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Mol Genet Genomics
DOI 10.1007/s00438-017-1347-8
ORIGINAL ARTICLE
Enriching an intraspecific genetic map and identifying QTL
for fiber quality and yield component traits across multiple
environments in Upland cotton (Gossypium hirsutum L.)
Xueying Liu1 · Zhonghua Teng1 · Jinxia Wang1 · Tiantian Wu1 · Zhiqin Zhang1 ·
Xianping Deng1 · Xiaomei Fang1 · Zhaoyun Tan1 · Iftikhar Ali1 · Dexin Liu1 ·
Jian Zhang1 · Dajun Liu1 · Fang Liu2 · Zhengsheng Zhang1
Received: 28 November 2016 / Accepted: 29 June 2017
© Springer-Verlag GmbH Germany 2017
Marker orders on the linkage map are highly consistent
with the corresponding physical orders on a G. hirsutum
genome sequence. Based on fiber quality and yield compo-
nent trait data collected from six environments, 113 QTLs
were identified through two analytical methods. Among
these 113 QTLs, 50 were considered stable (detected in
multiple environments or for which phenotypic variance
explained by additive effect was greater than environment
effect), and 18 of these 50 were identified with stability by
both methods. These 18 QTLs, including eleven for fiber
quality and seven for yield component traits, could be pri-
orities for MAS.
Keywords Fiber quality · Yield component · Stable QTL ·
Upland cotton
Introduction
As the best-known natural textile fiber crop and an impor-
tant potential source of plant oil and protein, cotton has
been cultivated for at least 7000 years (Lee and Fang,
2015). There are 45 diploid (2n = 2x = 26) and seven
tetraploid (2n = 4x = 52) species in the genus Gossypium
(Wendel and Grover 2015). Cultivated species include
tetraploids G. hirsutum (AD1) and G. barbadense (AD2),
and diploids G. herbaceum (A1) and G. arboreum (A2).
Although G. barbadense has superior fiber quality, its poor
adaptation and low yield limit its cultivation to specific
areas. G. hirsutum is extensively planted in more than 100
countries including China, the USA, India, and Pakistan,
providing approximately 95% of global cotton fiber output
(Chen et al. 2007).
Cotton fiber is the most crucial raw material for the
modern textile industry, but cotton produces much more
Abstract Cotton is a significant commercial crop that
plays an indispensable role in many domains. Constructing
high-density genetic maps and identifying stable quantita-
tive trait locus (QTL) controlling agronomic traits are nec-
essary prerequisites for marker-assisted selection (MAS). A
total of 14,899 SSR primer pairs designed from the genome
sequence of G. raimondii were screened for polymorphic
markers between mapping parents CCRI 35 and Yumian 1,
and 712 SSR markers showing polymorphism were used to
genotype 180 lines from a (CCRI 35 × Yumian 1) recombi-
nant inbred line (RIL) population. Genetic linkage analysis
was conducted on 726 loci obtained from the 712 polymor-
phic SSR markers, along with 1379 SSR loci obtained in
our previous study, and a high-density genetic map with
2051 loci was constructed, which spanned 3508.29 cM with
an average distance of 1.71 cM between adjacent markers.
Communicated by S. Hohmann.
Xueying Liu and Zhonghua Teng contributed equal work to this
paper.
Electronic supplementary material The online version of this
article (doi:10.1007/s00438-017-1347-8) contains supplementary
material, which is available to authorized users.
* Fang Liu
liufcri@163.com
* Zhengsheng Zhang
zhangzs@swu.edu.cn
1 Engineering Research Center of South Upland
Agriculture, Ministry of Education, Southwest University,
Chongqing 400716, China
2 State Key Laboratory of Cotton Biology/Cotton Research
Institute, Chinese Academy of Agricultural Sciences,
Anyang 455000, China
Mol Genet Genomics
1 3
than fiber. Cotton seed, a by-product of fiber, is increas-
ingly recognized to have excellent potential as a source
of food, feed, and biofuel (Coppock et al. 1987; Lukonge
et al. 2007). Furthermore, cotton is a model organism for
studying mechanisms of cell development and the rela-
tionship between maternal and filial tissues (Ruan et al.
2005). Worldwide, the annual value of cotton products
exceeds $100 billion (http://www.fas.usda.gov). There-
fore, cotton plays a crucial role in human life, scientific
research, and the world economy. Demand for cotton is
increasing with the rise in world population not only for
its fiber, but also for its seeds. With declining agricultural
acreage and shrinking farm labor, economical and effi-
cient technology to improve fiber quality and yield must
be utilized to meet growing demand for cotton.
Fiber quality and yield component traits with continu-
ous phenotypic variation are affected by alleles at multi-
ple quantitative trait locus (QTL) that tends to vary with
environments (Said et al. 2013). QTL by environment
interaction (QEI) is widely detected in genetic analysis
of crops including cotton (Shang et al. 2016b) and other
organisms (Li et al. 2015b). Studies on QEI contribute
to better understanding of genetic architecture of impor-
tant quantitative traits and the efficient use of marker-
assisted selection (MAS) in breeding (Boer et al. 2007).
Advances in molecular biology techniques have provided
a powerful strategy to study complex traits. MAS is now
widely applied in plant breeding to identify and evaluate
target characters. Many candidate genes for agronomic
trait-related QTLs have been cloned and characterized
in model plants such as rice (Li et al. 2003; Song et al.
2007). Identifying QTLs and underlying genes are still a
challenge in cotton because of the modest levels of poly-
morphism among cultivated cotton species and the com-
plex nature of most yield component traits (Zhang et al.
2012).
Over the last two decades, there have been numer-
ous genetic linkage maps produced based on intraspecific
Upland cotton populations (Zhang et al. 2005, 2016a;
An et al. 2010; Shao et al. 2014; Liu et al. 2015a; Jam-
shed et al. 2016). However, most of these cannot meet the
demands of accurate QTL mapping because of their large
distances between DNA markers. So far, Liu et al. (2015a)
constructed the most SSR marker-abundant genetic link-
age map of Upland cotton, including 1675 loci that span
3338.2 cM with an average distance of 1.98 cM between
adjacent markers. Genome sequences of G. raimondii
(Wang et al. 2012; Paterson et al. 2012), G. arboreum (Li
et al. 2014), G. hirsutum (Zhang et al. 2015b; Li et al.
2015a), and G. barbadense (Liu et al. 2015b; Yuan et al.
2015) have been completed, and provide enough DNA
markers for construction of high-resolution genetic maps
for accurately identifying QTL.
There have been more than 4000 QTLs identified in cot-
ton previously (Said et al. 2013, 2015a, b; http://www2.cot-
tonqtldb.org:8081/). Fiber quality traits that are of upmost
importance to cotton breeding programs have dominated
these studies. Although many studies have identified a
number of QTLs for cotton yield component traits, the
majority had small effects or were unstable across envi-
ronments. For example, Zhang et al. (2016b) identified
16 stable boll weight (BW) QTLs, but only qBW-chr13-7
had average phenotypic variance explained (PVE) of more
than 10%; An et al. (2010) identified five lint percentage
(LP) QTLs and four seed index (SI) QTLs, with the PVE of
qLP-c26-1 being as much as 87.1%. However, the detection
was only conducted in a single environment. Therefore, to
fully validate major and stable QTLs associated with cot-
ton fiber quality and yield component traits will be of vital
significance.
In the present study, a total of 712 polymorphic SSR
markers designed from the genome sequence of G. raimon-
dii were used to enrich a genetic map based on our previous
study. Five fiber quality and three yield component traits
collected in six environments were used to identify stable
QTLs. The results are expected to be valuable for QTL
fine-mapping and future research on molecular mecha-
nisms of cotton fiber quality and yield component traits.
Materials and methods
Mapping population
CCRI 35 and Yumian 1 are two Upland cotton cultivars,
which were used as parents in this study. CCRI 35, a high
yielding and disease resistant cultivar, was widely planted
in China in the last decade. Yumian1, characterized by high
fiber quality, was developed from a multiple-cultivar inter-
mating program (Zhang et al. 2009). The cross was made in
the summer of 2005 at Southwest University, Chongqing,
China, and 180 recombinant inbred lines were developed
as detailed by Tan et al. (2015). Multi-environment evalu-
ations were conducted in three locations, which included
Anyang in Henan province (HA), Ezhou in Hubei province
(HE), and Kuerle in Xinjiang province (XK), respectively,
in the summers of 2014 and 2015. The 180 lines and two
parents were planted in single-row plots (0.8 m wide and
5 m long, for 15 plants) in each environment.
Phenotyping
Fifty naturally-opened bolls were hand harvested from each
line to investigate traits. Yield component traits includ-
ing lint percentage (LP, %), boll weight (BW, g/boll), and
seed index (SI, g/100 seeds) were tested. LP was obtained
Mol Genet Genomics
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from lint weight divided by seed cotton weight. BW was
the average value of grams per boll. SI was measured by
weighing 100 cotton seeds. Fiber samples from each line
were evaluated for fiber quality traits using the HIV (High
Volume Instrument) system at the Supervision Inspection
and Testing Cotton Quality Center, Anyang, China. Data
were collected on fiber upper half mean length (FL, mm),
fiber micronaire (FM), fiber length uniformity ratio (FU,
%), fiber elongation (FE, %), and fiber strength (FS, cN/
tex). SPSS 20.0 (SPSS, Chicago, IL, USA) was used to
analyze correlations between these eight traits. For analysis
of variance (ANOVA), Microsoft Excel 2013 was used.
SSR development and genotyping
Cotton genomic DNA was extracted from young leaves of
mapping individuals and parents, using a modified CTAB
method (Zhang et al. 2005). To enrich markers on the
previous genetic linkage map (Tan et al. 2015), a total of
14,899 SSR primer pairs named SWU1593-SWU3931 and
SWU10001-SWU22560 were designed from the genome
sequence of G. raimondii (Paterson et al. 2012), and syn-
thesized by Beijing Genomics Institute (Beijing, China).
These primer pairs were designed using SSR locator 1
(http://comp.uark.edu/~ashi/MB/SSRLocator.html) based
on the following principles: (1) primer length from 18 to 27
bases; (2) annealing temperature between 55 and 65 °C; (3)
PCR product sizes ranging from 100 to 200 bp; and (4) GC
content of 45–65% with an optimum of 50%.
All primer pairs were first screened for polymorphism
between CCRI 35 and Yumian 1. PCR was conducted in a
total volume of 10 µl with 50 ng DNA template, 1× PCR
buffer, 2.0 mM MgCl2, 0.2 mM dNTPs, 0.5 µM concen-
trations of each primer, and 0.5 units of Taq polymerase
(Shanghai Sangon, China). The PCR conditions were as
follows: 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 55 °C
for 30 s, 72 °C for 1 min; 72 °C for 10 min; and 4 °C for
preservation. The PCR products were separated on 10%
(w/v) polyacrylamide gels and visualized by silver staining.
Polymorphic primers were defined by obvious differences
between two parents’ PCR products. Then, polymorphic
primers were used to genotype the RIL population with the
same PCR and electrophoresis procedures as screening.
All loci were named according to their respective primer
names. An “a” or “b” was added as a suffix for primers that
produced two loci.
Map construction
A high-density genetic linkage map was constructed using
Joinmap 4.0 (Van Ooijen 2006). All loci were grouped
and ordered with a log of odds (LOD) threshold range
of 4.0–8.0. Loci that could not be mapped to any groups
were eliminated from analysis. Recombination values were
converted into genetic distances (centimorgan, cM) by the
Kosambi map function. Chi-squared tests were used to
determine if any loci deviated significantly (p < 0.05) from
the expected Mendelian 1:1 segregation ratio in the RIL
population. Region containing more than three adjacent
loci which showed significant segregation distortion was
denoted as ‘segregation distortion region(s)’ (SDR) (Tang
et al. 2015).
A G. hirsutum genome sequence (Zhang et al. 2015b)
was downloaded from the CottonGen database (http://
www.cottongen.org). The sequences of public SSR mark-
ers (BNL, C2, CIR, CER, CGR, CM, COT, DC, DOW,
DPL, Gh, HAU, JESPR, MGHES, MUCS, MUSB, MUSS,
NAU, NBRI, PGML, SHIN, STV, and TMB) were down-
loaded from the Cotton Marker Database (http://www.
cottonmarker.org). The physical position of SSR mark-
ers on the G. hirsutum genome was determined by local
NCBI-blast-2.2.31+ (Camacho et al. 2009) using their
primer sequence. Circos-0.66 (Krzywinski et al. 2009) was
employed to check colinearity of loci between the linkage
map and the G. hirsutum physical map.
QTL analyses
Single-environment identification of QTL and evaluation of
their effects were carried out by MapQTL 6.0 (Van Ooijen
2009). A threshold of log of odds ratio (LOD) ≥2.0 was
used to declare suggestive QTL, following previous publi-
cations (Shen et al. 2007; Liu et al. 2015a), as suggested
by Lander and Kruglyak (1995). Positive additive effects
of QTL indicated that the CCRI 35 allele increased the
phenotypic value, whereas negative effects indicated that
the Yumian 1 allele increased the phenotypic value. QTLs
identified in two or more environments were considered to
be potential stable QTL.
QEI mapping of QTL was carried out by the multi-envi-
ronment trials (METs) function and the ‘Inclusive compos-
ite interval mapping’ (ICIM) method of ICIMapping 4.0
(Meng et al. 2015). The input file was defined in an Excel
spreadsheet with five sheets: ‘General Information’, ‘Chro-
mosome’, ‘Linkage Map’, ‘Genotype’, and ‘Phenotype’.
QTL identification was done with pre-adjusted IciMapping
parameters: scan = 5 cM, probability in stepwise regres-
sion = 0.001, and LOD threshold = 5. QTLs were con-
sidered as potential stable QTL only when the phenotypic
variance explained (PVE) by the average additive effect
(PVEA) was greater than the QEI effect (PVEAE).
MapChart 2.2 (Voorrips 2006) was used to graphically
represent the genetic map and QTL bars. QTLs were named
starting with ‘q’, followed by a trait abbreviation and the
chromosome number, then by the number of QTL affect-
ing the trait on the same chromosome (e.g., qSI02.2 for the
Mol Genet Genomics
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second seed index QTL on chromosome 2). QTLs for the
same trait across different environments were declared in
the same QTL region when their confidence intervals (CI)
overlapped.
Results
Performance of yield component traits
Descriptive statistics for fiber quality traits (FL, FU, FM,
FE, and FS) and yield component traits (LP, BW, and SI)
across six environments were summarized in Table 1.
These eight traits were observed to show continuous vari-
ation. Skewness and kurtosis tests showed that these traits
are all approximately normally distributed. Transgressive
segregation was observed for all traits.
Correlations among the tested traits, based on pheno-
typic data from six environments for the RIL population,
are shown in Table 2. Among the five fiber quality traits,
all trait pairs presented significant correlation except for
FM-FU and FM-FE. BW had significant positive cor-
relations with SI, FL, FU, FE, and FS. LP had significant
negative correlations with BW, SI, and FS, and significant
positive correlations with FU and FM. SI had a significant
negative correlation with FM and significant positive cor-
relations with the other four fiber quality traits. Analysis of
variance showed that all five fiber quality traits and three
yield component traits had significant genetic and environ-
mental effects (p < 0.01) (Table 3).
Construction of an updated genetic map
Among the 14,899 SWU SSR primer pairs screened, 712
(4.8%) showed clear polymorphism between CCRI 35 and
Yumian 1, as listed in Table S1. These primer pairs gen-
erated 726 loci in the RIL population, with 14 pairs seg-
regating at two loci. Along with 1379 loci obtained in our
previous study (Tan et al. 2015), a genetic map with 2051
loci was constructed, covering 3508.29 cM with an aver-
age distance of 1.71 cM between adjacent markers. The
positions of all mapped loci are listed in Table S2. Among
the 726 newly developed SWU loci, 707 were mapped,
including 152 on At subgenome and 555 on Dt subgenome
chromosomes. The average recombinational length of chro-
mosomes was 134.93 cM. Chr05 that spanned 218.44 cM
had the longest recombinant length, whereas chr03 had
the shortest, spanning only 82.86 cM. The At subgenome
included 650 loci (1634.99 cM) with an average inter-
val of 2.52 cM between loci, and the Dt subgenome pos-
sessed 1401 loci (1873.30 cM) with an average interval
of 1.34 cM. Twelve gaps (>20 cM) were identified on this
genetic map, with seven on the At subgenome and five on
the Dt subgenome. The largest gap was located on chr09,
spanning 37.36 cM. More details are listed in Table 4.
Segregation distortion of SSR markers
Among the 2051 mapped loci, 842 showed segregation
distortion (p < 0.05) (Table 4) with 296 new loci account-
ing for 41.1% of the total. These 842 loci comprised 55
SDRs (segregation distortion regions) on 23 chromosomes,
with 20 on the At subgenome and 35 on the Dt subgenome
(Table 4, Fig. 2). Forty-four (5.2%) of the 842 loci favored
CCRI 35 alleles and 799 (94.8%) favored Yumian 1 alleles.
Distorted loci were unevenly mapped on chromosomes,
with the most on Chr20, the least on Chr21, and the highest
density on Chr13 with as much as 82.9%.
Colinearity between the linkage and physical map
Figure 1 shows the colinearity of loci between the physi-
cal map and the enriched genetic linkage maps of various
chromosomes. Loci that were located in scaffolds or whose
physical location was not determined were removed from
the analysis (Table S8). The vast majority of loci on the
genetic map were in accordance with their locations on the
At or Dt subgenome sequence of G. hirsutum. In the At
subgenome, 1634.99 cM corresponded to 1.13 GB, which
spanned 97.0% of the subgenome. In the Dt subgenome,
1873.30 cM corresponded to 760.48 MB, which spanned
98.2% of the subgenome. The physical span of each chro-
mosome in the present map and their percentage in chro-
mosome assemble by Zhang et al. (2015b) are listed in
Table 4.
QTL mapping
A total of 113 QTLs were identified in this study. Among
them, 105 QTLs were identified with a range of 9–17 QTLs
per trait by MapQTL 6.0 (Table S3, Fig. 2). Their LOD
scores ranged from 2.0 to 6.6, and they explained 4.8–
15.6% of the phenotypic variance. Forty-two QTLs were
identified on the At subgenome and 63 on the Dt subge-
nome. Twenty-eight QTLs were detected in multiple envi-
ronments. Remarkably, ten QTLs were detected in three or
more environments, with one detected in all environments.
For each trait, favorable alleles were derived from both par-
ents. Through QEI analysis by ICIMapping 4.0, 45 QTLs
including 16 yield component QTLs and 29 fiber qual-
ity QTLs were identified on 19 chromosomes (Table S4,
Fig. 2). Most of these QEI were stable across environments,
and 37 were also identified in one or more environments by
MapQTL 6.0. Eighteen QTLs were detected with stability
across environments by both methods, including four FL
Mol Genet Genomics
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Table 1 Phenotypic variation of fiber quality and yield component traits for two parents and their RIL population
Environment Parents RIL population
CCRI 35 Yumian 1 Mean Max. Min. Std. deviation Kurtosis Skewness
Length (mm) 2014-HA 28.9 29.9 29.7 32.8 26.1 1.3 −0.1 −0.1
2014-HE 28.5 31.2 29.7 33.3 26.7 1.3 −0.3 0.2
2014-XK 29.4 28.9 30.2 35.3 25.8 1.3 1.3 0.3
2015-HA 27.3 26.7 26.6 31.7 23.8 1.1 1.8 0.4
2015-HE 28.5 29.3 29.1 32.5 26.0 1.3 0.1 0.0
2015-XK 29.8 26.6 28.4 32.8 25.8 1.2 0.3 0.3
AVERAGE 28.7 28.8 29.0 33.1 25.7 1.2 0.5 0.2
Uniformity 2014-HA 85.3 87.8 85.4 88.4 82.3 1.2 0.0 0.0
2014-HE 85.4 85.1 85.1 88.2 81.0 1.2 0.4 0.6
2014-XK 84.3 84.1 84.7 87.3 80.0 1.2 0.7 0.0
2015-HA 85.5 83.7 84.2 87.9 81.2 1.2 0.1 0.0
2015-HE 86.2 86.0 84.6 86.7 81.9 1.2 −0.5 0.0
2015-XK 86.4 81.6 84.4 87.4 81.0 1.3 −0.2 0.0
AVERAGE 85.5 84.7 84.7 87.7 81.2 1.2 0.1 0.1
Micronaire (%) 2014-HA 5.2 4.3 4.3 6.7 2.7 0.6 1.6 0.2
2014-HE 5.0 4.5 4.7 5.9 3.4 0.4 0.1 0.0
2014-XK 3.9 2.4 4.1 5.4 2.3 0.6 −0.1 −0.5
2015-HA 4.8 5.1 4.3 5.3 3.2 0.4 0.0 −0.5
2015-HE 5.1 5.2 5.0 5.9 4.0 0.4 −0.4 −0.3
2015-XK 5.3 5.0 4.8 6.3 3.5 0.4 1.4 0.1
AVERAGE 4.9 4.4 4.5 5.9 3.2 0.4 0.4 −0.2
Elongation (%) 2014-HA 6.7 6.8 6.8 6.9 6.6 0.1 −0.7 −0.3
2014-HE 6.8 6.8 6.8 6.9 6.6 0.1 −0.6 −0.2
2014-XK 6.8 6.8 6.8 6.9 6.6 0.1 0.3 −0.2
2015-HA 6.8 6.6 6.6 6.9 6.3 0.1 0.5 −0.3
2015-HE 6.8 6.8 6.8 7.1 6.6 0.1 0.4 0.5
2015-XK 6.8 6.5 6.8 7.0 6.4 0.1 0.3 −0.2
AVERAGE 6.8 6.7 6.8 7.0 6.5 0.1 0.1 −0.1
Strength (cN/tex) 2014-HA 29.5 32.4 32.5 40.5 25.1 2.4 0.7 0.5
2014-HE 30.4 32.9 31.6 37.8 26.7 2.2 −0.1 0.2
2014-XK 30.1 30.5 30.7 40.2 26.8 2.1 1.6 0.8
2015-HA 30.4 27.7 29.1 37.3 23.9 2.7 0.0 0.3
2015-HE 30.6 29.2 31.0 36.8 25.8 2.1 −0.2 0.1
2015-XK 29.3 25.2 28.8 36.0 23.5 1.9 1.1 0.4
AVERAGE 30.1 29.7 30.6 38.1 25.3 2.2 0.5 0.4
Boll weight (g) 2014-HA 4.2 5.1 5.1 6.8 3.7 0.6 0.0 0.0
2014-HE 5.4 5.1 5.0 6.3 4.2 0.4 0.2 0.6
2014-XK 5.1 3.2 4.6 6.5 3.1 0.7 −0.2 0.0
2015-HA 4.3 4.3 4.4 5.7 3.2 0.4 0.3 0.0
2015-HE 5.2 5.0 5.0 6.5 3.6 0.5 0.8 0.0
2015-XK 4.4 5.2 5.0 6.9 3.3 0.6 0.2 0.0
AVERAGE 4.8 4.7 4.9 6.5 3.5 0.5 0.2 0.1
Mol Genet Genomics
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QTLs, five FM QTLs, two FS QTLs, three LP QTLs, and
four SI QTLs (Table 5).
Lint percentage QTL
Thirteen QTLs were detected on ten chromosomes in sin-
gle-environment analysis, explaining 5.1–8.6% of the phe-
notypic variance (Table S3). Yumian 1 contributed favora-
ble alleles for qLP15.1 and qLP22.1, whereas favorable
alleles for the other 11 QTLs were conferred by CCRI 35.
Five LP QTLs identified in more than one environment
were located on Chr07, Chr14, Chr19, Chr20 and Chr23.
qLP19.1 explained the most phenotypic variance with a
value of 8.6%.
qLP14.1, qLP19.1, qLP23.1 and qLP24.1 were also
identified through QEI analysis, with LOD scores ranging
from 5.6 to 7.1 (Table S4). The first three QTLs were sta-
ble across environments. In particular, qLP24.1 exhibited
strong interaction with environment with PVEAE higher
than PVEA.
Boll weight QTL
In single-environment analysis, 10 QTLs, with PVE rang-
ing from 4.8 to 12.2%, were detected on eight chromo-
somes (Table S3). Among them, Chr21 and Chr22 pos-
sessed two BW QTLs, respectively. qBW07.1 with a LOD
score of 5.1 explained 12.2% of the phenotypic variance.
Favorable alleles at five QTLs (qBW08.1, qBW11.1,
qBW18.1, qBW21.1 and qBW21.2) derived from CCRI 35,
whereas the others derived from Yumian 1.
Two QTLs, qBW07.1 and qBW08.1, were identified
through QEI mapping (Table S4). However, the PVEAE of
qBW08.1 was much higher than PVEA, indicating a strong
interaction with environment. On the contrary, qBW07.1,
Table 1 continued
Environment Parents RIL population
CCRI 35 Yumian 1 Mean Max. Min. Std. deviation Kurtosis Skewness
Lint percentage (%) 2014-HA 51.2 46.8 42.2 52.0 32.2 3.2 1.1 0.6
2014-HE 41.4 40.9 39.0 44.6 31.8 2.3 0.1 −0.3
2014-XK 42.3 38.8 42.0 46.4 36.9 2.0 −0.5 −0.3
2015-HA 40.0 40.1 39.0 43.4 33.4 1.9 0.2 −0.6
2015-HE 40.0 40.7 39.7 44.0 33.7 1.8 0.1 −0.3
2015-XK 44.8 45.1 45.6 52.6 40.0 1.9 0.5 0.0
AVERAGE 43.3 42.1 41.2 47.2 34.7 2.2 0.3 −0.2
Seed index (g) 2014-HA 10.2 9.9 10.4 13.8 7.8 1.1 −0.3 0.2
2014-HE 10.0 10.3 10.9 13.2 8.6 0.9 0.2 0.1
2014-XK 10.3 10.2 10.2 14.3 6.7 1.1 0.2 0.1
2015-HA 9.4 9.2 9.8 12.0 7.5 0.9 −0.4 0.0
2015-HE 10.8 10.8 11.3 14.5 9.0 1.0 0.3 0.5
2015-XK 9.3 10.1 9.8 12.5 7.6 0.9 −0.1 0.4
AVERAGE 10.0 10.1 10.4 13.4 7.9 1.0 0.0 0.2
Table 2 Correlation
coefficients among all
traits in the Upland cotton
RIL population across six
environments
BW boll weight; LP lint percentage; SI seed index; FL fiber length; FU fiber uniformity; FM fiber micro-
naire; FE fiber elongation; FS fiber strength
*, ** Significant differences with a probability level of 0.05 and 0.01, respectively
Traits BW LP SI FL FU FM FE FS
BW 1
LP −0.239** 1
SI 0.632** −0.367** 1
FL 0.199** −0.242** 0.301** 1
FU 0.165*0.199** 0.221** 0.255** 1
FM −0.012 0.389** −0.261** −0.419** 0.145 1
FE 0.256** −0.119 0.426** 0.757** 0.453** −0.135 1
FS 0.200** −0.172*0.428** 0.787** 0.415** −0.352** 0.776** 1
Mol Genet Genomics
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with an LOD score of 9.1 and PVEA much higher than
PVEAE, was a potential stable QTL.
Seed index QTL
Fourteen QTLs with PVE ranging from 5.1 to 15.6% were
detected on 12 chromosomes through MapQTL6.0 (Table
S3). There were two SI QTLs on both Chr02 and Chr25,
respectively. The favorable alleles of qSI07.1, qSI 19.1,
qSI22.1, qSI24.1 and qSI25.1 were conferred by Yumian 1
and the others by CCRI 35. Five SI QTLs were detected in
multiple environments, with qSI17.1 and qSI18.1 detected
in two environments, qSI15.1 and qSI20.1 in three, and
qSI07.1 in all six, explaining phenotypic variance of as
much as 15.6%.
In addition to qSI 02.1, qSI 02.2, qSI 03.1, qSI 07.1,
qSI 15.1, qSI 17.1, qSI 20.1 and qSI 22.1, two more QTLs
(qSI03.2, qSI15.2) were identified by QEI mapping (Table
S4). All 10 SI QTLs showed PVEA higher than PVEAE
and average additive effects were higher than average QEI
effects.
Fiber length QTL
Fourteen QTLs were detected through single-environment
analysis, explaining 5.2–9.0% of the phenotypic vari-
ance (Table S3). Favorable alleles that increased FL at 13
loci were derived from Yumian 1, whereas the other one
(qFL01.2) positive alleles were contributed by CCRI 35.
Among these 14 QTLs, qFL01.1, qFL14.1, qFL17.1 and
qFL26.1 were identified in two environments.
Seven QTLs were identified by QEI mapping (Table S4).
All of these seven QTLs were thought to be potential sta-
ble with their PVEA higher than PVEAE. Besides, qFL04.1,
of which positive allele derived from CCRI 35, was not
detected by single-environment analysis.
Fiber uniformity QTL
Nine QTLs were mapped on eight chromosomes, which
explained 5.0–7.7% of the phenotypic variance (Table
S3). Alleles for increasing fiber uniformity at three loci,
including qFU05.2, qFU09.1 and qFU22.1, were contrib-
uted by Yumian 1. Another six were contributed by CCRI
35. Moreover, only qFU17.1 that PVEA higher than PVEAE
was identified through QEI mapping (Table S4).
Fiber micronaire QTL
In single-environment analysis, a total of 17 QTLs were
identified and located on 13 chromosomes, explaining 5.0–
9.3% of the phenotypic variance (Table S3). Among them,
favorable alleles of qFM03.1, qFM08.2, and qFM16.1 were
derived from Yumian 1. Nine QTLs were detected in more
than one environments, with qFM05.1, qFM17.1, qFM20.2,
qFM22.1, qFM22.2 and qFM23.2 in two environments,
qFM23.1 in three environments, qFM25.1 in four envi-
ronments, and qFM07.1 in five environments. Notably, all
favorable alleles of these nine QTLs were contributed by
CCRI 35.
Eleven QTLs were detected through QEI mapping,
including two new QTLs (qFM16.2 and qFM24.1) that
were not identified by single-environment analysis (Table
S4). Most of these eleven QTLs were thought to be poten-
tial stable except qFM17.1, which exhibited strong interac-
tion with environment with PVEA lower than PVEAE.
Fiber elongation QTL
15 QTLs were mapped on 11 chromosomes by single-
environment analysis, explaining 5.1–9.9% of the
Table 3 Analysis of variance (ANOVA) for all traits in the RIL pop-
ulation across six environments
FL fiber length; FU fiber uniformity, FM fiber micronaire, FE fiber
elongation, FS fiber strength, BW boll weight, LP lint percentage, SI
seed index
*, ** Significant differences with a probability level of 0.05 and 0.01,
respectively
Trait Source df Mean square F value
FL Genotype 179 4.43 4.63**
Environment 5 294.39 307.84**
Error 895 0.96
FU Genotype 179 2.20 1.71**
Environment 5 37.79 29.32**
Error 895 1.29
FM Genotype 179 0.56 4.04**
Environment 5 21.72 156.50**
Error 895 0.14
FE Genotype 179 0.01 2.27**
Environment 5 0.75 121.41**
Error 895 0.01
FS Genotype 179 12.13 3.31**
Environment 5 370.01 101.14**
Error 895 3.66
BW Genotype 179 0.54 2.47**
Environment 5 14.51 66.62**
Error 895 0.22
LP Genotype 179 13.32 3.89**
Environment 5 1170.88 340.62**
Error 895 3.43
SI Genotype 179 3.76 8.72**
Environment 5 63.95 148.08**
Error 895 0.43
Mol Genet Genomics
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phenotypic variance (Table S3). Among them, favora-
ble alleles of qFE08.2, qFE10.1, qFE14.1, qFE15.1,
qFE17.1, qFE23.1 and qFE25.1 were derived from CCRI
35, and the other eight were contributed by Yumian 1.
Two QTLs including qFE07.1 and qFE08.2 were identi-
fied in two environments, respectively.
Four QTLs (qFE01.1, qFE07.1, qFE14.2 and
qFE17.1) identified by single-environment analysis were
also detected by QEI mapping (Table S4). qFE01.1,
qFE14.2 and qFE17.1 were thought to be potential sta-
ble QTLs with their PVEA higher than PVEAE, whereas
qFE07.1 exhibited strong interaction with environment.
Fiber strength QTL
In single-environment analysis, a total of 13 QTLs were
identified explaining 5.4–13.2% of the phenotypic vari-
ance (Table S3). Favorable alleles of six QTLs (qFS07.1,
qFS14.2, qFS22.1, qFS23.1, qFS25.1 and qFS26.1) were
contributed by Yumian 1, and the others were derived from
CCRI 35. Three QTLs were identified in more than one
environment, with qFS07.1 and qFS26.1 in two environ-
ments and qFS14.2 in three environments.
In addition to qFS01.1, qFS07.1, qFS14.2 and qFS26.1,
two more QTLs (qFS12.1 and qFS14.1) were detected by
Table 4 Markers distribution and chromosomes parameters on the enriched genetic map
Chr chromosome, SDR segregation distorted region
a The percentage of present chromosome physical span in chromosome assembled by Zhang et al. (2015b)
Chr. Loci Distorted loci Distortion rate
(%)
SDR Gaps (>20 cM) Length (cM) Average interval
(cM)
Physical length
(MB)
Percentage of
genome coverage
(%)a
1 57 10 17.54 2 0 100.91 1.77 99.26 99.38
2 64 4 6.25 1 1 120.27 1.88 82.98 99.44
3 35 25 71.43 1 0 82.86 2.37 93.9 93.66
4 35 17 48.57 2 0 117.03 3.34 61.55 97.84
5 97 8 8.25 1 0 218.44 2.25 91.74 99.66
6 27 4 14.81 1 0 107.48 3.98 102.43 99.28
7 99 76 76.77 3 0 112.68 1.14 77.34 98.84
8 45 4 8.89 0 2 147.45 3.28 100.45 96.93
9 44 36 81.82 2 1 121.6 2.76 63.64 84.85
10 40 9 22.50 2 1 139.1 3.48 99.68 98.82
11 40 5 12.50 0 1 153.98 3.85 92.38 98.99
12 26 14 53.85 3 1 127.17 4.89 82.19 93.95
13 41 34 82.93 2 0 86.02 2.1 77.79 97.29
At 650 246 37.85 20 7 1634.99 2.52 1125.33 96.99
14 87 40 45.98 4 0 153.5 1.76 66.06 98.19
15 113 14 12.39 2 0 117.46 1.04 61.11 99.43
16 185 40 21.62 2 1 153.87 0.83 54.46 98.46
17 98 78 79.59 3 0 107.79 1.1 45.4 97.24
18 69 50 72.46 3 1 104.98 1.52 58.78 97.11
19 133 61 45.86 6 0 204.4 1.54 60.98 98.47
20 160 95 59.38 1 0 133.21 0.83 63.37 100.00
21 69 2 2.90 0 1 151.15 2.19 64.37 97.40
22 54 42 77.78 2 0 137.31 2.54 50.08 97.34
23 121 33 27.27 4 1 197.77 1.63 50.38 98.78
24 67 34 50.75 3 1 128.76 1.92 63.58 96.49
25 115 58 50.43 3 0 118.59 1.03 63.79 99.22
26 130 49 37.69 2 0 164.51 1.27 58.13 98.34
Dt 1401 596 42.54 35 5 1873.3 1.34 760.48 98.20
Total 2051 842 41.05 55 12 3508.29 1.71 1885.81 97.48
Mol Genet Genomics
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QEI mapping (Table S4). Five of them were stable across
environments; however, qFS26.1 exhibited strong interac-
tion with environment with a much higher PVEAE.
QTL‑cluster analysis
A QTL cluster is defined as a densely populated QTL
region of the chromosome that contains four or more
QTLs associated with various traits (Said et al. 2015b;
Zhang et al. 2016c). In this study, five clusters with 24
QTLs were identified on five chromosomes. The cluster
on Chr07, C7-cluster-1, of which the approximate posi-
tion was 58.9–78.7 cM, included seven QTLs. The PVE of
these seven QTLs ranged from 5.4 to 15.6%. The cluster on
Chr25, C25-cluster-1, contained five QTLs that explained
5.2–7.5% of the phenotypic variance. The details of each
cluster are summarized in Table S5.
Discussion
Polymorphism and distribution of newly developed SSR
markers
The relatively low polymorphism ratio of 4.78% for new
SSR markers was consistent with the narrow genetic back-
ground between Upland cotton cultivars, as reported in
many previous studies (Zhang et al. 2009, 2015a; Jamshed
et al. 2016). Because partial homology exists between At
and Dt subgenomes in allotetraploid cotton (Zhang et al.
2015b), the 707 mapped loci developed from the genome
sequence of G. raimondii, closely resembling the Dt sub-
genome progenitor of tetraploid G. hirsutum (Wendel and
Cronn 2003; Page et al. 2013; Wendel and Grover 2015.),
should be distributed approximately evenly on both At and
Dt genomes. However, only 152 loci were mapped on the
At subgenome and 555 on the Dt subgenome. One reason
for uneven distribution of polymorphic SSRs on the At and
Dt subgenome may be that these SSR primer pairs were
developed based on the sequence of the D genome dip-
loid cotton G. raimondii, resulting in less match of primer
sequences to the A subgenome. Another reason for this
might be that asymmetric evolution of At and Dt subge-
nomes caused more polymorphic SSR markers on the Dt
than the At (Guo et al. 2008; Yu et al. 2011; Zhang et al.
2012).
High‑density genetic map construction
A high-density genetic map is valuable for mapping QTL
for agronomically significant traits. Despite many genetic
maps having been constructed in intraspecific Upland cot-
ton populations (Zhang et al. 2005, 2016a; Wang et al.
2016; Shen et al. 2007; An et al. 2010; Shao et al. 2014;
Liu et al. 2015a; Jamshed et al. 2016), most are inad-
equate to provide for fine-mapping of QTL and candidate
gene identification. The main reasons for this inadequacy
are the large distance between adjacent markers and com-
paratively poor coverage of the genome in previous maps
(Zhang et al. 2005, 2012; Wang et al. 2006). With advances
in sequencing techniques, high-efficiency single nucleotide
polymorphism (SNP) markers have become widely applied
Fig. 1 Colinearity between the RIL genetic map and G. hirsutum
physical map. a Colinearity between the genetic maps of Chr01 to
Chr13 with corresponding physical maps of the At subgenome. b
Colinearity between the genetic maps of Chr14 to Chr26 with cor-
responding physical maps of the Dt subgenome
Mol Genet Genomics
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Fig. 2 Genetic map and QTL for yield component traits from RIL
population. Markers showing segregation distortion are indicated by
asterisks (*p < 0.05; **p < 0.01; ***p < 0.005) for makers skewed
toward the Yumian 1 alleles, and another sign (#p < 0.05; ##p < 0.01;
###p < 0.005) for markers skewed toward CCRI35 alleles. Bars along
the chromosomes indicate the confidence interval of QTL
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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to genetic map construction. Up to now, there are three
high-density Upland cotton SNP genetic maps (Zhang et al.
2016b; Wang et al. 2015c; Li et al. 2016) and one high-
density interspecific SNP genetic map (Wang et al. 2015a).
However, SNP markers will cost much when used to geno-
type a population with thousands of individuals for QTL
fine-mapping. Therefore, the role that SSR markers play in
MAS still cannot be overlooked.
In the present study, an enriched genetic map relative to
our previous report (Tan et al. 2015) was constructed. This
map contained 2051 loci spanning 3508.29 cM with an
average interval distance of 1.71 cM between adjacent loci,
and spanned as much as 97.48% of the G. hirsutum genome
(Zhang et al. 2015b). To our knowledge, this is the most
SSR marker-abundant intraspecific cotton genetic map to
date. On the other hand, there were still some gaps in the
present map, with new markers needed to saturate this map
for future fine-mapping.
Segregation distortion
Segregation distortion is common in plant mapping popu-
lations, including cotton. The RIL population, which has
experienced natural selection and artificial sampling for
several generations, has consistently shown segregation
distortion (Shen et al. 2007; Tang et al. 2015; Jamshed et al.
2016). In the present study, 41.08% of mapped loci showed
segregation-distorting, comprising 55 SDRs. In particular,
chr09 and chr13 possessed more than 80% distorted loci.
The vast majority of these loci and SDR skewed to par-
ent Yumian 1. Several similar results have been reported
in other populations developed from Yumian 1 (Liu et al.
2015a; Tang et al. 2015), suggesting that segregation dis-
torted alleles are widespread in the genome of Yumian 1
and affect many other genetic backgrounds. Yumian 1 was
developed from a multiple-cultivar intermating program,
and its complicated genetic background may play a role
in segregation distortion. The same situation also occurred
in other cultivars such as line 7235 (Chen et al. 2009;
Zhang et al. 2012), which was considered to contain intro-
gressed chromatin segments or genes from G. anomalum or
G.barbadense.
Segregation distortion may arise from lethality, gametic
selection, partial male or female sterility, zygotic selec-
tion, or pollen spine development (Song et al. 2006). In the
present study, many QTLs were identified on SDR, which
Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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can also be found in previous studies. However, segrega-
tion distortion does not intensively impact the estimation of
QTL position and effect (Zhang et al. 2010). For example,
qFS07.1 detected by Tan et al. (2015), which was located
on SDR, has been fine-mapped and found underlying can-
didate gene by Fang et al. (2017).
Comparison between QEI mapping
and single‑environment QTL mapping
The stability of QTL and their interaction with environ-
ment could be directly analyzed by QEI mapping (Li et al.
2015b). In this study, for example, qSI15.1 had average
Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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additive effect of 0.15 and QEI effects in the six environ-
ments of 0.00, −0.03, 0.02, −0.08, 0.00 and 0.09, respec-
tively. On the other hand, QEI effects for qBW08.1 were
−0.08, 0.01, 0.07, −0.02, −0.09 and 0.12, with average
additive effect of only 0.04. The much larger interactions
for qBW08.1 showed the higher level of QEI, and less sta-
bility. Most QTLs identified by QEI mapping can also be
detected in single-environment mapping. Furthermore,
single-environment mapping can detect some QTLs which
could not be identified by QEI mapping. For QTLs detected
by both methods, the LOD peak score by single-environ-
ment mapping fluctuated around the position estimated by
QEI mapping. Although LOD thresholds in QEI mapping
were much higher than in single-environment analysis,
additive effects calculated by QEI mapping were relatively
low. This phenomenon was also found in a previous study
(Li et al. 2015b), and may be caused by the different algo-
rithms used by them.
Common and stable QTL
In the present study, a total of 74 fiber quality and 39
yield component traits QTLs were identified. Based on
linked DNA markers in common with prior studies, 32
QTLs identified in this study, including 20 for fiber qual-
ity and 12 for yield component traits, were inferred to be
the same as QTLs that were reported before. In addition,
compared with previous reports, 28 fiber quality QTLs
and nine yield component QTLs were found to have simi-
lar positions to QTLs listed in the Cotton QTLdb (http://
Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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www.cottonqtldb.org) (Said et al. 2015a). The compari-
sons were, respectively, listed as supplementary material
in Tables S6 and S7. Besides, three clusters in the pre-
sent study were found to have similar positions to QTL
clusters reported by Said et al. (2015b) (Table S5). These
common QTLs and common clusters could be used for
future research on gene identification, and even breeding
practices through MAS.
Among the 113 QTLs in the present study, 29 were
identified by both methods. Twenty-eight QTLs were
Fig. 2 continued
Mol Genet Genomics
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Fig. 2 continued
Mol Genet Genomics
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identified as potential stable QTLs by single-environ-
ment analysis, and 40 were identified with potential
stability through QEI mapping. Among them, 18 QTLs,
including four FL QTLs, five FM QTLs, two FS QTLs,
three LP QTLs and four SI QTLs, were identified to be
potential stable QTL by both methods. Among the 18
QTLs, qFL14.1, qFL17.1, qFM07.1 and qFS07.1 had
been identified with stability by Tan et al. (2015), and
the other 14 stable QTLs were newly identified in the
present study (Table 5). The 18 stable QTLs may be
Gh088
0.0
TMB1638a CER0095a
0.6
NAU3985
0.9
NBRI1594*
1.7
DC40310*
2.0
PGML3199***
5.4
SWU21728***
6.9
PGML3974b***
10.6
PGML4893***
10.9
HAU2901***NBRI2026***
12.4
SWU07-136*** SW U21761*** SWU21756***
13.0
NBRI2188***
21.6
SWU00749***
23.6
MUCS535***
26.9
NBRI2071***
29.4
DPL0553*** NBRI1537*** DC20120***
35.3
SWU00619***
36.2
JESPR153b***
38.6
SWU22173***
38.9
PGML4089b***
39.1
COT009***
44.3
NBRI2101***
48.5
DC20092***
53.5
BNL1040*
56.3
SWU20561a
59.4
DPL0398
61.2
NBRI1292
61.5
MUSS603***
61.8
SWU22423***
66.9
SWU22413***
68.0
SWU22449***
73.2
NBRI1639***
73.6
BNL2571***
79.9
SWU06-026***
83.6
SWU22498***
86.0
Chr13
DPL0201*
0.0
NBRI1791*NBRI1663* STV016*
CER0095b*CER0084*
0.3
SWU00738**
0.6
TMB1638b**
1.2
BNL0645**
1.4
SWU21718***
1.5
CER0168
5.9
SWU21732
7.8
BNL1660
7.9
MUSB0285
9.1
PGML3974a
11.2
PGML0799b***
16.1
PGML3122
27.4
SWU00320 NAU2697
32.4
SWU10443
34.4
SWU13669
36.7
SWU01940
38.9
Gh443
39.3
NAU0748*
41.7
NAU3636**
42.3
SWU11353*
43.0
PGML1206***
44.3
SWU22061***
46.4
PGML1511***
49.2
BNL1721***
50.0
DC40094***
50.6
NAU3211***
52.4
PGML4750*** PGML4301*** JESPR153a***
BNL3280***
53.0
BNL3473***
53.2
SWU22174*** SWU22176***
54.1
BNL3479*** DC40426***
55.9
DPL0864***
56.1
PGML4089a***
59.3
DPL0420***
59.7
MUSB0551***
60.1
PGML3565*** NAU2443*** STV010***
61.6
MUSB0685***
61.9
TMB1767***
62.1
SWU22290*** SWU22296*** PGML4650***
65.5
SWU22301***
67.0
Gh060*** Gh501*** SWU22317***
67.3
DPL0390*
74.0
SWU22381
75.1
NAU3589
77.8
PGML0075
78.2
PGML3792
78.5
NBRI0822 NBRI1600
78.8
BNL2667
82.9
CIR216*** Gh303***
104.7
SWU07-104*** HAU2977***
105.0
qBW18.1
qSI18.1
qFM18.1
Chr18
SDR18.2
SDR18.
3
SDR18.1
SDR13.1
SDR13.2
Fig. 2 continued
Mol Genet Genomics
1 3
valuable resources for fiber quality and yield improve-
ment by pyramiding favorable alleles into improved cot-
ton cultivars.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethical standards This article does not contain any studies with
human participants or animals performed by any of the authors.
Funding This research was supported by the National Natural Sci-
ence Foundation of China (Grant Numbers 31571720 and 31371671).
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Table 5 Stable quantitative trait locus (QTL) for fiber quality and yield component traits identified in the RIL population
FL fiber length, FM fiber micronaire, FS fiber strength, LP lint percentage, SI seed index
Trait QTL Chr. Location (cM) Nearest Locus LOD Additive effect PVE (%) Compare with stable QTL
of Tan et al. (2015)
FL qFL01.1 1 0 NAU3346b 3.1 −0.4 7.7 New
qFL14.1 14 116.42 SWU14644 3.7 −0.35 9.0 Same
qFL17.1 17 25.53 SWU11976 3.2 −0.45 7.9 Same
qFL26.1 26 66.85 NAU1298 3.2 −0.35 7.8 New
FM qFM05.1 5 160.87 SWU03915 2.4 0.09 5.9 New
qFM07.1 7 68.82 PGML3165b 3.8 0.11 9.3 Same
qFM20.2 20 114.29 NAU6293b 2.3 0.09 5.6 New
qFM23.1 23 79.12 Gh499 3.0 0.16 7.5 New
qFM25.1 25 116.79 NAU3427 3.2 0.11 7.8 New
FS qFS07.1 7 67.95 SWU03760 5.5 −0.77 13.2 Same
qFS14.2 14 115.96 SWU14643 3.7 −0.85 9.1 New
LP qLP14.1 14 124.50 PGML0048b 2.4 0.51 6.0 –
qLP19.1 19 62.91 PGML3255 3.5 0.63 8.6 –
qLP23.1 23 48.94 NAU6418 2.8 0.73 6.8 –
SI qSI07.1 7 78.96 C2-0114 6.6 −0.36 15.6 –
qSI15.1 15 4.85 SWU10910 3.3 0.26 8.1 –
qSI17.1 17 86.62 NBRI1957 2.9 0.23 7.1 –
qSI20.1 20 52.70 HAU0748 3.6 0.28 8.8 –
Mol Genet Genomics
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