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JXB Advance Access originally published online on April 23, 2007
Journal of Experimental Botany 2007 58(8):1927-1933; doi:10.1093/jxb/erm054
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© The Author [2007]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

RESEARCH PAPER

Class prediction of closely related plant varieties using gene expression profiling

G Ancillo1, J Gadea2, J Forment2, J Guerri1 and L Navarro1,*

1Instituto Valenciano de Investigaciones Agrarias (IVIA), Carretera Moncada-Náquera, Km. 4.5, 46113 Moncada (Valencia), Spain
2Instituto de Biología Molecular y Celular de Plantas (IBMCP), Universidad Politécnica de Valencia, Laboratorio de Genómica, Avenida de los Naranjos, s/n, 46022 Valencia, Spain

* To whom correspondence should be addressed. E-mail: lnavarro{at}ivia.es

In recent years, class prediction experiments have been largely developed in cancer research with the aim of classifying unknown samples by examining their expression signature. In natural populations, a significant component of gene expression variability is also heritable. Citrus species are an ideal model to accomplish the study of these questions in plants, due to the existence of varieties derived from somatic mutations that are likely to differ from each other by one or a few point mutations but are phenotypically indistinguishable at early vegetative stages. The small genetic variability existing among these varieties makes molecular markers ineffective in distinguishing genotypes within a particular species. Gene expression profiles have been used to predict mandarin clementine varieties (Citrus clementina Hort. ex Tan.) by means of two independent supervised learning algorithms: Support Vector Machines and Prediction Analysis of Microarrays. The results show that transcriptional variation is variety-dependent in citrus, and supervised clustering methods may correctly assign blind samples to varieties when both training and test samples are under the same experimental conditions.

Key words: Citrus, class prediction, expression profiling, microarray, natural variation

Received 30 November 2006; Revised 23 February 2007 Accepted 27 February 2007


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