QTL Mapping of Agronomic and Economic Traits for Four F2 Populations in Upland Cotton

Abstract

Background: Upland cotton (Gossypium hirsutum) accounts for more than 90% of annual world cotton output due to its high yield potential. However, yield traits and fiber quality traits exhibit negative correlations in most cases. Here, to dissect simultaneously the genetic basis underlying complex traits such as yield and fiber quality as well as their genetic correlations in upland cotton, four F₂ populations were constructed using two normal lines and two introgression lines. Subsequently, phenotyping of 8 agronomic and economic traits along with QTL mapping were implemented.

Results: Extensive phenotype variations and transgressive segregation were found across segregation populations. Four genetic maps with length of 585.97cM, 752.45cM, 752.45cM and 1163.66cM were construct. The result of mapping displayed a total of 50 QTLs across four populations were identified, of which 27 were for fiber quality traits and 16 for yield traits. Multiple QTLs having the common maker, such as qBW4 and qBW2, or residing in the same QTL cluster, such as qLP9 and qFL9-1, were prioritized for further research.

Conclusions: These findings will provide insight into simultaneous improvement of yield and fiber quality in upland cotton breeding.

Introduction

Cotton represents the main source of natural textile fibers in the world and thus is the most prevalent raw materials used in the textile industry (Wang et al., 2018). To meet the demand of textile, both high yield and high-quality fibers are required in cotton. Upland cotton (Gossypium hirsutum) accounts for more than 90% of global cotton production due to its high yield potential and broader adaptability but with moderate fiber quality, whereas G. barbadense produces exceptionally high-quality fibers with lower fiber yield (Cai et al., 2014; Hu et al., 2019).

The majority of agronomic and economic traits, such as yield and fiber quality, are quantitative traits and controlled by multiple loci/genes. Moreover, they are influenced by the environment. Meanwhile, previous reports shown that there are the negative correlations between fiber quality traits and yield traits (Wang et al., 2015; Liu et al., 2018; Zhang et al., 2019). Therefore, dissecting the genetic basis of yield and fiber quality with the benefit of modern molecular genetics method, such as marker-assisted selection is essential, and so as to contribute to simultaneous improvement for yield and fiber quality.

As a modern molecular genetic method, molecular markers have been widely applied in cotton in the last decade. Recently, the molecular markers get a great rapid development with the release of assembled genome sequences of G. hirsutum (Li et al., 2015; Zhang et al., 2015; Wang et al. , 2018; Yang et al., 2019) and G. barbadense (Liu et al., 2015; Yuan et al., 2015). Numerous genetic linkage maps, including intraspecific map between G. hirsutum and interspecific map between G.hirsutum and G.barbadense, were constructed using RFLP (restriction fragment length polymorphism), SSR (simple sequence repeats) and SNP (single nucleotide polymorphism), etc. According to CottonQTLdb (Release 2.3, Said et al., 2013, Said et al., 2015), thousands of QTLs for yield and fiber quality in cotton had been detected. However, to date the researches about how to dissect simultaneously the genetic basis underlying complex traits and their genetic correlations in multiple upland cotton populations using QTL mapping were remain few.

In the present study, four F₂ populations were derived from the hybridization between two G. hirsutum normal lines (4133B and SGK9708) and two introgression lines (Suyuan04-3 and J02-247). Subsequently, four corresponding genetic linkage maps were constructed using SSR markers. Integrating phenotypic data of 8 agronomic and economic traits, including yield and fiber quality, QTL mapping was implemented and 50 QTLs were detected. These findings will not only contribute to dissecting the genetic basis underlying yield and fiber quality and their genetic correlations, but also provide insight into simultaneous improvement of yield and fiber quality in upland cotton breeding.

Materials And Methods

Plant Materials and Field Experiments

Two G. hirsutum normal lines (4133B and SGK9708), which is endowed with high yield potential but moderate fiber quality, and two introgression lines (Suyuan04-3 and J02-247), which is endowed with superior fiber quality, were as parents, respectively. Cotton materials were stored at the National Mid-term Gene Bank for Cotton of China.

In 2014, the seeds of four parents were sown in Anyang, Henan province and four cross combinations, including 4133B×Suyuan04-3, 4133B×J02-247, SGK9708×J02-247 and SGK9708×4133B, were constructed. To facilitate the description below, four populations were termed as 4Su, 4J, SgJ and Sg4, respectively. Winter breeding of cotton was carried out in Hainan province. In 2015, four F₂ populations (4Su, 4J, SgJ and Sg4), which consisting of 271, 248, 276 and 304 individuals respectively, were planted in Anyang. Field cultivation and management were implemented according to previous method.

Trait Measurements and Statistical Analysis

At mid-September, all plants in four F₂ populations were used to investigate the plant height (PH). During the harvesting season, all the seed cotton were collected, and then boll weight (BW) and lint percentage (LP) were calculated after the seed cotton were weighed and ginned. Fiber quality traits, such as fiber length (FL), fiber strength(FS), Fiber length uniformity (FU), Micronaire (MIC) and Fiber elongation (FE), were tested using an HVI1000 (Uster Technologies, Switzerland) with HVICC Calibration in the Cotton Quality Supervision, Inspection and Testing Center, Ministry of Agriculture, Anyang, China,

The descriptive statistics, including the maximum value, minimum value, mean value, standard deviation and coefficient of variation (CV), for the eight traits across four populations were processed by Microsoft Excel 2013. Correlation matrix was calculated and visualized using corrplot package in R (Wei and Simko, 2016).

SSR Makers Analysis

Genomic DNA of individuals from F₂ populations and their parents was extracted from the young leaves tissue using modified CTAB method (Paterson et al., 1993).

Polymorphism detection for four pairs of parents was run using 5713 SSR primers. The primers that amplify stable polymorphic products were selected for genotyping in F₂ population. The sequences of SSR primers were downloaded from CottonGen (https://www.cottongen.org/, Yu et al., 2014). In order to mapping SSRs to physical map, a local BLAST program was performed (Altschul et al., 1990). The sequences of SSRs were queried against the G. hirsutum genome sequences (Yang et al., 2019). The top 1 of blast-hit was selected for the further analysis. The PCR reaction, amplified products separating and silver staining were performed as detailed by Feng et al (2015).

Genetic map construction

Genetic linkage map was constructed using JoinMap 4.0 with regression mapping method and LOD threshold of 5.0. The Kosambi function was used to convert the recombination frequencies to map distances.

QTL mapping and Analysis

Win QTL Cartographer 2.5 was applied to identify QTLs with composite interval mapping (CIM) method. The main parameters were set as 1.0 cM for mapping step, 5 for control markers, and 1,000 for permutation tests. QTLs were declared significant if the corresponding LOD score was greater than 2.5. Meanwhile, the additive effect, dominant effect and R² (the percent of phenotypic variance explained by a QTL) were estimated. QTLs detected for the traits were named as follows: q-trait-linkage group No. (McCouch et al., 1988), where traits were PH, BW, LP, FL, FS, FU, FE and MIC. Graphic representing the linkage groups and QTLs was created by MapChart 2.2 (Voorrips, 2002).

The action mode of QTL was represented by dominance degree, i.e. an absolute value of dominant effect divided by additive effect (|D/A|; Stuber, 1987). It was additive if the dominance degree less than 0.2, partial dominance between 0.2 and 0.8, dominance between 0.81 and 1.2, over dominance more than 1.2.

The comparison between QTLs identified here and the CottonQTLdb database (Said et al., 2015) were carried out to determine whether QTLs were novel or detected before. Briefly, the QTLs in the present study that shared the same or overlapping confidence intervals with the QTLs in the database based on the common maker position were considered as QTLs identified in previous studies.

Results

Phenotypic Variation of four F₂ populations

The phenotype of eight agronomic and economic traits across four F₂ populations were evaluated, as a result, extensive phenotype variations and transgressive segregation, in which phenotypic value of individuals was better than the superior parent and worse than the inferior parent, were observed (Table 1 and Fig. 1). The CV values indicated that there was difference of variation degree 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 of eight traits shown that all the traits besides MIC were normally distributed (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 that a lot of correlations were observed for different traits within and between populations (Fig. 2). BW was negatively correlated with LP (-0.87 < r < -0.62) within three populations (4Su, 4J, Sg4); BW was positively correlated with FS, FU, FE, MIC (0.13<r<0.67) within 4J and SgJ populations. The negative correlations between BW and FL, FS, FU, FE (-0.89<r<-0.82) and the positive correlations between MIC (r =0.82) were observed within 4Su populations, in contrast, LP was positively correlated with FL, FS, FU, FE (0.95<r<0.99) and negatively correlated with MIC (r=-0.90). 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).

PH and BW in 4J population was positively correlated with PH, LP, FL, FS, FU, FE (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. 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.

Overall, within populations, the majority of correlations between two yield traits, BW and LP, were negative, while the majority of correlations among fiber qualities were positive, as well as between BW and fiber qualities. The correlations between LP and fiber qualities were positive or negative. Significantly correlations between multiple traits among 4Su, 4J and Sg4 populations were observed, 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, 208 between 4133B and J02-407, 158 between SGK9708 and J02-407, 170 between SGK9708 and 4133B. The polymorphism rate of primers was 3.55%, 3.64%, 2.77% and 2.98% respectively.

Joinmap 4.0 software was employed to construct 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 5: Table S1a). The average length of linkage groups was 58.6 cM and 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, a map of 752.45 cM was construct and 61 makers across 10 linkage groups were mapped (Table 2, Additional file 2: Fig. S2, Additional file 5: Table S1b). The average length of linkage groups was 75.2 cM and average distance of makers was 12.34 cM. LG7 contained the most makers (21) and LG3 contained the least makers (3).

For SgJ, 83 makers, approximately half of 158 polymorphism makers, were mapped in 15 linkage groups (Table 2, Additional file 3: Fig. S3, Additional file 5: Table S1c). The total length of map was 855.04 cM and the average length of linkage groups was 57 cM. The greatest adjacent maker interval was 21.46 cM on LG13 and least was 1.06 cM on LG14.

For Sg4, approximately a 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 5: Table S1d). The average length of linkage groups was 129.3 cM and 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 were for fiber quality traits and 16 for 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%). 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 allele 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). 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 additive effect and dominance effect were same.

For LP, all together 8 QTLs with 1.68%~18.11% R² were identified in 4Su (2), 4J (2) and SgJ (4). The additive effect of two major QTLs, qLP7_4J and qLP2, which with more than 10% R², were negative, indicating that the favourable allele comes from the parent J02-247. Moreover, the action mode of qLP7_4J 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. 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 of the 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 qFL7_4Su and qFL6, hinting that BNL2449 and NAU1211 were closely linked to FL. In addition, the additive effect of QTLs qFL2-2 was positive, meaning 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. 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.

For FE, a total of 4 QTLs with 0.16% ~ 5.62% R² were detected in 4Su, SgJ and Sg4 populations. 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. 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, meaning the action mode were 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, if we expanded the region range to 95.31cM ~105.81cM, there will be 8 QTLs, involving in 6 traits: 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 QTLs identified here and QTLs in CottonQTLdb database, the results showed that one fifth of QTLs (10/50) had been reported in previous papers, illustrating the reliability of the QTL mapping here. Meanwhile, 40 novel QTLs were detected in our study. The 10 QTLs reported 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 paper, a total of 9 QTL clusters were identified in 4Su (5), 4J (1) and Sg4 populations (3). The QTL cluster harboured the most QTLs was above-mentioned hotspot region, where there were 8 QTLs for 6 traits, in LG9 of 4Su population (Fig.3A). There was another QTL cluster that harboured QTLs for FU and MIC in the same LG (Fig.3A).

As we all 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 F₂ population. Among them, 6 paired trait QTLs had the same direction of addictive effect (Additional file 6: Table S2).

Discussion

To dissect the genetic basis underlying yield and fiber quality as well as their genetic correlations, two upland cotton normal lines (4133B and SGK9708) and two introgression lines (Suyuan04-3 and J02-247) were selected as parents respectively, and four populations were constructed. Among the populations, the female parents of three ones (4Su, 4J and SgJ) were high yield potential lines and the male parent were superior fiber quality lines. Thus, the extensive phenotypic variation were frequently observed in the cross combinations, whose parents are with distant kinship each other. Meanwhile, all traits exhibited transgressive segregation and approximately normally distributed across four F₂ populations (Table 1 and Fig. 1), suggesting that these traits were quantitative traits controlled by multiple genes and the two parents possessed a very different genetic diversity (Reyes 2019).

Furthermore, many individuals with transgressive phenotype were found based on the phenotypic data. For example, all the median values of FL and FS in 4Su, 4J and SgJ populations were higher than or nearly 30, fiber reaching double-thirty quality values (FL ≥ 30 mm and FS ≥ 30 cN·tex^{− 1}) is generally considered as high-quality. In plant breeding, transgressive segregation provide adaptive advantage for traits (Reyes 2019). To a certain extent, high yield and high-quality fibers is the outcome of adaptation for cotton. Therefore, it is not surprising that many instances of transgressive segregation were observed for these traits in F₂ populations. And then, some of these transgressive lines can be used to breed for high quality fiber. At the same time, above-mentioned phenomenon implied that the favourable alleles of fiber quality trait generally come from introgression lines’ parents.

Because quantitative traits are influenced by the environment, planting the mapping population in multiple environments was adopted to identify stable QTLs (Tang et al. 2015; Diouf et al. 2018; Zhang et al. 2019). Here, multiple stable QTLs such as qBW4 and qBW2, were detected simultaneously using four F₂ populations. Although these two QTLs were identified in 4J and SgJ populations, they had the common maker and their LGs anchored to the same chromosome, thus the two QTLs could be considered as one QTL. The process of breeding high-yield varieties will be accelerated through maker-assisted selection using closely linked maker.

The phenomenon of QTLs cluster was consistent with previous studies, i.e. QTLs for fiber quality are clustered on the same chromosome; and the D09 chromsome, where majority of makers in LG9 mapped, harboured important loci regulating fiber quality traits (He et al., 2007; Qiao et al., 2019). These results illustrated that QTLs in clusters might be closely linked or have pleiotropic effects (Vikram et al., 2015; Zhao et al., 2016; You et al., 2019; Yuan et al., 2018), which provides an explanation for the significant phenotypic correlations or linkage drag between related traits (Zhang et al., 2019). For paired trait QTLs, if they had the same QTL additive effect direction and showed significant medium or high positive correlations, it will be easy to simultaneously improve these traits (Zhang et al., 2019). In the present study, there are 6 paired trait QTLs according with the above conditions (Additional file 6: Table S2). These valuable information for simultaneous improvement of yield and fiber quality traits in cotton is provide from the 6 paired trait QTLs.

Based on above conclusion, we found that qLP9 for LP and qFL9-1 as well as qFL9-2 for FL were in same QTL cluster in LG9 of 4Su population, furthermore, the high positive correlation and the same direction of addictive effect between LP and FL were observed. Therefore, a further research plan is proposed to the breeding improvement, this QTL cluster maybe as one of priorities, using fine mapping of QTL clusters via large-scale secondary separation populations, gene editing technology and so on to break the negative correlation and improve yield and fiber quality.

Conclusion

In this study, four F₂ populations were derived from the hybridization between two G. hirsutum normal lines and two introgression lines. Four corresponding genetic linkage maps were constructed. Integrating phenotypic data of 8 agronomic and economic traits, QTL mapping was implemented. A total of 50 QTLs across four populations, in which 27 were for fiber quality traits and 16 for yield traits. The QTLs in same cluster, such as qLP9 for LP and qFL9-1 for FL, were prioritized for further research. These results will be helpful to dissect the genetic basis underlying yield and fiber quality, and lay a promising foundation for simultaneously improving of yield and fiber quality in upland cotton breeding.

Supplementary Information

Additional file 1: Fig.S1. The genetic map of 4Su population.

Additional file 2: Fig.S2. The genetic map of 4J population.

Additional file 3: Fig.S3. The genetic map of SgJ population.

Additional file 4: Fig.S4. The genetic map of Sg4 population.

Additional file 5: Table S1. The linkage group of four populations.

Additional file 6: Table S2. Integrated analysis of paired trait QTLs and direction of additive.

Declarations

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and helpful suggestions which help to improve the manuscript.

Authors’ contributions

Li HG, Pan ZE, and Du XM designed the experiments and drafted the manuscript. Pan ZE and He SP carried out the molecular maker experiments. Jia YH and Geng XL participated in the design of the study and performed the statistical analysis. Li HG, Chen BJ, Wang LR, and Pang BY conducted the phenotypic evaluations and collected the data from the field. Li HG, Pan ZE, and He SP constructed the genetic maps and performed QTL mapping. All the authors read and approved the final manuscript.

Funding

This research was supported by the National Key R&D Program of China (2017YFD0101601).

Availability of data and materials

The data and materials for supporting the results of this article are included within the article and its supplementary material files.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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