Construction Of Wheat Molecular Genetic Map And QTL Analysis For Agronomic And Quantity Traits

A associated RIL populations comprising302(Weimai8/Luohan2-derived, WL) lineswas used in the present study to construct a genetic linkage map using SSR, EST-SSR, RAPD,SRAP, STS and ISSR markers. Then the inclusive interval mapping method was utilized tomap QTL with additive effect by software Icimapping v3.0for agronomic traits, such asSpikelet number per spike(SPN), kernel number per spike(KNPS);1000-kernel weight(TKW),Grain weight per meter row (GWPM),Plant height (PH),heading date (HD),Blooming date (BD),flag leaf length (FLL),and quality traits, such as grain protein content(GPC), wet gluten content (WGC), flour whiteness (FW),kernel hardness (KH),waterabsorption (Abs), and dough stability time (DST). What's more, based on the novel moleculargenetic map above, multivariate conditional QTL mapping analysis was conducted to specifythe genetic characteristics of yield and yield-related traits, and quality traits respect toyield-related traits at the QTL level. The results were as follows:(1) Three thousand one handred and twenty three pairs of genomic primers were used togenotype the RIL populations and their parents, and in total246pairs of markers amplifiedclear and discrepant sites between Weimai8and Luohan2. The separation ratio of most sitesfit the1:1ratio, suggesting that the RIL population could be utilized in QTL mappinganalysis.(2) The genetic linage map, with23linkage groups, was constructed using the softwareMAPMAKER/EXP3.0and Joinmap v3.0, which was comprising348sites, with the wholegenome length of3132.20cM and an average distance of9.00cM between markers, and thenumber of markers being2on chromosomes3D to47on7B, showing that the mappingpopulation was suitable for QTL mapping.(3) Based on the genetic linkage map, the software IciMapping v3.0(with inclusiveinterval mapping method) was used to conduct QTL mapping of the agrononmic and qualitytraits, in three environments. Totally274QTL with additive effect for the twenty twoagronomic traits were mapped on all wheat chromosomes, with the explanation of phenotypic variation (PVE) from2.54%to40.64%of a single QTL. Of these,30of which accounted forat least10%of the phenotypic variation,14QTL showed significance in at least two trials ofE1, E1, E3and P, being major stable QTL. In total, up to50QTL with additive effect foreight traits were mapped, with the explanation of phenotypic variation (PVE) from3.60%to16.15%of a single QTL. Of these,7QTL showed significance in at least two trials of E1, E1,E3and P,10of which can explain at least10%of the phenotypic variation in one or twoenvironments, being major QTL.(4) For the first time, conditional QTL mapping was conducted for grain yield and yiledcomponents in wheat. The results showed that all of the yield related components, at the QTLlevel, thousand-grain-weight (TKW) contributed to yield (GWPM) the most, followed byspike number per meter row (SNPM), but the contribution of the two sizes is pretty much thesame, and next to the kernel number per spike of main stem (KNPS) and kernel weight per ear(KWPS), and the everage kernel number per spike (EKNPS) has the lest contribution to it.When protein content (GPC) conditional on yield related traits, the conditional QTL mappingshowed that thousand-kernel-weight (TKW) and kernel number per spike (KNPS) has equalcontribution to protein content, although they did not affected all the same QTL sites, bycontrast, the spike number per meter row (SNPR) has a little influence on it. When doughtstable time (DST) conditional on yield related traits, the conditional mapping analysis showedthat kernel number per spike (KNPS), thousand-kernel-weight (TKW) and spike number permeter row (SMPM) has little effect on the phenotypic variation of dought stable time (DST),which means that the grain yiled componens have little effects on quality trais of wheat, andthat makes the yield and flour processing quality improving simultaneously possible. Thepossible genetic relationships anasysis between plant height (PH) and its components showed,that spike length (SL) contributed the least to PH, the third internode length from the top(TITL) had the strongest influence on PH. Conditional and unconditional QTL mappingshowed that when the effects of kernel numbper per spike (KNPS), thousand-kernel-weight(TKW) and spike number per meter row (SNPR) were excluded, the QTL QDst-WL-2D,QGpc-WL-5B and QGpc-WL-7A can still be detected, which means that those three QTL wereaffected very little by the three yiled components, therefore, by Marker-asisted selection tothese three sites, it may makes the improving of the yield and quality at the same time.