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Molecular Genetics and Genomics

, Volume 294, Issue 6, pp 1385–1402 | Cite as

Dissecting the genetic basis of fiber quality and yield traits in interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense

  • Yuzhen Shi
  • Aiying Liu
  • Junwen Li
  • Jinfa Zhang
  • Baocai Zhang
  • Qun Ge
  • Muhammad Jamshed
  • Quanwei Lu
  • Shaoqi Li
  • Xianghui Xiang
  • Juwu Gong
  • Wankui Gong
  • Haihong Shang
  • Xiaoying Deng
  • Jingtao Pan
  • Youlu YuanEmail author
Open Access
Original Article

Abstract

Fiber quality and yield are important traits of cotton. Quantitative trait locus (QTL) mapping is a prerequisite for marker-assisted selection (MAS) in cotton breeding. To identify QTLs for fiber quality and yield traits, 4 backcross-generation populations (BC1F1, BC1S1, BC2F1, and BC3F0) were developed from an interspecific cross between CCRI36 (Gossypium hirsutum L.) and Hai1 (G. barbadense L.). A total of 153 QTLs for fiber quality and yield traits were identified based on data from the BC1F1, BC1S1, BC2F1 and BC3F0 populations in the field and from the BC2F1 population in an artificial disease nursery using a high-density genetic linkage map with 2292 marker loci covering 5115.16 centimorgans (cM) from the BC1F1 population. These QTLs were located on 24 chromosomes, and each could explain 4.98–19.80% of the observed phenotypic variations. Among the 153 QTLs, 30 were consistent with those identified previously. Specifically, 23 QTLs were stably detected in 2 or 3 environments or generations, 6 of which were consistent with those identified previously and the other 17 of which were stable and novel. Ten QTL clusters for different traits were found and 9 of them were novel, which explained the significant correlations among some phenotypic traits in the populations. The results including these stable or consensus QTLs provide valuable information for marker-assisted selection (MAS) in cotton breeding and will help better understand the genetic basis of fiber quality and yield traits, which can then be used in QTL cloning.

Keywords

Quantitative trait locus (QTL) Fiber quality Yield Interspecific backcross populations QTL cluster 

Introduction

Cotton is an important economic crop worldwide that produces natural fibers used as raw materials in the textile industry. With the development of spinning technologies and mechanization of cotton harvesting, the demand for higher cotton fiber quality has increased. The current goal of cotton breeding is the development of new cultivars with higher fiber yield and superb fiber quality. Gossypium barbadense and G. hirsutum are the two most economically important tetraploid cultivated species. The former (including Egyptian cotton, Pima cotton and Sea–Island cotton) has longer, stronger and finer fibers; However, its disadvantages were a low yield and narrow adaptation. The latter (Upland cotton), has a high yield and is widely grown in many countries, accounting for approximately 95% of global cotton production (Chen et al. 2007); however, it has a relatively low fiber quality. Therefore, it is of great significance to effectively transfer genes associated with high-quality traits from G. barbadense to G. hirsutum for synchronous genetic improvement of yield and fiber quality. No breakthrough has been reported in creating elite varieties with both high yield from G. hirsutum and high fiber quality from G. barbadense using traditional breeding methods (Zhang and Percy 2007). However, it was possible to transfer desirable genes from G. barbadense to G. hirsutum based on quantitative trait loci (QTL) mapping and new biotechnology.

Since the first cotton molecular genetic map was reported by Reinisch et al. (1994). More than 1000 QTLs for fiber quality and yield in cotton have been mapped using interspecific populations of G. hirsutum × G. barbadense and intraspecific populations of G. hirsutum (Fang et al. 2014; Jamshed et al. 2016; Keerio et al. 2018; Li et al. 2017a; Liu et al. 2017; Ma et al. 2017; Said et al. 2015a, b; Shang et al. 2016; Wang et al. 2015, 2016, 2017a, b; Yu et al. 2014; Zhai et al. 2016; Zhang et al. 2015b). The cotton genome draft sequences have laid a foundation for further QTL localization and molecular breeding at the genomic level (Li et al. 2014, 2015; Wang et al. 2012a; Zhang et al. 2015a). Many QTLs have been detected using natural populations, recombinant inbred or backcross inbred line populations or other populations based on single nucleotide polymorphism (SNP) markers (Ademe et al. 2017; Islam et al. 2016; Keerio et al. 2018; Li et al. 2016b; Ma et al. 2018; Sun et al. 2017; Zhang et al. 2016b). These studies provided important information for further study of QTLs/genes related to fiber quality and fiber yield and for marker-assisted selection (MAS) in breeding. However, only a few QTLs have been used in MAS (Cao et al. 2015). Most, if not all, linkage-based QTL mapping studies report results from only one mapping population.

To transfer desirable genes from G. barbadense to G. hirsutum, we developed different interspecific backcross populations and chromosome segment substitution line (CSSL) populations (Li et al. 2016a, 2017b; Lu et al. 2017; Song et al. 2017; Zhai et al. 2016; Lan et al. 2011; Liang et al. 2010; Kong et al. 2018) and constructed a high-density simple sequence repeat (SSR) genetic linkage map of the BC1F1 population, which comprised 2292 loci covering 5115.16 centimorgans (cM) of the cotton genome with an average distance of 2.23 cM between markers (Shi et al. 2015). This study reports the identification of QTLs related to fiber yield and fiber quality traits using a high-density map and 5 backcross populations from the same interspecific hybrid of the two cultivated tetraploid species. Consistent QTLs across different populations or environments were identified, which provides useful information to facilitate the understanding of the genetic basis of fiber quality and yield traits and lays a foundation for the simultaneous improvement of fiber quality and yield.

Materials and methods

Plant materials

Three backcross generations (BC1F1, BC1S1 and BC2F1) were derived from an interspecific cross between CCRI36 (G. hirsutum L.) and Hai1 (G. barbadense L.), of which CCRI36, the recipient parent, is a high yield cultivar bred by the Institute of Cotton Research (ICR), Chinese Academy of Agricultural Sciences (CAAS) and Hai1, the donor parent, is a cultivated cotton line of G. barbadense with superb fiber quality, and high resistance to Verticillium wilt (VW) and the dominant glandless trait (Shi et al. 2016).

The BC1F1 population included 135 individual plants, and the two parents were grown in the field (Anyang Experiment Farm, ICR, CAAS) in 2004. Each row was 8 m long and the rows were 0.8 m apart. The BC2F1 population (with 133 family lines), the BC1S1 population (with 120 family lines), and the two parents were planted in the field (Anyang Experiment Farm, ICR, CAAS) in 2005, with single-row plots for each family and two-row plots for the two parents. Each row was 8 m long and 0.8 m wide and included 32 plants. Additionally, 133 BC2F1 families were backcrossed with CCRI36, and the contemporary backcross bolls were named the BC3F0 population. The BC2F1 population (with 133 family lines) was planted in an artificial disease nursery (the BC2F1-DN population) in 2005 following a randomized complete block design with three replicates and one row (plot) for each family. Each row was 2.5 m long and contained 15 plants, and the rows were 0.6 m apart (Shi et al. 2016).

The glanded plants in the BC1F1, BC2F1 and BC1S1 population were removed at the seedling stage. Five populations (BC1F1, BC2F1, BC1S1, BC2F1-DN, and BC3F0) in 3 environments (the field in 2004, the artificial disease nursery in 2005, the field in 2005) were used to detect QTLs for fiber quality and yield traits in the present study.

Phenotypic evaluation

We harvested naturally opened self-pollinated bolls from each plant in the BC1F1 population in 2004, 30 naturally opened self-pollinated bolls from each plot for the BC1S1, BC2F1 and BC2F1-DN populations, and naturally opened backcrossed bolls (BC3F0) from each family in the BC2F1 population in the field in 2005 to evaluate fiber quality and yield traits. Three fiber quality traits and four yield components were analyzed, namely, boll weight (BW), lint percentage (LP), seed index (SI), lint index (LI), fiber length (i.e., mean upper-half length, FL), fiber strength (FS), and fiber micronaire (FM).

The phenotypic data for each of the 5 populations (BC1F1, BC2F1, BC1S1, BC2F1-DN, and BC3F0) were independent.

Map construction

Cotton genomic DNA was extracted from young leaves of the 135 BC1F1 plants and the two parents as described by Paterson et al. (1993). Genotyping and genetic linkage map construction for the BC1F1 population were previously described (Shi et al. 2015). Briefly, the genetic map consists of 2292 marker loci on 26 chromosomes with a total length of 5115.16 cM and an average distance of 2.23 cM between markers. This high-density genetic linkage map served as a foundation for QTL analysis of the 5 populations (BC1F1, BC1S1, BC2F1, BC2F1-DN and BC3F0).

QTL analysis

The QTLs for fiber quality and yield traits were analyzed using the composite interval mapping method (Zeng 1994) and Windows QTL Cartographer 2.5 (Wang et al. 2006) with a walk speed of 1 cM, 5 control markers, and 1000 permutation tests (Shi et al. 2015). Positive additive effects indicated that Hai1 alleles decreased the phenotypic trait values, and negative scores indicated that Hai1 alleles increased the phenotypic trait values. The CCRI36 alleles had the opposite effects. The QTLs were named as follows: (q + trait abbreviation) + chromosome/linkage group + QTL number. For the same trait, QTL across different generations or environments were considered stable when their confidence intervals overlapped (Sun et al. 2012).

Results

Performance of fiber quality and yield traits of the populations and their parents

The CCRI36 and Hai1 parents differed in yield and fiber quality traits in that the former had a greater BW and LI, lower SI and intermediate fiber quality, while the latter had longer, stronger fibers, a lower BW and LI, and a higher SI (Table 1).
Table 1

Performance of fiber quality and yield traits for interspecific populations and their parents

Trait

Interspecific backcross populations

Parents

Year

Generation (population)

Mean

Range

CV (%)

Hai1

CCRI36

FL (mm)

BC1F1

30.83

26.1–34.8

6.76

32.72

29.38

2004

BC1S1

28.54

24.6–33

5.49

33.66

28.40

2005

BC2F1

29.21

26.8–33.6

4.14

   

BC2F1-DN

29.04

26.3–32.5

4.12

   

BC3F0

29.13

26.8–31.7

3.62

   

FS (cN/tex)

BC1F1

32.04

26.6–38.3

9.28

40.77

28.65

2004

BC1S1

29.31

24.21–34.89

7.30

39.82

28.71

2005

BC2F1

30.12

26.07–33.81

5.29

   

BC2F1

30.62

27.34–34.69

5.44

   

BC3F0

30.71

26.26–35.57

5.51

   

FM (unit)

BC1F1

3.66

2–5

14.38

4.18

4.10

2004

BC1S1

3.87

2.96–5.47

11.80

4.17

4.25

2005

BC2F1

4.11

3.16–5.08

7.06

   

BC2F1-DN

4.27

3.53–5.18

7.36

   

BC3F0

4.00

3.2–4.89

7.12

   

BW (g)

BC1F1

3.55

1.88–5.73

22.95

3.10

4.50

2004

BC1S1

3.12

1.71–4.38

16.01

2.78

4.94

2005

BC2F1

4.49

3.67–5.51

8.88

   

BC2F1-DN

4.26

3.13–5.54

10.34

   

BC3F0

   

SI (g)

BC1F1

2004

BC1S1

11.59

8.4–15.7

11.39

12.37

10.71

2005

BC2F1

12.00

10.1–14.1

7.25

   

BC2F1-DN

12.15

9.8–14.5

7.43

   

BC3F0

12.83

10.3–14.8

7.24

   

LI (g)

BC1F1

2004

BC1S1

6.23

4.44–9.17

13.73

6.00

6.70

2005

BC2F1

6.73

4.74–8.53

8.27

   

BC2F1-DN

6.93

4.68–8.74

8.89

   

BC3F0

8.47

4.47–11.59

13.42

   
The mean values of FL and FS in the 5 populations were slightly higher than those of the recurrent parent (CCRI36), and the mean values of FM in the 5 populations were slightly lower or similar to those of the parent (CCRI36). The mean values of BW and LI in 4 populations were slightly higher than those of the parent Hai1. The mean values of SI were similar to that of the parent Hai1. Large variation in all traits was observed among interspecific populations and transgressive segregation was detected in all the populations (Table 1; Fig. 1). FM exhibited more variation than the other fiber quality traits, and BW displayed more variation than SI. As expected, the earlier-generation populations exhibited more phenotypic variation than the later-generation populations. The FL, FS and FM in all 5 populations, SI and LI in 4 populations (BC1S1, BC2F1, BC3F0 and BC2F1-DN), and BW in 4 populations (BC1F1, BC1S1, BC2F1 and BC2F1-DN) exhibited a continuous and normal distribution (Fig. 1).
Fig. 1

Frequency distribution of 6 traits of fiber quality and yield in the interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense

An overall description of the QTLs detected in the 5 populations

A total of 2292 marker loci distributed on 26 chromosomes (Shi et al. 2015) were used to map QTLs in the BC1F1, BC1S1, BC2F1, BC2F1-DN and BC3F0 populations. Two yield traits (SI and LI) were not measured in the BC1F1 population due to a low seed number for individual plants, and BW was not measured for the BC3F0 population because of artificial-hybridization.

In the 2004 BC1F1 population, a total of 22 QTLs were detected, including 4 for FL, 6 for FS, 7 for FM, and 5 for BW. In the 2005 BC1S1 population, 37 QTLs were detected, including 10 for FL, 5 for FS, 6 for FM, 5 for SI, 8 for LI, and 3 for BW, which showed that the number of QTLs detected for FL was the largest and the number of QTLs detected for BW was the smallest. In the 2005 BC2F1 population, more QTLs (44) were detected, including 7 for FL, 7 for FS, 7 for FM, 10 for SI, 6 for LI, and 7 for BW. In the 2005 BC2F1-DN population, a total of 37 QTLs were detected, including 7 for FL, 6 for FS, 5 for FM, 5 for SI, 8 for LI, and 6 for BW. In the 2005 BC3F0 population, 39 QTLs were detected, including 8 for FL, 5 for FS, 9 for FM, 11 for SI and 6 for LI. Most QTLs were detected in the BC2F1 population, the fewest QTLs in the BC1F1 population, and similar numbers of QTLs were identified in the BC1S1, BC2F1-DN, and BC3F0 populations.

QTL mapping of fiber quality traits in interspecific backcross populations

A total of 80 QTLs controlling fiber quality traits were identified in all 5 interspecific backcross populations: 33 for FL, 24 for FS, and 23 for FM. These QTLs were distributed on 19 chromosomes and explained 5.17–19.80% of the total phenotypic variation. Of these QTLs, 16 were stable, i.e., detected in 2 or 3 populations: 3 of them for FL, 5 for FS, and 8 for FM (Table 2; Fig. 2).
Table 2

Results of QTL mapping of three fiber quality traits in the 5 interspecific populations

Trait

QTL

Gen

Env

C

Position (cM)

Nearest marker

Interval (cM)

LOD

Add

PVE (%)

FL

qFL-C2-1

BC1F1

2004AY

C2

69.4

CICR0239

67.4–72.2

5.40

1.66

15.15

qFL-C2-2

BC1F1

2004AY

C2

79.3

NAU3875

78.3–89.7

2.67

1.26

8.37

qFL-C2-3

BC1S1

2005AY

C2

100.1

HAU2132

96.6–110.2

3.05

0.84

7.00

qFL-C2-4

BC2F1

2005AY

C2

146.6

HAU2690

137.5–158.4

3.32

0.67

7.27

qFL-C3-1

BC2F1

2005DN

C3

146.6

HAU0292

145.9–150.8

2.94

− 0.63

6.19

qFL-C4-1

BC3F0

2005AY

C4

61.6

DPL0107a

57.4–63.4

4.04

0.64

9.13

qFL-C4-2

BC3F0

2005AY

C4

65.5

HAU1300

64.4–66.7

2.73

0.53

6.23

qFL-C4-3

BC3F0

2005AY

C4

70.1

HAU1215

66.7–73.8

2.51

0.51

5.74

qFL-C8-1

BC1S1

2005AY

C8

175.2

JESPR092

168.1–184.8

4.25

1.06

11.31

qFL-C8-2

BC1S1

2005AY

C8

187.0

NAU2407

184.8–187.8

3.36

0.90

8.01

qFL-C8-3

BC1S1

2005AY

C8

189.8

CGR5130

187.8–195.5

3.33

0.90

7.96

qFL-C9-1

BC1F1

2004AY

C9

131.5

HAU0361

122.6–140.2

2.78

1.33

7.31

qFL-C10-1

BC1S1

2005AY

C10

167.5

CIR166

164.3–169.9

3.41

1.15

11.13

qFL-C10-2

BC1S1

2005AY

C10

178.4

HAU0949

176.5–179.9

5.38

1.15

12.94

qFL-C10-3

BC1S1

2005AY

C10

185.5

NAU2911

183.5–188.3

3.28

1.02

10.08

qFL-C10-4

BC1S1

2005AY

C10

191.3

NAU2991

188.3–194.7

2.71

0.97

9.28

qFL-C11-1

BC3F0

2005AY

C11

58.3

MUSS281

41.4–65.1

2.55

− 0.56

6.75

qFL-C11-2

BC2F1

2005AY

C11

77.2

BNL3442

75.7–79.2

4.70

− 0.80

6.75

qFL-C11-3

BC2F1

2005AY

C11

89.7

HAU0848

86.5–95.2

2.82

− 0.65

7.23

qFL-C11-4

BC2F1

2005AY

C11

175.2

TMB0064

172.3–180.6

3.42

− 0.66

5.90

qFL-C12-1

BC2F1

2005DN

C12

31.5

CGR6698a

29–31.8

2.81

0.62

5.90

qFL-C15-1

BC2F1

2005DN

C15

123.1

NAU3486

119–130.8

2.55

− 0.60

5.67

qFL-C16-1

BC2F1

2005DN

C16

146.8

DPL0048

144.3–149

5.04

0.89

12.12

qFL-C16-2

BC2F1

2005DN

C16

152.0

PGML00820

150.4–158.1

3.62

0.71

7.87

qFL-C16-3

BC1S1

2005AY

C16

160.3

BNL1604

159–165.4

3.52

1.08

11.00

qFL-C16-4

BC1S1

2005AY

C16

170.4

HAU1559

167–174.7

4.96

1.17

13.63

BC3F0

2005AY

C16

172.9

NAU2733

171.5–174.1

6.67

0.86

16.15

qFL-C16-5

BC2F1

2005AY

C16

179.7

DPL0283

174.1–182.5

3.25

0.66

7.25

BC3F0

2005AY

C16

175.7

NAU2556

174.1–176.7

7.66

0.92

18.25

qFL-C16-6

BC3F0

2005AY

C16

186.5

CGR6680

182.5–187.5

4.46

0.77

13.08

qFL-C20-1

BC2F1

2005DN

C20

12.3

CGR5565a

8.4–14.7

5.38

0.87

11.80

BC3F0

2005AY

C20

12.3

CGR5565a

5.9–15.4

2.77

0.52

5.95

qFL-C21-1

BC1F1

2004AY

C21

75.6

NAU3381

69.7–76

4.38

1.48

12.03

qFL-C21-3

BC2F1

2005AY

C21

98.4

PGML04104

93.5–102

3.75

0.73

8.70

qFL-C23-1

BC2F1

2005DN

C23

51.2

CICR0425

45.2–56.2

6.17

− 1.13

19.80

qFL-C25-1

BC2F1

2005AY

C25

104.1

MUSS275

97.1–108.4

4.16

− 0.73

8.89

FS

qFS-C4-1

BC3F0

2005AY

C4

71.1

HAU1215

69.3–73.9

3.30

0.99

8.45

qFS-C5-1

BC1F1

2004AY

C5

50.2

CGR6247

49.5–51.6

3.51

1.88

9.59

qFS-C5-2

BC1F1

2004AY

C5

52.6

CICR0065b

51.6–57.4

2.75

1.83

8.81

qFS-C5-3

BC3F0

2005AY

C5

102.5

BNL3992

99.7–105.5

3.50

− 0.95

7.77

qFS-C9-1

BC2F1

2005DN

C9

73.6

Gh112

63.1–83.7

3.36

− 1.14

10.35

qFS-C10-1

BC2F1

2005AY

C10

122.1

HAU2009

121.3–125.6

3.00

− 0.88

7.10

qFS-C10-2

BC2F1

2005AY

C10

133.2

DPL0116a

128.6–139.9

5.55

− 1.22

14.05

qFS-C10-3

BC2F1

2005DN

C10

215.6

CGR5040

213.7–221.3

3.36

1.05

8.40

qFS-C11-1

BC1S1

2005AY

C11

77.0

BNL3442

74–79.2

4.00

− 1.43

10.45

BC3F0

2005AY

C11

76.0

BNL3442

74.6–79.6

4.48

− 1.16

11.29

qFS-C11-2

BC1S1

2005AY

C11

89.7

HAU0848

85.5–92.2

2.92

− 1.23

7.98

BC3F0

2005AY

C11

88.7

HAU0848

85.5–92.3

3.73

− 1.03

8.76

qFS-C11-3

BC2F1

2005AY

C11

166.1

BNL0625

162.2–166.9

3.37

− 0.88

7.33

BC2F1

2005DN

C11

160.7

CGR6862

157.2–166.9

3.31

− 0.99

8.41

qFS-C11-4

BC2F1

2005AY

C11

167.6

DC40196

166.9–173.2

3.14

− 0.85

6.86

BC2F1

2005DN

C11

169.7

SHIN-0601

168.6–174.1

3.38

− 0.94

7.84

qFS-C12-1

BC1F1

2004AY

C12

81.7

DPL0303

80–83.7

3.10

− 1.77

8.41

qFS-C13-1

BC2F1

2005AY

C13

179.6

PGML04131a

173.8–184.3

4.12

− 0.98

9.10

qFS-C16-1

BC1S1

2005AY

C16

160.3

BNL1604

158.2–165.4

3.25

1.44

10.46

BC3F0

2005AY

C16

154.1

CGR5018

152–163.5

2.87

0.91

6.28

qFS-C16-2

BC1S1

2005AY

C16

171.4

HAU1559

167.6–172.9

4.08

1.43

10.54

qFS-C16-3

BC1S1

2005AY

C16

174.1

CICR0452

172.9–181.7

2.79

1.17

7.26

qFS-C16-4

BC1F1

2004AY

C16

190.1

HAU2060

188.3–191.4

2.57

1.53

6.20

qFS-C20-1

BC2F1

2005DN

C20

19.1

NAU5307

15.4–26

2.72

0.84

6.13

qFS-C21-1

BC1F1

2004AY

C21

31.5

CGR5747b

24.9–33.1

3.36

− 1.99

9.16

qFS-C21-2

BC1F1

2004AY

C21

33.8

SHIN-1579

33.1–39.5

2.61

− 1.85

7.22

qFS-C21-3

BC2F1

2005AY

C21

52.4

NAU4003

48.6–54.1

2.62

0.85

6.75

qFS-C21-4

BC2F1

2005AY

C21

60.0

DPL0582

58.2–65.1

4.46

1.02

9.89

qFS-C21-5

BC2F1

2005DN

C21

235.1

CICR0383

225.2–239.3

3.67

− 1.03

8.56

FM

qFM-C1-1

BC1F1

2004AY

C1

16.8

CGR5995

14–22.1

5.33

0.40

12.73

BC3F0

2005AY

C1

16.8

CGR5995

14.1–23.7

3.56

0.16

7.18

qFM-C5-1

BC1F1

2004AY

C5

25.6

DPL0247a

20–33.8

2.55

0.24

5.17

qFM-C10-1

BC1S1

2005AY

C10

59.7

NAU1066b

52.1–64.6

2.65

0.32

10.43

qFM-C10-2

BC1S1

2005AY

C10

67.6

NAU1066b

64.6–73

2.67

0.34

9.67

qFM-C10-3

BC1F1

2004AY

C10

181.8

NAU4008

178.4–183

2.91

− 0.28

6.80

qFM-C10-4

BC1F1

2004AY

C10

186.5

NAU2911

183–188.3

3.93

− 0.34

10.34

qFM-C10-5

BC1S1

2005AY

C10

189.3

NAU2991

186.1–194.9

2.57

− 0.24

6.93

BC1F1

2004AY

C10

190.3

NAU2991

188.3–198.9

4.16

− 0.36

11.34

qFM-C11-1

BC2F1

2005AY

C11

203.6

SWU0470

201.1–206.2

4.33

0.18

9.67

qFM-C15-1

BC1S1

2005AY

C15

74.9

CGR5236

73.3–76.6

5.16

0.33

13.10

qFM-C15-2

BC2F1

2005AY

C15

143.6

C2-0022C

142.2–145

4.70

0.19

10.79

qFM-C15-3

BC2F1

2005AY

C15

145.7

TMB1660

145–147.8

4.69

0.19

10.77

BC2F1

2005DN

C15

145.5

NAU3736

143.5–145.7

3.82

0.17

8.03

BC3F0

2005AY

C15

145.7

TMB1660

143.6–147.8

2.80

0.14

5.82

qFM-C15-4

BC2F1

2005AY

C15

151.5

TMB1664

149.7–153.5

5.36

0.20

12.22

BC3F0

2005AY

C15

151.5

TMB1664

149.4–153.5

3.95

0.17

8.06

BC2F1

2005DN

C15

154.5

PGML02824b

151.5–158.7

4.91

0.20

10.27

qFM-C21-1

BC2F1

2005DN

C21

68.8

NAU3156

63.4–74.5

5.83

− 0.23

13.75

BC3F0

2005AY

C21

70.8

NAU3156

66.4–75.6

4.41

− 0.19

11.24

qFM-C21-2

BC2F1

2005AY

C21

83.3

BNL3147

75.8–85

3.65

− 0.17

8.10

BC2F1

2005DN

C21

80.6

BNL3147

77.5–82.6

5.41

− 0.23

13.74

BC3F0

2005AY

C21

82.6

BNL3147

76.2–85

5.44

− 0.20

11.88

qFM-C21-3

BC2F1

2005AY

C21

86.6

HAU0423

85–90.1

3.83

− 0.17

8.47

BC3F0

2005AY

C21

86.0

BNL3449

85–90.1

4.99

− 0.20

11.51

qFM-C21-4

BC2F1

2005AY

C21

94.1

PGML04105b

90.1–96.1

2.75

− 0.16

7.34

BC3F0

2005AY

C21

94.1

PGML04105b

90.1–96.3

4.13

− 0.19

10.15

qFM-C21-5

BC1F1

2004AY

C21

97.4

PGML04104

92.7–102.7

3.91

− 0.33

9.06

qFM-C21-6

BC3F0

2005AY

C21

164.1

DC40025

153.4–178.1

3.27

0.15

6.70

qFM-C21-7

BC2F1

2005DN

C21

258.6

NAU6520

246.7–268.2

2.84

0.14

5.63

qFM-C24-1

BC3F0

2005AY

C24

26.6

MUSS021

24.9–30.7

2.69

− 0.13

5.36

qFM-C24-2

BC1S1

2005AY

C24

133.9

DPL0214b

131.2–136

4.66

− 0.32

11.75

qFM-C24-3

BC1S1

2005AY

C24

138.9

HAU2738

137.4–142.9

2.53

− 0.24

6.61

qFM-C25-1

BC1F1

2004AY

C25

129.2

CGR6799b

127.8–131.9

5.35

0.38

12.79

C chromosome, Gen generation, Env environment, AY Anyang Experiment Farm, DN artificial disease nursery, PVE phenotypic variation explained

Fig. 2

Chromosomal locations of QTLs for 6 traits of fiber quality and yield in the interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense. Asterisk, stable QTL; the number of asterisk, the number of environments in which the QTL was detected

Fiber length

A total of 33 QTLs for FL were identified in the 5 populations (BC1F1, BC1S1, BC2F1, BC2F1-DN and BC3F0), each with a phenotypic variation explained (PVE) value of 5.67–19.80%; the QTLs were distributed on 14 chromosomes: 6 on C16, 4 each on C2, C10 and C11, 3 each on C4 and C8, 2 on C21, and only 1 on C3, C9, C12, C15, C20, C23 and C25. Thus, the QTLs for FL were clustered on C16, C2, C10 and C11.

Twenty-five of the 33 QTLs had positive additive effects, in which CCRI36 alleles increased FL by about 0.51–1.66 mm, whereas 8 QTLs had negative additive effects, in which Hai1 alleles increased FL by about 0.56–1.13 mm.

Notably, three of them could be stable: qFL-C16-4 was detected in both the BC1S1 and BC3F0 populations, with a PVE of 13.63% and 16.15%, respectively, and qFL-C16-5 was detected in both the BC2F1 and BC3F0 populations, with a PVE of 7.25% and 18.25%, respectively. The QTL qFL-C20-1 was detected in both the BC2F1-DN and BC3F0 populations, with a PVE of 5.95% and 11.80%, respectively. CCRI36 alleles increased FL by approximately 0.52–1.17 mm.

Fiber strength

In total 24 QTLs for FS were detected in the 5 populations (BC1F1, BC1S1, BC2F1, BC2F1-DN and BC3F0), each with PVE 6.13–14.05%, and located on 10 chromosomes, 5 QTLs on C21, 4 each on C11 and C16, 3 each on C5 and C10, and only 1 on C4, C9, C12, C13 and C20, respectively. It showed that C21, C11 and C16 contained more QTL for FS. Thirteen of the 24 QTLs had negative additive effects, in which Hai1 alleles increased FS about 0.85–1.99 cN/tex.

Importantly, 5 of them could be stable: 2 (qFS-C11-1 and qFS-C11-2) were detected in both the BC1S1 and BC3F0, with PVE 7.98–11.29%, and 2 (qFS-C11-3 and qFS-C11-4) were detected in both the BC2F1 and BC1F1-DN, explaining the phenotypic variations by 6.86–8.41%. Hai1 alleles increased FS by approximately 0.85–1.43 cN/tex. One of them was mapped on C16, qFS-C16-1 was detected in both the BC1S1 and BC3F0, explaining the phenotypic variations by 6.28% and 10.46%. CCRI36 alleles increased FS by approximately 0.91–1.44 cN/tex.

Fiber micronaire

In total, 23 QTLs for FM were identified in the 5 populations, each with a PVE of 5.17–13.75%; the QTLs were distributed on 8 chromosomes: 7 on C21, 4 on C15, 5 on C10, 3 on C24, and only 1 on C1, C5, C11 and C25. Therefore, C21, C15, C10 and C24 contained most QTLs for FM. Hai1 alleles decreased FM by approximately 0.14–0.40 units for 12 of the 23 QTLs, whereas CCRI36 alleles decreased FM by approximately 0.13–0.36 units for the other 11 QTLs.

Notably, 8 of the QTLs were stable: qFM-C21-1 was detected in both the BC1F1-DN and BC3F0 populations, with a PVE of 11.24% and 13.75%, respectively, qFM-C21-2 was detected in 3 populations (BC2F1, BC1F1-DN and BC3F0), with a PVE of 8.10–13.74%, and qFM-C21-3 and qFM-C21-4 were detected in both the BC2F1 and BC3F0 populations, with a PVE of 7.34–11.51%. CCRI36 alleles decreased FM by approximately 0.16–0.23 units. The QTL qFM-C15-3 and qFM-C15-4 were simultaneously detected in the BC2F1, BC2F1-DN and BC3F0 populations, with a PVE of 5.82–10.77% and 8.06–12.22%, respectively. Hai1 alleles at the 2 QTLs decreased FM by approximately 0.14–0.19 units and 0.17–0.20 units, respectively. The QTL qFM-C10-5 was detected in both the BC1S1 and BC1F1 populations, with a PVE of 6.93% and 11.34%, respectively. Hai1 alleles increased FM by approximately 0.24–0.36 units. The QTL qFM-C1-2 was detected in both the BC1F1 and BC3F0 populations, with a PVE of 7.18% and 12.73%, respectively. Hai1 alleles decreased FM by approximately 0.16–0.40 units.

QTL mapping of yield traits in interspecific backcross populations

A total of 73 QTLs for 3 fiber yield traits were detected on 20 chromosomes in 4 of the 5 interspecific backcross populations. The PVE of each QTL was 4.98–18.92%. Seven of the QTLs were stable, i.e., detected in 2 or 3 populations simultaneously: 4 for SI and 3 for LI (Table 3; Fig. 2).
Table 3

Results of QTL mapping for 3 fiber yield traits

Trait

QTL

Gen

Env

C

Position (cM)

Nearest marker

Interval (cM)

LOD

Add

PVE (%)

BW

qBW-C2-1

BC2F1

2005DN

C2

171.3

NAU1246

165.9–176.6

2.91

0.24

7.17

qBW-C3-1

BC2F1

2005AY

C3

58.8

CICR0222

57.6–66.5

3.17

0.21

6.91

qBW-C4-1

BC2F1

2005DN

C4

85.8

MUCS570

82.3–89.8

2.67

0.23

6.53

qBW-C10-1

BC1F1

2004AY

C10

182.3

NAU2082

177.9–182.7

2.98

− 0.45

7.16

qBW-C10-2

BC1F1

2004AY

C10

187.5

NAU2991

184.1–192.8

4.42

− 0.57

11.31

qBW-C11-1

BC1F1

2004AY

C11

136.7

CICR0433

135.3–138.7

2.57

− 0.42

5.87

qBW-C14-1

BC2F1

2005AY

C14

40.0

NAU3648

35.4–45.2

5.47

0.29

13.15

qBW-C14-2

BC2F1

2005AY

C14

48.5

PGML03232

47.1–50.6

3.41

0.23

7.98

qBW-C15-1

BC1S1

2005AY

C15

6.1

NAU3102

0.9–12.6

4.35

0.45

14.31

qBW-C17-1

BC1S1

2005AY

C17

112.5

NAU4052

109.2–114.6

4.42

− 0.39

15.25

qBW-C17-2

BC1S1

2005AY

C17

119.4

CGR6185

117.7–122.6

4.30

− 0.34

11.44

qBW-C18-1

BC2F1

2005DN

C18

42.6

CGR6787

28.9–44.3

2.64

0.23

6.57

qBW-C18-2

BC2F1

2005DN

C18

47.5

PGML01649

44.3–50

3.06

0.25

7.56

qBW-C18-3

BC2F1

2005DN

C18

51.1

BNL1079

50–51.8

2.77

0.23

6.88

qBW-C18-4

BC2F1

2005DN

C18

52.1

NAU3816a

51.8–53.8

2.54

0.22

6.33

qBW-C19-1

BC2F1

2005AY

C19

89.3

PGML03375

88.2–91.6

2.82

0.20

6.14

qBW-C20-1

BC1F1

2004AY

C20

113.7

HAU1491b

107.5–121.5

3.89

0.57

9.09

qBW-C21-1

BC1F1

2004AY

C21

48.8

CGR5097

45–57.2

2.84

− 0.43

6.51

qBW-C21-2

BC2F1

2005AY

C21

137.1

DPL0867

133.7–140.9

3.47

0.24

8.77

qBW-C21-3

BC2F1

2005AY

C21

147.0

CGR5233

145.6–147.8

7.10

0.52

16.58

qBW-C21-4

BC2F1

2005AY

C21

155.7

TMB1976

153.8–157.7

5.32

0.40

15.36

SI

qSI-C3-1

BC3F0

2005AY

C3

97.0

BNL3441

96.2–99.8

2.72

− 0.46

5.67

qSI-C3-2

BC3F0

2005AY

C3

106.3

BNL2443

105–107.2

3.25

− 0.57

6.64

qSI-C3-3

BC3F0

2005AY

C3

109.6

CICR0034

108.6–112.4

4.06

− 0.58

9.13

qSI-C3-4

BC3F0

2005AY

C3

114.4

HAU0195b

112.9–114.8

4.48

− 0.57

8.94

qSI-C3-5

BC3F0

2005AY

C3

116.1

CGR6528

114.8–118.1

4.24

− 0.59

9.62

qSI-C6-1

BC2F1

2005DN

C6

58.6

CICR0461b

55.2–61.7

3.22

0.54

8.55

qSI-C8-1

BC1S1

2005AY

C8

77.7

CER0152c

71.1–87.3

2.52

0.71

5.38

qSI-C9-1

BC2F1

2005DN

C9

111.9

BNL1672

106.2–114.5

2.81

− 0.47

6.52

qSI-C10-1

BC3F0

2005AY

C10

41.9

DPL0485

41.1–49.8

2.51

0.43

4.98

qSI-C14-1

BC2F1

2005AY

C14

150.4

NAU3913

148.5–154.6

3.52

− 0.47

7.35

BC2F1

2005DN

C14

147.5

PGML01521

143.1–150.1

3.06

− 0.49

7.00

qSI-C14-2

BC2F1

2005AY

C14

161.6

PGML03864

155.2–163

3.39

− 0.47

7.15

qSI-C14-3

BC3F0

2005AY

C14

195.0

CER0138

190.8–200

4.25

− 0.58

9.25

qSI-C14-4

BC3F0

2005AY

C14

205.0

DC40046

203–209.4

3.56

− 0.52

7.69

qSI-C17-1

BC2F1

2005AY

C17

77.5

NAU3309

71–82.4

3.88

− 0.67

13.76

qSI-C17-2

BC2F1

2005AY

C17

88.4

NAU6542

83.7–92.3

6.28

− 0.70

15.75

BC2F1

2005DN

C17

85.4

NAU3309

82.4–88.8

6.37

− 0.81

18.92

qSI-C17-3

BC2F1

2005DN

C17

92.3

HAU0764

90.3–93.3

3.05

− 0.51

7.63

qSI-C17-4

BC2F1

2005AY

C17

95.9

JESPR195

93.3–101.7

4.69

− 0.59

11.29

qSI-C18-1

BC2F1

2005AY

C18

0.0

NAU6173

0–3.7

3.52

0.48

7.38

qSI-C20-1

BC2F1

2005AY

C20

56.1

SHIN-0087b

52.6–57.9

3.89

0.54

9.60

BC3F0

2005AY

C20

53.1

HAU1423b

48.2–57.1

4.66

0.59

9.48

qSI-C20-2

BC2F1

2005AY

C20

62.3

HAU0019

60.5–64.2

4.12

0.52

8.93

BC3F0

2005AY

C20

61.9

NAU4973

60.5–63.3

2.96

0.47

6.18

qSI-C20-3

BC2F1

2005AY

C20

67.2

HAU0175

64.2–68.1

4.84

0.57

10.39

qSI-C20-4

BC2F1

2005AY

C20

68.3

NAU5013

68.1–71.8

4.76

0.56

10.22

qSI-C21-1

BC3F0

2005AY

C21

100.6

NAU4014

97.4–108

3.48

0.50

6.93

qSI-C22-1

BC1S1

2005AY

C22

112.6

PGML00767

108.4–114

3.63

0.86

9.64

qSI-C22-2

BC1S1

2005AY

C22

116.0

PGML04137

114–118.3

5.01

0.99

13.18

qSI-C22-3

BC1S1

2005AY

C22

120.5

CGR5566

119.4–125

3.39

0.80

8.48

qSI-C25-1

BC1S1

2005AY

C25

135.3

DPL0705b

132.1–140.7

5.08

− 1.00

13.51

LI

qLI-C6-1

BC1S1

2005AY

C6

36.6

HAU3350a

34.3–38.3

4.10

0.56

10.29

qLI-C6-2

BC1S1

2005AY

C6

42.3

TMB2959

40.8–42.5

3.31

0.50

8.44

qLI-C6-3

BC1S1

2005AY

C6

45.9

PGML00996

44.5–46.6

3.68

0.53

9.31

qLI-C6-4

BC1S1

2005AY

C6

48.6

CGR6019

46.6–51.6

2.97

0.52

8.57

qLI-C8-1

BC2F1

2005DN

C8

39.0

HAU1390

38.5–40.5

2.51

− 0.31

6.14

qLI-C8-2

BC2F1

2005DN

C8

51.5

DPL0862

45.4–56.1

3.85

− 0.40

10.15

qLI-C8-3

BC2F1

2005DN

C8

60.1

DPL0589

58.1–61.8

2.73

− 0.34

7.33

qLI-C11-1

BC2F1

2005AY

C11

210.3

MUCS507

203.5–211

2.67

0.29

6.47

qLI-C11-2

BC2F1

2005AY

C11

219.1

HAU0512

213–231

3.01

0.33

8.42

qLI-C12-1

BC3F0

2005AY

C12

105.7

DPL0400

102.9–107.3

3.36

− 1.97

7.39

qLI-C14-1

BC2F1

2005AY

C14

210.3

DPL0521

203–214.3

2.50

− 0.33

8.34

BC1S1

2005AY

C14

214.3

HAU1219b

207.2–215.3

3.81

− 0.65

12.29

qLI-C16-1

BC2F1

2005DN

C16

143.2

NAU6664

140.7–144.9

3.89

− 0.40

9.54

qLI-C16-2

BC2F1

2005DN

C16

149.0

DPL0897

147.6–151.8

5.04

− 0.44

12.12

qLI-C16-3

BC2F1

2005DN

C16

154.1

CGR5018

151.8–157.1

4.33

− 0.41

10.53

qLI-C16-4

BC2F1

2005AY

C16

160.3

BNL1604

158.6–165.4

2.56

− 0.32

7.82

qLI-C16-5

BC2F1

2005AY

C16

171.5

HAU1559

167.4–174.1

5.25

− 0.42

13.23

BC3F0

2005AY

C16

169.4

HAU1559

165.7–174.1

4.22

− 1.95

10.93

qLI-C16-6

BC2F1

2005AY

C16

175.7

NAU2556

174.1–176.7

4.85

− 0.40

12.30

BC3F0

2005AY

C16

175.7

NAU2556

174.1–180

4.30

− 1.83

9.60

qLI-C16-7

BC3F0

2005AY

C16

187.5

CGR6680

182.5–188.1

2.51

− 1.49

6.26

qLI-C17-1

BC3F0

2005AY

C17

53.8

COT064

50–63.5

2.55

− 1.83

6.01

qLI-C17-2

BC1S1

2005AY

C17

111.5

NAU4052

106.3–115.6

3.59

0.59

11.57

qLI-C17-3

BC1S1

2005AY

C17

119.4

CGR6185

118.3–125.1

4.06

0.56

10.17

qLI-C21-1

BC2F1

2005DN

C21

128.5

TMB2493

125.3–132.5

3.11

− 0.34

7.22

qLI-C21-2

BC1S1

2005AY

C21

235.1

CICR0383

228.4–240

3.32

0.50

8.20

qLI-C23-1

BC2F1

2005DN

C23

50.2

CICR0425

42.2–56.3

4.08

0.49

14.85

qLI-C25-1

BC3F0

2005AY

C25

17.4

CGR5525a

13.1–19.1

3.01

1.55

6.54

C chromosome, Gen generation, Env environment, AY Anyang Experiment Farm, DN artificial disease nursery, PVE phenotypic variation explained

Boll weight

In total, 21 QTLs for BW were identified in the 4 populations (BC1F1, BC1S1, BC2F1, and BC1F1-DN), each with a PVE of 5.87–16.58%. Among the QTLs for BW, 5, 3, 7 and 6 QTLs were detected in the BC1F1, BC1S1, BC2F1 and BC1F1-DN populations, respectively. The QTLs were found on 12 chromosomes: 4 each on C18 and C21, 2 each on C10, C14 and C17, and only 1 on C2, C3, C4, C11, C15, C19 and C20. Six QTLs (qBW-C10-1, qBW-C10-2, qBW-C11-1, qBW-C17-1, qBW-C17-2, and qBW-C21-1) for BW on C10, C11, C17 and C21 had negative additive effects, in which Hai1 alleles increased BW from 0.34 to − 0.57 g. The other 15 QTLs had positive additive effects, in which the CCRI36 alleles increased BW from 0.20 to 0.57 g.

However, no stable QTLs were detected in 2 or more populations, generations or environments.

Seed index

A total of 27 QTLs for SI were identified in the 4 populations (BC1S1, BC2F1, BC1F1-DN and BC3F0), each with a PVE of 4.98–18.92%; the QTLs were distributed on 12 chromosomes: 5 on C3, 4 each on C14, C17 and C20, 3 on C22, and only 1 on C6, C8, C9, C10, C18, C21 and C25. Hai1 alleles increased SI by approximately 0.31–1.97 g for 15 of the 27 QTLs and decreased SI by approximately 0.29–1.55 g for the other 12 QTLs.

Notably, 4 of these QTLs were stable, and 2 that were detected in both the BC2F1 and BC2F1-DN populations mapped to C14 (qSI-C14-1) and C17 (qSI-C17-2), with a PVE of 7.00–7.35% and 15.75–18.92%, respectively. Hai1 alleles at the 2 QTLs increased SI by approximately 0.47–0.49 g and 0.70–0.81 g, respectively. Two QTLs were detected in both the BC2F1 and BC3F0 populations were mapped to C20 (qSI-C20-1 and qSI-C20-2), with a PVE of 9.48–9.60% and 6.18–8.93%, respectively. Hai1 alleles at the 2 QTLs decreased SI by approximately 0.54–0.59 g and 0.47–0.52 g, respectively.

Lint index

In total, 25 QTLs for LI were identified in 4 populations (BC1S1, BC2F1, BC1F1-DN and BC3F0), each with a PVE of 6.01–14.85%; the QTLs were found on 10 chromosomes: 7 on C16, 4 on C6, 3 each on C8 and C17, 2 each on C11 and C21, and only 1 on C12, C14, C23 and C25. Hai1 alleles increased LI by approximately 0.29–1.55 g for 14 of the 25 QTLs and decreased LI by approximately 0.31–1.97 g for the other 11 QTLs.

Importantly, 3 of the QTLs were stable. One detected in both the BC2F1 and BC1S1 populations mapped to C14 (qLI-C14-1), with a PVE of 8.34–12.29%. Hai1 alleles increased LI by 0.33–0.65 g. Two QTLs (qLI-C16-1 and qLI-C16-2) detected in both the BC2F1 and BC3F0 populations were mapped to C16, with a PVE of 10.93–13.23% and 9.60–12.30%, respectively. Hai1 alleles increased LI by approximately 0.42–1.95 g and 0.40–1.83 g, respectively.

QTL clusters

Using the same linkage map from the BC1F1 population, a total of 179 QTLs for fiber quality and yield traits (7 traits: FL, FS, FM, BW, LP, SI, LI) were identified, of which 26 QTLs for LP were identified on 9 chromosomes with phenotype data related to the BC1F1 population (Shi et al. 2015).

Through comprehensive analysis, we found that 10 QTL clusters, including 71 QTLs, were related to 2 or more traits, which is a common phenomenon in cotton (Rong et al. 2007; Lacape et al. 2010; Shen et al. 2007; Said et al. 2013, 2015b; Li et al. 2016b; Keerio et al. 2018). Two QTL clusters were detected on C16, C17 and C21, and one was detected on C4, C6, C10 and C11 (Table 4; Table S1).
Table 4

QTL clusters for fiber quality and yield traits in interspecific backcross populations

Cluster

Interval (cM)

C

No of QTL

QTL

C4-Cluster-1

61–86

C4

5

qFL-C4-1(+), qFL-C4-2(+)b, qFL-C4-3(+), qFS-C4-1(+), qBW-C4-1(+)

C6-Cluster-1

36–59

C6

5

qSI-C6-1(+), qLI-C6-1(+), qLI-C6-2(+), qLI-C6-3(+), qLI-C6-4(+)

C10-Cluster-1

167–192

C10

9

qFL-C10-1(+), qFL-C10-2(+)b, qFL-C10-3(+), qFL-C10-4(+)b, qFM-C10-3(−)b, qFM-C10-4(−), qFM-C10-5(−)a, qBW-C10-1(−) qBW-C10-2(−)b

C11-Cluster-1

63–90

C11

4

qFL-C11-2(−)b, qFL-C11-3(−), qFS-C11-1(−)a, qFS-C11-2(−)ab

C16-Cluster-1

137–161

C16

13

qFL-C16-1(+), qFL-C16-2(+)b, qFL-C16-3(+)b, qFS-C16-1(+)ab, qLP-C16-1(−)b, qLP-C16-2(−), qLP-C16-3(−), qLP-C16-4(−), qLP-C16-5(−), qLI-C16-1(−), qLI-C16-2(−), qLI-C16-3(−), qLI-C16-4(−)

C16-Cluster-2

169–193

C16

12

qFL-C16-4(+)a, qFL-C16-5(+)a, qFL-C16-6(+), qFS-C16-2(+), qFS-C16-3(+), qFS-C16-4(+), qLP-C16-6(−)ab, qLP-C16-7(−)a, qLP-C16-8(−), qLI-C16-5(−)a, qLI-C16-6(−)a, qLI-C16-7(−)

C17-Cluster-1

77–100

C17

7

qLP-C17-1(+)a, qLP-C17-2(+)ab, qLP-C17-3(+), qSI-C17-1(−), qSI-C17-2(−)a, qSI-C17-3(−), qSI-C17-4(−)

C17-Cluster-2

111–120

C17

6

qBW-C17-1(−), qLP-C17-4(+), qLP-C17-5(+)b, qBW-C17-2(−), qLI-C17-2(+), qLI-C17-3(+)

C21-Cluster-1

60–76

C21

4

qFL-C21-1(+), qFS-C21-4(+), qFM-C21-1(-)ab, qLP-C21-1(−)

C21-Cluster-2

83–101

C21

6

qFL-C21-2(+)b, qFM-C21-2(−)ab, qFM-C21-3(−)a, qFM-C21-4(−)a,qFM-C21-5(−), qSI-C21-1(+)

The data for LP QTLs are from our previous reports (Shi et al. 2015)

C chromosome

aStable QTL

bCommon QTL

There were 6 QTL clusters related to both fiber quality and yield traits, namely, C16-Cluster-1, C16-Cluster-2, C21-Cluster-1, C21-Cluster-2, C10-Cluster-1 and C4-Cluster-1. Thirteen QTLs were in C16-Cluster-1 on C16 (137–161 cM), including 3 for FL (qFL-C16-1, qFL-C16-2, and qFL-C16-3) and 1 for FS (qFS-C16-1) with positive additive effects and 5 for LP (qLP-C16-1, qLP-C16-2, qLP-C16-3, qLP-C16-4, and qLP-C16-5) and 4 for LI (qLI-C16-1, qLI-C16-2, qLI-C16-3, and qLI-C16-4) with negative additive effects. The same results were observed for C16-Cluster-2 on C16 (169–193 cM), which harbored 12 QTLs, including 3 for FL (qFL-C16-4, qFL-C16-5, and qFL-C16-6) and 3 for FS (qFS-C16-2, qFS-C16-3, and qFS-C16-4) with positive additive effects and 3 for LP (qLP-C16-6, qLP-C16-7, and qLP-C16-8) and 3 for LI (qLI-C16-5, qLI-C16-6, and qLI-C16-7) with negative additive effects. C21-Cluster-1, in a region (60–76 cM) on C21, contained 4 QTLs, namely, qFL-C21-1 for FL and qFS-C21-4 for FS with positive additive effects, and qFM-C21-1 for FM and qLP-C21-1 for LP with negative additive effects. C10-Cluster-1, in a region (167–192 cM) on C10, harbored 9 QTLs, including 4 for FL (qFL-C10-1, qFL-C10-2, qFL-C10-3, and qFL-C10-4) with positive additive effects and 3 for FM (qFM-C10-3, qFM-C10-4, and qFM-C10-5) and 2 for BW (qBW-C10-1 and qBW-C10-2) with negative additive effects. C4-Cluster-1, on C4 (61–86 cM), harbored 5 QTLs, 3 (qFL-C4-1, qFL-C4-2, and qFL-C4-3) for FL, one (qFS-C4-1) for FS and one (qBW-C4-1) for BW, all with a positive additive effect. C21-Cluster-2, on C21 (83–101 cM), harbored 6 QTLs, namely, qFL-C21-2 for FL and qSI-C21-1 for SI with positive additive effects and qFM-C21-2, qFM-C21-3, qFM-C21-4, and qFM-C21-5 for FM with negative additive effects.

There were 3 QTL clusters related to fiber yield, namely, C6-Cluster-1, C17-Cluster-1 and C17-Cluster-2. Six QTLs were located in C17-Cluster-2 on C17 (111–120 cM): 2 (qBW-C17-1 and qBW-C17-2) for BW with negative additive effects and 2 (qLP-C17-4 and qLP-C17-5) for LP and 2 (qLI-C17-2 and qLI-C17-3) for LI with positive additive effects. C6-Cluster-1, in a region (36–59 cM) on C6, harbored 5 QTLs, namely, qSI-C6-1 for SI and qLI-C6-1, qLI-C6-2, qLI-C6-3, and qLI-C6-4 for LI, all with positive additive effects. C17-Cluster-1, on C17 (77–100 cM), harbored 7 QTLs, namely, qLP-C17-1, qLP-C17-2, and qLP-C17-3 for LP with positive additive effects and qSI-C17-1, qSI-C17-2, qSI-C17-3, and qSI-C17-4 for SI with negative additive effects.

C11-Cluster-1, on C11 (63–90 cM), was associated with fiber quality, harboring qFL-C11-2 and qFL-C11-3 for FL and qFS-C11-1 and qFS-C11-2 for FS, all with negative additive effects.

Discussion

The location of QTLs

The linkage map was constructed with 2292 SSR marker loci, covering 5115.16 cM of the AD genome with an average distance of 2.23 cM between markers (Shi et al. 2015). The length of the linkage map is similar to that of the AD genome in cotton, and the SSR marker loci are distributed throughout the genome. We used the high-density genetic linkage map and data from 5 backcross populations (BC1F1, BC2F1, BC1S1, BC2F1-DN, and BC3F0) in 4 different generations (BC1F1, BC2F1, BC1S1, and BC3F0) to identify the QTLs of fiber yield and fiber quality in 3 environments (the field in 2004, an artificial disease nursery in 2005, and the field in 2005). A total of 153 QTLs were identified, including 80 QTLs for 3 fiber quality traits and 73 QTLs for 3 yield traits in the present paper (Tables 2, 3; Fig. 2). Twenty-three QTLs were consistently detected in 2 or 3 populations, including 3 for FL, 5 for FS, 8 for FM, 4 for SI, and 3 for LI.

Among the 153 QTLs detected in the present study, 30 QTLs were reported in previous studies with a common marker in the confidence interval on the same chromosome. Ten QTLs for FL (qFL-C3-1, qFL-C4-2, qFL-C8-1, qFL-C8-3, qFL-C10-2, qFL-C10-4, qFL-C11-2, qFL-C16-2, qFL-C16-3, and qFL-C20-1) were reported previously (Chen et al. 2018; Kong et al. 2018; Lan et al. 2011; Said et al. 2015b; Sun et al. 2012; Wang et al. 2011, 2013, 2016, 2017a; Zhai et al. 2016; Zhang et al. 2016a). Of these QTLs, qFL-C3-1 was the same as that in three previous reports (Wang et al. 2013, 2016; Chen et al. 2018), and qFL-C16-3 was the same as that in three previous reports (Wang et al. 2011; Said et al. 2015b; Lan et al. 2011). Eight QTLs for FS (qFS-C5-1, qFS-C5-3, qFS-C10-3, qFS-C11-2, qFS-C16-1, qFS-C20-1, qFS-C21-3, and qFS-C21-5) were consistent with those reported previously (Said et al. 2015b; Shao et al. 2014; Tang et al. 2015; Wang et al. 2016; Zhai et al. 2016; Kong et al. 2018). Among these 8 common QTLs for FS, qFS-C10-3 may be the same as that in three previous reports (Shao et al. 2014; Tang et al. 2015; Kong et al. 2018). Of 7 common QTLs for FM, one QTL (qFM-C10-3) was reported by three researchers (Said et al. 2015b; Si et al. 2017; Yu et al. 2013). One QTL (qFM-C5-1) was reported by Yu et al. (2013) and Said et al. (2015b). The other 5 QTLs for FM (qFM-C15-2, qFM-C21-1, qFM-C21-2, qFM-C24-3 and qFM-C25-1) were reported in the previous studies (Said et al. 2015b; Liang et al. 2010; Song et al. 2017; Yang et al. 2015). Five QTLs for yield traits (qBW-C10-2, qBW-C21-3, qSI-C14-1, qSI-C18-1 and qLI-C17-1) were reported previously (Kong et al. 2018; Ning et al. 2014; Si et al. 2017; Wang et al. 2015; Wu et al. 2009). Nine QTLs detected in previous studies were confirmed in our advanced backcross populations, including 3 QTLs (qFL-C20-1, qFS-C11-2, and qFS-C20-1) reported by Zhai et al. (2016), 3 QTLs (qBW-C10-2, qFL-C10-4, and qFS-C10-3) reported by Kong et al. (2018), and 3 QTLs (qFL-C16-3, qFM-C21-1 and qFM-C24-3) reported by Lan et al. (2011), Liang et al. (2010), and Song et al. (2017), respectively. Therefore, 30 of the QTLs detected in this study were previously reported, and the other 123 QTLs are considered novel.

In summary, 23 QTLs (qFL-C16-4, qFL-C16-5, qFL-C20-1, qFS-C11-1, qFS-C11-2, qFS-C11-3, qFS-C11-4, qFS-C16-1, qFM-C1-1, qFM-C10-5, qFM-C15-3, qFM-C15-4, qFM-C21-1, qFM-C21-2, qFM-C21-3, qFM-C21-4, qSI-C14-1, qSI-C17-2, qSI-C20-1, qSI-C20-2, qLI-C14-1, qLI-C16-5, and qLI-C16-6) were stable and detected in 2 or more populations in this paper and 30 common QTLs (qFL-C3-1, qFL-C4-2, qFL-C8-1, qFL-C8-3, qFL-C10-2, qFL-C10-4, qFL-C11-2, qFL-C16-2, qFL-C16-3, qFL-C20-1, qFS-C5-1, qFS-C5-3, qFS-C10-3, qFS-C11-2, qFS-C16-1, qFS-C20-1, qFS-C21-3, qFS-C21-5, qFM-C5-1, qFM-C10-3, qFM-C15-2, qFM-C21-1, qFM-C21-2, qFM-C24-1, qFM-C25-1, qBW-C10-2, qBW-C21-3, qSI-C14-1, qSI-C18-1, and qLI-C17-1) were reported in the previous studies (Table S3).

There were 47 stable or common QTLs (qFL-C3-1, qFL-C4-2, qFL-C8-1, qFL-C8-3, qFL-C10-2, qFL-C10-4, qFL-C11-2, qFL-C16-2, qFL-C16-3, qFL-C16-4, qFL-C16-5, qFL-C20-1, qFS-C5-1, qFS-C5-3, qFS-C10-3, qFS-C11-1, qFS-C11-2, qFS-C11-3, qFS-C11-4, qFS-C16-1, qFS-C20-1, qFS-C21-3, qFS-C21-5, qFM-C1-1, qFM-C5-1, qFM-C10-3, qFM-C10-5, qFM-C15-2, qFM-C15-3, qFM-C15-4, qFM-C21-1, qFM-C21-2, qFM-C21-3, qFM-C21-4, qFM-C24-3, qFM-C25-1, qBW-C10-2, qBW-C21-3, qSI-C14-1, qSI-C17-2, qSI-C18-1, qSI-C20-1, qSI-C20-2, qLI-C14-1, qLI-C16-5, qLI-C16-6 and qLI-C17-1), of which 6 QTLs (qFL-C20-1, qFS-C11-2, qFS-C16-1, qFM-C21-1, qFM-C21-2, and qSI-C14-1) were both stable and common QTLs. Therefore, 17 QTLs (qFL-C16-4, qFL-C16-5, qFS-C11-1, qFS-C11-3, qFS-C11-4, qFM-C1-1, qFM-C10-5, qFM-C15-3, qFM-C15-4, qFM-C21-3, qFM-C21-4, qSI-C20-2, qSI-C17-2, qSI-C20-1, qLI-C14-1, qLI-C16-5, and qLI-C16-6) were considered novel stable QTLs.

The detection of QTLs in multiple populations or different genetic backgrounds indicated the stabilities of the genetic effects. These stable and common QTLs might increase the reliability and efficiency of selection and play important roles in the simultaneous improvement of fiber yield and quality by MAS (Shen et al. 2007; Sun et al. 2012; Tang et al. 2015; Wang et al. 2015; Zhai et al. 2016; Zhang et al. 2009).

Contributions of the A and D subgenomes to allotetraploid cottons

In the past 20 years, most of QTL mapping studies have clearly shown that more QTLs for fiber quality traits were situated on the D subgenome than on the A subgenome for fiber quality traits (Fang et al. 2014; Jiang et al. 1998; Paterson et al. 2003; Rong et al. 2007; Said et al. 2013; Shen et al. 2007; Yang et al. 2015). Some studies have shown that almost equal numbers of QTLs are on the two subgenomes (Ning et al. 2014; Wang et al. 2012b, 2015).

In the present study, more QTLs for FL and FS were located on the A subgenome than on the D subgenome (35 on the A subgenome and 22 on the D subgenome), while more QTLs or genes for FM were located on the D subgenome than on the A subgenome (8 on the A subgenome and 15 on the D subgenome). Overall, the number of QTLs for fiber quality traits on the A subgenome was larger than that on the D subgenome (43 vs. 37). However, for yield component traits, more QTLs were distributed on the D subgenome than on the A subgenome in our study. Using the same map and the same populations, 26 QTLs for LP were identified (Shi et al. 2015). A total of 99 QTLs for yield traits (BW, SI, LI, and LP) were found, 33 of which were located on the A subgenome, and 66 QTLs were located on the D subgenome (Table 5).
Table 5

Distributions of QTLs on the A and D subgenomes of allotetraploid cotton

C

FL

FS

FM

BW

SI

LI

LP

Fiber quality trait

Yield trait

Sum

C1

  

1

   

0

4

0

4

C2

4

  

1

   

6

1

7

C3

1

  

1

5

 

0

1

6

7

C4

3

1

 

1

   

5

1

6

C5

 

3

1

   

3

7

3

10

C6

    

1

4

 

0

5

5

C7

       

0

0

0

C8

3

   

1

3

 

7

4

11

C9

1

1

  

1

 

1

2

2

4

C10

4

3

5

2

1

 

2

13

5

18

C11

4

4

1

1

 

2

 

9

3

12

C12

1

1

   

1

2

3

3

6

C13

 

1

     

1

0

1

C14

   

2

4

1

2

2

9

11

C15

1

 

4

1

  

2

7

3

10

C16

6

4

   

7

8

12

15

27

C17

   

2

4

3

5

0

14

14

C18

   

4

1

  

0

5

5

C19

   

1

   

0

1

1

C20

1

1

7

1

4

  

15

5

20

C21

2

5

 

4

1

2

1

13

8

22

C22

    

3

  

0

3

3

C23

1

    

1

 

2

1

3

C24

1

 

3

    

4

0

4

C25

  

1

 

1

1

 

9

2

11

C26

       

0

0

0

A subgenome

21

14

8

6

9

10

8

43

33

76

D subgenome

12

10

15

15

18

15

18

37

66

103

Sum

33

24

23

21

27

25

26

80

99

179

The data for LP QTLs are from our previous report (Shi et al. 2015)

C chromosome

In summary, more QTLs for both fiber quality and fiber yield traits were detected on the D subgenome than that on the A subgenome (76 vs. 103) (Table 5).

QTL clusters for fiber yield and quality

Ten clusters were identified in this study. These clusters harbored many stable QTLs or common QTLs, of which 17 (qFL-C16-4, qFL-C16-5, qFM-C10-5, qFM-C21-1, qFM-C21-2, qFM-C21-3, qFM-C21-4, qFS-C11-1, qFS-C11-2, qFS-C16-1, qLI-C16-5, qLI-C16-6, qSI-C17-2, qLP-C16-6, qLP-C16-7, qLP-C17-1, and qLP-C17-2) were stable QTLs and 16 (qFL-C4-2, qFL-C10-2, qFL-C10-4, qFL-C11-2, qFL-C16-2, qFL-C16-3, qFM-C10-3, qFM-C21-1, qFM-C21-2, qFS-C11-2, qFS-C16-1, qBW-C10-2, qLP-C16-1, qLP-C16-6, qLP-C17-2, and qLP-C17-5) were common QTLs. Thus, 6 QTLs (qFS-C11-2, qFS-C16-1, qFM-C21-1, qFM-C21-2, qLP-C16-6, and qLP-C17-2) were both stable and common QTLs (Table 4; Table S1).

Among the ten clusters, one (C16-Cluster-1) resembled the C16-Cluster-Gh-2 cluster reported by Said et al. (2015a, b) and Wang et al. (2011). The other 9 clusters are considered novel.

All QTLs for both FL and FS showed the same additive effect direction in all 5 clusters (C4-Cluster-1, C11-Cluster-1, C16-Cluster-1, C16-Cluster-2, and C21-Cluster-1), and QTLs for FL and FM showed opposite additive effect directions in each of 3 clusters (C10-Cluster-1, C21-Cluster-1, and C21-Cluster-2). These QTL clusters revealed a strong positive genetic correlation between FL and FS and a strong negative genetic correlation between FL and FM. Some previous reports revealed similar clusters (Sun et al. 2012; Zhang et al. 2013, 2016a; Fang et al. 2014; Zhai et al. 2016; Islam et al. 2016; Diouf et al. 2018), but these correlations were not explicitly mentioned in their reports. All QTLs for both LP and LI had the same additive effect direction in 3 clusters (C16-Cluster-1, C16-Cluster-2, and C17-Cluster-2), and QTLs for LP and SI had opposite additive effect directions in C17-Cluster-1. These QTL clusters revealed a strong positive genetic correlation between LP and LI and a strong negative genetic correlation between LP and SI. To the best of our knowledge, the same or similar results have not been reported. All QTLs for both FL and FS had positive additive effects and all QTLs for both LP and LI had negative additive effects in 2 clusters (C16-Cluster-1 and C16-Cluster-2). All QTLs for both FL and FS had positive additive effects and all QTLs for both LP and FM had negative additive effects in one cluster (C21-Cluster-1). These QTL clusters revealed a strong negative genetic correlation between LP and FL and between LP and FS. Some previous reports detected similar clusters (Wang et al. 2011; Li et al. 2016b; Si et al. 2017), but these correlations were not explicitly mentioned in their reports.

These results provide an explanation for the significant phenotypic correlations between the related traits in different populations (Table S2) and indicate that these loci might function as pleiotropic genes or are closely linked to various other genes (Rong et al. 2007; Yu et al. 2013; Zhai et al. 2016).

In other words, 5 of the 10 clusters (C10-Cluster-1, C11-Cluster-1, C16-Cluster-1, C16-Cluster-2, and C17-Cluster-1), with stable or common QTLs related to 2 or more different traits, are important clusters and warrant further study for the simultaneous genetic improvement of fiber yield and quality in cotton (Table 4; Table S1). However, 3 clusters (C10-Cluster-1, C16-Cluster-1, and C16-Cluster-2) harbored negatively correlated stable QTLs or common QTLs between fiber quality and yield traits and could hinder the simultaneous improvement of these traits. Therefore, an in-depth study of this linkage mechanism and breaking the linkage drag between unfavorable QTLs/genes through gene fine mapping (Cao et al. 2015), gene-editing technology (Dahan-Meir et al. 2018; Durr et al. 2018) or other new technologies and methods would play an important role in cotton molecular breeding. In addition, with the same two parents (CCRI36 and Hai1), we have developed CSSLs that can be used for further study.

Conclusion

In conclusion, a total of 153 QTLs for fiber quality and yield traits were identified in 5 interspecific backcross populations using a high-density genetic linkage map. Of these QTLs, 30 were consistent with those identified previously. Importantly, 23 QTLs were stably detected in 2 or 3 populations or generations, 17 of which were novel stable QTLs. Ten QTL clusters for different traits were found, and 9 of them were novel.

The results provide valuable information for MAS in cotton breeding, QTL/gene cloning, understanding the genetic basis of fiber quality and yield traits in the two cultivated tetraploid species of cotton (G. hirsutum L. and G. barbadense L.) and genetically improving fiber quality and yield in cotton.

Notes

Acknowledgements

This work was funded by the National Key R&D Program for Crop Breeding (2016YFD0100203) and the National Agricultural Science and Technology Innovation Project of the CAAS.

Author contributions

YY conceived and designed the experiments. YS and BZ performed the experiments. AL and JL participated in field trials. JZ and MJ helped in data analyses. QL, JG, GQ, HS, WG SL, XX, XD and JP contributed reagents/materials/analysis tools. YS analyzed the data and drafted the manuscript. YY and JZ revised the manuscript. All the authors have read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The authors declare that this study complies with the current laws of the country in which the experiments were performed. This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

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Supplementary material 1 (DOCX 31 kb)
438_2019_1582_MOESM2_ESM.docx (25 kb)
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Supplementary material 3 (DOCX 25 kb)

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Yuzhen Shi
    • 1
  • Aiying Liu
    • 1
  • Junwen Li
    • 1
  • Jinfa Zhang
    • 2
  • Baocai Zhang
    • 1
  • Qun Ge
    • 1
  • Muhammad Jamshed
    • 1
  • Quanwei Lu
    • 1
  • Shaoqi Li
    • 1
  • Xianghui Xiang
    • 1
  • Juwu Gong
    • 1
  • Wankui Gong
    • 1
  • Haihong Shang
    • 1
  • Xiaoying Deng
    • 1
  • Jingtao Pan
    • 1
  • Youlu Yuan
    • 1
    Email author
  1. 1.State Key Laboratory of Cotton Biology, Key Laboratory of Biological and Genetic Breeding of Cotton, The Ministry of Agriculture, Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangChina
  2. 2.Department of Plant and Environmental SciencesNew Mexico State UniversityLas CrucesUSA

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