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ONLINE RESOURCES

Association between SSR markers and fiber traits in sea-island cotton (Gossypium barbadense) germplasm resources

QI MA1,2†, JING ZHAO3†, HAI LIN1, XINZHU NING1, PING LIU1, FUJUN DENG1, AIJUN SI1

and JILIAN LI1*

1 Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science / Northwest

Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Shihezi 832000, P.R.China 2 State Key Laboratory of Cotton Biology, Anyang 455000, P.R.China

3 Crop Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000,

P.R.China † The authors contributed equally to this work

* For correspondence. E-mail: [email protected]

Running title: Marker-trait associations in sea-island cotton

Key words: Gossypium barbadense, Germplasm resources, Fiber traits, Association analysis, SSR

marker

Abstract

Identification of molecular markers associated with fiber traits can accelerate cotton marker-assisted

selection (MAS) programs. In this study, Gossypium barbadense germplasm accessions with diverse

origins (n = 123) were used to perform association analysis of fiber traits with 120 polymorphic simple

sequence repeat (SSR) markers. In total, 120 polymorphic primer pairs amplified 258 loci, with a mean of

2.15 loci per primer. Population structure analysis identified three main clusters for the accessions, which

indicated agreement of genetic and predefined populations. Marker–trait associations (n=58) were

detected for 10 fiber traits with 26 SSR markers located on 15 chromosomes. The R2 (phenotypic

variation explained) ranged from 3.19 % to 15.21 %. Two markers (NAU5465 and NAU3013) were

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found to be stably associated with boll number per plant (BN) and fiber uniformity (UI), respectively.

Four markers (BNL252, NAU3424, NAU3324, and CGR5202) associated with fiber quality traits

preferentially clustered on the D8 chromosome, which was thus identified as an important candidate

region for study molecular mechanisms underlying fiber quality and for use in breeding cotton cultivars

for improving fiber quality. This study generated molecular data with a potential for better understanding

of the genetic basis of the fiber traits and provided new markers for MAS in G. barbadense breeding

programs.

Introduction

Cotton is one of the most important natural fiber crops worldwide. Sea-island cotton (G. barbadense)

and upland cotton (G. hirsutum) are two cultivated tetraploid species, accounting for approximately 2%

and 95%, respectively, of the annual worldwide cotton production (Cai et al. 2014). Although G.

barbadense has some shortcomings such as low fiber yield, poor adaptability, and difficulty in picking, it

has superior fiber quality traits. The fiber traits of G. barbadense offer great potential for progress and

development of the textile industry with respect to fiber breeding; therefore, more research focus on these

traits is required. Because fiber yield and quality traits are complex quantitative traits, tagging these traits

will accelerate mining of novel genes and enable quick and efficient pyramiding of non-allelic

quantitative trait loci (QTLs) by marker-assisted selection (MAS).

Currently, linkage analysis using segregation populations and association analysis using natural

populations based on linkage map and linkage disequilibrium (LD), respectively, are the two main

methods used for studying QTLs (Wang et al. 2013). Since linkage map construction entails selecting

appropriate parents and then growing temporary or permanent populations, it is a particularly

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time-consuming process (Salvi and Tuberosa 2005). An additional limitation of linkage maps is that some

loci often do not separate and recombine when two particular parents are selected for constructing a

linkage map (Li et al. 2013).

Association analysis is an effective approach for linking phenotypes and genotypes in plants, when

information on population structure and LD is available (Thornsberry et al. 2001). With rapid

development of DNA-based molecular markers, association analysis was first successfully used for

identification of alleles at loci contributing to disease susceptibility in humans (Goldstein et al. 2003).

Recently, this effective approach has been widely used in various plant species such as wheat (Arief et al.

2009; Gouis et al. 2011; Wang et al. 2014), maize (Thornsberry et al. 2001), rice (Agrama et al. 2007;

Park et al. 2009), soybean (Gillman et al. 2014), and potato (Gebhardt et al. 2003; D’Hoop et al. 2014),

to identify marker-trait associations. Association analysis has also been used in cotton (Gossypium spp.),

especially G. hirsutum to identify associations between markers and a variety of phenotypic traits such as

fiber quality traits (Abdurakhmonov et al. 2009; Cai et al. 2014; Nie et al. 2016), yield traits

(Abdurakhmonov et al. 2007; Wu et al. 2008; Wang et al. 2013; Iqbal and Rahman 2017 ), agronomic

traits (Yang et al. 2013; Liang et al. 2014), early-maturating traits (Li et al, 2016), and salt tolerance

(Shao et al. 2015).

All association analysis studies mentioned above focused on the phenotypic traits of G. hirsutum.

However, G. barbadense has superior fiber quality traits and harbors many elite fiber genes. Therefore, it

is necessary to perform an association analysis to identify QTLs associated with fiber traits in G.

barbadense germplasm, with the objective of pyramiding elite alleles and promoting marker-assisted

selection in cotton breeding programs.

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Materials and methods

G. barbadense germplasm used in this study was derived from a natural population which consisted

of 123 representative G. barbadense accessions (Supplemental Table 1), including 113 accessions

developed in China, 7 accessions (Pima 3-79, Pima 90, Pima cotton, Pima 3, Pima 5, Pima Ø, and uoc620)

introduced from USA and 3 accessions (Bahatini, Minufei, and Luoxiya 1) introduced from Africa.

A field experiment was carried out at the Korla experimental station of the Xinjiang Academy of

Agricultural and Reclamation Science, Korla, Xinjiang province, a region most suitable for G. barbadense

growth in China. In both 2014 and 2015, 123 G. barbadense germplasm accessions (two replicates each)

were planted in a randomized plot design with a single row plot and 80 individuals per row. A specific

wide-narrow planting pattern was used, such that the row spacing was 66 (±10) cm, with 12 cm between

plants, and the plot size of each accession was 3.75 m2.

Yield traits were measured in early October of 2014 and 2015 (Supplemental Table 2). Ten plants

growing close to each other were selected to count the total boll number and the average was scored as

boll number per plant (BN). A total of thirty bolls were harvested to determine weight per boll (BW) and

lint percentage (LP). Seed cotton weight per plant (SWP) and lint weight per plant (LWP) were calculated

based on the BN, BW, and LP. After ginning, fibers were mixed well and 10–15g fibers were randomly

sampled for each plant material. Fiber samples were independently tested for fiber quality traits using

HVI system (HFT 9000, Uster Technologies, Switzerland) at 20 °C and 65% relative humidity, at the

Cotton Fiber Quality Inspection and Testing Centre, Ministry of Agriculture of China (Anyang, Henan,

China). Variables tested included upper half mean length of fiber (UHML), fiber strength (STR),

micronaire value (MIC), fiber elongation rate (ELO) and fiber uniformity (UI).

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Genomic DNA was isolated from the G. barbadense germplasm as described by Paterson et al. (1993).

SSR markers with broad genome-wide coverage (n =500) were used to screen the polymorphisms in 12

randomly selected accessions from the 123 G. barbadense germplasm accessions. Detailed information

on the microsatellite markers, including primer sequences, can be accessed at the cotton microsatellite

database (https://www.cottongen.org/search/markers).

Simple sequence repeat polymerase chain reaction (SSR-PCR) amplifications were performed using

PCR Veriti 96-well Thermal cycler (ABI, USA). The total reaction volume for PCR was 10 μl and

reaction mix consisted of 1µl DNA extract (20 ng/µl), each primer (300 nM), 400 µl dNTPs (lM), 0.5 U

of Taq polymerase, and 1 µl PCR reaction buffer. Amplifications were performed under the following

conditions: (i) 95 °C for 5 min, (ii)15 cycles of 94 °C for 45 s, 65 °C for 45 s with a reduction of 1 °C per

cycle, and 72 °C for 1 min, (iii) 25 cycles of 95 °C for 5 min, 45 s annealing at [optimum annealing

temperature for each primer pair (Tm) - 5°C], and 72 °C for 1 min, and (iv) a final step of 72 °C for 10

min. Electrophoresis and staining were performed as described by Zhang et al. (2000).

The STRUCTURE v2.3.4 (Pritchard and Wen 2004, http://pritch.bsd.uchicago.edu/software.html), a

model-based Bayesian method, was used to subdivide the G. barbadense germplasm accessions into

individual clusters, based on codominant genotypic data. For each run, the burn-in time was 50 000 and

the number of replications was 100 000 (Pritchard and Wen 2007). Pritchard introduced a model-based

clustering method to infer population structure and assign individuals to populations using multilocus

genotype data.

The TASSEL software (version 2.1, http://www.maizegenetics.net) was used to perform association

analysis of fiber yield and quality traits. The mixed linear model (MLM) approach was used to conduct

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marker- trait association tests. The MLM association test was performed by simultaneous accounting of

multiple levels of population structure (Q-matrix) and relative kinship among the individuals (K-matrix)

according to Yu et al. (2006). The population structure matrix (Q) was identified by running

STRUCTURE after K value was determined. The P-value determined whether a QTL was associated with

the marker and the R2-marker evaluated the magnitude of the QTL effects (Agrama et al. 2007). The R2

value represented the correlation between alleles at two loci, which is informative for evaluating the

resolution of association approaches (Kantartzi et al. 2008).

The SPSS 21.0 software (http://www.spss.com.cn/) was used to conduct variation, correlation

and principal component analysis (PCA). The broad-sense heritability (hB2) of each trait was estimated

using SAS 8.1 software (SAS Institute 1999).

Results

Fiber yield and quality properties of G. barbadense germplasm

123 G. barbadense germplasm accessions used in this study revealed a wide range of phenotypic

variation in fiber yield and quality traits including BW, BN, SWP, LWP, LP, UHML, UI, MIC, STR, and

ELO. LWP had the highest coefficient of variation (CV) of 48.53% in the E1 (2014 in Korla) environment,

whereas UI had the lowerest CV (1.66%) in the E2 (2015 in Korla) environment. The CV of most fiber

traits was all over 10%, which indicated a great variation in fiber traits in G. barbadense germplasm. The

broad sense heritability (hB2) for ten traits ranged from 30.43% to 73.14% in the E1 environment and

ranged from 32.46% to 80.23% in the E2 environment. The highest hB2 value was for LP (73.14% in the

E1 environment and 80.23% in the E2 environment), indicating that LP was less affected by

environmental factors than the other nine traits (Table 1).

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Correlation analysis of fiber yield and fiber quality traits

Correlations among five fiber yield traits and five fiber quality traits of G. barbadense germplasm

are listed in Table 2. We observed significant trait correlations among ten fiber traits in the E1 and E2

environments. The following variables were positively correlated (P < 0.01) in both environments: BW

with LWP, LP, MIC, and ELO; BN with LWP; SWP with LWP; LP with MIC; UHML with UI, STR, and

ELO; UI with STR, and ELO; MIC with ELO; and STR with ELO. The following variables were

negatively correlated (P < 0.01): BN with LP, STR, and ELO. These results indicated that there were

positive correlations among fiber yield traits and among fiber quality traits but negative correlations

between fiber yield and quality traits.

Principal component analysis of phenotypic traits

Results of the PCA of ten fiber traits of G. barbadense germplasm are shown in Table 3. Based on the

principle of eigenvalue > 1, the former three principal components, with cumulative rates of 83.475%,

were selected, which could relatively comprehensively reflect all the information. To be specific, the first

principal component showed the maximum contribution (43.754%). Among the eigenvectors of the first

principal component, the major phenotypic traits with relatively high load and positive sign were STR

and ELO, which mainly reflected the fiber quality traits. Among the eigenvectors of the second principal

component, major phenotypic traits with relatively high load and positive sign were SWP and LWP,

which mainly reflected the yield traits. Among the eigenvectors of the third principal component, major

phenotypic traits with relatively high load and positive sign were BN and BW, which also reflected the

yield traits. (Table 3)

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SSR-marker polymorphism

Of the 500 SSR markers with broad genome-wide coverage, 120 SSR markers (Supplemental Table

3 includes the list of 120 SSR primers with their repeat motif and chromosomal locations, as reported in

literature) showed polymorphism reproducibility and locus specificity and they covered 258 alleles

among the 123 G. barbadense germplasm accessions assayed. The number of polymorphic alleles per

locus ranged from 1 to 4, with a mean of 2.15. The genetic diversity index ranged from 0.000 to 0.693,

with an average of 0.673.

Population structure

The population structure of the G. barbadense germplasm was determined using the STRUCTURE

v2.3.4 software. The [LnP (D)] value increased continuously with K values ranging from 1 to 10 (Fig.

1A). Therefore, the most likely number of subpopulations (K) was determined according to Delta K value

(Evanno et al. 2005). The maximum (peak) Delta K value (148.84) was observed for K =3 (Fig. 1 B),

which indicated that the entire population could be divided into 3 subpopulations (Fig. 2).

The STRUCTURE model-based analysis showed that the model of three different subpopulations

had the highest posterior probability. The G. barbadense germplasm was assigned to three subpopulations

with 50% or higher probability. The three subpopulations, designated as group 1, group 2, and group 3,

consisted of 43, 41 and 29 germplasm accessions, respectively. A particularly noteworthy finding was that

several germplasm lines from USA, such as Pima 90, Pima 3-79, Pima 3 and Pima 5, with long fruit

branches and loose plant type, were in the group 1. The group 2 and 3 contained almost all germplasm

accessions with short fruit branches and compact plant type. In general, the germplasm accessions with

compact plant type and short fruit branches had better fiber quality than those with loose plant type and

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long fruit branches. These results indicated that fiber quality traits were very important factors for

clustering of the G. barbadense germplasm, which was substantially consistent with the result that fiber

quality traits were the first principal component analyzed by PCA. The remaining 10 germplasm

accessions failed to group with a probability higher than 50%. These germplasm accessions with mixed

ancestral genetic backgrounds, including a few Xinjaing G. barbadense germplasm, were artificially

assigned to the ‘‘mixed group’’.

Marker-trait association

Association analysis identified marker–trait associations (P < 0.05) for all the traits examined. In total,

58 marker–trait associations, including 33 marker loci associated with fiber yield and 25 loci associated

with fiber quality traits, were identified using MLM of Tassel 2.1 software, with 26 SSR markers located

on 15 chromosomes (Table 4 and 5). The R2 value ranged from 3.19% (BNL226) to 15.21% (HAU2768).

There were 38 and 20 marker loci detected in the E1 and E2 environments, respectively. It is worth

mentioning that the markers NAU5465 and NAU3013 were found to be associated with BN and UI,

respectively, in both environments. We also discovered that some SSR markers were simultaneously

associated with more than one fiber trait: HAU2146 with BW, SWP, and LWP; NAU3013 with BW, BN,

and LWP; and NAU3110 with BN, SWP, and LWP. Seven marker loci were significantly associated with

two fiber yield traits each: NAU797 and NAU5465 with BN and LWP, NAU5120 with BW and LP,

HAU2768 with BN and LP, HAU2146 with BW and SWP, NAU2687 with SWP and LWP, NAU803

with LWP and LP (Table 4). In addition, a single marker locus (NAU3110) was significantly associated

with four fiber quality traits (UHML, STR, MIC, and UI) whereas another one locus (NAU3791) was

significantly associated with two fiber quality traits (UHML and MIC). Three marker loci were

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significantly associated with three fiber quality traits: PGML01548 with UHML, STR, and ELO;

NAU3013 with UHML, STR, and UI; and HAU2828 with STR, MIC, and ELO (Table 5).

In the present study, we found that some SSR marker loci associated with fiber yield and quality

traits preferentially clustered on specific chromosomes. Three SSR marker loci (NAU797, NAU3110, and

NAU1102) associated with fiber yield traits (BN and YWP) clustered on the D5 chromosome, whereas

four marker loci (NAU3424, BNL252, CGR5202, and NAU3324) associated with fiber quality (UHML,

STR, MIC, and ELO ) clustered on the D8 chromosome. LD (R2 values) among markers varied from

3.19% to 15.21% (Table 4 and 5). Of the 58 marker–trait associations, the association of HAU2768 with

BN and LP accounted for 10% or more of the total variation.

Discussion

Association analysis was first used in human populations to identify loci controlling disease

susceptibility (Risch and Merikangas 1996; Schafer and Hawkins 1998) and this approach has since been

widely used in numerous plants to identify DNA polymorphisms associated with various phenotypes.

However, association analysis has almost rarely been used in G. barbadense because of its scarce

resources and poor adaptability. Therefore, identification of associations between markers and phenotypic

traits in G. barbadense in this study will contribute to promoting the use of the association analysis in

cotton research and furthermore, verify the associated markers identified in G. hirsutum.

Ideally, an association analysis should include as much phenotypic and genotypic diversity as can be

stably measured in a common environment. However, owing to scarce resources worldwide, our sample

was restricted to 123 germplasm accessions approximately double of those used in Wang et al. (2013).

The level of detected diversity was relatively high, with an average value of 0.673 (ranging from 0.000 to

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0.693). The mean number of polymorphic alleles per locus was 2.15, which was higher than that reported

by Wang et al. (2013) (1.66 loci per primer) based on 56 G. barbadense accessions. The genetic diversity

and the number of SSR loci in the population in our study for association analysis were higher and lower,

respectively, than those observed in prior studies in G. hirsutum (Mei et al. 2013; Qin et al. 2015), which

indicated that the germplasm in our study showed a high level of genetic variation.

Population structure is an important factor that typically leads to spurious associations. Therefore,

conducting population structure analysis on natural population is a prerequisite for association analysis

(Kline et al. 2001; Flint-Garcia et al. 2005). The results of the STRUCTURE analysis (K=3)

demonstrated that the G. barbadense germplasm was divided into 3 subgroups and the G. barbadense

cotton accessions from USA were significantly different from those from Xinjiang, China. Further

analysis revealed that the Xinjiang germplasm accessions did not belong to the same subgroup. The

results indicated that there was still frequent gene exchange among G. barbadense cotton of Xinjiang.

Because population structure analysis does not require any prior knowledge of the origin, geographic

distribution, phenotypic characteristics, and other factors, it can truly reflect the genetic differences

between materials, excluding the interference of human factors on the subpopulation divisions (Kantartzi

and Stewart 2008 ).

In the present study, marker-trait analysis using MLM revealed significant associations between

cotton fiber traits and SSRs in the G. barbadense germplasm. In total, 58 marker–trait associations were

identified, including 33 marker loci associated with fiber yield and 25 marker loci associated with fiber

quality traits. Ideally, the associations between markers and traits should be examined in two

ways–significance of marker–trait association (P-values) using TASSEL software and the marker–trait

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associations found in other QTL studies (Kantartzi et al. 2008). Although several markers were also found

in previous QTL studies in G. hirsutum, the traits associated with a particular marker were not the same as

in G. barbadense (Table 6). Therefore, we hypothesize that the marker–trait associations identified in G.

hirsutum and G. barbadense were inconsistent because of their dissimilar genomic structures and

phenotypic traits. However, it is noteworthy that the markers on the D8 chromosome (Chr. 24) associated

with fiber strength were identified and validated in previous studies (Chen et al. 2009; Kumar et al. 2012;

Cai et al. 2014). Simultaneously, our research indicated that there are important markers related to fiber

strength (BNL252) and other fiber quality traits (NAU3424, NAU3324, CGR5202) on the D8

chromosome. Therefore, we believe that this chromosome is an important candidate region for the study

of molecular mechanisms underlying fiber quality and for use in breeding cotton cultivars for improving

fiber quality.

In summary, whole-genome association studies are advantageous in that they enable the entire

genome to be assessed for trait-associated variants. These studies have been proved effective for

elucidating the genetic basis of complex traits in plants (Ingvarsson and Street 2011). Application of

association mapping to plant breeding appears to be a promising means of overcoming the limitations of

conventional linkage mapping. This study demonstrated that SSR markers associated with fiber traits of

G. barbadense germplasm provided a reference and basis for identification of more elite genes of fiber

traits in G. barbadense and enhanced the data from QTL studies for the implementation of MAS.

Acknowledgments

This work was financially supported by the State Key Laboratory of Cotton Biology Open Project Fund

(CB2015A10), the Youth Fund of Xinjiang Academy of Agricultural and Reclamation Science

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(YQJ201503), the National Natural Science Fund (31260360) and the Special Fund of Corps Breeding

(2016AC027).

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Yang X. L., Zhou X. D., Liu H. W., Wang X. F., Wu L. Q., Li Z. K. et al. 2013 Aflp markers associated with important agronomic traits in cotton. Cotton Science. 25, 211–216.

Yu J., Pressoir G., Briggs W. H., Vroh Bi. I., Yamasaki M., Doebley J. F. et al. 2006 A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics. 38, 203–208.

Yu Y., Yuan D. J., Liang S. G., Li X. M., Wang X. Q., Lin Z. X. et al. 2011 Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC1 population between gossypium hirsutum and G. barbadense. BMC Genomics. 12, 1–14.

Zeng L. H., Jr W. R. M., Gutiérrez O. A. and Boykin D. L. 2009 Identification of associations between ssr markers and fiber traits in an exotic germplasm derived from multiple crosses among gossypium, tetraploid species. Theoretical and Applied Genetics. 119, 93-–103.

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16

Received 3 December 2016, in final revised form 12 March 2017; accepted 24 March 2017 Unedited version published online: 28 March 2017

Figure Legends

Fig.1. Estimated LnP(D) and Delta K from 10 iterations obtained through STRUCTURE 2.3.3 analysis

(A)LnP(D) for k values from 1 to 10 for simulations using the G. barbadense germplasm.

17

B) ΔK for k values from 2 to 9 for the G. barbadense germplasm.

Fig. 2. The summary plot of sub-population structures in the G. barbadense germplasm analyzed using

STRUCTURE 2.3.3 by Q-matrix estimates (k = 3).

Groups are represented in different colors as shown in figure legends. Each column represents one G.

barbadense germplasm accession and partitioned into segments representing admixture of ancestral

composition. The length of segments represents the percentage of a single ancestral background in that

line. The columns (123 in total) were assigned to three groups

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Tables Table 1. Summary of fiber traits of G. barbadense germplasm in the E1 and E2 environments

Parameters a Environments b Fiber traits c

BW BN SWP LWP LP UHML UI MIC STR ELO

Mean E1 2.92 10.40 29.64 9.50 30.64 36.78 88.20 3.95 45.32 6.96

E2 3.09 10.20 31.47 10.03 31.78 35.82 86.74 3.68 41.50 6.77

Minimum E1 1.86 4.60 13.66 4.03 18.21 30.94 84.00 2.59 31.20 6.40

E2 2.32 4.60 13.82 4.46 21.19 29.56 81.90 2.23 28.60 6.40

Maximum E1 3.59 31.40 52.79 53.12 37.79 39.74 91.90 4.92 58.50 6.96

E2 4.19 17.40 58.52 18.21 36.76 39.43 89.80 4.69 54.90 7.10

SD E1 0.35 3.03 7.40 4.61 2.92 1.89 1.59 0.44 5.88 0.15

E2 0.33 2.33 7.81 2.81 2.85 2.16 1.44 0.44 5.64 0.15

CV(%) E1 11.99 29.13 24.97 48.53 9.53 5.14 1.80 11.14 12.97 2.16

E2 10.68 22.84 24.82 28.02 8.97 6.03 1.66 11.96 13.59 2.22

hB2(%) E1 42.36 30.43 48.47 54.86 73.14 52.15 40.14 46.32 52.21 60.34

E2 32.46 41.26 50.32 46.15 80.23 40.36 45.80 52.24 40.36 52.13a SD, standard deviation; CV, coefficient of variation; hB

2, the broad sense heritability. b E1, Korla in 2014; E2, Korla in 2015.

c BW, weight per boll; BN, boll number per plant; SWP, seed cotton weight per plant; LWP, lint weight per plant; LP,

lint percentage; UHML, upper half mean length of fiber; UI, fiber uniformity; MIC, micronaire value; STR, fiber

strength; ELO, fiber elongation rate.

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Table 2. Correlations of fiber yield and quality traits of G. barbadense germplasm in E1 and E2 environment

Traits a BW BN SWP LWP LP UHML UI MIC STR ELO

BW 0.814** 0.079 0.360** 0.373** 0.316** 0.287** 0.234** 0.351** 0.332**

BN 0.099 0.015 0.364** -0.538** 0.119 0.233** 0.552** -0.337** -0.383**

SWP 0.355** b 0.561** 0.898** 0.842** 0.060 0.122 0.035 0.134 0.085

LWP 0.255** 0.836** 0.345** 0.950** 0.198* 0.241** 0.142 0.287** 0.227*

LP 0.268** -0.392** 0.043 -0.187* -0.128 0.238** 0.316** 0.300** 0.280**

UHML 0.105 -0.113 -0.108 -0.080 -0.289** 0.716** -0.093 0.757** 0.616**

UI 0.156 -0.206* -0.071 -0.159 -0.078 0.632** 0.191* 0.792** 0.711**

MIC 0.353** -0.095 0.078 0.128 0.419** -0.235** 0.155 0.290** 0.402**

STR 0.228* c -0.236** -0.163 -0.092 -0.015 0.693** 0.745** 0.073 0.853**

ELO 0.355** -0.232** -0.113 0.003 0.204* 0.426** 0.614** 0.455** 0.703**

Correlation coefficients on the bottom left were the coefficients of the traits in E1, and those on the top right were

the coefficients of the traits in E2. a See Table 1 for abbreviations. b Significant at P<0.01 level. c Significant at P<0.05 level.

Table 3. Eigenvectors and percentages of accumulated

contribution of principal component analysis(PCA)

Phenotypic traits a Component

1 2 3

BW 0.612 -0.056 0.546*

BN 0.653 0.083 0.701*

SWP 0.447 0.757* -0.459

LWP 0.688 0.680* -0.193

LP 0.729 0.673 -0.021

UHML 0.622 -0.499 -0.412

UI 0.725 -0.445 -0.287

MIC 0.438 0.022 0.559

STR 0.814* b -0.441 -0.203

ELO 0.776* -0.431 -0.070

Eigen value 4.375 2.325 1.647

Contribution rate (%) 43.754 23.248 16.473

Cumulative percentage (%)

43.754 67.002 83.475

20

a See Table 1 for abbreviations. b The relatively high absolute value of each index in all factors

Table 4 Simple sequence repeat (SSR) markers associated with the same yield

component traits in two different environments using the MLM method

Traits a Environments b Marker loci Chromsome R2(%) P-value

BW E1 BNL226 Chr.03(A03) 3.19 0.0499 HAU2146 Chr.09(A09) 4.01 0.0282 NAU5120 Chr.07(A07) 3.62 0.0370 NAU2820 Chr.07(A07) 3.35 0.0446 NAU5163 Chr.01(A01) 6.35 0.0061 E2 NAU3189 Chr.26(D12) 4.27 0.0242 NAU3013 Chr.10(A10) 3.36 0.0452 BN E1 JESPR232 Chr.08(A08) 3.62 0.0368 NAU5465 Chr.14(D02) 3.79 0.0325 NAU3013 Chr.10(A10) 4.21 0.0245 NAU797 Chr.19(D05) 4.65 0.0182 HAU2768 Chr.06(A06) 12.08 0.0002 E2 NAU5465 Chr.14(D02) 6.13 0.0070 NAU3110 Chr.19(D05) 4.34 0.0225 SWP E1 HAU2146 Chr.09(A09) 5.26 0.0126 NAU2687 Chr.25(D06) 7.17 0.0037 E2 NAU3110 Chr.19(D05) 5.45 0.0109 NAU5465 Chr.14(D02) 4.96 0.0150 NAU1102 Chr.19(D05) 3.34 0.0451 LWP E1 HAU2146 Chr.09(A09) 3.22 0.0491 NAU797 Chr.19(D05) 3.43 0.0425 NAU3791 Chr.04(A04) 5.69 0.0094 NAU3013 Chr.10(A10) 5.89 0.0082 NAU2687 Chr.25(D06) 6.06 0.0074 E2 NAU3110 Chr.19(D05) 6.36 0.0060 NAU5465 Chr.14(D02) 5.18 0.0129 HAU2828 Unknown 3.94 0.0295 NAU803 Chr.14(D02) 3.60 0.0374 LP E1 NAU5120 Chr.16(D07) 3.33 0.0466 NAU803 Chr.14(D02) 3.87 0.0323 NAU1322 Chr.24(D08) 6.20 0.0070 BNL1604 Chr.16(D07) 8.47 0.0017 HAU2768 Chr.06(A06) 15.21 0.0000

a See Table 1 for abbreviations. b E1, Korla in 2014; E2, Korla in 2015

Table 5 Simple sequence repeat (SSR) markers associated with the same fiber quality traits in two different environments using the MLM method

Traits a Environments b Marker loci Chromsome R2(%) P-value

21

UHML E1 PGML0154 Unknown 3.26 0.0482 NAU3481 Chr.21(D11) 4.28 0.0241 NAU797 Chr.19(D05) 4.81 0.0168 NAU3791 Chr.04(A04) 4.90 0.0158 NAU3013 Chr.10(A10) 6.47 0.0058 E2 NAU3110 Chr.19(D05) 4.20 0.0253 NAU3424 Chr.24(D08) 3.56 0.0391

STR E1 BNL252 Chr.24(D08) 3.70 0.0345 NAU3110 Chr.19(D05) 3.91 0.0297 HAU2828 Unknown 4.33 0.0223 NAU3013 Chr.10(A10) 4.51 0.0198 PGML0154 Unknown 5.97 0.0075

MIC E1 HAU2828 Unknown 4.47 0.0222 NAU2083 Chr.01(A01) 5.50 0.0113 E2 NAU3791 Chr.04(A04) 6.47 0.0057 NAU3110 Chr.19(D05) 6.13 0.0071 CGR5202 Chr.24(D08) 4.03 0.0281

UI E1 NAU3013 Chr.10(A10) 3.39 0.0433 E2 NAU1102 Chr.19(D05) 3.50 0.0409 NAU3110 Chr.19(D05) 3.39 0.044 NAU3013 Chr.10(A10) 3.36 0.045

ELO E1 HAU2828 Unknown 3.47 0.0404 NAU3324 Chr.24(D08) 3.48 0.0401 PGML0154 Unknown 7.68 0.0026 E2 NAU2820 Chr.07(A07) 4.15 0.0262

a See Table 1 for abbreviations. b E1, Korla in 2014; E2, Korla in 2015

Table 6 Comparison of associated SSR markers with other researches. Our research Previous researches

Markers Traits a Traits a Reference NAU803 LWP FF b Cai et al. 2014; Shen et al. 2005 NAU5163 BW UHML Duan 2015 NAU2687 SWP, LWP STR,ELO Qian 2009 NAU3110 LWP,UHML UHML, STR Duan 2015; Qin et al. 2015 PGML01548 STR; ELO Senescence-related Trait Li 2015 BNL226 BW LP Qian 2009 BNL1604 LP LP Zeng et al. 2009 JESPR232 BN STR Qian 2009 NAU797 BN; UHML Verticillium wilt resistance Mei 2012 NAU2083 MIC BN Said et al. 2015

a See Table 1 for abbreviations. b FF, Fiber fineness

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Supplemental Table1 Name and origin of 123 G. barbadense germplasm accessions

No. Code Name Origin No. Code Name Origin

1 H1 Xinhai 47 Xinjiang, China 63 H63 07NH-15 Xinjiang, China

2 H2 S0717 Xinjiang, China 64 H64 07NH-17 Xinjiang, China

3 H3 k399 Xinjiang, China 65 H65 Chang A11-3 Xinjiang, China

4 H4 TH-314 Xinjiang, China 66 H66 08H-6 Xinjiang, China

5 H5 Yuanlong 5 Xinjiang, China 67 H67 HS11-3 Xinjiang, China



8 H8 Ta 08-362 Xinjiang, China 70 H70 02NH-10 Xinjiang, China

9 H9 Xinhai 28-1 Xinjiang, China 71 H71 07NH-68 Xinjiang, China

10 H10 Changfeng 1 Xinjiang, China 72 H72 06NH-2 Xinjiang, China

11 H11 Y-163 Xinjiang, China 73 H73 07NH-16 Xinjiang, China

12 H12 G-92 Xinjiang, China 74 H74 07NH-20 Xinjiang, China

13 H13 DJ08-378 Xinjiang, China 75 H75 08NH-2 Xinjiang, China

14 H14 Xinhai 28-2 Xinjiang, China 76 H76 08NH-3 Xinjiang, China



17 H17 k-011 Xinjiang, China 79 H79 08NH-7 Xinjiang, China

18 H18 Xinhai 21 line Xinjiang, China 80 H80 07NH-15 Xinjiang, China

19 H19 09NH-44 Xinjiang, China 81 H81 07NH-20 Xinjiang, China

20 H20 25-6H Xinjiang, China 82 H82 07NH-59 Xinjiang, China

21 H21 7-4H Xinjiang, China 83 H83 08NC-25H Xinjiang, China

22 H22 07NH-13 Xinjiang, China 84 H84 04NC-20H Xinjiang, China

23 H23 2-9H Xinjiang, China 85 H85 HS12-5 Xinjiang, China

24 H24 3-2H Xinjiang, China 86 H86 98107 Xinjiang, China


26 H26 03H-1 Xinjiang, China 88 H88 118 Xinjiang, China


28 H28 Qianjin 616-3-2 Xinjiang, China 90 H90 B3029 Xinjiang, China

29 H29 Shengli 1 Xinjiang, China 91 H91 08NC-25H Xinjiang, China

30 H30 Xinhai 3 Xinjiang, China 92 H92 07N-88H Xinjiang, China

31 H31 Xinhai 6 Xinjiang, China 93 H93 07N-89H Xinjiang, China

32 H32 Xinhai 7 Xinjiang, China 94 H94 06NC-28H Xinjiang, China

33 H33 H8645 Xinjiang, China 95 H95 08NC-25H Xinjiang, China

34 H34 H858 Xinjiang, China 96 H96 07NC-33H Xinjiang, China



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39 H39 Xinhai 15 Xinjiang, China 101 H156 Pima 3-79 USA

40 H40 Xinhai 16 Xinjiang, China 102 H157 Pima 90 USA

41 H41 Xinhai 20 Xinjiang, China 103 H158 Yuejin 1 China



44 H44 Xinhai 25 Xinjiang, China 106 H161 A6303 China

45 H45 Xinhai 26 Xinjiang, China 107 H162 Xinong 27 China

46 H46 Xinhai 28 Xinjiang, China 108 H163 Bahatini Africa

47 H47 Ba202 Xinjiang, China 109 H164 Yuan mou 1 China

48 H48 Tianchang 10 Xinjiang, China 110 H165 Minufei Africa

49 H49 Tianchang 16 Xinjiang, China 111 H166 Kangwei 2 China

50 H50 Xinhai 19 Xinjiang, China 112 H167 Xibei cotton China

51 H51 07NH-20 Xinjiang, China 113 H168 uoc620 USA

52 H52 08NH-42 Xinjiang, China 114 H169 Luoxiya 1 Africa

53 H53 07NH-15 Xinjiang, China 115 H170 Pima Ø USA

54 H54 07NH-17 Xinjiang, China 116 H171 Chnag rong 3 China




58 H58 06NH-2 Xinjiang, China 120 H175 Huadong cotton China

59 H59 06NH-16 Xinjiang, China 121 H176 Pimacotton USA

60 H60 05NH-7 Xinjiang, China 122 H177 Pima 3 USA

61 H61 06NC-12 Xinjiang, China 123 H178 Pima 5 USA

62 H62 07NH-5 Xinjiang, China

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Supplemental Table 2 Fiber Yield and quality properties of G. barbadense germplasm

in two environments E1: Korla in 2014

No. Code BW (g)

BN (No.)

SWP (g)

LWP (g)

LP

(%)

UHML (mm)

UI (%)

MICSTR

(cN.tex-1) ELO (%)

1 H1 2.80 12.20 34.10 9.82 28.80 38.65 88.40 4.23 51.20 7.10 2 H2 3.04 11.80 35.87 11.27 31.41 39.19 90.80 3.82 53.80 7.20 3 H3 2.86 13.40 38.26 13.07 34.15 37.09 88.80 4.05 44.80 7.00 4 H4 2.97 8.00 23.72 8.60 36.26 38.01 88.90 4.23 47.00 7.00 5 H5 3.00 12.80 38.40 11.26 29.33 37.81 87.50 4.00 42.90 6.90 6 H6 3.33 9.80 32.59 9.80 30.08 38.51 89.40 4.45 47.70 7.10 7 H7 2.70 9.80 26.41 8.04 30.43 38.49 89.10 3.63 45.40 6.90 8 H8 3.12 7.40 23.09 6.62 28.69 38.21 89.60 3.59 49.00 7.10 9 H9 2.66 17.20 45.75 13.42 29.32 38.42 88.90 3.49 44.60 6.90 10 H10 2.43 11.60 28.13 8.99 31.96 37.83 87.90 4.01 49.40 7.00 11 H11 3.12 10.20 31.82 8.82 27.72 36.90 87.10 3.25 40.40 6.80 12 H12 3.30 8.20 27.02 9.10 33.69 35.10 90.00 4.36 49.60 7.10 13 H13 3.28 9.40 30.83 10.25 33.23 34.95 89.80 4.41 51.80 7.00 14 H14 3.17 10.00 31.70 10.55 33.28 35.68 87.00 3.98 52.10 7.20 15 H15 2.87 8.80 25.26 7.88 31.18 38.02 90.40 4.57 52.50 7.10 16 H16 3.43 9.80 33.57 10.93 32.55 36.48 88.30 4.19 49.60 7.20 17 H17 3.14 16.00 50.24 16.96 33.76 37.53 86.50 3.14 47.70 6.80 18 H18 3.30 15.40 50.74 15.32 30.20 36.86 89.00 4.36 39.80 6.90 19 H19 3.04 14.40 43.71 12.82 29.32 36.72 90.50 4.15 47.40 7.00 20 H20 3.06 10.10 30.91 10.45 33.82 35.02 87.30 3.77 46.20 6.90 21 H21 3.25 7.20 23.36 6.80 29.12 38.89 91.00 4.04 56.10 7.20 22 H22 2.42 11.00 26.62 9.02 33.88 36.82 89.30 4.35 49.30 7.00 23 H23 2.91 10.60 30.79 9.65 31.33 37.67 88.80 4.20 43.20 7.10 24 H24 2.59 11.00 28.49 8.64 30.31 33.79 87.80 4.64 46.20 7.00 25 H25 2.93 6.80 19.92 6.19 31.06 35.08 86.90 4.08 41.70 6.90 26 H26 2.79 7.60 21.20 6.08 28.67 36.34 86.60 3.73 44.90 7.00 27 H27 3.09 9.60 29.66 8.74 29.45 36.51 87.90 4.03 46.10 6.90 28 H28 2.95 9.60 28.27 9.26 32.77 35.07 88.10 4.32 44.60 7.00 29 H29 2.88 7.80 22.46 6.79 30.21 37.88 91.00 4.62 46.20 7.20 30 H30 2.94 11.20 32.87 10.81 32.88 34.75 86.40 4.29 38.10 6.80 31 H31 3.02 9.60 28.99 8.88 30.63 34.88 88.20 4.07 43.70 6.90 32 H32 2.99 11.40 34.03 11.57 34.00 33.03 87.00 4.56 36.30 6.80 33 H33 3.26 13.20 42.97 12.34 28.73 34.53 87.90 4.10 44.00 6.90 34 H34 3.47 10.00 34.70 10.80 31.12 37.84 88.50 4.31 42.30 7.00

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35 H35 2.76 7.80 21.49 6.24 29.04 37.29 88.70 3.89 42.60 7.10 36 H36 3.04 14.40 43.70 12.82 29.32 36.76 90.50 4.13 47.40 7.00 37 H37 2.78 11.40 31.64 9.69 30.63 36.32 87.50 3.74 38.50 6.80 38 H38 3.51 8.00 28.04 7.92 28.25 38.11 89.60 3.72 49.10 7.00 39 H39 3.11 17.00 52.79 14.54 27.54 37.54 89.70 4.07 45.70 7.00 40 H40 2.77 9.00 24.93 8.19 32.85 35.47 88.30 3.97 42.80 7.00 41 H41 3.00 11.60 34.74 9.16 26.38 36.06 87.80 3.91 46.40 7.00 42 H42 2.96 9.00 26.60 7.29 27.41 38.79 90.90 3.87 46.80 7.00 43 H43 2.38 9.20 21.85 6.85 31.37 36.32 86.60 3.06 45.30 6.90 44 H44 2.75 10.80 29.70 8.86 29.82 37.36 88.90 4.34 47.90 7.00 45 H45 3.03 10.00 30.25 9.35 30.91 38.46 88.70 3.85 51.10 7.00 46 H46 3.41 8.00 27.24 8.56 31.42 39.25 89.70 3.74 58.50 7.30 47 H47 3.07 10.00 30.70 9.20 29.97 37.30 87.80 4.06 51.70 7.00 48 H48 3.35 11.20 37.46 10.53 28.10 38.99 91.60 3.74 53.70 7.10 49 H49 3.14 9.80 30.72 8.72 28.39 39.21 90.10 3.91 57.80 7.10 50 H50 2.97 4.60 13.66 4.03 29.46 38.78 87.90 3.50 49.50 6.90 51 H51 2.91 10.00 29.10 8.80 30.24 38.61 90.00 4.16 48.70 7.10 52 H52 2.56 10.40 26.57 7.12 26.81 37.69 90.50 4.35 52.40 7.10 53 H53 3.05 7.40 22.53 7.18 31.86 38.64 89.30 3.97 49.90 6.90 54 H54 3.46 7.40 25.57 7.55 29.52 38.87 89.40 3.58 51.30 7.00 55 H55 3.56 6.60 23.46 7.72 32.91 38.58 88.70 3.91 55.00 7.10 56 H56 2.97 9.60 28.51 8.83 30.98 38.66 88.00 3.47 51.00 6.90 57 H57 2.98 9.60 28.61 8.74 30.54 37.61 89.00 4.35 48.80 7.00 58 H58 2.64 6.80 17.92 5.47 30.55 37.57 88.20 4.02 48.70 7.00 59 H59 2.69 10.00 26.85 8.40 31.28 39.61 89.50 3.25 55.60 7.10 60 H60 2.74 10.00 27.40 9.35 34.12 37.57 87.20 3.33 52.10 6.90 61 H61 3.02 9.80 29.60 9.51 32.12 35.35 87.70 4.22 50.30 7.00 62 H62 2.52 9.00 22.68 7.02 30.95 37.27 87.80 3.46 42.70 6.90 63 H63 3.08 7.40 22.76 7.88 34.63 35.33 88.10 4.03 48.00 7.00 64 H64 2.27 9.20 20.88 6.35 30.40 37.72 89.60 3.97 50.10 6.90 65 H65 2.42 7.60 18.39 5.09 27.69 38.21 89.50 3.56 49.90 6.90 66 H66 3.19 10.20 32.49 10.05 30.93 39.74 90.20 3.84 54.80 7.20 67 H67 3.04 8.80 26.75 7.57 28.29 38.10 88.00 3.81 45.80 6.70 68 H68 3.18 6.60 20.96 6.07 28.98 38.58 89.60 4.38 47.40 7.10 69 H69 3.45 11.80 40.65 11.62 28.59 37.18 86.90 4.37 43.70 6.90 70 H70 2.96 10.40 30.73 9.15 29.78 36.73 85.90 4.30 44.00 6.90 71 H71 3.04 9.40 28.58 8.84 30.92 38.33 89.90 4.56 48.20 7.00 72 H72 2.89 8.20 23.70 7.75 32.70 38.47 88.30 4.20 45.50 6.90 73 H73 3.17 11.20 35.45 9.63 27.17 39.10 90.40 3.74 52.70 7.10 74 H74 2.62 8.40 22.01 6.59 29.96 38.57 89.60 4.11 50.80 7.10 75 H75 3.39 6.60 22.37 6.47 28.91 38.33 88.70 4.09 47.70 7.10

26


27

117 H172 3.02 9.80 29.55 9.46 32.01 33.76 85.00 3.50 35.70 6.70 118 H173 3.17 10.00 31.65 10.30 32.54 36.76 86.20 4.38 38.20 7.20 119 H174 3.13 10.40 32.50 9.46 29.12 37.53 86.70 3.49 37.40 6.80 120 H175 2.92 12.20 35.62 11.04 30.99 32.45 87.20 4.92 37.00 6.90 121 H176 3.06 31.40 17.40 53.12 18.21 37.38 86.40 4.02 44.90 7.00 122 H177 3.39 12.80 43.39 13.70 31.56 37.67 85.50 3.95 38.70 6.90 123 H178 2.97 12.10 35.88 9.98 27.82 37.65 86.90 3.09 38.90 6.70 E2: Korla in 2015 1 H1 3.35 33.90 12.20 40.91 13.79 38.58 88.20 3.43 49.60 6.90 2 H2 2.79 27.40 11.80 32.92 10.78 36.49 87.30 3.73 42.80 6.80 3 H3 3.37 34.30 13.40 45.11 15.32 36.22 87.90 4.27 47.30 6.90 4 H4 3.53 36.40 8.00 28.21 9.71 37.50 87.90 4.05 50.90 7.10 5 H5 3.61 36.80 12.80 46.17 15.70 37.75 88.90 3.82 52.60 7.00 6 H6 3.32 32.40 9.80 32.50 10.58 37.93 88.00 3.43 46.00 6.80 7 H7 3.20 33.90 9.80 31.33 11.07 38.10 88.10 4.13 47.50 6.90 8 H8 3.23 31.50 7.40 23.93 7.77 36.58 87.20 4.14 49.80 7.00 9 H9 3.42 29.30 17.20 58.82 16.80 38.24 87.70 3.96 48.60 6.90 10 H10 3.18 29.70 11.60 36.85 11.48 39.25 88.40 3.40 48.30 6.80 11 H11 3.49 34.00 10.20 35.56 11.56 39.00 89.10 3.98 54.60 7.10 12 H12 3.76 36.60 8.20 30.80 10.00 38.92 89.50 3.86 52.90 7.00 13 H13 3.16 29.80 9.40 29.70 9.34 36.63 88.40 4.21 51.10 7.00 14 H14 3.55 35.30 10.00 35.47 11.77 39.43 88.70 4.01 54.90 7.10 15 H15 3.28 31.60 8.80 28.89 9.27 37.13 87.60 3.76 48.40 6.90 16 H16 3.18 28.70 9.80 31.16 9.38 36.52 88.90 4.13 45.70 6.80 17 H17 2.95 29.30 16.00 47.20 15.63 35.63 85.00 3.49 39.90 6.70 18 H18 3.59 33.10 15.40 55.34 16.99 37.08 87.90 3.97 51.00 7.00 19 H19 3.13 27.20 10.20 31.96 9.25 34.92 85.80 3.73 42.70 6.80 20 H20 3.07 29.90 7.20 22.10 7.18 34.81 86.70 3.94 37.70 6.70 21 H21 3.38 30.30 11.00 37.22 11.11 36.74 88.50 3.68 43.90 6.90 22 H22 3.24 30.40 10.60 34.38 10.74 36.75 87.60 3.65 44.00 6.80 23 H23 2.84 27.80 11.00 31.28 10.19 34.22 85.30 3.55 35.50 6.80 24 H24 3.33 31.50 6.80 22.62 7.14 35.87 87.30 3.80 40.30 6.70 25 H25 3.70 33.50 7.60 28.15 8.49 36.68 86.80 3.53 38.20 6.70 26 H26 3.92 37.80 9.60 37.66 12.10 38.24 86.90 3.80 40.50 6.90 27 H27 3.53 30.60 9.60 33.92 9.79 35.18 86.40 3.72 41.50 6.80 28 H28 3.66 38.40 7.80 28.52 9.98 31.87 86.10 4.69 34.60 6.70 29 H29 2.90 27.20 11.20 32.44 10.15 35.75 86.20 3.47 39.70 6.70 30 H30 2.61 27.80 9.60 25.09 8.90 34.68 87.90 4.03 39.00 6.80 31 H31 2.86 26.60 11.40 32.60 10.11 36.13 85.50 3.90 39.20 6.70 32 H32 2.91 28.90 13.20 38.41 12.72 34.81 87.20 3.95 44.50 7.00 33 H33 3.17 27.80 10.00 31.70 9.27 37.67 88.30 3.29 39.90 6.90

28


29


30

116 H171 3.17 29.40 11.60 36.77 11.37 32.89 85.00 3.49 33.40 6.50 117 H172 2.95 28.80 9.80 28.94 9.41 31.99 84.40 3.93 34.30 6.70 118 H173 2.65 25.00 10.00 26.50 8.33 34.87 86.70 3.79 36.80 7.00 119 H174 2.73 23.20 10.40 28.39 8.04 32.66 86.00 3.83 40.60 6.70 120 H175 3.19 27.60 12.20 38.96 11.22 34.90 86.00 3.05 36.00 6.60 121 H176 3.00 27.10 11.40 34.24 9.28 33.39 85.30 3.60 35.40 6.60 122 H177 3.25 32.80 12.80 41.56 13.99 32.61 84.60 3.46 33.00 6.50 123 H178 2.55 25.40 12.10 30.90 7.85 34.32 84.80 3.45 36.70 6.60

Supplemental Table3 120 SSR primers with their Number of alleles, chromosomal

locations and reference

Code Marker Name Number of alleles Chr. Reference

1 NAU3110 4 chr.19 Guo et al. 2007

2 NAU2820 3 chr.16 Guo et al. 2007

3 NAU3324 1 chr.24 Guo et al. 2007

4 NAU5120 3 chr.16 Guo et al. 2007

5 PGML01548 1 unknown unknown

6 NAU797 2 chr.19 Guo et al. 2007

7 NAU1028 1 chr.17 Guo et al. 2007

8 NAU1093 2 chr.06 Qin et al. 2008

9 NAU1102 3 chr.19 Shen et al. 2007

10 HAU2146 2 chr.09 Yu et al. 2011

11 NAU2908 2 chr.17 Guo et al. 2007

12 HAU2828 3 unknown unknown

13 BNL226 2 chr.03 Liu et al. 2000

14 BNL1495 1 chr.13 Guo et al. 2008

15 CGR5202 1 chr.24 Xiao et al. 2009

16 NAU803 1 chr.14 Guo et al. 2007

17 BNL1604 1 chr.16 Yu et al. 2011

18 NAU2083 1 chr.01 Guo et al. 2008

19 NAU3791 1 chr.04 Yu et al. 2011

20 NAU2991 3 chr.20 Guo et al. 2008

21 NAU1322 2 chr.24 Guo et al. 2008

22 NAU2687 2 chr.25 Guo et al. 2008

23 NAU3424 2 chr.24 Yu et al. 2011

31

24 DPL0509 1 unknown Xiao et al. 2009

25 HAU2768 2 chr.06 Yu et al. 2011

26 NAU5163 2 chr.01 Guo et al. 2007

27 BNL3034 3 chr.14 Yu et al. 2012

28 NAU3189 2 chr.26 Guo et al. 2008

29 BNL169 2 chr.20 Liu et al. 2011

30 NAU3013 2 chr.10 Guo et al. 2008

31 NAU3346 1 chr.15 Guo et al. 2008

32 BNL252 2 chr.24 Guo et al. 2007

33 NAU5465 2 chr.14 Guo et al. 2008

34 NAU3481 1 chr.21 Guo et al. 2008

35 JESPR232 1 chr.08 Guo et al. 2007

36 NAU905 3 chr.06 Guo et al. 2007

37 NAU2200 3 chr.23 Guo et al. 2007

38 BNL2449 2 chr.13 Guo et al. 2007

39 BNL3823 2 chr.23 Guo et al. 2008

40 CGR5228 1 unknown Xiao et al. 2009

41 NAU2679 2 chr.06 Guo et al. 2007

42 NAU3433 4 chr.15 Yu et al. 2011

43 NAU3384 1 chr.01 Yu et al. 2011

44 NAU5107 1 chr.15 Yu et al. 2011

45 BNL3580 3 chr.01 Yu et al. 2011

46 BNL3888 3 chr.01 Yu et al. 2011

47 BNL3590 2 chr.02 Yu et al. 2011

48 NAU5233 3 chr.03 Yu et al. 2011

49 NAU5444 1 chr.03 Guo et al. 2007

50 BNL3259 2 chr.03 Guo et al. 2007

51 NAU3405 3 chr.19 Yu et al. 2011

52 NAU2562 4 chr.05 Yu et al. 2011

53 NAU5088 2 chr.05 Yu et al. 2011

54 NAU5400 3 chr.05 Yu et al. 2011

55 BNL3995 2 chr.05 Xiao et al. 2009

56 NAU3243 2 chr.06 Yu et al. 2011

57 NAU2156 2 chr.06 Yu et al. 2011

58 BNL1064 3 chr.06 Yu et al. 2011

59 NAU1048 1 chr.07 Guo et al. 2007

60 NAU3101 3 chr.09 Guo et al. 2007

61 BNL3626 1 chr.09 Xiao et al. 2009

32

62 NAU2166 1 chr.10 Guo et al. 2007

63 NAU3284 1 chr.21 Yu et al. 2011

64 NAU3117 2 chr.11 Guo et al. 2007

65 NAU3377 1 chr.11 Yu et al. 2011

66 BNL3592 2 chr.11 Yu et al. 2011

67 NAU3519 4 chr.12 Guo et al. 2007

68 NAU3398 2 chr.18 Yu et al. 2011

69 NAU5345 2 chr.13 Yu et al. 2011

70 NAU3540 2 chr.13 Yu et al. 2011

71 NAU3989 1 chr.13 Yu et al. 2011

72 NAU3576 3 chr.15 Guo et al. 2007

73 BNL3145 3 chr.14 Guo et al. 2007

74 NAU3449 4 chr.17 Guo et al. 2007

75 NAU2955 4 chr.22 Guo et al. 2007

76 BNL1047 2 chr.25 Yu et al. 2011

77 NAU2932 4 chr.05 Yu et al. 2011

78 NAU3095 3 chr.19 Yu et al. 2011

79 NAU2942 2 chr.19 Guo et al. 2007

80 NAU2801 2 chr.19 Guo et al. 2007

81 NAU5121 2 chr.19 Yu et al. 2011

82 NAU5255 1 chr.05 Yu et al. 2011

83 NAU4884 1 chr.19 Yu et al. 2011

84 NAU5447 1 chr.19 Yu et al. 2011

85 NAU3306 1 chr.25 Guo et al. 2007

86 JESPR224 2 chr.25 Xiao et al. 2009

87 NAU2974 2 chr.16 Yu et al. 2011

88 NAU2626 3 chr.16 Guo et al. 2007

89 NAU2627 3 chr.16 Yu et al. 2011

90 BNL1395 4 chr.07 Xiao et al. 2009

91 BNL3084 2 chr.24 Yu et al. 2011

92 BNL3860 3 chr.24 Xiao et al. 2009

93 NAU3137 2 chr.20 Guo et al. 2007

94 BNL3646 2 chr.20 Xiao et al. 2009

95 NAU4865 3 chr.21 Yu et al. 2011

96 NAU3240 3 chr.21 Guo et al. 2007

97 BNL3649 4 chr.21 Xiao et al. 2009

98 NAU3293 3 chr.26 Guo et al. 2007

99 BNL1079 1 chr.18 Xiao et al. 2009

33

100 BNL1705 1 chr.21 Xiao et al. 2009

101 BNL193 4 chr.18 Yu et al. 2011

102 BNL2646 3 chr.15 Yu et al. 2011

103 NAU3995 1 chr.03 Guo et al. 2007

104 NAU4042 2 chr.19 Guo et al. 2007

105 NAU3588 2 chr.25 Yu et al. 2011

106 NAU5433 2 chr.06 Guo et al. 2007

107 HAU0878 1 chr.05 Yu et al. 2011

108 HAU0883 2 chr.14 Yu et al. 2011

109 HAU0975 1 chr.06 Yu et al. 2011

110 HAU1058 2 chr.15 Yu et al. 2011

111 HAU1185 2 chr.19 Yu et al. 2011

112 HAU1195 3 chr.16 Yu et al. 2011

113 HAU2873 3 chr.10 Yu et al. 2011

114 NAU3665 2 chr.10 Yu et al. 2011

115 HAU1809 4 chr.11 Yu et al. 2011

116 HAU1951 2 chr.14 Yu et al. 2011

117 HAU2119 2 chr.06 Yu et al. 2011

118 HAU2367 2 chr.25 Yu et al. 2011

119 HAU2414 3 chr.13 Yu et al. 2011

120 NAU3096 2 chr.19 Yu et al. 2011

[1] Yu Y., Yuan D. J., Liang S. G., Li X. M., Wang X. Q., Lin Z. X. et al. Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC1 population between gossypium hirsutum and G. barbadense. BMC Genomics, 2011, 12, 1-14. [2] Xiao J., Wu K., Fang D. D., Stelly D. M., Yu J. and Cantrell R. G. 2009 New SSR markers for use in cotton (Gossypium spp.) improvement. Journal of Cotton Science, 13, 75–157. [3] Guo W. Z, Cai C. P., Wang C. P., Han Z. G., Song X. L., Wang K. et al. 2007 A microsatellite-based, gene-rich linkage map reveals genome structure, function and evolution in Gossypium. Genetics 176, 527-541.

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