Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers
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 (T...
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Published in | Genetics (Austin) Vol. 186; no. 2; pp. 713 - 724 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Genetics Society of America
01.10.2010
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Communicating editor: M. Kirst These authors contributed equally to this work. Supporting information is available online at http://www.genetics.org/cgi/content/full/genetics.110.118521/DC1. |
ISSN: | 1943-2631 0016-6731 1943-2631 |
DOI: | 10.1534/genetics.110.118521 |