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| 101. |  | RIOS, A.; GIMÉNEZ, A. Soja: Control de malezas In: GIMÉNEZ, A.; RESTAINO, E. (Eds.). Girasol y soja : algunos aspectos tecnológicos de producción para el litoral oeste de Uruguay. Montevideo (Uruguay): INIA, 1994. p109-114 (INIA Boletin de Divulgación; 47) Convenio INIA - Cámara de Aceites Comestibles - Facultad de Agronomía| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/07/2017 |
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Actualizado : |
20/02/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
LADO, B.; BATTENFIELD, S. D.; GUZMÁN, C.; QUINCKE, M.; SINGH, R. P.; DREISIGACKER, S.; PEÑA, R. J.; FRITZ, AL.; SILVA, P.; POLAND, J.; GUTIÉRREZ, L. |
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Afiliación : |
BETTINA LADO, Statistics Dep., Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay; SARAH D. BATTENFIELD, Kansas State University, Department of Plant Pathology, Manhattan, United States; CARLOS GUZMÁN, Centro Internacional de Mejoramiento de Maiz y Trigo, Global Wheat Program, Mexico City, Mexico; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAVI PRAKASH SINGH, Centro Internacional de Mejoramiento de Maiz y Trigo, Global Wheat Program, Mexico City, Mexico; SUSANNE DREISIGACKER, Centro Internacional de Mejoramiento de Maiz y Trigo, Applied Biotechnology Center, Mexico City, Mexico; ROBERTO JAVIER PEÑA, Centro Internacional de Mejoramiento de Maiz y Trigo, Global Wheat Program, Mexico City, Mexico; ALLAN K. FRITZ, Kansas State University, Manhattan, United States; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JESSE A. POLAND, Kansas State University, Department of Plant Pathology and Department of Agronomy, Manhattan, United States; LUCÍA GUTIÉRREZ, Kansas State University, Department of Plant Pathology and Department of Agronomy, Manhattan, United States. |
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Título : |
Strategies for selecting crosses using genomic prediction in two wheat breeding programs. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
The Plant Genome, 2017, v.10, Issue 2, 12p. OPEN ACCESS |
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ISSN : |
1940-3372 |
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DOI : |
10.3835/plantgenome2016.12.0128 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: Dec 14, 2016 // Accepted: Mar 18, 2017 // Published: July 6, 2017.
B. Lado and S. Battenfield contributed equally.Assigned to Associate Editor Nicholas Tinker.
This is an open access article distributed under the CC BY-NC-ND license. |
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Contenido : |
ABSTRACT.
The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (VLD) or assuming linkage equilibrium (VLE). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat (Triticum aestivum L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses.
© Crop Science Society of America |
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Palabras claves : |
GENOMIC SELECTION; WHEAT; WHEAT BREEDING PROGRAMS. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12466/1/tpg-10-2-plantgenome2016.12.0128.pdf
https://dl.sciencesocieties.org/publications/tpg/articles/10/2/plantgenome2016.12.0128
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Marc : |
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100 1 $aLADO, B.
245 $aStrategies for selecting crosses using genomic prediction in two wheat breeding programs.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: Dec 14, 2016 // Accepted: Mar 18, 2017 // Published: July 6, 2017. B. Lado and S. Battenfield contributed equally.Assigned to Associate Editor Nicholas Tinker. This is an open access article distributed under the CC BY-NC-ND license.
520 $aABSTRACT. The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (VLD) or assuming linkage equilibrium (VLE). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat (Triticum aestivum L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. © Crop Science Society of America
650 $aTRIGO
653 $aGENOMIC SELECTION
653 $aWHEAT
653 $aWHEAT BREEDING PROGRAMS
700 1 $aBATTENFIELD, S. D.
700 1 $aGUZMÁN, C.
700 1 $aQUINCKE, M.
700 1 $aSINGH, R. P.
700 1 $aDREISIGACKER, S.
700 1 $aPEÑA, R. J.
700 1 $aFRITZ, AL.
700 1 $aSILVA, P.
700 1 $aPOLAND, J.
700 1 $aGUTIÉRREZ, L.
773 $tThe Plant Genome, 2017$gv.10, Issue 2, 12p. OPEN ACCESS
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