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Molecular Genetics and Genomics (2019) 294:1123–1136
https://doi.org/10.1007/s00438-019-01566-8
ORIGINAL ARTICLE
QTL mapping andgenetic eect ofchromosome segment substitution
lines withexcellent ber quality fromGossypium hirsutum ×
Gossypium barbadense
Shao‑qiLi1· Ai‑yingLiu1· Ling‑leiKong1· Ju‑wuGong1· Jun‑wenLi1· Wan‑kuiGong1· Quan‑weiLu2· Peng‑taoLi2·
QunGe1· Hai‑hongShang1· Xiang‑huiXiao1· Rui‑xianLiu1· QiZhang1· Yu‑zhenShi1· You‑luYuan1
Received: 6 November 2018 / Accepted: 3 April 2019 / Published online: 27 April 2019
© The Author(s) 2019
Abstract
Chromosome segment substitution lines (CSSLs) are ideal materials for identifying genetic effects. In this study, CSSL
MBI7561 with excellent fiber quality that was selected from BC4F3:5 of CCRI45 (Gossypium hirsutum) × Hai1 (Gossypium
barbadense) was used to construct 3 secondary segregating populations with 2 generations (BC5F2 and BC5F2:3). Eighty-one
polymorphic markers related to 33 chromosome introgressive segments on 18 chromosomes were finally screened using
2292 SSR markers which covered the whole tetraploid cotton genome. A total of 129 quantitative trait loci (QTL) associ-
ated with fiber quality (103) and yield-related traits (26) were detected on 17 chromosomes, explaining 0.85–30.35% of the
phenotypic variation; 39 were stable (30.2%), 53 were common (41.1%), 76 were new (58.9%), and 86 had favorable effects
on the related traits. More QTL were distributed in the Dt subgenome than in the At subgenome. Twenty-five stable QTL
clusters (with stable or common QTL) were detected on 22 chromosome introgressed segments. Finally, the 6 important
chromosome introgressed segments (Seg-A02-1, Seg-A06-1, Seg-A07-2, Seg-A07-3, Seg-D07-3, and Seg-D06-2) were identi-
fied as candidate chromosome regions for fiber quality, which should be given more attention in future QTL fine mapping,
gene cloning, and marker-assisted selection (MAS) breeding.
Keywords CSSLs· Chromosome introgressed segments· Fiber quality· QTL· Genetic effects
Introduction
As an allopolyploid cash crop, cotton is important for
genetic research and provides the textile industry with the
most important natural fiber raw material. Among cultivated
cotton species, two tetraploid cottons, Upland cotton (Gos-
sypium hirsutum L./G.h) with higher yield and Sea-island
cotton (Gossypium barbadense L./G.b) with better fiber
quality are the most widely cultivated in agricultural pro-
duction (Ulloa etal. 2005; Zhang etal. 2009). Therefore,
it is interesting to introgress the favorable genes for fiber
quality from G.b to G.h to improve the fiber quality and yield
simultaneously. However, introgression is very difficult for
breeders to implement using conventional breeding, because
all of the related traits are quantitative traits controlled by
multiple genetic loci, and the fiber quality and yield-related
traits are usually negatively correlated (Clement etal. 2012;
Ma etal. 2014; Yu etal. 2016). Fortunately, with the rapid
development of high-precision molecular marker technology
and gene mapping, an increasing number of genetic maps
Communicated by S. Hohmann.
Shao-qi Li and Ai-ying Liu have contributed equally to this work
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s0043 8-019-01566 -8) contains
supplementary material, which is available to authorized users.
* Yu-zhen Shi
shiyzmb@126.com
* You-lu Yuan
yuanyoulu@caas.cn
1 State Key Laboratory ofCotton Biology, Key Laboratory
ofBiologiacl andGenetic Breeding ofCotton, The Ministry
ofAgriculture, Institute ofCotton Research, Chinese
Academy ofAgricultural Science, Anyang455000, Henan,
China
2 School ofBiotechnology andFood Engineering, Anyang
Institute ofTechnology, Anyang455000, Henan, China
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1124 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
and QTL have been identified (Jia etal. 2016; Kushanov
etal. 2016).
After the first molecular genetics map of cotton was con-
structed (Reinisch etal. 1994), a wide variety of populations
were used to perform QTL mapping, which predominantly
consists of F2 (Brown etal. 2005; Wang etal. 2010; Yu
etal. 2013), double haploid (DH) (Lu etal. 2014; Cai etal.
2015), backcross (BC) (Chen etal. 2012; Shang etal. 2016;
Wang etal. 2016b), backcross inbred lines (BILs) (Pang
etal. 2012; Nie etal. 2015; Zhang etal. 2015a), recombi-
nant inbred lines (RILs) (Zhang etal. 2015c; Jamshed etal.
2016; Shang etal. 2016) and CSSLs (Liang etal. 2010; Li
etal. 2016; Shi etal. 2016; Zhai etal. 2016). The complex
genetic background in most populations makes it difficult to
estimate the positions and effects of QTL. There are a few
differences between CSSLs and recurrent parents, which is
favorable for QTL mapping, genetic effect identification and
gene cloning (Wang etal. 2016c; Lu etal. 2017). Therefore,
more attention has been paid to the development and utiliza-
tion of CSSLs in genetic research, although CSSLs are time
consuming and costly to construct (Wan etal. 2008; Zhao
etal. 2009).
The first CSSLs were constructed in tomatoes by Eshed
and Zamir (1994). Subsequently, researchers launched a
wide range of studies and applications of CSSLs in rice
(Wan etal. 2004), corn (Li etal. 2014), wheat (Liu etal.
2006) and other crops. The first set of cotton CSSLs was
developed with TM-1 as the recipient parent by Stelly etal.
(2005), and was used to analyze genetic effects, as well as
genetic relationships for fiber quality and yield component
traits with substitution of one chromosome (Stelly etal.
2005). After this study, a series of studies were carried out
on cotton CSSLs (Wang etal. 2008, 2016c Chen etal. 2012;
Su etal. 2014; Lu etal. 2017). Yang etal. (2009) detected
51 QTLs using 116 CSSLs originating from CCRI45 (G.h)
and Hai1 (G.b). Wang etal. (2012) indicated the inheritance
of long staple fiber qualities using the CSSLs developed by
TM-1 and Hai7124. Fu etal. (2013) detected 12 QTLs asso-
ciated with fiber quality and yield using the CSSLs from
TM-1 and Sub18.
Although CSSLs are effective in QTL mapping, there
is less information for detecting genetic effects from
introgressing chromosome segments from Island cotton
into Upland cotton. A series of CSSLs were constructed
through the hybridization of CCRI45 (G.h), CCRI36 (G.h)
and Hai1 (G.b) by our team(Shi etal. 2008). Subsequently,
a high-density genetic linkage map was constructed that
contained 2292 SSR markers and covered 5115.16 cM of
the cotton AD genome (Shi etal. 2015), and many QTL
were identified using populations with various generations
(BC4F1, BC4F3, BC4F3:5 and BC4F4) (Yang etal. 2009; Ge
etal. 2012; Ma 2014; Guo etal. 2015; Lan etal. 2015). The
genetic effects and heterosis of yield and yield component
traits were analyzed through hybridizing 10 CSSLs accord-
ing to North Carolina Design II (Li etal. 2016). A total
of 70 QTL and their genetic effects for fiber yield-related
traits and 29 QTL for fiber quality traits on 13 chromosomes
were detected using CSSLs (BC5F3, BC5F3:4 and BC5F3:5)
(He 2014). Twenty two QTL associated with fiber quality
and yield traits on seven chromosomes were detected in F2
and F2:3 with two CSSLs of MBI9749 and MBI9915 as par-
ents (Guo etal. 2018). A total of 24 QTLs for fiber qual-
ity and 11 QTLs for yield traits were detected in the three
segregating generations of double-crossed F1 and F2 and
F2:3, which were constructed using four CSSLs as parents
(MBI9804×MBI9855) × (MBI9752×MBI9134) (Zhai etal.
2016). Eighteen QTL for fiber quality and 6 QTL for yield-
related traits across 7 chromosomes were detected using
BC6F2 and BC6F2:3 with two parents of CCRI36 (recurrent
parent) and MBI9915(CSSL) (Song etal. 2017).
In the present study, BC5F2 and BC5F2:3 populations were
constructed by hybridization of CCRI45 (recurrent parent)
and MBI7561 (BC4F3:5) with excellent and stable fiber qual-
ity, to evaluate the genetic effects of the introgressed seg-
ments by SSR markers. This study is expected to lay the
foundation for future studies, such as genetic mechanism
exploring, QTL fine mapping, gene cloning and MAS breed-
ing applications.
Materials andmethods
Plant materials andpopulation development
MBI7561 as the female parent was selected from the CSSLs
BC4F3:5 which was constructed by advanced backcross and
selfing of combination of CCRI45 (G.h) and Hai1 (G.b).
The recurrent parent CCRI45 was a glandular cotton cultivar
widely grown with high yield and resistance to budworm
(Ma 2014), which was developed by the Institute of Cotton
Research of Chinese Academy of Agricultural Sciences (Shi
etal. 2008, 2015). The donor parent Hai1 was a dominant
glandless G. barbadense with excellent fiber quality. The
fiber quality and yield component traits of MBI7561 were
excellent and stable (Table4).
We constructed F1 (BC5F1) by backcrossing CCRI45
(male) and MBI7561 in Anyang in the summer of 2013.
BC5F1 was planted and self-crossing seeds (BC5F2) were
harvested in Hainan province in the winter of 2013. In
2014, a total of 310 BC5F2 (PopE1) individual plants were
developed and fiber and seeds (BC5F2:3) were collected
from individual plants. Both BC5F1 and BC5F2 populations
were planted in the Anyang experimental farm of the Insti-
tute of Cotton Research of Henan Province of China, with
row length of 8 m, row spacing of 0.8 m, and plant spacing
of 0.25 m. In 2015, a total of 253 BC5F2:3 (PopE2) family
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1125Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
lines were planted in one-row plot with a row length of
5 m on the Anyang experimental farm, and another 250
BC5F2:3 (PopE3) family lines were planted in two-narrow-
row plots, with row length of 3 m and plant spacing of 0.12
m on the Alar experimental farm of the Institute of Cotton
Research of Xinjiang Autonomous Region of China. The
plastic-membrane covering technique and a wide/narrow
row spacing pattern were used. The row spacing alterna-
tion was 0.2 m and 0.6 m. In addition, there were 212
common BC5F2:3 lines in the two different environments.
Investigation ofphenotypic traits
Collection ofphenotypic data
In 2014, naturally opened bolls were collected from the
F2 individual plants for phenotype evaluation, including
boll weight (BW), lint percentage (LP), fiber length (FL),
fiber strength (FS), fiber micronaire (FM), fiber uniform-
ity (FU), and fiber elongation (FE). In 2015, 30 naturally
opened bolls were harvested from the plot for F2:3 lines.
BW and LP were calculated using seed cotton weight,
fiber weight, and boll number. The fiber quality traits were
tested with HFT9000 using HVICC international calibra-
tion cotton samples in the Cotton Quality Supervision and
Testing Center of the Ministry of Agriculture of China.
Analysis ofphenotypic traits
The observed phenotypic data were analyzed using 3 soft-
wares. BW, LP, and transgressive rate over the recurrent
parent (TRORP) were calculated using EXCEL 2010. The
SAS statistical software (version 8.1, SAS Institute, Cary
NC) was used to perform the descriptive statistical analy-
sis of phenotypes. The statistical values included mean,
maximum, minimum, standard deviation (SD), skewness,
kurtosis, and coefficient of variation (CV). For ANOVA,
correlation analysis and all significance tests were per-
formed using the SPSS20.0 software (SPSS, Chicago,
IL, USA). All correlation values were Pearson Correla-
tion Coefficients. A higher FM value is not necessarily for
indicative of better fiber quality, and the fineness of the
fiber is evaluated by the FM value grading, (A level (best):
[3.7,4.2]; B level (better): [3.5, 3.6] and[4.3, 4.9]; C level
(bad): [0,3.4] and [5.0, ∞)). The FM values of CCRI45
in all environments were in the C level, thus, we selected
individuals in the A level and B level for the calculation
of transgressive rate values, and the range of FM values of
the selected individuals should be ≥ 3.5 and ≤ 4.9.
Identication ofgenotypes
DNA extraction andmarker detection
The young leaves of F2 individual plants and parents were
sampled in 2014. Genomic DNA was extracted using the
modified CTAB method(Paterson etal. 1993). The products
of PCR amplification were isolated and identified by 8%
non-denaturing vertical polyacrylamide gel electrophoresis.
The DNA segments in the gel were visualized by the silver
staining method (Zhang etal. 2000). In this study, we used
2292 SSR markers from the genetic linkage map with a total
genetic length of 5115.16 cM (Shi etal. 2015) to screen the
recurrent parent of CCRI45, donor parent of Hai1, MBI7561
and F1(MBI7561*CCRI45). The polymorphic markers were
used to identify genotypes of the F2 individual plants. The
sequences of each primer were obtained from the Cotton
Genome Database (www.cotto ngen.org) and synthesized by
Bioethics Engineering (Shanghai) Co., Ltd.
Analysis ofgenotypes
Genotyping analysis of each sample and distribution analysis
of chromosome introgressed segments were performed by
GGT2.0 software (http://www.plant breed ing.wur.nl) (Van
Berloo 2008), including the number, length and positions of
introgressed segments, and the genetic background recovery
rate of each sample. The nomenclature of segments was as
follows: Seg + the serial number of the chromosome (AD)
+ the serial number of the cluster on the chromosome.
Genetic eects analysis
QTL mapping
The QTL IciMappingV4.1 (www.isbre eding .net/softw
are/?type=detai l&id=18) software was used to perform
QTL mapping. A likelihood of odds (LOD) threshold of 2.5
was used to declare significant additive QTL. The resulting
linkage map and QTL were drawn using MapChart2.2 soft-
ware (Voorrips 2002). The QTL nomenclature was: q + the
English abbreviation of trait + the serial number of chromo-
some + the serial number of the QTL on the chromosome
associated with the same trait + (the direction of the additive
genetic effect). For example, qFL-16-2 (+) represents the
second QTL associated with the FL on chromosome 16 with
a positive additive genetic effect from the G. barbadense
introgressing segments.
QTL‑cluster analysis
The QTL cluster analysis was performed by Biomercator 4.2
software (Arcade etal. 2004). QTL were projected on the
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1126 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
genetic map and QTL cluster analysis were performed for
all traits. Four models were thus generated, and each had an
Akaike information criterion (AIC) value. The model with
the lowest AIC value was selected and used for the position
identification of QTL clusters. The nomenclature of QTL
cluster was: Clu + the English abbreviation of trait +the
serial number of chromosome + the serial number of the
cluster on the chromosome associated with the same trait.
Results
Phenotypic performance ofCSSLs populations
The descriptive statistics of phenotypic data for fiber yield
and fiber quality traits, as well as their recurrent parent
CCRI45, are presented in Table1. In the three populations,
the average performance of BW was smaller than that of
CCRI45, with a significant difference in PopE3; the aver-
age performance of FU was higher than that of CCRI45,
with a significant difference in PopE3; and the other traits
were significantly better than those of CCRI45 except for
LP in PopE2. The TRORP for BW was 17.52–32.83% and
53.62–99.26% for other traits. Overall, there were many
transgressive separations for most of traits in the popula-
tions. All traits showed a continuous normal distribution in
three populations (Table1, Fig. S1), which was consistent
with the characteristics of quantitative traits. Significant
positive correlations were found among most traits (FL and
FS/FE/FU, FS and FU/FE, FU and FE/FM/BW, and FM and
FE/LP/BW), whereas significant negative correlations were
found between FS and FM, LP, and FE / FL in most popula-
tions (Table2).
Analysis ofintrogressive segments
A total of 81 pairs of polymorphic markers were screened
between the parents and 33 introgressive segments distrib-
uted on 18 chromosomes (Fig.1). The genetic background
recovery rate of MBI7561 was 95.60%, and the proportion
of homozygous introgressive segments (138.11 cM, 2.7%)
was significantly higher than that of heterozygous seg-
ments (86.96 cM, 1.7%). Four chromosomes [Chr15(D1),
Ch25(D6), Chr07(A7), and Chr16(D7)] had more introgres-
sive segments than others.
The 81 pairs of SSR markers were used to screen the gen-
otype of the BC5F2 population, and 6 pairs of markers did
not show the polymorphism. The average rate of background
Table 1 Phenotypic performance of fiber quality and yield-related traits in three populations
Sta statistic, SE std. error, TRORP transgressive rate over the recurrent parent, Ske skewness, Kur kurtosis, FL fiber length, FS fiber strength, FM
fiber micronaire, FU fiber uniformity, FE fiber elongation, BW boll weight, LP lint percentage
Environment Trait CCRI45 Population
Mean SD Gener. Mean Min. Max. SD Ske. Kur. TRORP. (%) CV (%)
PopE1 BW (g) 5.63 1.02 BC5F25.30 2.48 9.37 0.88 0.36 0.84 32.83 16.53
LP (%) 37.54 3.22 39.90** 25.52 52.69 2.76 −0.28 1.55 66.46 6.91
FL (mm) 29.48 1.19 30.77** 25.84 35.29 1.20 −0.21 0.55 86.39 3.90
FS (cN/tex) 28.02 1.38 31.47** 23.50 39.60 2.13 −0.02 0.33 94.55 6.77
FM (unit) 4.83 0.56 4.38** 2.56 6.09 0.60 −0.37 −0.16 72.11 13.71
FU (%) 83.98 1.52 84.41 78.20 88.00 1.53 −0.67 0.69 65.34 1.81
FE (%) 6.79 0.06 6.84** 6.50 7.10 0.06 −0.22 0.39 94.60 0.94
PopE2 BW (g) 5.38 0.26 BC5F2:3 5.02 3.30 6.57 0.68 −0.01 −0.55 31.77 13.54
LP (%) 36.74 3.34 38.63 32.61 44.13 2.17 −0.14 −0.22 79.51 5.62
FL (mm) 28.57 1.65 30.86** 26.30 34.90 1.73 −0.18 −0.73 88.45 5.62
FS (cN/tex) 28.65 3.39 34.94** 28.00 44.00 2.78 0.22 −0.07 99.26 7.94
FM (unit) 5.07 0.31 4.58** 3.00 5.70 0.47 −0.32 0.24 77.70 10.35
FU (%) 84.35 1.37 85.26 82.10 87.90 1.25 −0.33 −0.39 77.23 1.47
FE (%) 6.80 0.14 6.94** 6.60 7.30 0.13 0.16 −0.11 76.72 1.82
PopE3 BW (g) 5.74 0.16 BC5F2:3 5.36** 3.54 7.23 0.43 0.06 1.45 17.52 8.08
LP (%) 38.20 1.45 40.84** 32.99 48.00 2.15 −0.25 0.21 89.40 5.28
FL (mm) 28.07 0.91 29.80** 26.40 34.10 1.25 0.08 0.06 92.59 4.18
FS (cN/tex) 26.25 1.08 28.96** 24.40 35.00 1.83 0.20 −0.06 94.18 6.31
FM (unit) 5.09 0.37 4.80** 3.40 5.80 0.43 −0.34 −0.02 60.67 8.95
FU (%) 84.30 0.90 85.24** 82.00 88.10 1.16 −0.25 −0.15 78.23 1.36
FE (%) 6.79 0.09 6.88** 6.50 7.20 0.10 −0.13 0.34 91.18 1.48
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1127Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
Table 2 Correlation of
phenotypic traits in the three
populations
FL fiber length, FS fiber strength, FM fiber micronaire, FU fiber uniformity, FE fiber elongation, BW boll
weight, LP lint percentage
*Significant correlation at the 0.05 level (2-tailed), **Significant correlation at the 0.01 level (2-tailed)
BW LP FL FS FM FU
PopE1
LP −0.19**
FL 0.07 −0.36**
FS −0.08 −0.14 0.5**
FM 0.47** 0.04 −0.05 −0.36**
FU 0.29** −0.19** 0.32** 0.20** 0.37**
FE 0.23** −0.19** 0.46** 0.35** 0.21** 0.43**
PopE2
LP 0.33**
FL 0.46** 0.20**
FS 0.01 −0.03 0.61**
FM 0.57** 0.39** 0.11 −0.22**
FU 0.26** 0.18** 0.32** 0.22** 0.24**
FE 0.50** 0.29** 0.79** 0.55** 0.29** 0.37**
PopE3
LP −0.05
FL −0.12 −0.38**
FS −0.13 −0.16* 0.66**
FM 0.33** 0.34** −0.49** −0.38**
FU −0.04 0.18* 0.13 0.25** −0.05
FE 0.03 −0.16* 0.62** 0.50** −0.13 0.24**
Fig. 1 The graphical genotypes of MBI7561. Note A: genetic background (recurrent parent CCRI45); B: homozygous introgressive segments
(donor parent Hai1); H: Heterozygous introgressive segments
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1128 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
recovery in the BC5F2 population was 97.95% and ranged
from 97.3 to 99.2%. The average rate of homozygous intro-
gressive segments was 1.11% and ranged from 0 to 2%. The
average rate of heterozygous introgressive segments was
0.94% and ranged from 0.3 to 2.4%.
QTL mapping
In total, 65 markers on 17 chromosomes were associated
with the QTL of the seven traits, and 48 of these markers
were associated with QTL in multiple populations. Nineteen
markers located on 8 chromosomes existed in the At subge-
nome, and forty-six markers were located on 9 chromosomes
in the Dt subgenome. Based on the method of inclusive com-
posite interval mapping (ICIM), a total of 129 QTL were
identified in 29 introgressive segments of 17 chromosomes
in the three populations (Table3, TableS1, Fig. S2), with
each explaining 0.85% to 30.35% of the phenotypic variation
(PVE). There were 103 and 26 QTL related to the five fiber
quality traits and two yield component traits, respectively.
Forty-one QTL were distributed in the At subgenome, and
88 were distributed in the Dt subgenome. In addition, there
were 107 QTL distributed in 7 pairs of homologous chro-
mosomes. Forty-five QTL (35% of the total number) were
detected in multiple environments, and 39 of them were
stable. Eighty-six QTL showed positive additive effects, 36
showed negative additive effects, and seven showed unstable
additive effects.
Fiber length
There were 21 QTL for FL on 11 chromosomes with the
PVE ranging from 1.67% to 11.93%; 7 and 14 of these
QTL were distributed in the At- and Dt subgenomes,
respectively. Chr16 and Chr25 were the top 2 chromo-
somes with the largest number of QTL. Sixteen QTL with
additive effects from 0.06 to 0.75 mm indicated that Hai1
alleles increased FL, and four QTL with additive effects
from −0.60 to −0.22 mm indicated that CCRI45 alleles
increased FL. The qFL-16-3 had unstable genetic effects,
which could be detected in two environments, with addi-
tive effect of −0.23 mm in PopE2 and 0.58 mm in PopE3.
Three QTL (qFL-16-5, qFL-25-2and qFL-25-3) could
be stably detected in more environments. The qFL-25-
2 linked to CGR5525 could explain 2.84%, 2.57%, and
5.51% of the observed phenotypic variations with addi-
tive effects of −0.57, −0.50, and −0.57 mm in PopE1,
PopE2, and PopE3, respectively. The qFL-16-5 linked to
NAU5408 and NAU3594 could explain 5.93% and 9.60%
of the observed phenotypic variations in PopE1 and PopE3
with the additive effect of 0.23 and 0.29 mm in two gener-
ations, respectively. The qFL-25-3 linked to GH537 could
explain 3.39% and 6.43% of the observed phenotypic vari-
ations in PopE1 and PopE3 with the additive effect of 0.22
and 0.50 mm in two generations, respectively.
Table 3 Distribution of QTL on
chromosomes
FL fiber length, FS fiber strength, FM fiber micronaire, FU fiber uniformity, FE fiber elongation, BW boll
weight, LP lint percentage
BW (g) LP (%) FL (mm) FS (cN·tex-1) FM (unit) FU (%) FE (%) Total
Chr01(A1) 0 1 0 1 1 1 1 5
Chr02(A2) 1 0 1 2 1 0 1 6
Chr04(A4) 1 0 1 0 0 0 0 3
Chr06(A6) 1 0 1 1 1 0 1 5
Chr07(A7) 1 1 2 3 1 1 2 11
Chr08(A8) 0 0 0 0 0 1 0 1
Chr09(A9) 0 1 1 1 0 1 2 6
Chr10(A10) 0 1 1 1 1 0 0 4
Chr15(D1) 1 4 2 3 1 4 5 20
Chr16(D7) 4 0 5 5 3 0 1 18
Chr17(D3) 0 0 1 2 1 2 0 6
Chr19(D5) 1 0 2 1 2 2 2 10
Chr20(D10) 0 0 0 0 1 0 0 1
Chr22(D4) 1 1 0 0 1 1 0 4
Chr23(D9) 0 1 0 1 0 0 0 2
Chr24(D8) 0 0 0 1 1 1 1 4
Chr25(D6) 3 2 4 5 3 2 4 23
Total 14 12 21 27 18 16 21 129
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1129Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
Fiber strength
There were 27 QTL for FS on 13 chromosomes with the
PVE ranging from 0.85% to 13.19%; 9 and 18 of them
were distributed in the At- and Dt subgenomes, respec-
tively. Chr16 and Chr25 were the first 2 chromosomes with
the most QTL. Seventeen QTL with additive effects from
0.03 to 3.11 cN tex−1 indicated that Hai1 alleles increased
FS, nine QTL with additive effects from −1.25 to −0.07
cN tex−1 indicated that CCRI45 alleles increased FS.
The qFS-17-1 had unstable genetic effects, which could
be detected in two environments, with additive effects of
0.35 cN tex−1 in PopE2 and −0.07 cN tex−1 in PopE3.
Ten QTL (qFS-02-2, qFS-06-1, qFS-07-2, qFS-10-1,
qFS-16-1, qFS-16-4, qFS-16-5, qFS-19-1, qFS-25-3, and
qFS-25-4) could be stably detected in more environments.
The qFS-16-1 linked to CGR6894a could explain 6.06%,
7.25%, and 7.38% of the observed phenotypic variations
with the additive effect of 0.44, 0.66, and 0.42 cN tex−1
in PopE1, PopE2, and PopE3, respectively. The qFS-16-5
linked to NAU5408 and NAU3594 could explain 6.55%,
7.42%, and 10.92% of the observed phenotypic variations
with additive effects of 0.66, 0.78, and 0.76 cN tex−1 in
PopE1, PopE2, and PopE3, respectively. The qFS-25-
3 linked to DPL0166a and SHIN-0885 could explain
13.09%, 8.16%, and 7.91% of the observed phenotypic
variations with additive effects of 0.04, 3.11, and 0.81 cN
tex−1 in PopE1, PopE2, and PopE3, respectively. The qFS-
25-4 linked to CGR5201 and SHIN1131 could explain
11.95%, 6.95%, and 4.90% of the observed phenotypic
variations with additive effects of 0.89, 0.81, and 0.54 cN
tex−1 in PopE1, PopE2, and PopE3, respectively. The qFS-
02-2 linked to DPL0450 and PGML04760 could explain
2.10% and 0.85% of the observed phenotypic variations in
PopE2 and PopE3 with additive effects of 1.10 and 0.65cN
tex−1, respectively. The qFS-06-1 linked to DC40067 and
DPL0918 could explain 3.99% and 5.92% of the observed
phenotypic variations in PopE2 and PopE3 with additive
effects of 1.48 and 1.16 cN tex−1, respectively. The qFS-
07-2 linked to NAU2002 and CGR6381 could explain
5.85% and 10.38% of the observed phenotypic variations
with additive effects of 0.58 and 0.78 cN tex−1 in PopE1
and PopE3, respectively. The qFS-10-1 linked to DPL0468
could explain 5.03% and 5.95% of the observed pheno-
typic variations with additive effects of 0.66 and 0.48 cN
tex−1 in PopE1 and PopE3, respectively. The qFS-16-4
linked to PGML02608 could explain 6.98% and 6.32% of
the observed phenotypic variations with additive effect of
0.66 and 0.55 cN tex−1 in PopE1 and PopE3, respectively.
The qFS-19-1 linked to DC40425 and BNL3089 could
explain 4.90% and 2.32% of the observed phenotypic vari-
ations with additive effects of 0.21 and 0.15 cN tex−1 in
PopE1 and PopE2, respectively.
Fiber micronaire
A total of 18 QTL for FM on 13 chromosomes with the
PVE ranging from 1.60 to 10.28% were identified; 5 and
13 of them were distributed in the At- and Dt subgenomes,
respectively. Chr16 and Chr25 were the two most prominent
chromosomes containing the largest number of QTL. Two
QTL (qFM-16-1 and qFM-17-1) with additive effects of 0.03
and 0.18 indicated that CCRI45 alleles decreased FM, and
the remaining 16 QTL with additive effects from −0.52 to
−0.06 indicated that Hai1 alleles decreased FM. Nine QTL
(qFM-02-1, qFM-10-1, qFM-15-1, qFM-16-2, qFM-19-1,
qFM-22-1, qFM-24-1, qFM-25-1, and qFM-25-2) could be
stably detected in more environments. The qFM-02-1 linked
to PGML02861 and DPL0450 could explain 2.07%, 9.24%,
and 6.25% of the observed phenotypic variations with addi-
tive effects of −0.14, −0.52, and −0.15 in PopE1, PopE2,
and PopE3, respectively. The qFM-15-1 linked to NAU3177
could explain 2.35%, 3.22%, and 4.73% of the observed phe-
notypic variations with additive effects of −0.14, −0.08 and
−0.16 in PopE1, PopE2 and PopE3, respectively. The qFM-
10-1 linked to DPL0468 could explain 4.56% and 3.56% of
the observed phenotypic variations with additive effects of
−0.17 and −0.13 in PopE1 and PopE3, respectively. The
qFM-16-2 linked to HAU1836 and BNL2634 could explain
9.88% and 1.98% of the observed phenotypic variations with
additive effect of −0.29 and −0.11 in PopE2 and PopE3,
respectively. The qFM-19-1 linked to PGML01289 could
explain 1.88% and 1.72% of the observed phenotypic vari-
ations with additive effects of −0.10 for both PopE2 and
PopE3, respectively. The qFM-22-1 linked to JESPR230 and
DPL0489 could explain 5.76% and 3.28% of the observed
phenotypic variations in PopE2 and PopE3, respectively,
with the additive effect of −0.08 and −0.15. The qFM-24-
1 linked to NAU2914 could explain 1.87% and 1.60% of
the observed phenotypic variations with additive effects of
−0.09 and −0.08 in PopE1 and PopE3, respectively. The
qFM-25-1 linked to DPL0166a and Gh537 could explain
2.05% and 6.22% of the observed phenotypic variations
with additive effects of −0.06 and −0.25 in PopE1 and
PopE3, respectively. The qFM-25-2 linked to BNL3806 and
TMB0313 could explain 10.28% and 5.91% of the observed
phenotypic variations in PopE2 and PopE3, respectively,
with additive effects of −0.18 and −0.09.
Fiber uniformity
Sixteen QTL for FU were identified on 10 chromosomes
with PVE ranging from 1.06% to 12.67%; 4 and 12 of them
were distributed in the At- and Dt subgenomes, respec-
tively. Chr15 was the most prominent chromosome with the
most QTL. Eleven QTL with additive effects from 0.01 to
0.65% indicated that Hai1 alleles increased FU. Two QTL
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1130 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
(qFU-19-1 and qFU-24-1) with additive effects of −0.49%
and −0.28% indicated that CCRI45 alleles increased FU.
Three QTL (qFU-15-1, qFU-15-3 and qFU-15-4) could
be detected in two environments, but had unstable genetic
effects. Two QTL (qFU-01-1 and qFU-07-1) could be sta-
bly detected in two environments. The qFU-01-1 linked to
BNL2921 and NAU3901 could explain 8.44% and 5.59%
of the observed phenotypic variations in PopE2 and PopE3,
respectively, with additive effects of 0.37% and 0.12%. The
qFU-07-1 linked to NAU1048 and CICR0226 could explain
1.89% and 1.82% of the observed phenotypic variations in
PopE2 and PopE3, respectively, with the additive effect of
0.38% and 0.1%.
Fiber elongation
There were 21 QTL for FE on 11 chromosomes with PVE
ranging from 1.32% to 10.06%; 8 and 13 of these QTL were
distributed in the At- and Dt subgenomes, respectively.
Chr15 and Chr25 were the top 2 chromosomes with the larg-
est number of QTL. Twelve QTL with additive effects from
0.001 to 0.05% indicated that Hai1 alleles increased FE, and
eight QTL with additive effects from −0.14 to −0.01% indi-
cated that CCRI45 alleles increased FE. The qFE-02-1 could
be detected in PopE1 and PopE2, but had unstable genetic
effects. Five QTL (qFE-06-1, qFE-16-1, qFE-19-2, qFE-
25-2, and qFE-25-4) could be stably detected in two envi-
ronments. The qFE-06-1 linked to DC40067 and DPL0918
could explain 6.87% and 3.25% of the observed phenotypic
variations in PopE2 and PopE3, respectively, with addi-
tive effects of 0.02% and 0.04%. The qFE-16-1 linked to
BNL2634 could explain 6.5% and 3.52% of the observed
phenotypic variations in PopE1 and PopE2, respectively,
with additive effects of −0.04% and −0.09%. The qFE-19-
2 linked to DC40425 and HAU1400 could explain 7.13%
and 6.31% of the observed phenotypic variations in PopE2
and PopE3, respectively, with additive effects of −0.01%
and −0.04%. The qFE-25-2 linked to CICR0701 and Gh537
could explain 7.14% and 4.64% of the observed phenotypic
variations in PopE1 and PopE3, respectively, with addi-
tive effects of 0.01% and 0.04%. The qFE-25-4 linked to
DPL0124 could explain 2.21% and 5.97% of the observed
phenotypic variations in PopE2 and PopE3, respectively,
with additive effects of −0.05% and −0.03%.
Boll weight
A total of 14 QTL for BW were identified on 9 chromosomes
with PVE ranging from 5.80 to 15.57%; 4 and 10 of them
were distributed in the At- and Dt subgenomes, respectively.
Chr16 and Chr25 were the first 2 chromosomes with the
most of QTL. Five QTL with additive effects from 0.01 to
0.28 g indicated that Hai1 alleles increased BW, and nine
QTL with additive effects from −0.57 to −0.01 g indicated
that CCRI45 alleles increased BW. Five QTL (qBW-06-1,
qBW-07-1, qBW-16-2, qBW-16-4 and qBW-25-3) could be
stably detected in more environments. The qBW-06-1 linked
to DC40067 and DPL0918 could explain 6.61%, 6.72%,
and 6.07% of the observed phenotypic variations with addi-
tive effects of −0.11, −0.22, and −0.08 g in the PopE1,
PopE2, and PopE3, respectively. The qBW-07-1 linked to
NAU2002 and NAU1085 could explain 8.61% and 6.98% of
the observed phenotypic variations with additive effects of
−0.36 and −0.20 g in PopE1 and PopE2, respectively. The
qBW-16-2 linked to HAU1836 and BNL2634 could explain
6.18% and 8.55% of the observed phenotypic variations
with additive effects of −0.31 and −0.18 gin PopE1 and
PopE2, respectively. The qBW-16-4 linked to NAU5408 and
NAU3594 could explain 7.78% and 6.92% of the observed
phenotypic variations with additive effects of −0.35 and
−0.21 gin PopE1 and PopE2, respectively. The qBW-25-3
linked to BNL3806 and SHIN1131 could explain 7.85% and
15.57% of the observed phenotypic variations in the PopE2
and PopE3, respectively, with additive effects of −0.22 and
−0.20 g.
Lint percentage
Twelve QTL for LP were identified on 8 chromosomes with
the PVE ranging from 3.39% to 30.35%, 4 and 8 of them
were distributed in At- and Dt subgenomes, respectively.
Chr15 was the most prominent chromosome with the larg-
est number of QTL. Nine QTL with the additive effect from
0.01 to 0.84% indicated that Hai1 alleles increased LP, and
four QTL with the additive effect from −2.28 to −0.52%
indicated that CCRI45 alleles increased LP. Five QTL
(qLP-09-1, qLP-15-1, qLP-15-2, qLP-15-3 and qLP-15-4)
could be stably detected in more environments. The qLP-
09-1 linked to DPL0171 could explain 30.35% and 4.92%
of the observed phenotypic variations with additive effects
of −2.28% and −0.60% in PopE1 and PopE3, respectively.
The qLP-15-1 linked to DPL0346a could explain 4.19% and
5.85% of the observed phenotypic variations with additive
effects of 0.01% and 0.52% in PopE1 and PopE3 respec-
tively. The qLP-15-2 linked to MUSS085 and SWU0280b
could explain 5.80% and 11.47% of the observed pheno-
typic variations with additive effects of 0.78% and 0.84%
in the PopE1 and PopE3, respectively. The qLP-15-3 linked
to MUCS410 and HAU0059 could explain 5.55%, 6.54%
and 5.42%of the observed phenotypic variations with addi-
tive effects of 0.01%, 0.25% and 0.72% in PopE1, PopE2
and PopE3, respectively. The qLP-15-4 linked to NAU5138
could explain 6.04%, 6.82% and 7.14% of the observed phe-
notypic variations with additive effects of 0.01%, 0.73% and
0.79% in PopE1, PopE2 and PopE3, respectively.
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1131Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
QTL cluster
QTL clusters are chromosome regions that contain multiple
QTL (≥ 3) related to various traits (Rong etal. 2007). In
the present study, 26 QTL clusters including 115 QTL were
identified on 23 introgressive segments of 14 chromosomes
(Table3, Fig. S2); 8 and 19 of them were distributed in the
At- and Dt subgenomes, respectively. The genetic length of
the clusters varied from 1 to 22 cM and was concentrated
between 1 and 5 cM. There were more QTL clusters on
Chr15, Chr16, and Chr25 than on the other chromosomes.
Twenty-three QTL clusters had stable QTL with the same
additive effect direction in more environments, and 11 of
these QTL clusters had stable QTL for FS or FL. Two QTL
clusters (Clu-16-5 and Clu-25-3) had stable QTL both for
FS and FL. The Clu-16-5 on Chr16 at 78-80 cM had 4 QTL,
the additive effects indicated that Hai1 alleles increased FL
and FS, but decreased FM and BW. The Clu-25-3 on Chr25
at 25-28 cM had five QTL, and the additive effects indicated
that Hai1 alleles increased FL, FS, FU and LP, but decreased
BW. Eight QTL clusters (Clu-02-1, Clu-06-1, Clu-07-2,
Clu-10-1, Clu-16-1, Clu-16-4, Clu-19-2, and Clu-25-4) had
stable QTL for FS. Except for Clu-16-1, and the additive
effects indicated that Hai1 alleles increased FL and FS, but
decreased FM and BW. One QTL cluster (Clu-25-1) had
stable QTL for FL, the additive effects indicated that Hai1
decreased FL and FS.
Discussion
Selection ofgenetic linkage map andimportance
ofCSSLs
In total, 23,569 pairs of SSR markers distributed in the
whole genome were used to screen for polymorphisms
between CCRI36 (G.h) and Hai1 (G.b). A genetic link-
age map with 2292 SSR loci on the 26 cotton chromo-
somes was developed from a BC1F1 population of CCRI36
× Hai1, covering the whole tetraploid cotton genome of
5115.16 cM with an average distance of 2.23 cM between
adjacent markers (Shi etal. 2015). Genetic diversity is
considered a critical issue in QTL mapping of complex
traits. CSSLs have the potential to enrich the diversity of
genetic background and uncover favorable alleles related
to important fiber yield and quality traits(Ali etal. 2010;
Wu etal. 2010; Tyagi etal. 2014). In addition, CSSLs are
ideal materials for QTL mapping, genetic effects identify-
ing and gene cloning(Yang etal. 2015), and are more con-
venient to study the minor and dominant effects of genes
(Wan etal. 2004; He etal. 2015; Li et al. 2016; Qiao
etal. 2016). In this study, the female parent MBI7561 was
selected from a CSSL constructed by G.h and G.b, which
had stable and significant advantages for fiber quality com-
pared with the recurrent parent of CCRI45, to produce
BC5F1 (Table4). The length of introgressed segments from
Hai1 for MBI7561 was 229.47 cM, of which homozygous
introgressed segments were significantly longer than het-
erozygous introgressed segments. The total proportion of
the introgressed segments for MBI7561 was small at the
whole genome (4.40%). However, the proportion of intro-
gression in this study was relatively larger than that in
the previous studies (Song etal. 2017; Guo etal. 2018).
Thus, it is necessary to purify the genetic background
using self-crossing and backcrossing. The diversity of the
MBI7561 genotypes and the dominance of the phenotype
were mutually beneficial, which showed that introgression
segments had important effects on the phenotype. Moreo-
ver, this study laid an important foundation to construct a
segregating population for identifying a large number of
QTL related to fiber yield and quality. The performance of
phenotypes (Tables1, 2, Fig. S1) and genotypes (Fig.1)
Table 4 Phenotypic
performance of fiber quality and
yield-related traits for parents
FL fiber length, FS fiber strength, FM fiber micronaire, FU fiber uniformity, FE fiber elongation, BW boll
weight, LP lint percentage
*Significant difference at the 0.05 level (2-tailed), **significant difference at the 0.01 level (2-tailed)
Year Parents BW (g) LP (%) FL (mm) FS (cN/tex) FM (unit) FU (%) FE (%)
2012 CCRI45 5.69 36.83 28.58 28.52 4.2 84.39 6.5
MBI7561 4.70** 36.25 31.83** 34.75** 3.85** 86.4** 6.5
Hai1 3.18** 32.31** 32.18** 37.7** 4.81** – –
2013 CCRI45 6.02 33.67 29.73 29.96 4.86 85.41 6.1
MBI7561 4.8** 37.04** 32.13** 36.65** 3.97** 86.40* 5.9**
Hai1 2.7** 30.9** 33.53** 37.7** 4.21** 83.30** 6.0**
2014 CCRI45 5.63 37.54 29.48 28.02 4.83 83.98 6.8
MBI7561 4.99** 39.56** 31.58** 33.59** 4.15** 84.86* 6.8
2015 CCRI45 5.38 36.74 28.57 28.65 5.07 84.35 6.8
MBI7561 4.72** 39.28** 31.5** 32.6** 4.40** 85.00** 7.0**
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1132 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
in the BC5F2 and BC5F2:3 populations showed a large
number of superior materials and stable QTL in multiple
environments.
Common QTL shared withprevious research
A total of 129 QTL were identified in the present study,
and 39 of them were stable (TableS1, Fig. S2). When QTL
had the same linkage markers or confidence interval overlap
between previous studies and our studies, we defined it as
a common QTL interval (Zhang etal. 2016b). Fifty three
common QTL detected in the present study were reported in
the previous researches (Zhang etal. 2008, 2015b, c, 2016a,
b; Liu 2009; Qin etal. 2009; Yang etal. 2009; Liang etal.
2010; Jia etal. 2011; Zhang 2012; Wang etal. 2013, 2016a,
b; He 2014; Ma 2014; Cao etal. 2015; Guo etal. 2015; Nie
etal. 2015; Rong etal. 2015; You 2015; Jamshed etal. 2016;
Ademe etal. 2017; Li 2017; Song etal. 2017; Guo etal.
2018), and 15 of them were stable (TableS3). The remain-
ing 76 of the 129 QTL were identified for the first time,
and 24 of the 39 stable QTL(qFL-16-5(+), qFL-25-2(−),
qFS-02-2(+), qFS-16-1(+), qFS-16-4(+), qFS-16-5(+),
qFS-19-1(+), qFM-02-1(−), qFM-10-1(−), qFM-15-1(−),
qFM-16-2(−), qFM-19-1(−), qFU-01-1(+), qFE-06-1(+),
qFE-16-1(−), qFE-19-2(−), qFE-25-4(−), qBW-07-1(−),
qBW-16-2(−), qBW-16-4(−), qBW-25-3(−), qLP-09-1(−),
qLP-15-2(+) and qLP-15-4(+)) were newly identified stable
QTL in the present study. Both stable QTL and common
QTL were suggestive of the stabilities of the genetic effects
(Zhai etal. 2016). Therefore, these 77 stable or common
QTL were very important for marker assisted breeding and
exploration of genetic mechanisms.
QTL clusters withcommon andstable QTL
QTL clusters are common phenomena in cotton (Said etal.
2015; Wang etal. 2015; Zhai etal. 2016; Song etal. 2017).
A large number of QTL were enriched in these hotspots,
which indicated that these chromosome segments might
contain pleiotropic or linked genes related to different traits
(Abdelraheem etal. 2017). These clustered QTL may belong
to the same genetic factor group contributing to the complex
network of fiber development and affecting the multiple fiber
traits(Lacape etal. 2010). QTL clusters allow cotton breed-
ers to focus their efforts on regions with pleiotropic or linked
loci. In the present study, a total of 26 QTL clusters were
identified; 23 of them had stable QTL and 22 of them had
49 common QTL (TableS2, TableS3) (Zhang etal. 2008,
2015b, c, 2016a, b; Liu 2009; Qin etal. 2009; Yang etal.
2009; Liang etal. 2010; Jia etal. 2011; Zhang 2012; Wang
etal. 2013; He 2014; Ma 2014; Cao etal. 2015; Guo etal.
2015; Nie etal. 2015; Rong etal. 2015; You 2015; Jamshed
etal. 2016; Wang etal. 2016a, b; Ademe etal. 2017; Li
2017; Song etal. 2017; Guo etal. 2018). The remaining
four clusters (Clu-02-1, Clu-04-1, Clu-16-4 and Clu-16-
5) were new. Twenty-five QTL clusters contained the 77
stable or common QTL with stable genetic effects, which
were regarded as stable QTL clusters. There were 19 stable
QTL clusters for FL or FS, 7 (Clu-07-1, Clu-07-2, Clu-16-
1, Clu-16-5, Clu-19-2, Clu-25-3 and Clu-25-4) of them for
FL and FS, 5 (Clu-15-3, Clu-16-2, Clu-16-3, Clu-17-1 and
Clu-25-1) of them mainly for FL, and 7 (Clu-02-1, Clu-06-
1, Clu-10-1, Clu-15-1, Clu-15-4, Clu-16-4 and Clu-24-1) of
them mainly for FS.
Fifteen of the 19 stable QTL clusters had common QTL
for FS or FL. Four QTL clusters (Clu-07-1, Clu-07-2, Clu-
25-3 and Clu-25-4) had common QTL for both FS and
FL, and the additive effects indicated most of Hai1 alleles
increased FL, FS and LP, but decreased FM and BW. Six
QTL clusters (Clu-15-3, Clu-16-1, Clu-16-2, Clu-16-3, Clu-
17-1 and Clu-19-2) had common QTL for FL. Except for
Clu-15-3 and Clu-16-3, the additive effects of the others
indicated Hai1 alleles increased FL and FS. Five QTL clus-
ters (Clu-06-1, Clu-10-1, Clu-15-1, Clu-15-4 and Clu-24-1)
had common QTL for FS. The additive effects of Clu-15-
1, Clu-15-4 and Clu-24-1 indicated that most of the Hai1
alleles increased FL and FS.
Three (Clu-02-1, Clu-16-4 and Clu-16-5) of the 4 new
QTL clusters were new stable QTL clusters. Their addi-
tive effects were similar to the effects of the 4 QTL clusters
which had common QTL for both FS and FL.
Genetic eects oftheimportant chromosome
introgressed segments
Analyzing the genetic effects of the chromosome segments is
necessary to develop a breeding strategy with precise direc-
tion in MAS (Zhai etal. 2016; Song etal. 2017), and to
explore genetic mechanisms. In this study, the genetic effects
of 29 chromosome introgressed segments were identified for
fiber quality and yield-related traits. The genetic effects of 23
chromosome introgressed segments were identical to those
of the QTL clusters associated with them. Four segments
(Seg-A08-2, Seg-A09-1, Seg-D09-1, and Seg-D06-3) had
stable genetic effects without any QTL clusters (TableS2).
A total of 13 chromosome introgressed segments were
important for fiber quality (10) and LP (4) improvement on
the 7 chromosomes [Chr15(D1), Chr02(A2), Chr19(D5),
Chr06(A6)–Ch25(D6), Chr07(A7)–Chr16(D7)]. The
additive effects of Seg-D06-2 with 3 stable QTL clusters
(with stable or common QTL) on Chr25(D6) at 23-33 cM
indicated that most of the introgressed Hai1 alleles stably
increased FL, FS, FU and FE, but stably decreased FM.
The additive effects of 9 segments (Seg-A07-1, Seg-A07-2,
Seg-A07-3, Seg-A02-1, Seg-A06-1, Seg-A10-2, Seg-D07-1,
Seg-D07-3, and Seg-D05-2), with single stable QTL cluster
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1133Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
(with stable or common QTL), indicated that most of the
introgressed Hai1 alleles stably increased FL and FS, but
stably decreased FM (except for Seg-D07-1). In addition,
the additive effects of 4 important chromosome introgressed
segments (Seg-A07-1, Seg-D01-2, Seg-D01-3, and Seg-D01-
4) with single-stable QTL cluster (with stable or common
QTL) indicated that the introgressed Hai1 alleles stably
increased LP.
Distribution ofimportant chromosome introgressed
segments forexcellent lines
To further verify which of the 13 important chromosome
introgressed segments had major genetic effects, we ana-
lyzed the distribution of introgressed segments for 5 excel-
lent lines with the best comprehensive phenotypes of FL,
FS, FM, and LP in multiple populations (Table5, Fig.2).
There were 6 common important chromosome introgressed
segments (Seg-A02-1, Seg-A06-1, Seg-A07-2, Seg-A07-3,
Seg-D07-3 and Seg-D06-2) in all five lines, and the additive
effects for the related QTL indicated that the introgressed
Hai1 alleles stably improved FL, FS and FM. There were
three important chromosome introgressed segments (Seg-
D01-2, Seg-D01-3 and Seg-D01-4), with stable genetic
effects for LP, enriched in the region (169-189 cM) on
Chr15(D1). The introgressed region was detected in all 5
excellent lines. The distribution of the important chromo-
some introgressed segments (or region) in excellent lines
further confirmed that further research should be focused
on the 7 common important chromosome introgressed
segments(or region) for fine mapping, genetic mechanisms
exploring, and MAS breeding applications.
In conclusion, developing CSSLs is an effective method
for identifying genetic effects. We selected the CSSLs
Table 5 Phenotypics and introgressed segments distribution of 5 lines with excellent fiber quality
Ind. ID Phenotype value of fiber quality in three environments Chromosome introgressed seg-
ments
Fiber length (mm) Fiber strength (cN/tex) Fiber micronaire Lint percentage (%) Number Len (cM) Rate (%)
Pop
E1
Pop
E2
Pop
E3
Pop
E1
Pop
E2
Pop
E3
Pop
E1
Pop
E2
Pop
E3
Pop
E1
Pop
E2
Pop
E3
288-02 34.58 31.30 32.50 34.70 36.70 32.70 4.51 4.10 5.20 41.50 36.20 39.90 14 106.00 2.07
291-06 35.29 31.20 34.10 37.90 36.70 33.40 3.90 3.80 3.90 38.18 35.72 39.91 14 100.00 1.95
291-19 32.62 32.60 31.20 35.90 40.50 30.90 3.83 4.30 4.80 34.86 36.01 41.47 13 104.00 2.03
292-05 34.43 33.10 33.10 39.30 38.40 32.40 3.24 4.00 5.10 33.45 35.45 38.86 14 107.00 2.09
243-04 30.06 33.90 31.00 31.70 39.40 33.30 3.95 3.90 3.80 37.17 37.31 38.61 11 71.00 1.39
MBI7561 31.58 31.50 31.01 33.59 32.60 30.40 4.15 4.40 4.42 39.56 39.28 40.80 33 127.00 2.48
CCRI45 29.48 28.57 28.07 28.02 28.65 26.25 4.83 5.07 5.09 37.54 36.74 38.20 – – –
Fig. 2 Genotype distribution of individuals with excellent fiber quality
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1134 Molecular Genetics and Genomics (2019) 294:1123–1136
1 3
containing 33 chromosome introgression segments as a
parent to construct the three segregated populations. A
total of 129 QTL associated with fiber quality (103) and
yield-related traits (26) were detected on 17 chromosomes,
explaining 0.85–30.35% of the phenotypic variation, 39
were stable, 53 were common, 76 were new, and 86 had
favorable effects on the related traits. More QTL were dis-
tributed in the Dt subgenome than in the At subgenome.
Twenty-five stable QTL clusters (with stable or common
QTL) were detected on 22 chromosome introgressed seg-
ments. Finally, the 6 important chromosome introgressed
segments (Seg-A02-1, Seg-A06-1, Seg-A07-2, Seg-A07-3,
Seg-D07-3 and Seg-D06-2) were identified as candidate
chromosome regions for fiber quality, which should be given
more attention in future QTL fine mapping, gene cloning,
and MAS breeding.
Acknowledgements This study was funded by the National Key R
& D Program for Crop Breeding (2016YFD0100306), the National
Natural Science Foundation of China (31101188) and the Agricul-
tural Science and Technology Innovation Program for CAAS (CAAS-
ASTIP-ICRCAAS). Thanks to the Quantitative Genetics Group of
CAAS (Beijing, China) providing the software ICIMapping and help
in QTL identification.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethical approval This article does not contain any studies with human
participants or animals performed by any of the authors.
Informed consent Informed consent was obtained from all individual
participants included in the study.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, 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.
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