Phenotypic variation, performance and correlation
Phenotypic characters (27 traits) of the “ZM175 / XY60” (ZX) RIL population and their parents were investigated under CK and S treatments in three trials. Based on the correlation analysis, significant positive relationships were found among three trials (Table S2). For most traits, correlation coefficients were about 0.4–0.7 under CK treatments and 0.3–0.6 under S treatments. The coefficients for the ionic traits (K+, Na+ and K+/Na+ ratio) were less than 0.4 indicating their vulnerability to environmental influences. The phenotypes of 254 RILs and their parents and narrow-sense heritability (h2) of all traits were summarized in Table 1. According to our experiments, the parental lines ZM175 and XY60 were significantly different in root related traits (RL, TRL, TRSA, TRT, TRAD, LRL, LRSA, LRT, MRL and MRT), SPAD and the cation contents. Although all root related traits were significantly inhibited under S treatments, XY60 had a more developed root system than ZM175 regardless of salt levels. After suffering salt stress, XY60’s old leaves still stayed green while those of ZM175 became yellow or even died. The K+ concentration and K+/Na+ ratio in XY60’s shoot were higher than those in ZM175, whereas the opposite result occurred for Na+ concentration. Under salt stress, the maximum, minimum and average values of the RILs for all seedling traits except YN and Na+ concentration decreased distinctly compared with those under CK treatment. The h2 for all measured traits were also obviously declined when the plants were treated with salt stress. For most traits, the skewness and kurtosis were small (less than 1.0) which demonstrated that the phenotype values followed normal distribution. In conclusion, ANOVA indicated that treatments, genotypes and genotype × treatment interaction significantly affected all of the traits related to seedling growth.
Correlation analysis was also carried out among different traits (Table S3). SFW, SDW and RDW presented significant and positive correlations with TDW and the correlation coefficients were more than 0.8 under CK and S treatments. SH, TN, LN, SPAD and RSATs were also positively correlated with TDW, while YN and sNa were negatively correlated with TDW. It was reasonable that there existed a positive correlation between sK and TDW under S treatment, but it was an exception that they were negatively related under CK treatment. The correlation between SH, RL, SPAD, TRT, LRT, RDW and TDW became higher under S treatment compared with those under CK treatment. In addition, obviously positive correlations were observed between sNa and YN under S treatment and between sK and SWC under CK treatment.
QTL mapping
In this study, two mapping softwares (IciMapping and WinQTLCart) were firstly used to detect the QTL for 27 seedling traits with the simple mean and BLUE values of three trials. It was shown that about 70% of the QTL detected by two softwares were the same, and the major QTL were hardly different (not shown). Then, QTL for all traits were detected by IciMapping with data sets of each trial (CK1, CK2, CK3, S1, S2, and S3). It was found that 153 repeatable QTL (detected with two or more data sets) and 5 QTL for rK, rNa and rK/Na (rK and rNa were assayed only in the first trial) were distributed on all wheat chromosomes but 3A and 4D (Table 2). These loci individually explained 2.35%–46.43% of the total phenotypic variation with LOD scores ranging from 2.61 to 40.38. Among them, 39 QTL were detected under both CK and S treatments, while 80 and another 39 QTL were detected under only CK and S treatment, respectively. A total of 12 QTL could explain more than 10% of the phenotypic variation and 80 QTL explained 5%–10% of the phenotypic variation. The additive effects of 100 QTL were derived from XY60 alleles, whereas the effects of the other 58 QTL were from ZM175 alleles. Epistatic effect analysis showed that a total of 94 pairs of loci mainly for YN, SPAD, TRAD, and MRT were detected but none was co-localized with the additive QTL. In particular, most of them just explained little phenotypic variation, and only 10 pairs of loci explained more than 2% of the phenotypic variation (Table S4). Here, a total of 20 QTL were found to interact with treatment (Table 3), and 19 of them were major additive QTL in Table 2. Among them, the interaction effects at five loci explained over 10% of the phenotypic variation, especially the interactions between cQRl-2B and treatment (25.98%) and between cQSh-4B and treatment (20.11%).
Seven and nine QTL were detected for SH and RL respectively. Among them, QSh-4B.2 and QRl-2B.1 were detected with significant additive × treatment (at) effects. QSh-4B.2 explained the maximum phenotypic variation (36.55%) with a LOD score of 29.35. However, it was only detected under CK treatment. Interestingly, QSh-4B.1 was found under S treatment nearby QSh-4B.2. Similarly, QRl-2B.1 could explain the maximum phenotypic variation (46.43%) with a LOD score of 38.4 under CK treatment; while QRl-2B.2 was detected 14 cM away from QRl-2B.1 under both CK and S treatments. Eight and 12 QTL were detected for TN and LN, respectively. Three QTL for TN (QTn-2A, QTn-2D, QTn-5B) and six for LN (QLn-2A.2, QLn-2D.2, QLn-3D, QLn-5A, QLn-5B.1 and QLn-6A) were detected under both CK and S treatments, and QTn-7A, QTn-7B, QLn-2D.1, QLn-5B.2 and QLn-6D-1 were discovered only under S treatments. QTn-5B and QLn-2B explained the maximum phenotypic variation for corresponding traits. A total of six QTL (QTn-5B, QLn-2A.2, Ln-2D.1, QLn-5A, QLn-5B.1 and QLn-6A) were found with significant at effects, but they only explained a little phenotypic variation (<3%).
For shoot and root biomass related traits (SFW, SDW, RDW and TDW), there were seven, five, nine and six QTL detected, respectively. Among them, three intervals on chromosomes 2A, 2B and 5A were found to contribute to all the four biomass related traits, and two intervals on 1B and 4B were proved to be related with all biomass traits but RDW under both treatments or only under S treatment. For SWC, nine QTL were mapped on chromosomes 1B, 2B (2), 2D, 3B, 4B, 5B, 6B and 7B. QSwc-4B explained the maximum phenotypic variation with a LOD score of 6.94 under CK treatment. QSwc-6B was detected under both CK and S treatments and explained 7.20% of the phenotypic variation.
After salt treatment, Na+ content in root and shoot tissues increased rapidly while K+ absorbing capacity decreased. It was previously verified that K+ and Na+ concentrations and K+/Na+ discrimination were very important to salt tolerance (Byrt et al. 2014; Dubcovsky et al. 1996; Dvorak and Gorham 1992; Gorham et al. 1987; Lindsay et al. 2004; Munns et al. 2012; Shah et al. 1987). Here, cation (K+ and Na+) contents in root and shoot were assayed in one and three trials, respectively. We found that the QTL (on chromosomes 1D, 3B, 5D and 6A) for K+, Na+ and K+/Na+ ratio in root were completely different from those (on chromosomes 1D, 2B, 2D, 4B, 5D, 6A and 6B) in shoot. Although QTL for Na+ in shoot were discovered with only one data set, two QTL (QsNa-2B and QsNa-5A) could be detected with the mean value by both mapping softwares (Fig. S1). Interestingly, QsNa-2B, QsK-2B and QsK/Na-2B were mapped to the same interval of 0–3.5-cM on chromosome 2B under CK and S treatments or just S treatment. QsK-4B and QsK/Na-4B were mapped to the same interval (21.5–34.5 cM) on the chromosome 4B, and they could explain the maximum phenotypic variation. It has been shown that sNa was positively correlated with YN, while they had a significantly negative relationship with SPAD under S treatment (Table S3). Here, QSpad-1A and QYn-1A were detected in the same interval 37.5–40.5 cM on chromosome 1A and QSpad-3D and QYn-3D.1 were co-located in 74.5–81.5 cM on chromosome 3D. Coincidently, the additive effects of QSpad-1A and QSpad-3D were derived from XY60 alleles, while the additive effects came from ZM175 alleles at QYn-1A and QYn-3D.1. In addition, QSpad-1A explained the maximum phenotypic variation under S treatment and QYn-1A had significant at effects.
For 10 RSATs, a total of 60 QTL were detected. Among these, 13 QTL were discovered under both CK and S treatments, 37 and 10 QTL were under only CK and S treatment, respectively. Besides, nine (QTrt-5A, QTrt-5D, QTrad-2B, QTrsa-2A, QTrsa-2B, QMrl-2B, QLrl-5A, QLrt-5A and QLrt-5D) of all 60 QTL had significant at effects. The at effects of cQLrl-5A and cQLrsa-5A explained more than 10% of the phenotypic variation. Significantly, two chromosome intervals (i.e., 0–4.5 cM on chromosome 2B and 29.5–40.5 cM on chromosome 2A) were significantly important for root related traits. The interval on chromosome 2B contributed to all the root related traits except MRT, and it could explain the maximum phenotypic variation for all the traits but TRT and LRT. The interval on chromosome 2A was related to all the root traits except TRAD and MRT, and it could stably explain 5%–10% of the phenotypic variation. In addition, chromosome 7B was important for root tip number. QTrt-7B and QLrt-7B were mapped in the same interval (67.5–69.5 cM) and QMrt-7B was close to them in 78.5–80.5 cM.
QTL clusters
QTL for different traits could clustered together in one interval on a certain chromosome, which was usually pleiotropic and important. In the present study, nearly half of the QTL (78/158) were identified to gather on group-2 and -5 chromosomes, as well as chromosomes 4B and 7D (Fig. 1), which were designated as C2A, C2B, C2D, C5A, C5B, C5D, C4B, C7D-1 and C7D-2, respectively. In C2A, there were 14 QTL for LN, RL, SFW, SDW, RDW, TDW and RSATs (TRL, TRT, TRSA, MRL, MRSA, LRL, LRT and LRSA) in the interval of 27.5–48.5 cM. The additive effects of them were all from XY60 alleles. Only two QTL had significant at effects, which could just explain 3.94% (QTrsa-2A) and 0.21% (QLn-2A) of the phenotypic variation. In C2B, a total of 19 QTL for TN, LN, SPAD, RL, sK, sK/Na, SFW, SDW, RDW, TDW and RSATs (TRL, TRT, TRAD, TRSA, MRL, MRSA, LRL, LRT and LRSA) were in the region of 0–4.5 cM, and their additive effects were all derived from XY60 alleles, too. The at effects of cQRl-2B.1, cQTrsa-2B, cQTrad-2B and cQMrl-2B explained 25.98%, 5.47%, 7.96% and 4.40% of the phenotypic variation, respectively. In the interval 117.5–141.5 cM, six QTL for SH, TN, LN, YN, sK and SWC assembled to form C2D. None of these six QTL had significant at effect, and the additive effects of them except for QSh-2D were derived from ZM175 alleles. Seven QTL for SH, LN, sK, sK/Na, SDW, SWC and TDW were located in C4B (17.5–33.5 cM). QSh-4B.2 and QsK-4B had significant at effects (20.11% and 12.14%) as well as high additive effects (36.55% and 12.87%). There were 10 QTL for TN, LN, SFW, SDW, RDW, TDW and RSATs (TRL, TRSA, MRSA and LRSA) in the block of 18.5–39.5 cM on chromosome 5A (C5A), at which the ZM175-derived alleles had positive effects on corresponding traits. Only QLn-5A was observed with significant at effect explaining 0.2% of the phenotypic variation. In C5B, four QTL for TN, LN, RDW, and MRSA clustered in the region of 39.5–55.5 cM, and the alleles from XY60 expressed positive effects on the corresponding traits. Among them, QTn-5B was detected under both CK and S treatments, and its additive effect could explain 8.80% of the phenotypic variation while its at effect only explained 2.81%. QLn-5B.1 was also detected under both treatments, and it contributed 5.19% to the phenotypic variation with just 0.05% of the at effects. The positions of ten QTL (QRdw-5D, QsK-5D, QTrl-5D, QTrt-5D, QTrsa-5D, QMrl-5D, QMrsa-5D, QLrl-5D, QLrt-5D and QLrsa-5D) on chromosome 5D were not very consistent for different data sets which led to a wide physical distance (124.5–185.5 cM). But their additive effects were all from XY60 alleles. Two QTL clusters (C7D-1 and C7D-2) were found in 57.5–58.5 cM and 115.5–119.5 cM on chromosome 7D, respectively. All QTL in them were for RSATs, and they only explained 2–5% of the phenotypic variation with no significant at effects. The alleles from XY60 at all the four QTL (QTrt-7D, QLrl-7D.1, QLrt-7D and QLrsa-7D) in the cluster C7D-1 could increase the corresponding traits values, while the alleles from ZM175 at all the five QTL (QRdw-7D, QTrsa-7D, QMrl-7D, QMrsa-7D and QLrl-7D.2) in the cluster C7D-2 showed positive effects. Fortunately, the additive effects of QTL above in one cluster usually derived from a same parent’s alleles, which would promote their effective utilization.
Validation of the QTL
Although most QTL were simultaneously detected by different mapping softwares, we valuated them in “Hanxuan 10 / Lumai 14” (LH) DH population and “Xiaoyan 54 / Jing 411” (XJ) RIL population. Here, the additive effects of seven QTL intervals were verified (Fig. 2, Fig. S2 and Table S5). QTL for SFW, SDW and TDW on chromosome 1B were detected in the same interval in LH population as well, explaining the phenotypic variation by 2.56%, 6.46% and 8.88%, respectively (Table S5). Moreover, two common SNP markers (AX-109819289 and AX-108785293) linked to these QTL were found in ZX and LH populations (Fig. 2). QLn-6A was found to be linked to five common SNPs in ZX and LH populations. On chromosome 2B, QTL for TDW was detected in 13.45–14.15 cM in LH population and QTL for SPAD and root traits were found in 70.5–77.5 cM in XJ population, which sharing many SNPs with those in ZX population. It was worth mentioning that QRl-2B(XJCK), QTrl-2B(XJCK) and QTrsa-2B(XJCK) explained extensive phenotypic variation (42.20%, 24.56% and 14.46%, respectively) in XJ population, which was similar with those in ZX population. QTL for SH, SDW, TDW and SWC on chromosome 4B were discovered to be linked to eight identical SNP markers between ZX and XJ populations. In particular, QSh-4B(XJCK) could explain remarkable phenotypic variation (31.74%) in XJ population as QSh-4B.2 (36.55%) in ZX population. On chromosome 5B, QTL for TN was detected under S treatment in both ZX and XJ populations, and two common SNPs (AX-109928742 and AX-89400290) were linked to it. QTL for SWC detected under both CK and S treatments were found in ZX and XJ populations and linked to 12 same SNP markers (Fig. S2).
KASP markers developing
To apply important QTL associated with salt tolerance to wheat breeding, six SNPs, i.e., AX-109383322 (1A) linked to QSpad-1A and QYn-1A, AX-109819289 (1B) linked to QTL for biomass (QSfw-1B, QSdw-1B and QTdw-1B), AX-109366069 (2A) linked to QTL for RSATs (QRl-2A, QTrl-2A, QTrsa-2A and QTrt-2A), AX-111606522 (2B) linked to QTL for root related traits (QRl-2B.1, QTrl-2B, QTrad-2B,QTrsa-2B and QTrt-2B) , AX-110967528 (5B) linked to QTn-5B and AX-109593935 (6A) linked to QLn-6A were successfully converted to KASP markers (Fig. S4 and Table S6), which would also play a role in the process of gene cloning.