Introduction

Cotton is an important economic crop worldwide that produces natural fibers used in the textile industry. It is essential that fiber quality is improved in order to keep pace with the development of spinning technology and cotton harvesting mechanization. However, the narrow genetic variation in Upland cotton limits the improvement in cotton varieties (Qin et al. 2008). It has been a long-term challenge for cotton breeders to improve fiber quality and yield to meet the needs of cotton producers and the textile industry. Cotton (Gossypium spp.) contains 52 species (Li et al. 2014), including two important cultivated tetraploid species: G. hirsutum (Upland cotton), with a high fiber yield, wide adaptability and medium fiber quality, and G. barbadense (Sea-Island, Egyptian or Pima cotton), with a low fiber yield, and narrow adaptability but high fiber quality (Lu et al. 2018). Therefore, the use of traditional breeding methods alone for fiber quality breeding is neither accurate nor efficient. Molecular marker-assisted selection (MAS) is a fast and effective method for improving Upland cotton fiber quality.

In the past 20 years, researchers have identified a large number of QTLs related to fiber quality in G. barbadense using interspecific segregating populations of G. hirsutum×G. barbadense or natural populations of G. barbadense (Abdullaev et al. 2017; Said et al. 2015a), but most of the map** populations are early segregating populations of G. hirsutum×G. barbadense such as F2 populations (Jiang et al. 1998; Kohel et al. 2001; Lin et al. 2005; Mei et al. 2004; Paterson et al. 2003), F2:3 populations (He et al. 2007) and early backcross generation populations (BC1, BC2, BC2S1) (Lacape et al. 2005; Shi et al. 2015, 2016). A few map** populations are recombinant inbred line (RIL) populations (Lacape et al. 2009, 2010) or backcross introgression line (BIL) populations (Nie et al. 2015; Yu et al. 2013), as well as natural populations of G. barbadense (Abdullaev et al. 2017; Wang et al. 2013a). Due to the complex background of these map** populations, it is difficult to accurately identify and precisely locate QTLs (Islam et al. 2016). Therefore, most of the QTL map** results cannot be applied to the genetic improvement in Upland cotton (Sun et al. 2017). Introgression lines (ILs), also known as chromosome segment introgression lines (CSILs) or chromosome segment substitution lines (CSSLs), are constructed by hybridization, backcrossing, self-pollination and MAS. Only the introgressed segment differs between a CSSL and its recipient parent. As the same set of CSSLs has the same or a similar genetic background but differs only in a specific genetic region on one chromosome (thus eliminating the influence of complex genetic backgrounds), CSSLs are ideal materials for studying quantitative traits and QTL map** in crops. The construction and utilization of CSSLs have been widely reported in tomato, rice, maize and other crops (Balakrishnan et al. 2019; Bouchez et al. 2002; Monforte and Tanksley 2000; Okada et al. 2018; Qi et al. 2013; Qiu et al. 2013), and a QTL for FL, qFL-chr1, was fine mapped and analyzed using near-isogenic introgression lines (NIILs) of Upland Tamcot 2111 (G. hirsutum, the recurrent parent) × Pima S-6 (G. barbadense, the donor parent) (Xu et al. 2017). Some QTLs related to fiber quality in G. barbadense were identified based on segregating populations of derived progenies of one IL (Chen et al. 2018; Wang et al. 2011, 2016). To date, most of the CSLs, ILs or CSSLs reported in cotton have been detected in the obsolete TM-1 genetic background. Although many QTLs for fiber quality traits have been detected in cotton, few of them have been used in MAS in breeding (Cao et al. 2014). This may be due to inaccurate QTL map** in populations with complex genetic backgrounds, the use of different environments or the use of different genetic backgrounds in QTL map** populations and breeding populations. In addition, using the QTLs identified in map** populations in breeding populations is a challenge. To date, the genetic background effects on QTL expression have not been reported.

To transfer beneficial genes from G. barbadense into Upland cotton cultivars, we constructed two sets of CSSLs with two different Upland cotton genetic backgrounds in which Hai1 (G. barbadense) was the donor parent and CCRI36 and CCRI45 (G. hirsutum) were the recipient parents (Li et al. 2016, 2019a; Lu et al. 2009). Some of the CSSLs were genetically evaluated, and some QTLs for yield and fiber quality were identified using secondary segregating populations derived from one, two or four CSSLs as parents (Guo et al. 2015, 2018; Li et al. 2019b; Song et al. 2016). With the rapid development of biotechnology, multiple cotton genomes have been sequenced, providing a foundation for further cotton gene identification and molecular breeding at the genome level (Hu et al. 2019; Li et al.

Materials and methods

Development of two sets of cotton CSSLs and multi-environment field experiments

Two sets of CSSLs were derived from two interspecific crosses with Hai1 (G. barbadense) as the donor parent and CCRI36 or CCRI45 (G. hirsutum) as the recipient parent. CCRI45 (also called CCRI221) is a late-maturing Upland cotton (G. hirsutum) cultivar, and CCRI36 is an early-maturing Upland cotton cultivar; both cultivars have high yield and were bred by the Institute of Cotton Research (ICR), the Chinese Academy of Agricultural Sciences (CAAS), Anyang, Henan Province. Hai1 is a cultivated line of G. barbadense with very high fiber quality.

First, two crosses (resulting in two F1 populations) were performed, with Hai1 as the male parent and CCRI36 or CCRI45 (G. hirsutum) as the female parent. Subsequently, BC5F3 populations with the CCRI36 background were obtained by five generations of successive backcrossing (with CCRI36 as the recurrent parent) and two generations of self-pollination and MAS (Li et al. 2016, 2019a). Similarly, BC4F3 populations with the CCRI45 background were also obtained by four generations of backcrossing (with CCRI45 as the recurrent parent) and two generations of self-pollination and MAS (Yang et al. 2009; Li et al. 2016).

In 2009, 2660 CCRI36 × Hai1 BC5F3 individuals and 2328 CCRI45 × Hai1 BC4F3 individuals were grown in the field in Anyang in Henan Province (Anyang Experiment Farm, ICR, CAAS). In 2010, BC5F3:4 individuals of CCRI36 × Hai1 and BC4F3:4 individuals of CCRI45 × Hai1 were planted in progeny rows. The single-row length was 5 m and the row width was 0.8 m in the 2009 Anyang (09HNA) and 2010 Anyang (10HNA) experiments.

On the basis of the above design, two subpopulations in each genetic background were randomly selected, including 408 CSSLs in the CCRI36 background, named 36Pop, and 332 CSSLs in the CCRI45 background, named 45Pop.

Subsequently, 36Pop was evaluated in a total of five environments as follows. In 2011, individual CSSLs in 36Pop (BC5F3:5) and their recurrent parent were grown in three environments at three different locations: Anyang in Henan Province (11HNA), Liaoyang in Liaoning Province (11LNL) and Shihezi in ** for fiber quality traits across multiple generations and environments in upland cotton. Mol Breed 30:569–582" href="/article/10.1007/s00122-020-03578-0#ref-CR54" id="ref-link-section-d94658705e1023">2012).

Based on the genetic linkage map comprising 2292 marker loci distributed on 26 chromosomes and covering almost the whole cotton AD genome (5115.16 cM) with an average marker interval of 2.23 cM, 551 simple sequence repeat (SSR) markers with an average interval of 10 cM between two markers were selected for the screening of genotypes in two sets of CSSLs (Shi et al. 2015). Chromosome (C) 4 had the least number of markers (11 SSRs), while C11, C19 and C21 had the largest (30 SSRs). The longest genetic distance between two markers was 25.99 cM, and the shortest was 0.45 cM. The details of selected markers and their adjacent markers on the genetic map are in Table S1. The sequences of each primer used in this report can be downloaded at http://www.cottonmarker.org and were synthesized by Bioethics Engineering Co., Ltd (Shanghai).

Data analysis and QTL map**

The phenotypic data from the CSSLs with the CCRI36 background in seven environments (09HNA, 10HNA, 11HNA, 11LNL, 11XJS, 14XJN, 14XJS) and with the CCRI45 background in nine environments (09HNA, 10HNA, 11HNA, 11XJK, 14XJA, 14XJK, 14HNZ, 15HNZ, 15HNA) were used for analysis.

Descriptive statistical analysis, correlation analysis and analysis of variance (ANOVA) were performed using SPSS 20.0 software (SPSS, Chicago, IL, America). Genotypic analysis of populations and analysis of chromosome introgressed segments calculations (including background recovery rate of the CSSLs, the number and length of introgressed segments) were performed using GGT 2.0 software developed by van Berloo (http://www.plantbreeding.wur.nl/UK/software_ggt.html) (van Berloo 2008).

QTL map** was performed with QTL IciMap** (version 4.0), and the RSETP-LRTADD map** method was applied with a logarithm of odds (LOD) threshold of 2.5 (Li et al. 2007; Wang et al. 2019).

The QTLs were named as follows: (q + trait abbreviation) + chromosome/linkage group + QTL number. QTLs for the same trait in different environments and populations were considered stable when their confidence intervals overlapped (Shi et al. 2015, 2016; Sun et al. 2012).

The resulting linkage map and QTLs were drawn using MapChart ver.2.2 software (Voorrips 2002).

Results

Evaluation of CSSLs and fiber quality

The results of the descriptive statistical analysis of each trait in the different populations are shown in Table 1. The average FL, FS and FM values of the recurrent parent were generally consistent with those of the corresponding population in all environments. The FL of the populations in all environments was slightly greater than that of the recurrent parents, except in 14XJN; the FS of the populations in the other environments was slightly higher than that of the recurrent parents, except in 10AY; and the FM value of the populations in all environments was slightly lower than that of the recurrent parents. The average FL, FS and FM values of 36 and 45 recurrent parents were similar in all environments, with medium fiber quality. The descriptions of the statistical analysis of quality for 45Pop in 09HNA, 10HNA, 11HNA and 11XJK follow those reported by Ma et al. (2013).

Table 1 Fiber quality in CSSL populations and their recipient parents CCRI36 and CCRI45 in multiple environments
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In all environments, the range and coefficient of variation of each trait in all the populations were large. For the same population in all environments, the variation in FM was the greatest among the three traits, and the variation in FS was greater than that in FL.

These results indicate abundant genetic variation in the CSSL populations produced by advanced backcrossing and continuous self-crossing.

The absolute skewness of all traits in all populations and environments was less than 1, thus following a normal distribution (Table 1, Fig. S1).

The correlation coefficients of each trait between different environments were significant (Table S2), indicating that these materials were stable across multiple environments. Most of the correlation coefficients among environments for FL and FM were larger than 0.5, whereas those for FS were smaller than 0.5, indicating that FS was more greatly affected by the environment than FL and FM.

ANOVA revealed highly significant effects of genotype (G), the environment (E) and the interaction between genotype and the environment (G × E interaction) on all three traits in the populations (Table 2). The broad-sense heritability values, calculated by partitioning the variance into genetic and G × E effects, were above 85% for all three traits.

Table 2 Analysis of variation (ANOVA) for fiber quality traits of the two CSSL populations with different genetic backgrounds across multiple environments
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Through the evaluation and analysis of multiple environments, some of the CSSLs with excellent and stable CSSLs were screened out (Li et al. 2017; Lu et al. 2012; Said et al. 2015a, b; Zhai et al. 2016). A total of 23 QTL clusters were found in this paper, with at least two stable or common QTLs affecting at least two or more different traits. These clusters were distributed on 13 chromosomes (C2, C7, C10, C12, C13, C14, C15, C16, C17, C19, C20, C21 and C22) (Table 4, Table S4).

Table 4 QTL clusters for fiber quality traits in CSSLs populations
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BI- and SE-QTL regions (BISERs) are those containing BI- and SE-QTLs affecting two or more different traits. SE-QTL regions (SERs) are those containing at least two SE-QTLs affecting at least two different traits. Twenty-three QTL clusters included six BISERs and 17 SERs. Six BISERs were involved in the control of two or three traits. BISER-C7-1 was located near the NAU1085 marker (92.24 cM) on C7, which harbored two BI-QTLs (qFL-C7-3 and qFS-C7-4 with a positive additive effect) and one SE-QTL (qFM-C7-3 with a negative additive effect). BISER-C15-1 was located near the NAU3177 marker (43.61 cM) on C15, with two BI-QTLs (qFL-C15-1 and qFS-C15-3, with positive additive effects). BISER-C16-1 was located near the BNL2634 marker (65.97 cM) on C16, with two BI-QTLs (qFL-C16-2 and qFS-C16-3, with positive additive effects). BISER-C17-1 was located near the NAU2909 marker (47.26 cM) on C17, containing one BI-QTL (qFM-C17-2 with a negative additive effect) and two SE-QTLs (qFL-C17-3 and qFS-C17-1 with a positive additive effect). BISER-C17-2 was located near the HAU0195a marker (122.79 cM) on C17, containing one BI-QTL (qFS-C17-3 with a positive additive effect) and two SE-QTLs (qFL-C17-5 with a positive additive effect and qFM-C17-4 with a negative additive effect). BISER-C2-1 was located near the NAU2277 marker (178.82 cM) on C2, which contained one BI-QTL (qFL-C2-6 with a positive additive effect) and one SE-QTL (qFS-C2-2 with a positive additive effect). The Hai1 alleles in the three BISERs (BISER-C7-1, BISER-C17-1 and BISER-C17-2) simultaneously increased FL and FS and decreased FM, and those in the other three BISERs simultaneously increased FL and FS.

Among the 17 SERs, 15 affected both FL and FS. These 15 SERs (SER-C2-1, SER-C7-1, SER-C10-1, SER-C12-1, SER-C12-2, SER-C13-1, SER-C13-2, SER-C14-1, SER-C14-2, SER-C16-2, SER-C19-1, SER-C20-1, SER-C21-1, SER-C21-2 and SER-C22-1) were located near HAU1980b (54.78 cM) on C2; near PGML01950 (53.19 cM) on C7; near BNL3563 (178.11 cM) on C10; near HAU1361 (84.57 cM) and NAU4889 (110.86 cM) on C12; near BNL1707 (8.68 cM) and CGR5242 (164.25 cM) on C13; near NAU3648 (40.73 cM) and HAU1219a (206.88 cM) on C14; near BNL3065 (176.71 cM) on C16; near NAU3405 (17.39 cM) on C19; near HAU1491a (139.9 cM) on C20; near PGML00972 (238.32 cM) and Gh132 (241.43 cM) on C21; and near NAU1325 (152.64 cM) on C22. The Hai1 alleles in two regions (SER-C14-2 and SER-C16-2) simultaneously decreased FL and FS, and those in the other 13 SERs simultaneously increased FL and FS. The other two SERs (SER-C15-1 and SER-C16-1) affected FS and FM, and they were located near HAU1058a (26.9 cM) on C15 and near CGR5149 (16.79 cM) on C16. The Hai1 alleles in the two regions simultaneously increased FS and FM (Table 4, Table S4).

Discussion

Characteristics of the materials used in this study

CSSLs are valuable genetic resources for basic and applied research on the improvement in complex traits (Balakrishnan et al. 2019). The materials used in this paper were CSSLs with the Upland cotton background and one or more introgressed segments from G. barbadense. Only the introgressed segments differed between the CSSLs and their recipient parents. A set of CSSLs, which had the same or a similar genetic background and differed only in a specific genetic region, can eliminate the influence of a complex genetic background, making CSSLs ideal materials for researching quantitative trait inheritance and gene identification in crops and advantageous in the identification of QTLs. The CSSLs are similar to their recurrent parents in terms of field-observed phenotypes but with one or more specific traits of G. barbadense (Ma et al. 2013; Li et al. 2019b).

In this paper, the CSSLs exhibited high genetic diversity in fiber quality traits (Table 1, Fig. S1). Through multiple environmental evaluation, some stable and high-quality lines were obtained (Li et al. 2017; Lu et al. QTLs for fiber quality traits

Cotton fiber quality traits are very important traits that are largely affected by both genetic backgrounds and environmental factors. In the present study, a total of 76 QTLs (29 FL-QTLs, 26 FS-QTLs and 21 FM-QTLs) and 120 QTLs (49 FL-QTLs, 52 FS-QTLs and 19 FM-QTLs) were detected in the two sets of CSSLs of the CCRI36 and CCRI45 backgrounds, respectively (Fig. 2, Table S3). Nine QTLs (four FL-QTLs, four FS-QTLs and one FM-QTL) were simultaneously detected in both backgrounds (the CCRI36 background and the CCRI45 background) (Table 3, Table S3). Thus, a total of 187 QTLs were identified in this study, including 74 FL-QTLs, 39 FM-QTLs and 74 FS-QTLs.

By comparison, 36 of the 76 QTLs detected in the CCRI36 background and 49 of the 120 QTLs detected in the CCRI45 background were identical or similar to previously reported QTLs (Table S3) as they shared common markers in the confidence interval on the same chromosome. In the CCRI36 background: 11 FL-QTLs (qFL-C1-1, qFL-C2-4, qFL-C5-2, qFL-C7-3, qFL-C16-2, qFL-C16-3, qFL-C19-5, qFL-C20-1, qFL-C20-4, qFL-C21-3 and qFL-C22-1), 12 FM-QTLs (qFM-C1-1, qFM-C1-2, qFM-C3-1, qFM-C3-2, qFM-C7-3, qFM-C12-1, qFM-C14-1, qFM-C16-1, qFM-C17-2, qFM-C17-4, qFM-C20-1 and qFM-C25-5) and 13 FS-QTLs (qFS-C7-4, qFS-C11-1, qFS-C13-3, qFS-C15-3, qFS-C15-5, qFS-C16-3, qFS-C16-4, qFS-C17-3, qFS-C20-1, qFS-C20-2, qFS-C20-4, qFS-C20-5 and qFS-C25-3) were previously reported (Deng et al. 2019; Fang et al. 2014; Guo et al. 2018; Jamshed et al. 2016; Li et al. 2019b; Ma et al. 2017; Said et al. 2015b; Shao et al. 2014; Shen et al. 2005; Shi et al. 2012; Wang et al. 2016, 2017a, b; Yang et al. 2015; Zhai et al. 2016; Zhang et al. 2012, 2016). In the CCRI 45 background: 16 FL-QTLs (qFL-C2-1, qFL-C2-3, qFL-C7-3, qFL-C10-1, qFL-C10-2, qFL-C14-1, qFL-C14-2, qFL-C14-3, qFL-C14-5, qFL-C15-4, qFL-C16-2, qFL-C17-4, qFL-C19-1, qFL-C19-2, qFL-C25-1 and qFL-C26-3) were previously reported, 10 FM-QTLs (qFM-C5-2, qFM-C5-3, qFM-C7-1, qFM-C7-2, qFM-C15-1, qFM-C17-2, qFM-C24-2, qFM-C25-1, qFM-C25-2 and qFM-C25-3) and 23 FS-QTLs (qFS-C7-3, qFS-C7-4, qFS-C9-1,qFS-C10-1, qFS-C10-2, qFS-C11-5, qFS-C11-6, qFS-C12-1, qFS-C14-1, qFS-C15-2, qFS-C15-3, qFS-C15-6, qFS-C16-3, qFS-C17-1, qFS-C17-3, qFS-C19-2, qFS-C21-1, qFS-C21-2, qFS-C21-4, qFS-C22-1, qFS-C24-1, qFS-C25-1 and qFS-C25-2) were previously reported (Chen et al. 2018; Deng et al. 2019; Fang et al. 2014; Guo et al. 2015; He et al. 2007; Huang et al. 2018; Jamshed et al. 2016; Li et al. 2019b; Lin et al. 2005; Ning et al. 2014; Said et al. 2015b; Shao et al. 2014; Shen et al. 2005; Shi et al. 2012; Tang et al. 2015; Wang et al. 2011, 2012, 2015, 2016; Zhai et al. 2016; Zhang et al. 2012). Notably, seven of QTLs (qFL-C7-3, qFL-C16-2, qFM-C17-2, qFS-C7-4, qFS-C15-3, qFS-C16-3 and qFS-C17-3) were BI-QTLs. Therefore, 77 (41.17%) of the QTLs detected in this study were previously reported, and the other 110 QTLs were considered novel.

It is worth mentioning that 49 of the QTLs were confirmed in our earlier-generation interspecific backcross populations (Shi et al. 2019b; Song et al. 2016; Guo et al. 2015, 2018). These QTLs included 14 QTLs (qFL-C2-4, qFL-C10-2, qFL-C16-3, qFL-C20-1, qFM-C1-2, qFM-C24-2, qFS-C11-1, qFS-C11-6, qFS-C12-1, qFS-C13-3, qFS-C16-4, qFS-C20-1, qFS-C21-1 and qFS-C21-4) confirmed in our earlier-generation interspecific backcross populations (Shi et al. 2019b), seven QTLs (qFL-C7-3, qFL-C20-4, qFL-C22-1, qFS-C7-4, qFS-C20-2, qFS-C20-4 and qFS-C20-5) reported by Song et al. (2016), two QTLs (qFS-C15-5 and qFS-C22-1) reported by Guo et al. (2018) and two QTLs (qFS-C19-2 and qFS-C21-2) reported by Guo et al. (2015). Of them, seven (qFL-C7-3, qFL-C16-2, qFM-C17-2, qFS-C7-4, qFS-C15-3, qFS-C16-3 and qFS-C17-3) were BI-QTLs (Table S3).

In the CCRI36 background, a total of 39 SE-QTLs (20 FL-QTLs, six FS-QTLs and 13 FM-QTLs) were stably expressed in at least two environments, 23 of which were previously reported, including nine FL-QTLs (qFL-C1-1, qFL-C2-4, qFL-C5-2, qFL-C7-3, qFL-C16-3, qFL-C19-5, qFL-C20-1, qFL-C20-4 and qFL-C22-1), nine FM-QTLs (qFM-C1-1, qFM-C3-1, qFM-C7-3, qFM-C12-1, qFM-C14-1, qFM-C16-1, qFM-C17-2, qFM-C17-4 and qFM-C25-5) and five FS-QTLs (qFS-C7-4, qFS-C16-3, qFS-C16-4, qFS-C17-3 and qFS-C20-5) (Table S3). In the CCRI45 background, a total of 79 SE-QTLs (31 FL-QTLs, 39 FS-QTLs and nine FM-QTLs) were stably expressed in multiple environments, 32 of which were previously reported, including nine FL-QTLs (qFL-C2-1, qFL-C2-3, qFL-C7-3, qFL-C10-2, qFL-C14-1, qFL-C14-5, qFL-C16-2, qFL-C19-1 and qFL-C19-2), eight FM-QTLs (qFM-C5-3, qFM-C7-2, qFM-C15-1, qFM-C17-2, qFM-C24-2, qFM-C25-1, qFM-C25-2 and qFM-C25-3) and 15 FS-QTLs (qFS-C7-3, qFS-C7-4, qFS-C9-1, qFS-C11-6, qFS-C12-1, qFS-C14-1, qFS-C15-2, qFS-C15-3, qFS-C16-3, qFS-C17-1, qFS-C17-3, qFS-C21-2, qFS-C21-4, qFS-C22-1 and qFS-C24-1) (Table S3). Among them, seven QTLs (qFL-C7-3, qFL-C16-2, qFM-C17-2, qFS-C7-4, qFS-C15-3, qFS-C16-3 and qFS-C17-3) were BI-QTLs and stably expressed in multiple environments of each of both backgrounds (Table 3, Table S3). Therefore, among the 109 SE-QTLs, 48 were confirmed in previous reports and 61 were considered novel. These QTLs are likely to exhibit genetic stability and warrant further clarification by QTL fine map** and cloning to better understand the genetics and molecular mechanisms underlying fiber development.

In the present study, more QTLs were located on the D subgenome than the A subgenome (29 on the A subgenome and 47 on the D subgenome in the CCRI36 background, and 50 on the A subgenome and 70 on the D subgenome in the CCRI45 background), which was consistent with most previous reports (Fang et al. 2014; Jiang et al. 1998; Lacape et al. 2010; Paterson et al. 2003; Said et al. 2013; Yang et al. 2015).

Effects of genetic backgrounds and environments on the expression of QTLs

Fiber quality traits in cotton are complex quantitative traits. One of the difficulties in improving complex traits is the environmental sensitivity of the identified QTLs. The percentages of SE-QTLs for the three traits (FL, FS and FM) were 60.00% and 49.00% in previous papers reported by Sun et al. (2012) and Jamshed et al. (2016), respectively. In the present paper, the overall percentage of SE-QTLs was 62.50%. These results were consistent with the results of previous reports (Sun et al. 2012; Jamshed et al. 2016), indicating that environmental factors have a large influence on fiber quality traits (Tan et al. 2018).

To date, there has been no report on the effect of genetic background on the QTL expression of fiber quality in cotton. In the present study, among the 187 QTLs detected overall, only nine (4.81%) were BI-QTLs, which indicated that genetic background has a strong influence on fiber quality traits in cotton. In rice, some studies show that the expressions of the QTLs for complex traits are strongly affected by genetic background (Qiu et al. 2019b). These provide a material foundation for further gene cloning and fiber quality improvement.

Conclusion

This study represents the first report using two sets of CSSLs with different genetic backgrounds but with the same donor parent to dissect the stability of QTLs of fiber quality traits across multiple environments in cotton. A total of 76 and 120 QTLs were identified in the CSSLs with the CCRI36 and CCRI45 backgrounds, respectively. Among them, nine BI-QTLs were found, and 78 (41.71%) of the detected QTLs were reported previously. Thirty-nine and 79 were SE-QTLs in at least two environments in the CCRI36 and CCRI45 backgrounds, respectively. Forty-eight SE-QTLs, including seven BI-QTLs, were confirmed in previous reports and 61 SE-QTLs, including two BI-QTLs, were considered novel. Twenty-three clusters with BI- and/or SE-QTLs were identified, 19 of which harbored favorable alleles from G. barbadense for two or three fiber quality traits. In summary, these results revealed the BI- and/or SE-QTL regions, indicated that genetic background has a stronger effect on fiber quality traits than environmental factors and provided insights into the effects of genetic background and environment on the expression of fiber quality QTLs in cotton. This study provides valuable information and new stable QTL regions for further QTL cloning and improvement in fiber quality by MAS in cotton breeding.