QTL Mapping For Salt Tolerance At The Seedling Stage In Maize (Zea Mays L.)

Salt stress is one of the most important factor constraining the growth and development of maize, and the salt tolerance is a quantitative trait controlled by multiple genes. In the present study, we constructed a high density genetic map based on high quality SNPs from161F2:5RIL populations derived from the cross between two maize inbred lines contrasting in salinity tolerance. Hydroponic system in the greenhouse and the saline field in Nanpi county, this two system were used to culture our RIL population as the materials for the experiment.For the hydroponic culture, the parents and the RIL population were grown in hydroponic system at seedling stage. When grown to3leaves and a half stage, they were treated with160mmol/L NaCl for the salt treatment and0mmol/L NaCl for the normal one. After7days of treatment, the shoots of S and N treatment were harvested separately, the salt tolerance rating (STR), shoot fresh weight (SFW), shoot dry weight (SDW), shoot water content (SWC), and the Na+and K+contents of the shoots (SNC, SKC) were measured.As for the saline field, we mainly measured the germination rate (FGR) and the salt tolerance rating (FSTR). Then the QGAstation software was used to analyze these phenotype data to get the conditional data. By analyzing these data with the QTLnetwork2.1software, We detected a total of20additive and9epistatic QTLs,12of additive and2of epistatic QTLs showed significant Q×T interaction effects. Conditional analysis detected9conditional QTLs.In saline field, only1additive QTL was identified for FGR and FSTR, respectively. The two QTLs, QFgrl and QFstrl were co-located, which could account for30.43%and58.33%of phenotypic variation, respectively.In greenhouse condition,3additive QTLs were found for STR, Qstr1、Qstr3Qstr7, which could account for14.25%、24.98%、3.37%of phenotypic variation, respectively. For TWC,4additive QTLs,2epistasis QTLs and1conditional QTL were detected. The4additive QTLs, QTwc1、QTwc3、QTwc7and QTwc9, could account for1.65%、11.65%、2.41%and4.19%of phenotypic variation, respectively. And all the4additive QTLs had additive×treatment effect, which could account for6.97%、10.11%、2.15%and3.85%of phenotypic variation, respectively. The2epistasis QTLs, QTwc1/QTwc3and QTwc3/QTwc9, could account for0.10%and1.41%of phenotypic variation, respectively.1conditional QTL QTwcs|twcn3, could account for27.12%of phenotypic variation.2additive QTLs,1epistasis QTL and1conditional QTL were detected for SNC.2additive QTLs QSnc3and QSnc5, could account for4.33%and4.37%of phenotypic variation, respectively. And all the2QTLs were detected to have additive x treatment interaction effects, which could account for7.59%and4.05%of phenotypic variation, respectively.1epistasis QTL QSnc1/QSnc6, could account for10.86%of phenotypic variation.4additive QTLs and5conditional QTLs were detected for SKC. The4additive QTLs, QSkc3、QSkc5、QSkc7and QSkc9, could account for15.43%、8.22%、4.36%and5.43%of phenotypic variation, respectively. And among them, QSkc3and QSkc5were detected to have additive×treatment interaction effects, which could account for16.24%and1.81%of phenotypic variation, respectively.5conditional QTLs,QSkcs|skcnl、QSkcs|skcn3.1、QSkcs|skcn3.2、QSkcs|skcn4and QSkcs|skcn5could account for12.85%、49.07%、47.96%、7.11%and4.67%of phenotypic variation, respectively.5additive QTLs,3epistasis QTLs and2conditional QTLs were detected for SKN.5additive QTLs QSkn3、QSkn4.1、QSkn4.2、QSkn5and QSkn6, could account for2.32%、16.81%、16.85%、1.21%and1.17%of phenotypic variation, respectively. Among them, QSkn3%QSkn4.1. QSkn4.2and QSkn5were detected to have additive×treatment interaction effects, which could account for2.29%、25.07%、24.88%and0.98%of phenotypic variation, respectively.3epistasis QTLs, QSkn4.1/QSkn6、 QSkn5/|QSkn6and QSkn6/QSkn10, could account for0.58%、0.79%and0.76%of phenotypic variation, respectively.2conditional QTLs, QSkns|sknn3and QSkns|sknn5, could account for32.54%and9.28%of phenotypic variation, respectively.