW, Australia, 2017. 7 p.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 80. | | CARRACELAS, G.; GUILPART, N.; GRASSINI, P.; CASSMAN, K.G.; ZORRILLA DE SAN MARTÍN, G. Yield potential and yield gaps of irrigated rice in Uruguay and other rice producing countries. [Presentación oral]. In: International Temperate Rice Conference, 6-9 de marzo, Griffith, NSW, Australia, 2017.| Tipo: Presentaciones Orales |
| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| Registros recuperados : 132 | |
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