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| Registros recuperados : 74 | |
| 44. |  | BALMELLI, G.; RESQUÍN, F.; SIMETO, S.; GONZÁLEZ, M.; SCOZ, R.; BRITO, G.; ROSSI, C.; MARANGES, F. Montes con INIA Sombra: protección del ganado y diversificación productiva. En: DÍA DE CAMPO, 2019, UNIDAD EXPERIMENTAL PALO A PIQUE (UEPP), TREINTA Y TRES, UY. p. 15.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 45. | | MAISONNAVE, J.; CARBALLO, M.; BATTISTONI, J.; BENAVIDEZ, U.; GONZÁLEZ, M.C.; PAZOS, A. Identificación, caracterización y evaluación de antígenos para el diagnóstico de la fascioliasis en rumiantes. ln: Jornadas Uruguayas de Buiatría, 19., 1991, Paysandú, Uruguay Paysandú (Uruguay): Centro Médico Veterinario de Paysandú, 1991. C.C.3, p. 1-3| Biblioteca(s): INIA Treinta y Tres. |
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| 46. | | GONZÁLEZ, M.V.; FERREIRA, V.; SIRI, M.I.; GONZÁLEZ-ARCOS, M.; GALVÁN, G.A. Identificación de fuentes de resistencia contra Clavibacter michiganensis subsp. michiganensis en el cultivo de tomate. In: Sociedad Uruguaya de Fitopatología Jornada Uruguaya de Fitopatología, 6., Jornada Uruguaya de Protección Vegetal, 4., 21-22 octubre, 2021, Montevideo, Uruguay. Libro de resúmenes. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2021. p. 44 Financiamiento: Programa CSIC Grupos I+D - Bacterias fitopatógenas: mecanismos de resistencia hospedera y de interacción planta-patógeno.| Biblioteca(s): INIA Treinta y Tres. |
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| 47. | | BALMELLI, G.; RESQUÍN, F.; SIMETO, S.; GONZÁLEZ, M.; SCOZ, R.; BRITO, G.; ROSSI, C.; MARANGES, F. INIA SOMBRA: una alternativa para la protección del ganado y para la diversificación productiva. Revista INIA Uruguay, 2019, no. 56, p. 79-83. (Revista INIA; 56).| Biblioteca(s): INIA Tacuarembó. |
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| 52. | | MENESES, L.; CALISTRO, P.; GONZALEZ, M.; PESSIO, M.; DO CANTO, J.; CIBILS-STEWART, X. Uso de endófitos comerciales (AR584) para mitigar el daño de Diloboderus abderus en Festuca arundinacea. [Poster]. In: Jornada Uruguaya de Fitopatología, 5., III Jornada Uruguaya de Protección Vegetal, 3., Montevideo, 11 de octubre de 2019.| Biblioteca(s): INIA Tacuarembó. |
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| 53. | | MENESES, L.; CALISTRO, P.; GONZALEZ, M.; PESSIO, M.; DO CANTO, J.; CIBILS-STEWART, X. Uso de endófitos comerciales (AR584) para mitigar el daño de Diloboderus abderus en Festuca arundinacea. [Resumen]. In: Jornada Uruguaya de Fitopatología, 5., III Jornada Uruguaya de Protección Vegetal, 3., Montevideo, 11 de octubre de 2019.| Biblioteca(s): INIA Tacuarembó. |
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| 55. | | LOPRETTI, M.I.; BALDYGA, N.T.; GONZALEZ, M.; OLAZABAL, L.B.; TORRES, M.G.; RESQUÍN, F.; CARRASCO-LETELIER, L. Biodegradation pretreatment of wood of E. grandis, E. dunnii, and E. benthamii to work in biorefinery processes. Journal of Renewable Materials, v. 4, n. 1, p. 66-71, 2016. Article history: Received October 13, 2015/ Accepted January 21, 2016.| Biblioteca(s): INIA La Estanzuela; INIA Tacuarembó. |
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| 56. | | CIBILS-STEWART, X.; GONZALEZ, M.; PESSIO, M.; CALISTRO, P.; ROSSI, C.; GIAMBIASI, M.; CHIARAVALLE, W.; ABBATE, S.; BARAIBAR, N. Crónica del daño de la lagarta medidora (Rachiplusia nu) en soja Intacta®. Cultivos. Revista INIA Uruguay, Marzo 2023, no.72, p.53-58. (Revista INIA; 72).| Biblioteca(s): INIA Las Brujas. |
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| 60. | | AROCENA, R.; CHALAR, G.; FABIÁN, D.; PACHECO, J.P.; OLIVERO, V.; GONZÁLEZ, M.; SILVA, M.; GARCÍA, P.; PERDOMO, C. Impacto de la lechería en los ecosistemas acuáticos continentales. Montevideo (UY) : INIA, 2016 39 p. (Serie FPTA-INIA; 60) Proyecto FPTA-179: Medidas para la mitigación del impacto de la lechería en la calidad de agua de la cuenca lechera del embalse Paso Severino. Período de Ejecución: May.2009-Abr.2012.| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
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| Registros recuperados : 74 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
18/12/2018 |
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Tipo de producción científica : |
Artículos Indexados |
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Autor : |
MISZTAL, I.; FRAGOMENI, B.; LOURENÇO, D. A. L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T. J. |
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Afiliación : |
IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Efficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY). |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Interbull Bulletin, 2015, v. 49, p. 111-116. |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture. MenosABSTRACT.
The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and
GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular... Presentar Todo |
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Palabras claves : |
BIG POPULATION; GENOMIC RECURSION; INVERSION; SINGLE-STEP METHOD. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12204/1/1387-2387-1-PB.pdf
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Marc : |
LEADER 02658naa a2200253 a 4500
001 1012458
005 2018-12-18
008 2015 bl uuuu u00u1 u #d
100 1 $aMISZTAL, I.
245 $aEfficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY).$h[electronic resource]
260 $c2015
520 $aABSTRACT. The purpose of this study was to evaluate properties of the inverse of the genomic relationship matrix derived with the algorithm for proven and young (APY) and the accuracy of genomic selection in single-step genomic best linear unbiased prediction (ssGBLUP). The APY implements genomic recursions on a subset of genotyped animals. When that subset is small, the cost of APY is approximately linear in memory and computations, effectively removing restrictions on the number of genotypes. Tests involved 10 102 702 final scores from 6 930 618 Holstein cows. A total of 100 000 animals with genotypes were used in the analyses and included 23 174 sires, 27 215 cows and 49 611 young animals. Genomic estimated breeding values (GEBVs) were calculated using ssGBLUP with a regular inverse of the genomic relationship matrix (G) and with G inverse from APY. Many subsets were tested including only sires, only cows and random samples from 2 000 to 20 000 animals. When the number of animals in the subset was ≥15,000, the correlations between GEBV with APY and GEBV with the regular inverse were ≥0.99. Best convergence rate was achieved with random samples. A theory on APY was derived and is based on the fact that additive effects of animals in the subset are linear functions of the effects of independent chromosome segments (ICSs); the number of segments is a function of the effective population size. Accuracy of GEBV with APY can be slightly superior to that of a regular inverse. The inverse with APY is computed from G, which in turn is derived from single nucleotide polymorphism (SNP) BLUP and indirectly from BayesB or other SNP-based prediction methods. Strategies like SNP selection, SNP weighting, and use of causative SNPs from sequence analysis can be incorporated in APY without additional cost. The APY removes size limitations from ssGBLUP and facilitates a model with a complex genetic architecture.
653 $aBIG POPULATION
653 $aGENOMIC RECURSION
653 $aINVERSION
653 $aSINGLE-STEP METHOD
700 1 $aFRAGOMENI, B.
700 1 $aLOURENÇO, D. A. L.
700 1 $aTSURUTA, S.
700 1 $aMASUDA, Y.
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aLAWLOR, T. J.
773 $tInterbull Bulletin, 2015$gv. 49, p. 111-116.
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