Introduction

Cotton (Gossypium spp.) also known as “White gold” is one of the major cash crops around the globe which is mainly cultivated to produce raw fiber for the textile industry (Singh 2017; Rehman et al. 2019; Jans et al. 2020). World fiber production equaled approximately 110 million metric tons in 2018, including 32 million tons of natural fibers and 79 million tons of chemical fibers. Cotton accounted for 80% of natural fiber production by weight (Townsend 2020) which shows its significance in international economies. Pakistan is the fourth largest lint producer of cotton (Shuli et al. 2018; Lalwani 2020).

Distinctness, uniformity, and stability (DUS) testing remain the sole scientific criteria for the protection and registration of new varieties in past (Pourabed et al. 2015). Earlier morphological and biochemical markers were used for DUS testing. The use of these markers produces inconsistent results because morphological and biochemical markers are influenced by the plant age, the environment and other factors. With the availability of molecular markers, it became possible to conduct rapid and accurate identification at the DNA level without the impact of environmental factors (Iqbal et al. 2017; Santhy et al. 2019). DNA fingerprinting is the rapid, easy, and most common method to discriminate, identify and characterize various cultivars to protect PBRs and promote marker-assisted breeding (Kalia et al. 2011). The technique has been revolutionized since the past three decades to distinguish the DNA polymorphism, biological identification, and documentation of species. Genetic profiling recapitulates the biological determination of species as well as traceability of diverse crop samples using the short tandem repeats. Through this PCR based approach, individual plant hybrids/varieties can be identified by acquiring a specific pattern of genetic profiles (Zhang et al. 2013).

The DNA fingerprints are stored in databases and sequences could be used for direct selection and identification of cotton hybrids and parents for future crop production programs. Moreover, the International Union for the Protection of new Varieties of Plants (UPOV) has encouraged the use of molecular markers in DUS testing for the identification of crop cultivars (UPOV-BMT 2002). Molecular markers are frequently used for effective selection, robust assessment of polymorphism and to explore the relativity of diverse genetic groups of cultivars with their wild relatives (Shah et al. 2009; Király et al. 2012). Previously, a restricted cotton gene pool has been classified by using Random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP). SSR/Microsatellites are proven to be an ideal tool for DUS testing of new varieties because of high polymorphism, multi-allelic, co-dominant inheritance, good reproducibility, abundant distribution all over the genome and short amplification product and widely used for molecular characterization of genotypes to accelerate the effective selection (Jamil et al. 2020). About >1000 primers are identified from the cotton genome that is available in genome libraries (Nguyen et al. 2004; Yu et al. 2014).

Although some studies were conducted for DNA fingerprinting and genetic diversity assessment of cotton varieties in Pakistan previously (Mumtaz et al. 2010; Ullah et al. 2012). As far as Mumtaz et al. (2010) is concerned they have used RAPDs markers which are less reliable and non-reproducible. Ullah et al. (2012) although used SSR markers for DNA fingerprinting however genotypes used in their study were all primitive and number of SSR markers (104) used were relatively low which are unable to reveal genetic diversity in Pakistani cotton genotypes having narrow genetic base. Keeping in view of above said facts in our study we utilized 297 SSR markers for DNA fingerprinting and genetic diversity analysis of 25 cotton genotypes. Cluster analysis was conducted for estimation of genetic distance and to provide a reliable picture of a diverse grouping of genotypes for effective utilization of genetic information in cotton breeding programs. Structure analysis and dendrogram provides an insight into different sets of allelic richness in GM cotton genotypes. DNA fingerprints of GM cotton will provide a molecular basis to identify and authenticate the seed purity in the market.

Materials and Methods

Plant material

The research work was conducted at Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute Faisalabad, Pakistan for DNA fingerprinting of cotton genotypes for variety protection and registration under Plant Breeders Rights Rules 2017. Seeds of 25 GM cotton genotypes (Pure-lines) were obtained from various institutes and stations from all over Punjab (Table 1) and were sown in pots in the greenhouse at 28°C following the standard agriculture practices. Each genotype was planted in 5 different pots wherein, each pot contained 2 seeds per genotype. After germination and seedling development till 3–4 leaves, 05 seedlings for each genotype were harvested and stored at -40°C for DNA extraction.

DNA isolation and PCR amplification

DNA was isolated from 100 mg of young leaves of GM cotton plants. The leaves were finely ground to powder using liquid nitrogen while DNA extraction was performed using the modified cetyltrimethylammonium bromide (CTAB) method (Allen et al. 2006). DNA samples for all genotypes were analyzed for the quality and quantity by NanoDrop spectrophotometer and also running them on Agarose Gel Electrophoresis as elaborated in our previous study (Jamil et al. 2020).

PCR was assembled using 2X DreamTaq Green PCR master mix ThermoFisher Scientific (K1082) as recommended by the manufacturer. The master mix aids us in direct loading the samples (PCR product) on gel and green dye does not cause any inconvenience during PCR reaction. For 2X we prepared 50 µL PCR reaction mixture which was comprised of 25 µL master mix, 200 ng template DNA, 2 µM primer (forward & reverse) and volume make up to 50 µL using nuclease-free water. PCR profile was set as follows: 1 cycle of initial denaturation at 95°C for 5 min, 35 cycles of denaturation at 95°C for 60 sec, annealing at 55°C for 1 min and extension at 72°C for 1 min, the final extension for 5 min at 72°C. The PCR product was stored at 4°C before electrophoresis.

Poly acrylamide gel electrophoresis (PAGE)

PCR amplicons were analyzed on Electrophoresis System model POWERPRO-3AMP (cleaver scientific limited) using 6% (W/V) polyacrylamide gel and performed at 16 watts power. PAGE gel was stained by Silver nitrate staining for visualization according to the previously described staining protocol by (Caetano-Anolles 1997). Resulting patterns were analyzed and captured using Syngene trans-illuminator Gel Documentation System.

Statistical analysis

The size of the PCR amplicon for each genotype was estimated by comparing them with banding patterns of 50 bp DNA ladder (Invitrogen™) loaded in the PAGE gel and scored as 1 or 0 indicating the presence and absence of particular band size. NTSYSpc 2.0 software (Rohlf 1998) and Structure v. 2.3.4 were used for statistical analysis. Structure analysis utilizes a model-based Bayesian clustering approach to obtain pedigree information that provides different sets of allelic richness in cotton (Pritchard et al. 2000). NTSYSpc 2.0 uses an un-weighted pair Group Method of Arithmetic Means (UPGMA) to analyze multivariate diverse data and generates a dendrogram. The structure analyses were performed according to the following parameters: No admission model; K-value (1–10); Burn-in periods: 10,000; 2 number of in-iteration burns 10,000; Markov Chain Monte Carlo Simulation: 100,000. The structure was determined by using LnP (K) values against ΔK values using Evanno Test (Evanno et al. 2005).

Results

SSR Polymorphism

A total of 297 SSR markers evenly distributed on 26 cotton chromosomes were used for DNA fingerprinting and genetic diversity studies of 25 cotton genotypes (Table S1). Among 297 SSR markers, 25 markers were not amplified whereas 28 were monomorphic and the remaining 244 were polymorphic. The polymorphic 244 SSR markers amplified a total of 1537 alleles among which 1294 (84.18%) were polymorphic and 243 alleles (15.82%) were monomorphic. Minimum numbers of alleles 2 were amplified by 13 SSR markers namely BNL0347, BNL2570, BNL3103, BNL3140, CIR0208, CIR0210, DPL0058, DPL0156, DPL0163, DPL0273, JESPR85, MUCS0515 and TMB2920. Maximum numbers of alleles (19) were amplified by SSR marker BNL0137 among which 16 were polymorphic (Fig. 1). Maximum polymorphic alleles (PA) 18 were amplified by BNL-228. Lowest PIC value (0.08) was observed for DPL0156 whereas the highest PIC value (0.93) was recorded for seven SSR markers i.e., BNL0137, BNL0387, BNL3977, JESPR220, JESPR222, MGHES44 and TMB0471 collectively. The average number of alleles and polymorphic alleles was 6.3 and 5.3 respectively. The average PIC value was 0.73 whereas the size of amplicon ranged from 80 to 1000 bp (Table 2).

DNA fingerprinting

Fifty-seven SSR markers were able to differentiate 25 cotton genotypes. There were two groups of genotypes concerning to DNA fingerprinting. Group, I comprised 19 genotypes that amplified unique alleles and were identifiable using single SSR marker. Group II was comprised of 06 genotypes which were not identifiable using unique alleles hence a two-step identification method was used for their identification (Table 3).

SSR marker BNL0119 amplified unique alleles for four genotypes i.e., MNH-1016, MNH-1020, BH-221, and IUB-13. Similarly, BNL0228 amplified unique allelic patterns for three genotypes i.e., MNH-886, FH-142, and IUB-13. IUB-13 was identifiable with the help of nine SSR markers, MNH-1016 was identifiable using six SSR markers, NIAB-878 amplified unique allelic pattern with five SSR markers, MNH-1020 and RH-668 with four markers, FH-326 and BH-221 with three markers, MNH-886, VH-327, RH-647, RH-662, SLH-8, SLH-19 and BS-15 with two markers and VH-Gulzar, FH-142, SLH-06 and BH-201 by one marker as given in Table 3.

Genetic diversity studies

The data of 244 polymorphic SSR markers were used to generate a UPGMA dendrogram to study the extent of genetic diversity among 25 cotton genotypes using the SHAN similarity matrix. The similarity coefficient between 25 cotton genotypes varied from 0.63 to 0.91. The dendrogram divided the genotypes into two distinct clusters (Fig. 2). The highest similarity was observed between FH-152 and FH-142 in cluster I sharing 91% of the genetic loci; whereas the lowest genetic similarity was observed between MNH-1020 and the rest of 24 genotypes sharing 63% of genetic loci in common. A domestic relationship exists between cultivar distribution and agro-ecological zones as is evident from the UPGMA dendrogram. Genotypes bred in different agro-ecological zones i.e., Multan, Sahiwal, Vehari, Faisalabad, and Bahawalpur tend to appear in the same clade in cluster analysis. However slight variation was observed for MNH-886, MNH-1020, RH-668, and BH-178 which did not follow geographical distribution (Fig. 2).

In most cases, cluster analysis results fitted well with pedigree parentage information. Genotypes lying in clade IA have a common parentage with one another except VH-383 and VH-Gulzar. Similarly, varieties present in clade IB i.e., except SLH-08, SLH-19, and RH-668 have one parent in common with each other. Genotypes present in clade III i.e., BH-201 and BH-221 do not have common parentage; whereas genotypes present in cluster II i.e., IUB-13 and BS-15 have shared parentage except for NIAB-878 and MNH-886 (Fig. 2 and Table 1).

Population structure studies

The model-based Bayesian approach was used to infer the population structure of twenty-five genotypes of cotton. Population Structure was determined by plotting the LnP (K) against ΔK values through Evanno Test (Evanno et al. 2005). The highest ΔK (10212.75) was observed for K=2 suggesting the existence of two sub-populations i.e., population 1 (P1) and population 2 (P2). P1 was comprised of 21 genotypes whereas P2 was comprised of four genotypes. The expected heterozygosity was high among the individuals of P1 (0.132) as compared to P2 (0.10). Whereas genetic diversity among the individuals of P2 (Fst_2 = 0.82) was high as compared to P1 (Fst_1 = 0.69) (Fig. 3). The results suggested that all genotypes originated from the Government of Punjab Agriculture set up (Ayub Agricultural Research Institute, AARI) have similar blood except MNH-886. Similarly, varieties bred from Institute other than AARI i.e., IUB-13, BS-15, and NIAB-878 have similar blood.

Discussion

DNA fingerprinting and genetic diversity studies are of prime importance for germplasm maintenance, PBRs protection, and seed production in cotton (Santhy et al. 2019). For a cotton breeder, presence of genetic variability guides for interspecific or intraspecific hybridization (Sheidai et al. 2014). Estimation of genetic diversity and DNA fingerprinting characterizes the individuals and assign them to different heterotic groups for the choice of parental genotypes for hybridization-based breeding programs (Noormohammadi et al. 2018; Ul-Allah et al. 2019).

Table 1: List of genotypes used in the study along with pedigree/parentage information

Institute Name	Variety name	Pedigree/Parentage
CRI, Multan	MNH-1016	MNH-786 (Non Bt.) × MNH-456 (Bt)
	MNH-1020	96016 × MNH-456
	MNH-1026	C-26 (MNH-6070 × MNH-786) × FH-207
	MNH-886	(FH-207 × MNH-770) × Bollgard-I
CRI, Khanpur	RH-647	RH-500 × FH-113
	RH-662	319/08 × FH-113
	RH-668	VH-259 × RH-620
CRS, Sahiwal	SLH-06	SLH-334 × Neelum-121
	SLH-8	SLS-1 × FH-142
	SLH-19	SLH-336 × FH-114
CRS, Vehari	VH-327	VH-289 × VH-291 (Bt.)
	VH-Gulzar	VH-281 × VH-211 (Bt.)
	VH-189	VH-319 (Bt.) × FH-142 (Bt.)
	VH-383	VH-211 (Bt.) × VH-326
CRS, Bahawalpur	BH-178	(BH-162 × MNH-6070) × Neelum-121
	BH-201	(BH-172 × BH-126) × Neelum-121
	BH-221	(BH-160 × BH-176) × BH-121
CRS, Faisalabad	FH-142	FH-114 × FH-207
	FH-Lalazar	FH-207 × NuCot-N-33B (Bollgard-I)
	FH-152	FH-207 × FH-113
	FH-326	FH-942 × FH-114
	FH-490	FH-113 × FH-2006
Islamia University Bahawalpur	IUB-13	IUB-09 × MNH-789
Bandesha Seed Corporation	BS-15	IB 2009 × MNH-786
NIAB, Faisalabad	NIAB-878	B-111 × NIAB-Kiran

Fig. 1: The amplification product of BNL-0137. The most informative SSR marker with 19 number of alleles among which 16 are polymorphic

In past different types of molecular markers i.e., RFLPs, RAPDs, AFLPs, ISSRs, and SSR were used for DNA fingerprinting and genetic diversity studies in cotton (Becelaere et al. 2005; Khan et al. 2010; Badigannavar et al. 2012; Noormohammadi et al. 2013). However, the present study evidenced that SSR markers are still an effective tool to differentiate cotton cultivars due to high polymorphism, ease of use, and high reproducibility. However, to exploit genetic variation we have to use a very large set of SSR markers which is an indication of a narrow genetic base in the cotton germplasm (Fig. 2).

Unlike most of the previous studies (Zhang et al. 2013; Noormohammadi et al. 2018), not all the cotton varieties produced unique allelic patterns as six varieties in the present study did not amplify unique bands. Some informative SSR markers showing a high level of polymorphism are BNL0137, BNL-228, BNL0387 TMB0471, JEPSR220 BNL0140, CIR0251, BNL2616, JESPR222, BNL3590, and BNL3977.

Table 2: List of SSR markers used along with Polymorphism information, Number of Alleles (NOA) Polymorphic Alleles (PA), Polymorphic Information Contents (PIC) and annealing temperature (TA)

Sr. No.	Name	Polymorphism	NOA	PA	PIC	Sr. No.	Name	Polymorphism	NOA	PA	PIC
1.	BNL0113	Polymorphic	5	5	0.78	150.	CIR0099	Monomorphic
2.	BNL0116	Polymorphic	4	4	0.66	151.	CIR0133	Polymorphic	8	6	0.86
3.	BNL0117	Polymorphic	8	7	0.87	152.	CIR0135	Not Amplified
4.	BNL0118	Polymorphic	3	3	0.61	153.	CIR0143	Polymorphic	5	5	0.80
5.	BNL0119	Polymorphic	14	13	0.89	154.	CIR0169	Monomorphic
6.	BNL0128	Polymorphic	5	5	0.78	155.	CIR0180	Monomorphic
7.	BNL0134	Monomorphic				156.	CIR0181	Monomorphic
8.	BNL0135	Polymorphic	4	2	0.75	157.	CIR0203	Polymorphic	7	5	0.70
9.	BNL0137	Polymorphic	19	16	0.93	158.	CIR0208	Polymorphic	2	2	0.50
10.	BNL0140	Polymorphic	14	14	0.91	159.	CIR0210	Polymorphic	2	1	0.50
11.	BNL0148	Polymorphic	6	6	0.83	160.	CIR0221	Polymorphic	3	1	0.67
12.	BNL0150	Polymorphic	10	8	0.90	161.	CIR0224	Polymorphic	4	3	0.75
13.	BNL0153	Polymorphic	7	7	0.85	162.	CIR0246	Polymorphic	6	6	0.71
14.	BNL0162A	Not Amplified				163.	CIR0251	Polymorphic	14	14	0.92
15.	BNL0174	Polymorphic	10	6	0.89	164.	CIR0272	Polymorphic	4	4	0.75
16.	BNL0193	Polymorphic	5	5	0.80	165.	CIR0294	Polymorphic	4	4	0.75
17.	BNL0197	Polymorphic	3	3	0.67	166.	CIR0307	Polymorphic	10	10	0.89
18.	BNL0206	Polymorphic	7	5	0.86	167.	CIR036	Polymorphic	8	5	0.87
19.	BNL0218	Polymorphic	10	10	0.89	168.	CIR0372	Polymorphic	7	7	0.85
20.	BNL0219	Polymorphic	3	3	0.67	169.	CIR0393	Polymorphic	6	6	0.83
21.	BNL0220	Polymorphic	3	3	0.53	170.	CIR0413	Polymorphic	3	3	0.67
22.	BNL0223	Polymorphic	10	9	0.88	171.	CIR0415	Polymorphic	5	3	0.78
23.	BNL0225	Polymorphic	12	10	0.91	172.	CIR049	Polymorphic	3	3	0.67
24.	BNL0226	Not Amplified				173.	CIR060	Polymorphic	4	1	0.75
25.	BNL0228	Polymorphic	18	18	0.92	174.	CIR062	Polymorphic	4	3	0.75
26.	BNL0234	Polymorphic	7	7	0.85	175.	CIR122	Polymorphic	7	5	0.86
27.	BNL0236	Polymorphic	4	4	0.75	176.	CM14	Not Amplified
28.	BNL0237	Polymorphic	4	3	0.72	177.	CM17	Not Amplified
29.	BNL0244	Polymorphic	6	5	0.82	178.	CM32	Not Amplified
30.	BNL0285	Polymorphic	7	6	0.86	179.	CM4	Polymorphic	6	5	0.83
31.	BNL0300	Polymorphic	4	4	0.75	180.	CM45	Polymorphic	13	4	0.92
32.	BNL0329	Polymorphic	9	8	0.85	181.	CM6	Not Amplified
33.	BNL0341	Polymorphic	9	9	0.88	182.	CM60	Polymorphic	7	3	0.86
34.	BNL0343	Polymorphic	4	4	0.74	183.	CM63	Not Amplified
35.	BNL0347	Polymorphic	2	2	0.50	184.	CM66	Monomorphic
36.	BNL0358	Polymorphic	5	1	0.80	185.	CM67	Polymorphic	7	7	0.76
37.	BNL0379	Not Amplified				186.	CM68	Not Amplified
38.	BNL0386	Polymorphic	10	10	0.88	187.	CM7	Not Amplified
39.	BNL0387	Polymorphic	17	17	0.93	188.	CM8	Not Amplified
40.	BNL0390	Polymorphic	6	6	0.82	189.	DPL0035	Polymorphic	9	9	0.89
41.	BNL0391	Polymorphic	5	5	0.80	190.	DPL0041	Polymorphic	6	6	0.83
42.	BNL0448	Polymorphic	9	3	0.87	191.	DPL0058	Polymorphic	2	1	0.50
43.	BNL0530	Not Amplified				192.	DPL0079	Polymorphic	4	3	0.67
44.	BNL0584	Polymorphic	3	3	0.66	193.	DPL0133	Polymorphic	5	4	0.79
45.	BNL0597	Polymorphic	4	1	0.75	194.	DPL0149	Polymorphic	5	5	0.70
46.	BNL0686	Monomorphic				195.	DPL0156	Polymorphic	2	2	0.08
47.	BNL0827	Polymorphic	3	2	0.67	196.	DPL0163	Polymorphic	2	2	0.50
48.	BNL0829	Polymorphic	6	6	0.83	197.	DPL0264	Polymorphic	6	4	0.83
49.	BNL0830	Polymorphic	2	2	0.27	198.	DPL0273	Polymorphic	2	2	0.50
50.	BNL0834	Polymorphic	8	8	0.88	199.	DPL0348	Monomorphic
51.	BNL0891	Polymorphic	5	5	0.80	200.	DPL0385	Polymorphic	4	4	0.75
52.	BNL0946	Polymorphic	5	5	0.76	201.	DPL0443	Monomorphic
53.	BNL1017	Monomorphic				202.	DPL0489	Monomorphic
54.	BNL1161	Polymorphic	8	4	0.87	203.	DPL0528	Polymorphic	4	3	0.75
55.	BNL1253	Polymorphic	5	5	0.77	204.	DPL0534	Polymorphic	4	3	0.75
56.	BNL1317	Polymorphic	8	8	0.84	205.	DPL0542	Polymorphic	7	4	0.82
57.	BNL1403	Polymorphic	3	3	0.67	206.	HAU0119	Polymorphic	7	3	0.82
58.	BNL1417	Polymorphic	7	7	0.84	207.	JESPR0102	Monomorphic
59.	BNL1418	Monomorphic	4	0	0.75	208.	JESPR0135	Polymorphic	9	9	0.89
60.	BNL1441	Polymorphic	5	5	0.80	209.	JESPR0232	Polymorphic	9	7	0.89
61.	BNL1531	Polymorphic	6	3	0.83	210.	JESPR0240	Monomorphic
62.	BNL1592	Polymorphic	2	2	0.50	211.	JESPR1	Polymorphic	4	3	0.70
63.	BNL1597	Polymorphic	9	7	0.88	212.	JESPR100	Polymorphic	4	4	0.72
64.	BNL1605	Not Amplified				213.	JESPR101	Not Amplified
65.	BNL1667	Polymorphic	5	4	0.79	214.	JESPR103	Polymorphic	8	8	0.87
66.	BNL1681	Not Amplified				215.	JESPR108	Polymorphic	3	3	0.63
67.	BNL1688	Polymorphic	6	1	0.83	216.	JESPR114	Polymorphic	11	11	0.89
68.	BNL1694	Polymorphic	5	3	0.80	217.	JESPR134	Polymorphic	10	9	0.88
69.	BNL2443	Monomorphic				218.	JESPR153	Polymorphic	11	11	0.88
70.	BNL2448	Polymorphic	6	5	0.83	219.	JESPR156	Polymorphic	4	4	0.71
71.	BNL2527	Polymorphic	11	11	0.91	220.	JESPR160	Polymorphic	3	2	0.64
72.	BNL2544	Polymorphic	5	5	0.80	221.	JESPR173	Polymorphic	6	6	0.83
73.	BNL2564	Polymorphic	4	4	0.75	222.	JESPR178	Polymorphic	5	5	0.80
74.	BNL2570	Polymorphic	2	2	0.50	223.	JESPR185	Polymorphic	5	5	0.79
75.	BNL2572	Polymorphic	4	4	0.75	224.	JESPR186	Not Amplified

Table 2: Continue

76.	BNL2590	Polymorphic	9	6	0.88	225.	JESPR194	Polymorphic	8	8	0.87
77.	BNL2597	Polymorphic	5	3	0.80	226.	JESPR200	NA
78.	BNL2599	Polymorphic	3	3	0.67	227.	JESPR202	NA
79.	BNL2616	Polymorphic	15	14	0.91	228.	JESPR205	Polymorphic	6	4	0.82
80.	BNL2632	Polymorphic	12	12	0.88	229.	JESPR209	Polymorphic	2	2	0.50
81.	BNL2634	Polymorphic	12	10	0.91	230.	JESPR215	Polymorphic	13	13	0.91
82.	BNL2652	Polymorphic	4	4	0.73	231.	JESPR218	Monomorphic
83.	BNL2681	Polymorphic	4	2	0.75	232.	JESPR220	Polymorphic	15	15	0.93
84.	BNL2700	Polymorphic	10	9	0.89	233.	JESPR222	Polymorphic	14	14	0.93
85.	BNL2750	Polymorphic	2	1	0.50	234.	JESPR227	Polymorphic	6	6	0.79
86.	BNL2762	Polymorphic	6	6	0.83	235.	JESPR229	Monomorphic
87.	BNL2772	Polymorphic	5	4	0.80	236.	JESPR232	Polymorphic	7	4	0.83
88.	BNL2827	Monomorphic				237.	JESPR236	Polymorphic	7	4	0.83
89.	BNL2835	Polymorphic	11	6	0.91	238.	JESPR242	Polymorphic	6	6	0.82
90.	BNL2882	Polymorphic	9	5	0.89	239.	JESPR244	Monomorphic
91.	BNL2986	Monomorphic				240.	JESPR246	Polymorphic	11	11	0.90
92.	BNL3029	Polymorphic	3	3	0.67	241.	JESPR250	Polymorphic	8	8	0.78
93.	BNL3034	Polymorphic	4	2	0.74	242.	JESPR270	Polymorphic	7	3	0.86
94.	BNL3071	Not Amplified				243.	JESPR272	Not Amplified
95.	BNL3090	Polymorphic	6	3	0.83	244.	JESPR291	Monomorphic
96.	BNL3103	Polymorphic	2	2	0.50	245.	JESPR292	Polymorphic	3	2	0.49
97.	BNL3140	Polymorphic	2	2	0.50	246.	JESPR296	Polymorphic	4	4	0.74
98.	BNL3147	Polymorphic	4	4	0.75	247.	JESPR310	Polymorphic	6	5	0.83
99.	BNL3255	Polymorphic	9	4	0.88	248.	JESPR42	Polymorphic	11	7	0.90
100.	BNL3279	Polymorphic	7	7	0.85	249.	JESPR80	Not Amplified
101.	BNL3319	Polymorphic	5	5	0.78	250.	JESPR84	Polymorphic	8	5	0.87
102.	BNL3324	Polymorphic	3	3	0.67	251.	JESPR85	Polymorphic	2	2	0.50
103.	BNL3345	Polymorphic	5	4	0.63	252.	JESPR94	Polymorphic	3	3	0.65
104.	BNL3379	Polymorphic	7	3	0.86	253.	JESPR95	Polymorphic	7	7	0.84
105.	BNL3383	Polymorphic	7	7	0.85	254.	MGHES11a	Polymorphic	6	6	0.83
106.	BNL3408	Polymorphic	10	10	0.89	255.	MGHES11b	Polymorphic	4	4	0.73
107.	BNL3414	Polymorphic	5	3	0.80	256.	MGHES18	Polymorphic	3	3	0.65
108.	BNL3432	Polymorphic	5	5	0.80	257.	MGHES24	Polymorphic	11	11	0.91
109.	BNL3449	Polymorphic	8	8	0.86	258.	MGHES30a	Monomorphic
110.	BNL3452	Polymorphic	5	5	0.80	259.	MGHES32	Not Amplified
111.	BNL3523	Polymorphic	7	2	0.85	260.	MGHES40	Polymorphic	7	7	0.85
112.	BNL3556	Monomorphic				261.	MGHES41	Polymorphic	9	8	0.88
113.	BNL3558	Polymorphic	3	3	0.67	262.	MGHES44	Polymorphic	14	13	0.93
114.	BNL3563	Polymorphic	7	3	0.86	263.	MGHES46	Not Amplified
115.	BNL3582	Polymorphic	3	2	0.66	264.	MGHES48	Polymorphic	13	13	0.92
116.	BNL3590	Polymorphic	14	14	0.92	265.	MGHES59	Polymorphic	3	3	0.64
117.	BNL3592	Polymorphic	5	3	0.80	266.	MGHES6	Polymorphic	3	3	0.56
118.	BNL3599	Polymorphic	13	13	0.92	267.	MGHES70	Polymorphic	8	6	0.80
119.	BNL3601	Polymorphic	4	2	0.71	268.	MGHES71	Polymorphic	5	5	0.79
120.	BNL3646	Polymorphic	3	1	0.67	269.	MGHES73	Polymorphic	11	11	0.89
121.	BNL3649	Monomorphic				270.	MGHES75	Polymorphic	5	5	0.76
122.	BNL3661	Polymorphic	8	5	0.86	271.	MGHES76	Polymorphic	6	6	0.82
123.	BNL3799	Monomorphic				272.	MUCS0515	Polymorphic	2	1	0.50
124.	BNL3860	Polymorphic	13	8	0.92	273.	MUSB1121	Polymorphic	5	5	0.71
125.	BNL3903	Polymorphic	4	1	0.75	274.	NAU0808	Polymorphic	5	3	0.78
126.	BNL3935	Polymorphic	11	7	0.91	275.	NAU2083	Polymorphic	9	9	0.83
127.	BNL3948	Polymorphic	3	1	0.64	276.	NAU2540	Polymorphic	5	5	0.73
128.	BNL3976	Polymorphic	3	1	0.67	277.	NAU2580	Polymorphic	4	1	0.75
129.	BNL3977	Polymorphic	14	14	0.93	278.	NAU2679	Polymorphic	4	4	0.62
130.	BNL3985	Polymorphic	4	4	0.74	279.	NAU2715	Polymorphic	4	1	0.73
131.	BNL3988	Polymorphic	6	6	0.83	280.	NAU2954	Polymorphic	5	5	0.80
132.	BNL3995	Polymorphic	3	3	0.67	281.	NAU3100	Polymorphic	8	8	0.87
133.	BNL4011	Polymorphic	3	2	0.62	282.	NAU6672	Polymorphic	4	3	0.75
134.	BNL4015	Not Amplified				283.	TMB0034	Not Amplified
135.	BNL4030	Not Amplified				284.	TMB0471	Polymorphic	16	15	0.93
136.	BNL4078	Polymorphic	3	3	0.67	285.	TMB0603	Polymorphic	4	1	0.75
137.	BNL4080	Monomorphic				286.	TMB0770	Polymorphic	5	5	0.80
138.	BNL4082	Polymorphic	6	6	0.83	287.	TMB1296	Polymorphic	6	4	0.83
139.	BNL4092	Polymorphic	7	7	0.86	288.	TMB1356	Polymorphic	6	1	0.83
140.	BNL786	Polymorphic	7	7	0.78	289.	TMB1456	Monomorphic
141.	BNL834	Polymorphic	7	7	0.85	290.	TMB1548	Polymorphic	6	6	0.83
142.	CGR5641	Polymorphic	4	2	0.74	291.	TMB1638	Polymorphic	4	3	0.75
143.	CGR6692	Polymorphic	4	4	0.75	292.	TMB1639	Polymorphic	6	2	0.82
144.	CGR6692	Polymorphic	3	2	0.63	293.	TMB1838	Polymorphic	3	1	0.66
145.	CGR6824	Polymorphic	7	7	0.80	294.	TMB1919	Polymorphic	5	5	0.80
146.	CIR0054	Polymorphic	4	4	0.75	295.	TMB2920	Polymorphic	2	2	0.50
147.	CIR0061	Polymorphic	7	7	0.85	296.	TMB2945	Polymorphic	3	2	0.66
148.	CIR0082	Polymorphic	11	11	0.91	297.	TMH05	Monomorphic
149.	CIR0094	Polymorphic	10	9	0.85

Note: Annealing temperature of all primers was 55°C

Table 3: List of SSR markers that can distinguish twenty-five varieties of cotton using direct or indirect method

Genotypes	DNA Fingerprints
MNH-886	BNL0228, MGHES24
MNH-1016	BNL0123, CIR0203, NAU2679, BNL0119, MGHES75, JESPR153
MNH-1020	BNL0119, BNL0391, BNL2634, JESPR232
MNH-1026	Identifiable using pair of SSR markers (BNL2632 & BNL0123) and (BNL0341 & CIR0230)
VH-327	MGHES75, JESPR215
VH-Gulzar	BNL0134
VH-189	Identifiable using pair of SSR markers (BNL0830 & BNL0119) and (DPL0153 & BNL0134)
VH-383	Identifiable using pair of SSR markers (BNL3601 & BNL0119) and (BNL3449 & CIR0391)
FH-142	BNL0228
FH-Lalazar	Identifiable using pair of SSR markers (BNL0830 & JESPR232) and (BNL0237 & CIR0203)
FH-152	Identifiable using pair of SSR markers (BNL834 & BNL1253) and ( BNL786 & BNL448)
FH-326	DPL0542, CIR0246, DPL0149
FH-490	Identifiable using pair of SSR markers ( TMB2926 & BNL0123) and ( BNL3988 & JESPR232)
RH-647	BNL1253, DPL0133
RH-662	BNL2616, MGHES73
RH-668	DPL0156, CIR0094, UAU0119, BNL0329
SLH-06	BNL0448
SLH-8	MGHES6, JESPR153
SLH-19	BNL0137, JESPR250
BH-178	Identifiable using pair of SSR markers ( BNL1592 & BNL3529) and (BNL0329 & JESPR153)
BH-201	JESPR236
BH-221	BNL3529, BNL0220, BNL0119
NIAB-878	NAU2083, BNL2540, BNL2599, BNL0140, JESPR114
IUB-13	HAU0119, CIR0307, BNL4082, BNL0390, BNL0150, BNL0228, BNL0119, BNL0236, JESPR100
BS-15	BNL2835, MGHES24

$Description: E:\Director\2018-2019\Rahil Experiments\PARB 908\Cotton\Cotton Barcoding Paper\PMBP\Fig 2. Dendrogram.png$

Fig. 2: Dendrogram of 25 cotton genotypes generated using data of 244 polymorphic SSR markers through SHAN similarity matrix and unweighted pair group method

These SSR markers may be used for DNA fingerprinting and genetic diversity studies in the future. Our results are in line with the previous studies (Bertini et al. 2006; Lacape et al. 2007) which also reported informative SSR markers for genotyping and genetic diversity studies.

The average alleles and polymorphic alleles per locus 6.3 and 5.3 respectively reported in our study were higher than many of previously published studies. Zhu et al. (2019) reported 6.02 alleles per locus in a study comprising of 557 G. hirsutism accessions. Javaid et al. (2017) reported 3.72 alleles per locus in a study of genetic diversity in 22 cotton accessions using 30 SSR markers. Similarly, Gurmessa (2019) reported 3.8 alleles per locus with 0.50 PIC value. Whereas according to our knowledge only one study of McCarty et al. (2018) reported a high number of alleles (7.9) per locus. This is expected

Fig. 3: Structure Analysis of Cotton varieties grown in Punjab Pakistan. Parameters: no admission model; K = 02; 10,000 Burn-in period; 100000 Rep

because they used landraces and genetic diversity in landraces is more than the cultivated varieties. However, Average PIC value reported in our study 0.73 is highest among all the previously published reports. High number of alleles in our study and high PIC value corresponds to large set of SSR markers used in our study (Table 2).

Different studies have reported a continuous decline in cotton productivity in Pakistan for the past 03 years (Ashraf et al. 2018; Ali et al. 2019b; Rana et al. 2020; Jamil et al. 2021). Whereas some model-based future predictions are pointing out that this trend will continue for another four to five years (Ashraf et al. 2018). The question arises what are major factors that are hampering cotton productivity? One possible answer to this question is the lack of genetic divergence in the cultivated cotton genotypes as proved through our results. The varieties used in this study covered almost 60% of the cropped area under cotton cultivation. However, when it comes to genetics there are only two types of blood as is evident from structure analysis. About 84% of genotypes (21) have similar genetic makeup and formulate P1 (Fig. 3). The pedigree parentage dictates that five genotypes have FH-207 as a common parent. The same is the case with Neelum-121 which is used as a parent in breeding of three genotypes and many other such examples exists in Table 1.

The pressure for higher productivity in cotton farming and continuous artificial selection have narrows down the genetic base which is a major hurdle for successful cotton breeding programs (Noormohammadi et al. 2018). It happens when you start with a broad genetic base but if the base material (Pedigree/Parentage) is itself has narrow genetic makeup as is our case, what will be its outcome? Crops will be more prone to biotic and abiotic stresses as is happening in cotton i.e., Whitefly (Ahmad and Akhtar 2018), Jassids, aphids, thrips (Akhtar et al. 2018) and bollworms (Ahmad et al. 2019) heavily infest almost all cotton varieties and cause almost 15–20% crop losses every year (Khan et al. 2016; Khanzada et al. 2016). Our breeding and selection efforts have narrowed down genetic base which needs to be broadened for the revival of cotton (Khanzada et al. 2016; Ali et al. 2019a).

Conclusion

DNA fingerprints were developed for twenty-five GM cotton genotypes grown in Punjab. The genetic diversity studies grouped the genotypes to two distinct groups P1 (20 genotypes), P2 (04 genotypes) whereas MNH-1020 did not follow clustering. The genetic makeup of cotton genotypes used in the study was narrow. We reported polymorphism information of 244 polymorphic SSR markers and proposed a core set of markers for future DNA fingerprinting and genetic diversity studies. Our study will provide a platform for the protection of Plant Breeders Rights and will help in registration of variety under Plant Breeders Rights Registry.

Acknowledgments

The Authors are highly thankful to Punjab Agricultural Research Board for providing funding to conduct this research work through PARB Project No. 908 entitled “DNA barcoding/fingerprinting for identification of Cotton, Wheat, Maize, Potato, Tomato and Date Palm varieties”. Mr. Baber Ali Lab Assistant, for technical assistance during research work. The Cotton Research Stations and Institute across the Province for providing the plant material and technical support.

Author Contributions

SJ, RS, MZI and SUR obtained funding, SJ, RS and EY conducted research experimentation, SJ, RS conducted statistical data analysis, SJ, RS and EY drafted the manuscript, SUR and MZI critically reviewed the manuscript. SJ, RS, MZI and SUR supervised the research experimentation and all process. SJ and RS corresponded to journal for submission and review process.

Conflict of Interest

The authors declare no conflict of interest among them

Data Availability declaration

We hereby declare that data, primary or supplementary related to this article, are available with the corresponding author and will be produced on demand

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