Abstract
QTL for days to flowering in rice under drought condition were mapped using a DH population derived from a cross between a deep-rooted upland adapted japonica genotype CT9993-5-10-1-M and a lowland adapted shallow-rooted moderately drought tolerant indica genotype IR62266-42-6-2. QTL mapping was performed following three different mapping models viz. simple (SIM), composite (CIM) and multiple mapping model (MIM) using WinQTL Cartographer version 2.5.006. SIM located 12 QTL for days to flowering spread over nine chromosomes whereas CIM and MIM each located 5 QTL with a threshold LOD score of 2.5. A comparison of the QTL detected by three different models identified five QTL that were common across at least two models for days to flowering. In MIM analysis, the detected QTL (qHD-1-b) between flanking markers (RG109 – ME1014) located on chromosome 1 recorded positive effect (1.4090) but the remaining four QTL had negative effect. The QTL (qHD-3-a) detected between flanking markers (RG104 – RG409) by both MIM and SIM in the present study was also reported earlier as linked with the marker RG104. The five common QTL detected by at least two models could be considered as stable QTL for days to flowering under drought and might be of practical use in marker assisted selection.
Highlights
Rice (Oryza sativa L.) is one of the most important cereal crops in the world and feeds nearly 50% of the world population
Plant material The plant material used for mapping of the QTL for days to flowering consisted of a population of 154 double haploid (DH) lines derived from a cross between a deeprooted upland adapted japonica rice genotype, ‘CT99935-10-1-M’ and a lowland indica rice genotype with shallow roots having moderate drought tolerance, ‘IR62266-42-62’
The present study provides insight into the genetic control of days to flowering in rice under moisture stress condition
Summary
Rice (Oryza sativa L.) is one of the most important cereal crops in the world and feeds nearly 50% of the world population. The analysis of the quantitative variation of such trait, especially its potential genetic basis, is of prime importance to a plant breeder (Asins, 2002). Plant breeders make use of the genetic variation and the genetic basis of the quantitative trait for formulating an efficient, time-bound breeding program for genetic enhancement of the trait. Because of their features such as large number of genes, small effects and greater influence of environment, the phenotype of the quantitative trait does not provide ample insight into its genotype as against simple monogenic traits (Kearsey, 2002). Molecular marker-mediated genetic analysis is widely used to dissect such complex quantitative trait into individual Mendelian factors for better understanding of the genetics of the quantitative trait (Pathak and Zhu, 2007)
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