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. 2015 Oct 24;21(4):531–539. doi: 10.1007/s12298-015-0326-y

Assessing genetic diversity among six populations of Gossypium arboreum L. using microsatellites markers

Khushboo Sethi 1, Priyanka Siwach 1,✉, Surender Kumar Verma 2
PMCID: PMC4646864  PMID: 26600679

Abstract

Among the four cultivated cotton species, G. hirsutum (allotetraploid) presently holds a primary place in cultivation. Efforts to further improve this primary cotton face the constraints of its narrow genetic base due to repeated selective breeding and hence demands enrichment of diversity in the gene pool. G. arboreum (diploid species) is an invaluable genetic resource with great potential in this direction. Based on the dispersal and domestication in different directions from Indus valley, different races of G. arboreum have evolved, each having certain traits like drought and disease resistance, which the tetraploid cotton lack. Due to lack of systematic, race wise characterization of G. arboreum germplasm, it  has not been explored fully. During the present study, 100 polymorphic SSR loci were  used to genotype 95 accessions belonging to 6 races of G. arboreum producing 246 polymorphic alleles; mean number of effective alleles was 1.505. AMOVA showed 14 % of molecular variance among population groups, 34 % among individuals and remaining 52 % within individuals. UPGMA dendrogram, based on Nei’s genetic distance, distributed the six populations in two major clusters of 3 populations each; race ‘bengalense’ was found more close to ‘cernuum’ than the others. The clustering of 95 genotypes by UPGMA tree generation as well as PCoA analysis clustered ‘bengalense’ genotypes into one group along with some genotypes of ‘cernuum’, while rest of the genotypes made separate clusters. Outcomes of this research should be helpful in identifying the genotypes for their further utilization in hybridization program to obtain high level of germplasm diversity.

Electronic supplementary material

The online version of this article (doi:10.1007/s12298-015-0326-y) contains supplementary material, which is available to authorized users.

Keywords: Asiatic cotton, Polymorphism information content, Genetic distance, SSR, UPGMA, PCoA

Introduction

Cotton is the world leading natural fiber crop on which the textile industries worldwide are largely based on. The genus Gossypium has 45–50 species, 40–45 are diploid (2n = 2× = 26) while 5 are allotetraploid (2n = 4× = 52). Spinnable fibers are obtained from two allotetraploid (G. hirsutum and G. barbedense) and two diploid (G. herbaceum and G. arboreum) species. Presently, tetraploid cotton (dominantly G. hirsutum) occupies a major fraction (>90 %) of world cotton cultivation because of superior fiber quality and has achieved the status of primary cotton; diploid species being cultivated only in traditional cotton growing areas of India, Pakistan, China, Bangladesh and Iran (Kulkarni et al. 2009). Efforts to further improve the plant and fiber traits of primary cotton face the constraints of narrow genetic base due to continuous selective breeding and selection. Enrichment of gene pool with genetic diversity is strongly needed for future gains in fiber industry (Abdalla et al. 2001). Transfer of allelic variation from diverse cotton germplasm resources to the primary cotton breeding gene pools by intraspecific and interspecific hybridization would be an important step in this direction.

G. arboreum (also known as Asiatic cotton) germplasm collection is an important genetic resource for tetraploid cotton improvement. G. arboreum has certain inherent qualities, which the tetraploids lack, like the ability to withstand drought and salinity (Maqbool et al. 2010; Tahir et al. 2011) and remarkable tolerance to several pests and disease, including bollworms (Dhawan et al. 1991), aphids and leafhoppers (Nibouche et al. 2008), rust, fungal (Wheeler et al. 1999) and viral (Mehetre et al. 2004; Akhtar et al. 2010) diseases. Natural G. arboreum fibers display various colours (e.g. white, off-white and tan) also and some of the accessions produce fibers with high strength (Mehetre et al. 2003). Some efforts have been put for introgressive breeding using G. arboreum as donor species to improve tetraploid cotton, especially for disease resistance and insect tolerance (Ansingkar et al. 2004; Kulkarni 2002), though the achievement was limited. A major problem in such efforts is the poor understanding of G. arboreum germplasm at molecular level.

A huge collection of G. arboreum germplasm is maintained at different centres worldwide (Kulkarni et al. 2009). Domestication of G. arboreum initiated during Indus valley civilization (3300–1300 BCE) (Hutchinson 1954) and from there it spread to different direction worldwide. During this dispersal it became adapted to diverse climate and soil conditions by developing distinct genetic and morphological features, based on which six different races were classified viz. ‘indicum’, ‘burmanicum’, ‘sinense’, ‘soudanense’, ‘bengalense’, ‘cernuum’ (Silow 1944; Hutchinson 1954; Brubaker et al. 1999). Each race has its own characteristics traits like race ‘indicum’ (cultivated in west India and coastal Tanzania) yield long fibers, race ‘cernuum’ (cultivated in North-east India) bear big bolls, higher lint% is observed with race ‘bengalense’ (cultivated in North and central India) while race ‘soudanense’ is well adapted to dry climatic conditions of Egypt and North Africa. ‘Sinense’ and ‘burmanicum’ are annual forms domesticated and cultivated popularly in China and Myanmar respectively with some cultivation in North-eastern regions of India also. Earlier the races were sown year by year by local farmers and diversity was dynamically maintained. During modern agriculture, new varieties have been introduced, often originating from crosses among elite inbred lines. Because of higher yields, the new varieties have largely replaced the old races. Therefore, analysis of genetic variation between and within races of G. arboreum is prerequisite for exploiting this germplasm in modern cotton improvement programs.

During the last two decades, molecular markers have been extensively used for studying genetic diversity as well as genetic relationship among genotypes across species including cotton species, though much focus has been on the tetraploid cotton germplasm (Abdalla et al. 2001; Lu and Myers 2002; Han et al. 2006, Abdurakhmonov et al. 2008, 2009; Azmat and Khan 2010; Noormohammadi et al. 2011; Surgun et al. 2012; Dahab et al. 2013). Efforts have also been made for genetic diversity analysis of selected G. arboreum germplasm using different markers like RAPD (Rana and Bhat 2004; Mahmood et al. 2009; Mandaliya et al. 2010; Deosarkar et al. 2010; Dongre et al. 2011), ISSR (Dongre et al. 2007; Khandagale et al. 2007; Bardak and Bolek 2012) and microsatellites or simple sequence repeats (SSRs) (Guo et al. 2006; Liu et al. 2006; Dongre et al. 2007; Kantartzi et al. 2009; Deosarkar et al. 2010; Dongre et al. 2011; Noormohammadi et al. 2013), though no study explored the polymorphism among the six races of G. arboreum. So, the present work was designed to study genetic diversity among elite genotypes of six different races of G. arboreum using microsatellite markers, since microsatellites markers have edge over other marker system in cultivar fingerprinting and diversity studies,

Materials and methods

Plant materials and DNA extraction

Ninety five cotton genotypes belonging to six races of G. arboreum, as described in Table 1, were selected for the present study. The cotton plants were cultivated in two rows of 6 m length with 30 cm interplant distance in the experimental field of Central Institute of Cotton Research (CICR), regional station, Sirsa, Haryana, India, in a completely randomized design (CRD) with 3 replications. Single plant, having fresh and young leaves, was selected randomly from any of the three replicates of each genotype. Fresh and young leaves of selected plants were subjected to total genomic DNA extraction using CTAB method (Saghai-Maroof et al. 1984). Quality and quantity of extracted DNA was examined by running on 0.8 % agarose gel as well as by UV-Spectrophotometer method.

Table 1.

The genotypes, belonging to six different races of G. arboreum, selected for the present work

No. Accession Population Group & source of collection No. Accession Population group & source of collection
1 CISA-6-187 Population group 1
(Bengalense race)
Collected from C.I.C.R., Regional Station, Sirsa (Haryana) India		 49 AKA-0106 Population group 1
2 CISA-6-123 50 CINA-369
3 CISA-6-209 51 CAN-1006
4 CISA-6-214 52 HD-485
5 CISA-6-256 53 GAM-150
6 CISA-6-295 54 JTAPTI-007
7 CISA-6-350 55 CCA-8
8 CISA-614 56 LD-694
9 CISA-6 57 RG-8
10 CISA-7 58 HD-123
11 CISA-8 59 PA-255
12 CISA-9 60 LD-987
13 CISA-10 61 RG-579
14 CISA-294 62 LD-919
15 CISA-64 63 LD-936
16 CISA-310 64 LD-1010
17 LD-327 65 RG-595
18 LD-733 66 C- 1 Population group 2
(Cernuum race)
Collected from Genbank, C.I.C.R. Nagpur (Maharashtra)
19 ARBAS-105 67 C- 2
20 TKA-9102/03 68 C-3
21 MDL-2617 69 C-4
22 GBaV-107 70 C-5
23 PA-532 71 C-6
24 PA-686 72 C-7
25 RG-526 73 C-8
26 RG-540 74 C-9
27 RG-541 75 C-10
28 RG-514 76 Id-1 Population group 3
(Indicum race)
Collected from Genbank, C.I.C.R. Nagpur (Maharashtra)
29 FDK-118 77 Id-2
30 TKA-9102 78 Id-3
31 KWP-902 79 Id-4
32 DLSA-17 80 Id-5
33 DLSA-1005 81 S-1 Population group 4
(Soudanese race)
Collected from Genbank, C.I.C.R. Nagpur (Maharashtra)
34 DLSA-1006 82 S-2
35 LD-960 83 S-3
36 LD-909 84 S-4
37 FDK-124 85 S-5
38 PAIG-8/1 86 Sin-1 Population group 5
(Sinense race)
Collected from Genbank, C.I.C.R. Nagpur (Maharashtra)
39 DAS-802 87 Sin-2
40 CCA-4 88 Sin-3
41 RAAS-931 89 Sin-4
42 GBaV-105 90 Sin-5
43 GBaV-120 91 Bur-1 Population group 6
(Burmanicum race)
Collected from Genbank, C.I.C.R. Nagpur (Maharashtra)
44 ARBHA-0853 92 Bur-2
45 ARBAS-104 93 Bur-3
46 RAAS-36 94 Bur-4
47 RAAS-8 95 Bur-5
48 GAM-158
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Microsatellite analysis

One hundred thirty microsatellite primer pairs were obtained from BNL (Brookhaven National Laboratory), NAU (Nanjing Agricultural University) and MUSS (M- Microsatellite, U- Last name of Principal Investigator, SS- Simple Sequences), sources for initial screening. Out of these, only 100 primers produced polymorphic and reproducible band pattern and hence these were selected for present study (Table 2). The sequence information of these primers is available at http://www.cottonmarker.org.

Table 2.

The polymorphic 100 SSR markersused in present study with number of alleles, size range and PIC values

S. No Primer name No. of alleles Size range (bp) PIC S. No Primer name No. of alleles Size range (bp) PIC
1 NAU-1067 3 160–156 0.528 51 MUSS-243 2 180–176 0.375
2 NAU-3911 2 194–190 0.040 52 BNL-1395 2 250–245 0.021
3 NAU-2083 2 224–220 0.040 53 BNL-1694 4 289–272 0.695
4 MUSS-422 2 265–260 0.040 54 BNL-1604 2 120–115 0.021
5 BNL-3580 4 197–190 0.517 55 BNL-1531 2 239–235 0.021
6 BNL-3888 2 130–126 0.021 56 NAU-2432 2 207–204 0.040
7 BNL-3090 2 240–234 0.040 57 NAU-2308 3 154–146 0.567
8 NAU-2095 2 219–215 0.040 58 NAU-3590 2 223–220 0.021
9 BNL-3424 2 197–192 0.040 59 NAU-3793 3 262–255 0.574
10 MUSS-73 2 208–205 0.041 60 BNL-3792 2 243–240 0.021
11 MUSS-599 2 230–227 0.021 61 BNL-3257 2 130–127 0.021
12 NAU-5383 4 162–110 0.666 62 BNL-1017 2 205–202 0.040
13 BNL-3971 2 200–196 0.021 63 NAU-2407 2 105–102 0.021
14 BNL-1897 2 190–186 0.040 64 NAU-920 2 155–150 0.021
15 BNL-1434 3 148–137 0.554 65 MUSS-189 2 216–210 0.021
16 NAU-5499 3 145–154 0.581 66 BNL-686 3 210–202 0.581
17 BNL-3259 2 105–102 0.021 67 BNL-1162 2 134–130 0.021
18 MUSS-207 2 224–220 0.021 68 BNL-1672 2 180–177 0.021
19 MUSS-192 2 220–217 0.040 69 NAU-2322 2 240–236 0.040
20 MUSS-172 2 219–215 0.021 70 NAU-3052 2 190–185 0.021
21 NAU-1070 2 250–245 0.021 71 NAU-1009 2 125–122 0.021
22 BNL-226 2 244–240 0.021 72 NAU-1046 2 200–197 0.021
23 NAU-1190 2 190–185 0.021 73 BNL-4053 2 220–215 0.021
24 BNL-3441 2 180–175 0.021 74 NAU-3467 2 224–220 0.021
25 BNL-2443 2 196–190 0.021 75 MUSS-68 2 205–203 0.021
26 NAU-1167 2 190–186 0.021 76 NAU-3454 2 234–230 0.021
27 NAU-3083 2 145–140 0.021 77 BNL-2530 2 180–176 0.021
28 NAU-2363 3 192–180 0.593 78 BNL-256 2 230–226 0.040
29 BNL-4047 3 183–177 0.557 79 BNL-2631 2 219–215 0.021
30 BNL-530 2 120–116 0.040 80 BNL-3895 2 240–236 0.372
31 NAU-3093 2 170–167 0.021 81 NAU-1182 2 206–200 0.021
32 BNL-4049 4 143–127 0.692 82 MUSS-123 2 200–195 0.021
33 BNL-2572 4 207–170 0.586 83 BNL-1231 2 191–188 0.021
34 NAU-2865 2 204–200 0.040 84 BNL-1066 4 206–198 0.593
35 NAU-2000 4 195–180 0.700 85 NAU-1162 2 235–229 0.021
36 MUSS-99 3 320–310 0.577 86 BNL-1404 2 225–219 0.021
37 BNL-3995 2 174–170 0.021 87 BNL-3147 2 107–104 0.021
38 BNL-3992 3 205–196 0.570 88 NAU-3377 2 237–234 0.021
39 BNL-542 3 204–195 0.590 89 MUSS-26 2 200–196 0.021
40 BNL-3241 6 230–170 0.800 90 NAU-3426 4 260–220 0.700
41 NAU-934 3 152–147 0.589 91 NAU-4047 2 190–186 0.021
42 BNL-3359 2 205–202 0.021 92 BNL-3261 2 149–146 0.021
43 BNL-2569 2 175–170 0.021 93 NAU-1278 2 215–209 0.021
44 BNL-1440 2 201–197 0.021 94 BNL-1673 2 234–229 0.021
45 NAU-1151 4 233–220 0.659 95 BNL-1679 2 211–208 0.021
46 NAU-2580 2 205–202 0.040 96 NAU-2038 3 210–200 0.500
47 NAU-3206 3 247–240 0.564 97 NAU-1141 2 156–150 0.021
48 NAU-3427 3 197–190 0.579 98 BNL-2652 2 175–170 0.021
49 NAU-933 3 173–165 0.554 99 BNL-4029 2 220–217 0.021
50 NAU-4030 2 159–155 0.021 100 BNL-1707 4 180–140 0.702
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The bold names are the SSR primers which exhibited PIC value greater than or equal to 0.5

PCR amplification was performed in a volume of 20 μl containing 2 μl of DNA (50 ng/μl), 0.5 μM of each primer (Sigma-Aldrich), 200 μM of dNTPs (Sigma-Aldrich), 0.5 U Taq polymerase (Sigma-Aldrich) and 1X PCR buffer (Sigma-Aldrich). Thirty five cycles, each consisting of 1 min denaturation at 95 °C, 1 min at annealing temperature (optimized separately for each primer pair, generally Tm-5 °C) and 2 min polymerization at 72 °C, were performed in a thermo cycler (Bio-Rad, USA). The PCR products were separated by electrophoresis in a horizontal gel system at 100 V for 4 h in 4 % metaphor gel and polymorphism was visualized by staining with ethidium bromide. Finally the gel was photographed under Gel Documentation system (Bio-Rad, USA).

Data analysis

The profiles revealed by SSR markers were scored as present (1) or absent (0) for each of the SSR loci. Genetic diversity was calculated at each locus by means of allelic polymorphism information content (PIC) (Anderson et al. 1993), with program CERVUS version 3.0 based on allelic frequencies among all 95 genotypes. PIC values for each locus were calculated as: PICj = 1-∑p2lj, plj is the frequency if the lth allele for locus j and is summed over its L alleles. Markers were classified as informative when PIC ≥0.5.

Several other genetic diversity parameters were determined viz. number of SSR locus (N), number of different allele (Na), effective number of allele (Ne), Shannon’s index (I), observed heterozygosity (Ho) and expected heterozygosity (He). The fixation index (F) which is equal to (Hexp-Ho)/Hexp, was also computed for all the loci and population being studied. This was accompanied by Analysis of Molecular Variance (AMOVA) in order to reveal significant difference between various genotypes and population groups. UPGMA (Unweighted Paired Group using Mean Average) dendrogram of 6 population groups was drawn based on Nei’s genetic distance, modified from Neighbour procedure of PHYLIP ver. 3.5. Similarity matrices were generated among the cultivars studied using ‘Simqual’ subprogram of software NTSYS and used for grouping of the genotypes by UPGMA clustering method. Ordination based on principle coordinate analysis (PCoA) was also done. All computations for determination of genetic parameters, clustering, AMOVA and PCoA analysis was done using softwares- NTSYS ver 2.02, POPGENE ver. 3.2 and GenAlex 6.5.

Results and discussion

Microsatellite diversity

The hundred selected microsatellite primer pairs, when used to amplify genomic DNA of selected 95 genotypes of G. arboreum, yielded a total of 240 alleles (all polymorphic), quite distinct on metaphor gels (Supplementary Fig. 1). The mean number of alleles obtained per locus was 2.4 while the number of alleles per locus varied from 2 to 6. The PIC values ranged from 0.021 to 0.80 (average 0.206) (Table 2). The average PIC obtained during the present study was less to that obtained by Kantartzi et al. (2009) (average PIC 0.42), while analyzing genetic diversity in G. arboreum cultivars using microsatellites, though the range obtained by them was also different (0.00 to 0.68). Liu et al. (2006) also reported high average PIC (0.31), compared to that obtained in present study, and the average PIC value obtained by Lacape et al. (2007) was also higher (average 0.55) than our value. This variation can be attributed to selection of different genotypes and primers for the study.

Of the 100 selected SSRs loci, 27 loci were found highly informative as they yielded PIC value of ≥0.5. As our selection of SSR loci was biased towards di-nucleotides, so it was difficult to correlate the polymorphism with the repeat type of SSR loci. SSR polymorphism has, however, sometimes been correlated with repeat length, and dinucleotide AT-rich repeats have been found more polymorphic than other kinds of repeats by some groups like Cavagnaro et al. (2010) and Kantartzi et al. (2009). Guo et al. (2007) found the polymorphic rate of tetranucleotide and dinucleotide repeat types slightly higher than that of trinucleotide repeat types.

Analysis of genetic diversity over all loci showed the mean number of effective alleles (Ne) = 1.505, mean value of Shannon’s information index (I) = 0.343 and a mean value of 0.203 expected heterozygosity (He) (Table 3). The highest value of Ne (1.595) was observed for population group 1, while highest value of I (0.359) and He (0.209) occurred in population group 5. Highest observed heterozygosity (Ho) value (0.144) was found in population group 6 whereas lowest value for the same parameters occurred in population group 3.

Table 3.

Genetic diversity parameters in six population group of G. arboreum

Pop N Na Ne I Ho He F
Pop 1 Mean 65.000 1.860 1.595 0.351 0.127 0.201 0.507
SE 0.000 0.122 0.099 0.052 0.032 0.030 0.067
Pop 2 Mean 10.000 1.830 1.544 0.355 0.134 0.204 0.564
SE 0.000 0.109 0.087 0.048 0.033 0.027 0.065
Pop 3 Mean 5.000 1.650 1.447 0.311 0.120 0.188 0.509
SE 0.000 0.097 0.075 0.043 0.032 0.025 0.071
Pop 4 Mean 5.000 1.710 1.470 0.334 0.126 0.203 0.510
SE 0.000 0.101 0.072 0.044 0.032 0.026 0.072
Pop 5 Mean 5.000 1.770 1.499 0.359 0.136 0.215 0.527
SE 0.000 0.101 0.076 0.044 0.034 0.025 0.073
Pop 6 Mean 5.000 1.720 1.475 0.344 0.144 0.209 0.469
SE 0.000 0.096 0.070 0.043 0.035 0.025 0.079
Total Mean 15.822 1.757 1.505 0.343 0.131 0.203 0.515
SE 0.000 0.043 0.033 0.019 0.013 0.011 0.029
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Na Number of different allele, Ne Effective number of allele, I Shannon’s index, Ho Observed heterozygosity, He Expected heterozygosity, F Fixation index

AMOVA analysis (after 999 numbers of permutations) was performed among populations, within population group and within individuals. The analysis indicated that 14 % of molecular variance is due to 6 population groups, 34 % is due to genetic variations among accessions in each population group and remaining 52 % is observed within individuals (Table 4). Present analysis showed significant difference among population groups, among individuals of a group and within individuals (p = 0.001). In previous similar study by Wang et al. (2011), 92 % of total variation was found confined to within population variation whereas only 8 % of total variation was due to among population variation, as analyzed by AMOVA. Noormohammadi et al. (2013), in diploid cotton genotypes after analysis by AMOVA, found 2 % of total variation due to population groups and 98 % due to genetic variations among accessions in each population group; such a low polymorphism among population groups could be due to inclusion of inter-specific hybrids, second backcross progenies and F5 plants of the same cross.

Table 4.

Parameters obtained by Analysis of molecular variance (AMOVA), during present study

Source df SS MS Est. Var. % Molecular variance
Among Population 5 249.02 49.80 1.80 14 %
Among individuals (in each population group) 89 1319.95 14.83 4.21 34 %
Within individuals 95 609 6.41 6.41 52 %
Total 189 2177.97 12.42 100 %
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df Degree of freedom, SS Sum of squares, MS Mean Square

UPGMA dendrogram based on Nei’s genetic distance distributed 6 population groups into 2 main clusters. Population group 1 (race ‘bengalense’), 2 (race “cernuum’) and 6 (race ‘burmanicum’) formed the first main cluster whereas population group 3 (race ‘indicum’), 4 (race ‘soudanense’) and 5 (race ‘sinense’) formed the second main cluster (Fig. 1). The genetic distance coefficient among 6 population groups ranged from 0.046–0.094 (Table 5). Evolutionary studies indicate race ‘indicum’ as most primitive perennial form in western India, dispersal of which to various regions evolved other races like ‘burmanicum’, ‘soudanense’, ‘sinense’ and ‘bengalense’. In the present study, indicum showed maximum genetic similarity to race sinense (0.9508) followed by soudanense (0.9273), bangalense (0.9165) and burmanicum (0.9142). ‘Cernuum’ and ‘indicum’ were found most distant (0.094). Evolutionary studies indicate ‘Cernuum’ to have evolved independently in the Assam hills of North-East India and Chittagang hills of Bangladesh (Kulkarni et al. 2009). However, during the present investigation, cernuum did not appear as independent group but exhibited remarkable similarities with bengalense and burmanicum, together forming one main cluster in the dendrogram while the rest three formed another main cluster (Fig. 1). Since, the evolution of these races is not very primitive, the loci used during present investigation may not be polymorphic enough for accurate grouping.

Fig. 1.

Fig. 1

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A phylogenetic UPGMA tree of six G. arboreum populations, based on Nei’s genetic distance and generated by POPGENE ver 3.2 software

Table 5.

Nei’s genetic distance (below diagonal) and Nei’s genetic identity (above diagonal), among six population groups

Pop1 Pop2 Pop3 Pop4 Pop5 Pop6
Pop1 0.9542 0.9165 0.9383 0.9265 0.9243
Pop 2 0.0469 0.9100 0.9273 0.9392 0.9421
Pop 3 0.0871 0.0943 0.9289 0.9508 0.9142
Pop 4 0.0636 0.0755 0.0737 0.9389 0.9183
Pop 5 0.0763 0.0628 0.0504 0.0631 0.9380
Pop 6 0.0787 0.0596 0.0897 0.0853 0.0640
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UPGMA tree based on similarity matrix of the 95 G. arboreum accessions, using 100 times bootstrapping, depicted cophenetic correlation value of r = 0.82. The cluster tree analysis distributed the genotypes into two major groups (Fig. 2). Group 1 consists of all the accessions belonging to race ‘bengalense’ and 5 accessions of race ‘cernuum’. Group 2 consists of other 4 races (5 genotypes each) and 5 genotypes of race ‘cernuum’ which are distributed randomly, not indicating a clear differentiation of subgroups. The result was similar to dendrogram drawn on the basis of Nei’s genetic distance. The similarity coefficient among 95 accessions ranged from 0.73–0.91. The maximum similarity of 0.91 has been observed between genotype 53 and 56 (both belong to race bengalense) and genotype 68 and 70 (both of race cernuum), followed by similarity coefficient of 0.905 between genotype 15 and 42 (race bengalense). The two major groups shared similarity coefficient of 0.75.

Fig. 2.

Fig. 2

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A UPGMA tree of 95 genotypes of G. arboreum generated by NTSYSpc2.02 software

Principal coordinate analysis (PCoA) is a technique which highlights the similarities and differences in the given data by reducing the number of dimensions without much loss of information. The PCoA plot of cotton genotypes after 999 reiterations (Fig. 3) supported the grouping obtained by clustering by UPGMA methods. The PCoA plot exhibited one major and distinct group constituting all the genotypes of bengalense along with five accessions of cernuum, while a second diffused group of rest of genotypes of all the races (Fig. 3).

Fig. 3.

Fig. 3

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Clustering of 95 genotypes of G. arboreum obtained by ordination based on Principal Coordinate Analysis (PCoA)

A narrow genetic base has been reported in Gossypium species (Iqbal et al. 1997; Abdalla et al. 2001). Plant breeders desire to use Gossypium arboreum as an invaluable genetic resource for improving both diploid and tetraploid cotton production. No study till date has been reported for genetic diversity and population structure characterization of all six races of G. arboreum. The comprehensive molecular characterization of selected cotton germplasm collections, during the present study gives insights regarding the level and distribution of genetic diversity in existing resources and provides insights into genetic subdivisions within each race.

Electronic supplementary material

Supplementary Fig. 1 (189KB, doc)

(DOC 189 kb)

Acknowledgments

Financial support was provided by the University Grants Commission (UGC), Ministry of Human Resource Development, Government of India, New Delhi, India in the form of Major Research Project to Priyanka Siwach (Principal Investigator) and Surender Kumar Verma (Co-Principal Investigator). The authors are grateful to the Director, CICR, Sirsa and Director CICR, Nagpur for providing the germplasm. We also acknowledge the infrastructural facilities provided by Chaudhary Devi Lal University, Sirsa, Haryana, India for all the lab work. Acknowledgement is also extended to CICR, Sirsa for allowing the use of experimental field facility and polyhouse facility.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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