Phenotypic variation of four F2 populations
The phenotype of eight agronomic and economic traits across four F2 populations was evaluated. As a result, the extensive phenotype variations and transgressive segregation were observed (Table 1 and Fig. 1), the transgressive segregation means that some individuals’ phenotypic values were better than the superior parent and some’ were worse than the inferior parent (Reyes 2019). The CV values indicated that there was a difference in variability between eight traits (Table 1). For PH, BW and LP, the CV value of LP was smaller (5.96%~7.98%), whereas the CV values of PH and BW were higher and similar (PH: 16.9%~21.95%; BW: 15.66%~19.7%). For FL, FS, FU, FE and MIC, the CV value of FU was minima (1.59%~2.61%) and MIC was maximum (13.87%~22%). Frequency distribution analysis depicted normal distribution of eight traits besides MIC (Fig. 1), suggesting that these traits were quantitative traits controlled by multiple genes and suitable for QTL mapping.
Correlation analysis
Correlation analysis between 32 sets of phenotypic data from eight traits across four populations illustrated the presence of significant correlations for different traits within and between populations (Fig. 2). BW showed significant negative correlation with LP (-0.87 < r < -0.62) within three populations (4Su, 4J, Sg4); while it depicted significant positive correlations with FS, FU, FE, MIC (0.13<r<0.67) within 4J and SgJ populations (Fig. 2). The negative correlations between BW and FL, FS, FU, FE (-0.89<r<-0.82) and the positive correlations between BW and MIC (r =0.82) were observed within 4Su populations (Fig. 2). In contrast, LP was positively correlated with FL, FS, FU, FE (0.95<r<0.99) and negatively correlated with MIC (r=-0.90) (Fig. 2). For fiber quality traits, the positive correlations between FL and FS, FU, FE; FS and FU, FE; FU and FE were observed within all four populations (0.19 < r <0.998) (Fig. 2).
PH and BW in 4J population were positively correlated with PH, LP, FL, FS, FU and FE in 4Su population (0.25< r <0.88), and negatively correlated with BW, MIC in 4Su population (-0.776< r< -0.772), respectively; as opposed to PH and BW, negative correlations (-0.83<r < -0.15) and positive correlations (0.68<r<0.77) between LP, FL in 4J population and corresponding traits in 4Su population were observed (Fig. 2). In addition, PH, LP, FE in Sg4 population was positively correlated with LP, FL, FS, FU, FE in 4Su population (0.11< r <0.17) and negatively correlated with BW (-0.14<r< -0.13), respectively; LP, FE, MIC in Sg4 population was positively correlated with PH, BW in 4J population (0.14<r< 0.27) and negatively correlated with LP, FL (-0.24<r< -0.15), respectively (Fig. 2).
Overall, within populations, the majority of correlations between two yield traits, BW and LP, were negative. In contrast, the majority of correlations among fiber qualities were positive, as well as between BW and fiber qualities (Fig. 2). The correlations between LP and fiber qualities were either positive or negative. The significant correlations between multiple traits among 4Su, 4J and Sg4 populations were observed (Fig. 2), suggesting the influence of common parent 4133B on traits.
Genetic map construction
5713 SSR primers were used to detect polymorphism for four pairs of parents, respectively. 739 polymorphism primers with clearly amplified bands were retained, including 203 polymorphism primers between 4133B and Suyuan04-3 (Additional file 5: Table S1a), 208 between 4133B and J02-407(Additional file 5: Table S1b), 158 between SGK9708 and J02-407 (Additional file 5: Table S1c), 170 between SGK9708 and 4133B (Additional file 5: Table S1d). The polymorphism rate of primers was 3.55%, 3.64%, 2.77% and 2.98% respectively.
Joinmap 4.0 software was employed to construct a genetic linkage map. For 4Su population, a total of 71 makers were assigned to 10 linkage groups (LGs) with a total map length of 585.97 cM (Table 2, Additional file 1: Fig. S1, Additional file 6: Table S2a). The average length of linkage groups was 58.6 cM, and the average distance of makers was 8.25 cM. The longest LG, LG9, contained the most makers (27), but half of LGs contained only three makers.
For 4J population, a map of 752.45 cM was constructed and 61 makers across 10 linkage groups were mapped (Table 2, Additional file 2: Fig. S2, Additional file 6: Table S2b). The average length of linkage groups was 75.2 cM, and the average distance of makers was 12.34 cM. LG7 contained most makers (21) and LG3 contained the least makers (3).
For SgJ population, 83 makers, approximately half of 158 polymorphism makers, were mapped in 15 linkage groups (Table 2, Additional file 3: Fig. S3, Additional file 6: Table S2c). The total length of the map was 855.04 cM and the average length of linkage groups was 57 cM. The highest adjacent maker interval was 21.46 cM on LG13 and least was 1.06 cM on LG14.
For Sg4 population, approximately one third of polymorphism makers (52/170) were assigned to 9 linkage groups, covering a genetic distance of 1163.66 cM (Table 2, Additional file 4: Fig. S4, Additional file 6: Table S2d). The average length of linkage groups was 129.3 cM, and the average distance of makers was 22.38 cM.
Mapping of QTLs
Win QTL Cartographer 2.5 was employed to identify the QTLs using the CIM algorithm for eight traits of four populations. As a result, a total of 50 QTLs were identified with 0.1%~59.24% R2, of which 27 corresponds to fiber quality traits and 16 corresponds to yield traits. 23, 4, 8 and 15 QTLs were detected in 4Su, 4J, SgJ and Sg4 populations, respectively (Table 3, Fig 3). The LG9 in 4Su population harboured the most QTLs (13), following by LG6 (6) and LG1 (5) both in Sg4 population.
For PH, 7 QTLs identified, of which 6 in 4Su population, were all minor effect (0.11% < R2< 4.02%; Table 3, Fig 3). The additive effect of two QTLs, qPH2-1 and qPH2-2, which with the higher R2 (2.66% and 4.02%), were positive, indicating that the favourable alleles come from the parent Suyuan04-3. And the action mode of qPH2-1 and qPH2-2 were over dominance based on dominance degree value.
For BW, a total of 8 QTLs with 1.17%~9.31% R2 were identified in 4J (1), SgJ (1) and Sg4 (6) (Table 3, Fig 3). It is noteworthy that both of the LGs harbouring one QTL in 4J (qBW4) and SgJ (qBW2) were anchored to A05 chromosome; meanwhile, a common SSR maker, NAU1255, was detected nearby the QTL interval. It was inferred that NAU1255 was a marker closely linked to BW. Furthermore, the directions of the additive effect and dominance effect were the same.
For LP, overall 8 QTLs with 1.68%~18.11% R2 were identified in 4Su (2), 4J (2) and SgJ (4) (Table 3, Fig 3). The additive effect of two major QTLs, qLP74J and qLP2, which with more than 10% R2, were negative, indicating that the favourable alleles come from the parent J02-247. Moreover, the action mode of qLP74J and qLP2 were over dominance and dominance, respectively.
For FL, the most QTLs (11) were detected. There were 6, 1 and 4 QTLs identified in 4Su, 4J and SgJ populations, respectively (Table 3, Fig 3). Multiple QTLs were in the same LG of a population, for example, qFL9-1, qFL9-2 and qFL9-3, which with 0.35% ~7.70% R2, were in LG9 of 4Su population. Interestingly, both LG7 in 4Su population and LG6 in SgJ population were anchored to A13 chromosome. Meanwhile, the common SSR makers, BNL2449 and NAU1211, were detected nearby the interval of QTLs qFL74Su and qFL6, hinting that BNL2449 and NAU1211 were closely linked concerning FL. In addition, the additive effect of QTLs qFL2-2 was positive, suggesting that the favourable alleles come from the male parent, Suyuan04-3 and J02-247, which is endowed with superior fiber quality.
For FS, a total of 5 QTLs were identified, 4 QTLs with R2 of 2.95% ~7.15% in 4Su population and 1 major QTL with R2 of 15.10% in Sg4 population (Table 3, Fig 3). The additive effect of 4 QTLs in 4Su population were positive, whereas 1 major QTL in Sg4 population was negative, implying that parent 4133B may not confer the favourable allele.
For FU, only two QTLs in the same LG of 4Su population with minor R2 (0.10% ~1.21%) were identified (Table 3, Fig 3).
For FE, a total of 4 QTLs with 0.16% ~ 5.62% R2 were detected in 4Su, SgJ and Sg4 populations (Table 3, Fig 3). The additive effect of one QTL, qFE8, was negative and action mode was additive, whereas, the other three QTLs were positive and over dominance.
For MIC, a total of 5 QTLs were detected across 3 LGs in 4Su and Sg4 populations (Table 3, Fig 3). As a major QTL, the R2 of qMIC2, which in LG2 of Sg4 population was up to 59.24%, the other four QTLs R2 were minor (0.15% ~6.29%). The dominance degree value of all QTLs but qMIC9-2 were up to 9.41~92.03, suggesting the action mode was over dominance.
There was a hotspot region in LG9 of 4Su population (Fig.3A). Three QTLs (qFL9-1, qFS9-1 and qFE9) were identified only at the position of 96.31cM; Further expansion of this region from 95.31cM to 105.81cM revealed presence of 8 QTLs corresponding 6 traits viz.PH (105.81cM), LP (95.31 cM), FL (96.31cM, 102.81 cM), FS (96.31cM, 101.81 cM), FE (96.31cM) and MIC(100.81cM). Therefore, this QTL interval maybe an important genome region that affects agronomic and economic traits in cotton. At the same LG, two QTLs, qFU9-1 and qMIC9-1 were identified at the position of 41.71cM.
QTLs Comparison and Analysis
We compared the identified QTLs here and QTLs in CottonQTLdb database, the results showed that one-fifth of QTLs (10/50) overlapped with previously reported QTLs, illustrating the reliability of the QTL mapping in the present paper. Meanwhile, 40 novel QTLs were detected in our study. The overlapped 10 QTLs reportedly involved in FL (4), FS (2), PH (1), BW (1), LP (1) and FE (1) traits. There were the most identified QTLs both in the present research (11) and CottonQTLdb database (494) for FL, which perhaps will increase the probability of hit.
QTLs for different traits that shared the same or overlapping confidence intervals were considered to reside in QTL clusters. In the present study, a total of 9 QTL clusters were identified in 4Su (5), 4J (1) and Sg4 populations (3). The QTL cluster harbouring the most QTLs was above-mentioned hotspot region, with 8 QTLs for 6 traits, in LG9 of 4Su population (Fig.3A). There was another QTL cluster that harbouring QTLs for FU and MIC in the same LG (Fig.3A).
As we know, BW and LP represented yield traits, FL, FS, FU, FE and MIC represented fiber quality traits. With this prerequisite, the analysis of paired trait QTLs was employed. There were 19 paired trait QTLs within 6 paired traits (BW and FL, FE; LP and FL, FS, FU, FE) that exhibited significant medium or high positive correlations (|r| >0.3) in the F2 population. Among them, 6 paired trait QTLs had the same direction of addictive effect (Additional file 7: Table S3).