Phenotypic variation of the four F2 populations
The phenotypes of eight agronomic and economic traits across four F2 populations were evaluated. Extensive phenotype variations and transgressive segregation were observed (Table 1 and Fig. 1). Transgressive segregation means that the phenotypic values of some individuals were better than those of the superior parent or worse than those of the inferior parent (Reyes 2019). The CV values revealed differences in variability among the eight traits (Table 1). The CV value for LP was low (5.96%-7.98%), whereas both CV values of PH and BW were high and similar (PH: 16.9%-21.95%; BW: 15.66%-19.7%). Among FL, FS, FU, FE, and MIC, the CV value was lowest for FU (1.59%-2.61%) and highest for MIC (13.87%-22%). Frequency distribution analysis showed normal distribution for seven of the traits, MIC was the exception (Fig. 1), suggesting that these traits were quantitative traits controlled by multiple genes and suitable for QTL mapping.
Correlation analysis between 32 sets of phenotypic data from the eight traits across the four F2 populations revealed significant correlations for different traits within and between populations (Fig. 2). BW and LP were significantly negatively correlated (-0.87<r<-0.62) in three populations (4Su, 4J, Sg4), whereas BW had significant positive correlations with FS, FU, FE, and MIC (0.13<r<0.67) in the 4J and SgJ populations (Fig. 2). Negative correlations between BW and FL, FS, FU, and FE (-0.89<r<-0.82) and positive correlations between BW and MIC (r =0.82) were observed in the 4Su population (Fig. 2). Conversely, LP was positively correlated with FL, FS, FU, and FE (0.95<r<0.99) and negatively correlated with MIC (r= -0.90) (Fig. 2). For the fiber quality traits, the positive correlations were found between FL and FS, FU, FE; FS and FU, FE; FU and FE in all four populations (0.19<r<0.998) (Fig. 2).
PH and BW in the 4J population were positively correlated with PH, LP, FL, FS, FU, and FE in the 4Su population (0.25<r<0.88) and negatively correlated with BW and MIC in the 4Su population (-0.776<r< -0.772); Conversely, 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). PH, LP, and FE in the Sg4 population were positively correlated with LP, FL, FS, FU, and FE in 4Su population (0.11<r<0.17) and negatively correlated with BW (-0.14<r< -0.13); LP, FE, and MIC in the Sg4 population were positively correlated with PH and BW and negatively correlated with LP and FL (-0.24<r< -0.15) in 4J population (0.14<r< 0.27) (Fig. 2).
Overall, within populations, most of correlations were negative between the two yield traits (BW and LP), whereas, most of the correlations were positive among the fiber quality traits, as well as between BW and the fiber quality traits (Fig. 2). Significant correlations were found between multiple traits among the 4Su, 4J, and Sg4 populations (Fig. 2), suggesting the influence of the common parent 4133B on the traits.
Genetic map construction
5 713 SSR primers were used to detect polymorphisms in the four pairs of parents. 739 polymorphism SSR 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), and 170 between SGK9708 and 4133B (Additional file 5: Table S1d). The polymorphism rates of the primers for the four comparisons were 3.55%, 3.64%, 2.77%, and 2.98%, respectively.
Joinmap 4.0 was employed to construct a genetic linkage map. For the 4Su population, 71 markers 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 the LGs was 58.6 cM, and the average distance of markers was 8.25 cM. The longest LG, LG9, contained the most markers (27), and half of the LGs contained only three markers.
For the 4J population, 61 markers were assigned to 10 linkage groups with a total map length of 752.45 cM (Table 2, Additional file 2: Fig. S2, Additional file 6: Table S2b). The average length of the LGs was 75.2 cM, and the average distance of markers was 12.34 cM. LG7 contained the most markers (21) and LG3 contained the least markers (3).
For the SgJ population, 83 markers, approximately half of the 158 polymorphism markers, were assigned to 15 linkage groups with a total map length of 855.04 cM (Table 2, Additional file 3: Fig. S3, Additional file 6: Table S2c). The average length of linkage groups was 57 cM. The longest average distance of markers was 21.46 cM on LG13 and the shortest was 1.06 cM on LG14.
For the Sg4 population, 52 markers, approximately one-third of the 170 polymorphism markers were assigned to nine linkage groupswith a total map length of 1 163.66 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 markers was 22.38 cM.
The Win QTL Cartographer 2.5 using the CIM algorithm was used to identify the QTLs for eight traits in the four F2 populations. As a result, a total of 50 QTLs with R2 of 0.1%-59.24% were identified, 27 were for fiber quality traits and 16 were for yield traits. A total of 23, 4, 8, and 15 QTLs were detected in the 4Su, 4J, SgJ, and Sg4 populations, respectively (Table 3, Fig 3). LG9 in the 4Su population harbored the highest number of QTLs (13), following by LG6 (6) and LG1 (5) in the Sg4 population.
Seven QTLs for PH were identified, but six of them in the 4Su population, had only minor effects (0.11%<R2<4.02%; Table 3, Fig 3). The additive effects of QTLs qPH2-1 and qPH2-2, which with the higher R2 (2.66% and 4.02%), were positive, indicating that the favorable alleles were from the parent Suyuan04-3. The action modes of qPH2-1 and qPH2-2 were over-dominance according to the dominance degree values.
Eight QTLs for BW were identified with R2 of 1.17%-9.31% in the 4J (1), SgJ (1), and Sg4 (6) populations (Table 3, Fig 3). It is noteworthy the LGs that harbored one of QTLs in the 4J (qBW4) and SgJ (qBW2) populations were anchored to the chromosome A05, and the common SSR marker, NAU1255, was detected close to the QTL interval implying that NAU1255 was closely linked to BW. Furthermore, the directions of the additive and dominance effects of these QTLs were the same.
Eight QTLs for LP were identified with R2 of 1.68%~18.11% in the 4Su (2), 4J (2), and SgJ (4) populations (Table 3, Fig 3). The additive effects of two major QTLs, qLP74J and qLP2 with R2 >10% were negative, indicating that the favorable alleles were from parent J02-247. The action modes of qLP74J and qLP2 were over-dominance and dominance, respectively.
Eleven QTLs for FL were identified in the 4Su (6), 4J (1), and SgJ (4) populations (Table 3, Fig 3). Multiple QTLs were in the same LG of a population, for example, qFL9-1, qFL9-2, and qFL9-3 with R2 of 0.35%-7.70% were in LG9 of the 4Su population. Interestingly, both LG7 in the 4Su population and LG6 in the SgJ population were anchored to the chromosome A13 (Table 2). The common SSR markers, BNL2449 and NAU1211, were detected near QTLs qFL74Su and qFL6, hinting that BNL2449 and NAU1211 may be closely linked to FL. In addition, the additive effect of QTL qFL2-2 was positive, suggesting that the favorable alleles come from the male parents, Suyuan04-3 and J02-247, that is endowed with superior fiber quality.
Five QTLs for FS were identified: four with R2 of 2.95%-7.15% in the 4Su population and one major QTL with R2 of 15.10% in the Sg4 population (Table 3, Fig 3). The additive effects of these four QTLs in the 4Su population were positive, whereas the additive effect of the one major QTL in the Sg4 population was negative, implying that the parent, 4133B may not have conferred the favorable allele.
Only two QTLs for FU were identified with R2 of 0.10%–1.21% in the same LG of the 4Su population (Table 3, Fig 3).
Four QTLs for FE were identified with R2 of 0.16%-5.62% in the 4Su, SgJ and Sg4 populations (Table 3, Fig 3). The additive effect of one QTL, qFE8, was negative and the action mode was additive, whereas the additive effects of the other three QTLs were positive and the action modes were over-dominance.
Five QTLs for MIC were detected in three LGs in the 4Su and Sg4 populations (Table 3, Fig. 3). A major QTL, qMIC2 with R2 of up to 59.24%, was in LG2 of the Sg4 population, the other four QTLs were minor with R2 0.15%-6.29%. The dominance degree values of all QTLs, except qMIC9-2, were up to 9.41-92.03, suggesting the action modes were over-dominance.
A hotspot region was detected in LG9 of the 4Su population (Fig.3A). Three QTLs (qFL9-1, qFS9-1, qFE9) were identified only at the position of 96.31 cM and expansion of this region from 95.31 cM to 105.81 cM revealed the presence of eight QTLs for six traits: PH (105.81 cM), LP (95.31 cM), FL (96.31 cM, 102.81 cM), FS (96.31 cM, 101.81 cM), FE (96.31 cM) and MIC(100.81 cM). Therefore, this hotspot region maybe an important genome region that affects agronomic and economic traits in cotton. Two other QTLs, qFU9-1 and qMIC9-1, were identified in the same LG9 at 41.71 cM.
QTLs comparison and analysis
We compared all the identified QTLs with the QTLs in CottonQTLdb database. The results showed that one-fifth of our QTLs (10/50) overlapped with previously reported QTLs, illustrating the reliability of our QTL mapping and indicating the other 40 QTLs were novel QTLs. The 10 common QTLs were reported to be asscociated with FL (4), FS (2), PH (1), BW (1), LP (1), and FE (1) traits. QTLs for FL were the most identified QTLs in both the present study (11) and CottonQTLdb database (494), which may have increased the probability of a hit.
QTLs for different traits that shared the same or overlapping confidence intervals were considered to be in QTL clusters. In the present study, a total of nine QTL clusters were identified in the 4Su (5), 4J (1), and Sg4 (3) populations. The QTL cluster harboring the most QTLs was the hotspot region described above, with eight QTLs for six traits. Another QTL cluster in the same LG (LG9 in the 4Su population) contained QTLs for FU and MIC (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 six paired traits (BW and FL, or FE; LP and FL, FS, FU, or FE) that had significant medium or high positive correlations (|r| >0.3) in the F2 populations. Six of the 19 paired trait QTLs had the same direction of addictive effects (Additional file 7: Table S3).