Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers
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- José Crossa
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Gustavo de los Campos
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Paulino Pérez
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Daniel Gianola
- Departments of Animal Science, Dairy Science, and Biostatistics and Medical Informatics, University of Wisconsin , Madison, Wisconsin 53706
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- Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- José Luis Araus
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Jianbing Yan
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Vivi Arief
- School of Land Crop and Food Sciences , University of Queensland, 4072, Sta. Lucia, Queensland, Australia
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- Marianne Banziger
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
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- Hans-Joachim Braun
- International Maize and Wheat Improvement Center (CIMMYT) , 06600, México DF, México
抄録
<jats:title>Abstract</jats:title> <jats:p>The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.</jats:p>
収録刊行物
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- Genetics
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Genetics 186 (2), 713-724, 2010-10-01
Oxford University Press (OUP)