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OJBTM
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
ABSTRACT
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.