Study On Qtl Mapping For Cold Tolerance Of Japonica Rice In Cold

Rice was the word’s most important food crops,chilling injury was the main limiting factor that affecting rice production and quality.In the Northeast area of China,Heilongjiang Province was the main rice producing area,occupied a pivotal position in China’s grain production,however,due to the general occurrence of the delayed chilling injury and barriers chilling injury,it caused substantially cut by japonica in cold,and seriously threat to the safe production of rice.Studying on rice cold tolerance characteristics,cultivating the cold tolerance of rice varieties to resist the adverse effects of cold damage was to ensure the safe production of japonica in cold the most economical and effective way.Cold tolerance was controlled by several quantitative trait locis (QTL) in rice, With constructing of high density molecular linkage map,QTL localization for the cold-tolerant related traits of rice had been extensively studied by molecular biology methods at home and abroad,however, the study was concentrated in the bud bursting period and seeding stage, In recent years,there were some process in the booting stage,but it was rare in QTL localization for the cold-tolerant related at tillering stage.This study explored QTL localization for the phenotypic values and CRI of the cold-tolerant related traits under natual condition and cold water irrigation at tillering and booting stage by using a F2:3population including180lines derived from a cross between two Japonica rice in cold that were different in cold tolerance "Dongnong422"and "Kongyu131",studying on the phenotypic changes at tillering and booting stage under natual and cold water stress condition,and QTL mapping for cold tolerance related traits,the purpose was to reveal the locis or the cold-tolerant related traits of rice at tillering and booting stage,In order to lay the foundation for marker-assisted breeding and gene cloning.The results were as follows.1. Linkage analysis for88pairs of SSR polymorphism makers by Mapmaker/EXP3.0b,a genetic linkage map of75SSR makers was constructed,it covered a total length of1457.68cM with an average distance of19.43cM between adjacent markers in rice genome.2. Under natual and cold water stress condition, the phenotypic values and cold water response index (CRI) of chlorophyll contents (CC^seeding height (SH)、tiller number (TN)、above ground biomass (AGB)、heading date (HD)、panicle number (PN)、panicle length (PL)、primary branch number (PBN)、secondary branch number (SBN)、spikelet number per panicle (SP)、seed setting rate (SSR) and grain weight per plant (GWP) were displayed a continuous variation and approximately normal distribution in F3lines,indicating all of these traits were quantitative inherited,suitale for QTL mapping. 3. The same trait of CC、SH、HD、PN、PL、PBN、SBN、SP、GWP under the two conditions were highly significant positive correlation TN、AGB and SSR were negative correlation under the two conditions,it showed that TN、AGB and SSR were more sensitive in cold-water stress. SSR were negative correlation under the natual condition,but was highly significant positive correlated with GWP under cold water stress condition,,it explained that SSR was more sensitive to cold water stress at the booting stage,and directly impacted GWP.4. Under natual and cold water stress condition at the tillering stage,18QTLs controlling CC、 SH、TN and AGB were detected,these QTLs were located on chromosome2、3、4、5、6、7、8and12,respectively.l QTL associated with CC was detected under two conditions,explained21.0%and27.0%phenotypic variation.Allete from Dongnong422increased CC.4QTLs associated with CRI were detected.5. Under natual and cold water stress condition at the booting stage,49QTLs controlling HD、 PL、PN、PBN、SBN、NP、SSR and GWP were detected,these QTLs were located on chromosome2、3、4、5、6、7、8、11and12, respectively.12QTLs were detected under two conditions,had the same direction of gene effect.In which4QTLs associated with HD were detected, qHD-7-1and qHD-12could explain the larger phenotypic variation,2QTLs associated with PBN were detected, qPBN-2and qPBN-7-1.3QTLs associated with SBN were detected, qSBN-7-2could explain the largest phenotypic variation,23.0%and25.0%under two conditions.1QTL associated with SP was detected,explained15.0%and24.0%phenotypic variation under two conditions.2QTLs associated with GWP were detected, qGWP-2-1、qGWP-7-1,which explained the same phenotypic variation under two conditions,9.0%and12.0%, respectively..13QTLs associated with CRI were detected.