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Online Journal of Bioinformatics ©
Volume 13(1):1-13, 2012
In Silico analysis of a consensus QTL for drought resistance in rice (Oryza Sativa L.)
N. Pradeepa*, P. Shanmuga Priya*, K. Silvas Jebakumar Prince, S. Kavitha, R. Poornima,
Mankar Sumeet Prabhakar and R. Chandra Babu1
Department of Plant Molecular Biology and Bioinformatics, Centre for Plant Molecular Biology and biotechnology, Tamil Nadu Agricultural University, Coimbatore, India *Equal contribution
Pradeepa N, Shanmuga PP, Jebakumar Prince KS, Kavitha S, Poornima R, Prabhakar MS . Chandra Babu R., In Silico analysis of a consensus QTL for drought resistance in rice (Oryza Sativa L.), Online J Bioinformatics, 13(1):1-13, 2012. Drought stress is a major limitation to rice, (Oryza sativa L.) production and yield stability in rainfed ecosystems. Mapping QTLs for drought resistance can be used to develop rice cultivars suitable for water-limited environments. Though numerous QTLs for drought resistance have been mapped, their use in breeding is limited. Identifying candidate QTLs and their underlying genes and regulatory elements are critical. Based on our research findings and published literature, the QTL on chromosome 1 spanning 139 - 150 cM was found to be a consensus region for drought resistance in rice. In silico analysis using ‘Rice Gene Thresher’ revealed this region to contain nearly 1400 transcriptional active genes with 183 genes of known biological functions. Based on stress gene catalogues, 35 genes were identified to be up-regulated under drought stress in rice. The drought responsive genes are involved in cellular metabolism, transport and signal transduction, transcription and hormonal regulation. The up-regulated genes encode proteins such as protein kinases, cytochrome P450 and choline/ethanolamine kinase involved in abiotic stress adaptation and yield improvement in rice. Thus the results of in silico analysis of the consensus QTL may be useful in map-based cloning of candidate genes and genetic engineering for drought resistance in rice and other cereals.
Keywords: Oryza sativa, rice, drought resistance, consensus QTL, allele mining, in silico analysis.