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Article

Genetic Diversity and Population Structure Analysis to Construct a Core Collection from Safflower (Carthamus tinctorius L.) Germplasm through SSR Markers

by
Gaddam Prasanna Kumar
1,
Pooja Pathania
1,
Nitu Goyal
1,
Nishu Gupta
1,
R. Parimalan
1,
J. Radhamani
1,
Sunil Shriram Gomashe
2,
Palchamy Kadirvel
3 and
S. Rajkumar
1,*
1
ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi 110012, India
2
ICAR-National Bureau of Plant Genetic Resources Regional Station, Dr PDKV Campus, Akola 444104, India
3
ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad 500030, India
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(4), 836; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13040836
Submission received: 22 February 2023 / Revised: 31 March 2023 / Accepted: 3 April 2023 / Published: 6 April 2023
(This article belongs to the Special Issue Breeding, Genetics and Genomics to Enhance Crop Production, towards Global Food Security)
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Abstract

:
Genetic resources are the fundamental source of diversity available to plant breeders for the improvement of desired traits. However, a large germplasm set is difficult to preserve and use as a working collection in genetic studies. Hence, the present study evaluates the genetic diversity of 3115 safflower accessions from the Indian National Gene Bank, including Indian cultivars, to develop a manageable set of accessions, with similar genetic variations of germplasm studied. A total of 18 polymorphic SSR markers were used. The genetic diversity analysis revealed that germplasm accessions were highly diverse and there is no correlation between genetic diversity and the geographical collection of germplasm or sourcing of germplasm. A core set was developed using a core hunter software with different levels of composition, and it was found that 10% of the accessions showed maximum gene diversity and represented an equal number of alleles and major allele frequency in the germplasm studied. The developed core consisted of 351 accessions, including Indian cultivars, and they were validated with various genetic parameters to ascertain that they were a true core set for the studied accessions of safflower germplasm.

1. Introduction

Carthamus tinctorius L., commonly known as “safflower”, is a member of the Asteraceae family and is an oil seed crop grown primarily for its nutritionally desirable high percentage of unsaturated fatty acids [1], with an estimated haploid genome size of 1.4 GB and chromosome number 2n = 24 [2]. It is a multipurpose crop with diverse uses, such as the extraction of dyes, the extraction of medicinal properties and edible oil extraction [3,4,5,6]. It has also been exploited for the production of biofuel and industrial oil [7]. Additionally, it became a good alternative to other oilseed crops in arid regions, due to its resilience to high salinity and low moisture conditions [8,9,10,11]. Safflower evolution is believed to have seven diversity centers, including regions in the Far East, India-Pakistan, the Middle East, Egypt, Sudan, Ethiopia and Europe [12]. Iran-Afghanistan, Israel-Jordan-Iraq-Syria and Turkey are three gene pool centers within Middle East [13]. However, Chapman et al. [14] proposed that safflower may have a single origin somewhere west of the Fertile Crescent, followed by a subsequent spread in Asia, Europe and Africa.
Globally, safflower is cultivated in more than 60 countries for various purposes. Among them, the USA, India, Kazakhstan, Mexico, Turkey and China are the largest producers of safflower [15]. In spite of significant fluctuations in the acreage under Safflower cultivation, India was the highest producer of the crop from 1994 to 2016. Nevertheless, Safflower has not been able to create a functional unit for itself as a major oilseed crop. Some factors that deter its cultivation are low yield, presence of spines and susceptibility to different biotic stresses [16]. Therefore, genetic improvement of safflower is essential for its acceptance as one of the major oilseed crops of global importance.
Conventional breeding programs in several crop species have resulted in the development of cultivars with improved yield and resistance to several diseases. The implication of molecular markers in crop breeding has been established as a powerful method for upgrading several crop species [17]. The multi-locus DNA markers namely amplified fragment length polymorphism (AFLP) [18,19] and randomly amplified polymorphic DNA (RAPD) [20], and inter-simple sequence repeats (ISSR) [21,22,23,24,25] and sequence-related amplified polymorphism (SRAP) [26] have been used extensively to identify genetic diversity and species relationships in Safflower. Microsatellites or simple sequence repeats (SSRs) are considered ideal genetic markers for assessment of diversity in germplasm collections due to their desirable properties: abundant, locus specific, co-dominant, multi-allelic, high throughput genotyping [27], high polymorphism and reproducibility. Efforts have been made to develop expressed sequence tag (EST) and genome-based SSR markers [28,29,30,31] and to apply them in safflower germplasm characterization [14,32,33].
Although a large number of plant germplasm is conserved in genebanks, the voluminous sample size and the meagre knowledge on the population structure and genetic diversity of these germplasm is a limitation for the successful utilization of their genetic potential [34]. Efficient management and utilization of the existing germplasm in the gene bank could be achieved by reducing the number of accessions systematically from the large collection of germplasm [35]. Frankel [36] suggested the concept of a core collection, which is a subset of accessions with minimum redundancy and maximum genetic diversity selected from an entire collection. Various studies have proposed subsampling proportions ranging from 5 to 30% for a core collection [37,38,39]. The development of core collections has been considered the most important activity in the conservation and utilization of plant genetic resources (PGRs) [40]. The development of core collections has already been reported for many crops, such as rice [41], soybean [42] and wheat [43]. The development of core collections was based at first on passport data, geographical distribution and phenotypic data [44,45,46]. Subsequently, the advance of molecular markers, which are an efficient means of confirming genetic diversity, has allowed the development of more powerful core collections in many crop species, either alone [47] or in combination with phenotypic data [48]. From amongst various molecular markers, SSRs are often selected for genetic studies, such as for genetic diversity and the development of core collections [49,50]. Although there are a number of useful methods for selecting a core collection, core hunter software [51] is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. It is a mixed replica search including both distance measures and allelic diversity indices [51]. Using this program, core collections have been developed in various crops, such as wheat [52] and barley [53]. The previous study on core development in safflower germplasm either used morphological characterization [54] or a small set of germplasm [55]. However, the Indian Gene Bank is conserving a large set of safflower germplasm collections, i.e., 7453 from different locations in India and across the globe, which provides enough information to study the variations for crop improvement programs. In this study, our aim was to analyze a set of 3115 accessions for genetic diversity and population structure using SSR markers, and to develop a core set that represents maximum genetic diversity for further utilization of potential genotypes in a varietal development program.

2. Materials and Methods

2.1. Plant Material

A total of 3115 diverse safflower accessions were used for the study (Table 1) and were procured from the National Gene Bank of the Indian Council of Agriculture Research–National Bureau of Plant Genetic Resources, (ICAR–NBPGR), New Delhi, India. Out of 3115 safflower accessions, 1512 accessions were collected from India, representing 15 states, and 595 accessions were breeding material used for varietal development (Table S1). There were 1008 accessions from exotic collections belonging to 7 different countries (Figure 1).
The accessions obtained from the National Gene Bank were purified by selfing for one generation at ICAR–NBPGR Regional Station, Akola, Maharashtra, India. Seeds from a single head were collected and germinated in germination papers for 7–10 days, young seedlings of about 5–7 were harvested and pooled for DNA extraction using DNeasy Plant Kit (Qiagen, Hilden, Germany). The quality and quantity of the extracted DNA were assessed using spectrophotometer and gel electrophoresis.

2.2. Genotyping through SSR Markers

Eighteen polymorphic SSR markers identified from the earlier study [28] (Table 2), distributed across the genome, were used for the present study. The polymorphic SSR markers were identified based on preliminary analysis with 43 safflower varieties on the basis of PCR product separated in 3% MetaphorTM agarose (Lonza, Rockland, NY, USA) gel electrophoresis. Polymorphic markers with varying sizes of amplified products were multiplexed into 5 sets and 4 markers (CAT-46, 48, 65, 96;) with overlapping sizes of amplified products individually amplified for efficient use of metaphor agarose and efficient scoring.
For PCR amplification for SSR primers, 10 µL PCR mixture was prepared containing 1X PCR buffer, 200 mM of dNTPs, 0.2 µM of forward and reverse primer and 0.5 units of Taq Polymerase (GeneDireX Inc. USA). PCR reaction set up involved initial denaturation at 95 °C for 2 min followed by 20 cycles of 95 °C for 45 s, 54 °C for 45 s, 72 °C for 45 and final extension at 72 °C for 4 min. The DNA amplification products were separated by gel electrophoresis using 3% MetaphorTM agarose (Lonza, Rockland, NY, USA) in 1X TBE buffer along with 50 bp DNA ladder (GeneDireX Inc., Taoyuan, Taiwan). The amplified bands are visualized in UV gel doc analyzer (Analytik Jena, Jena, Germany). Gel images were obtained and analyzed using AlphaView SA software.

2.3. Genetic Diversity and Population Structure Analysis

Amplified fragments for each SSR primer were scored and Power Marker version 3.25 [56] was used to get polymorphic information content (PIC). GenAlex 6.503 was used to calculate the number of alleles, allele frequency and genetic diversity index (GDI), such as major allele frequency (MAF) and gene diversity (GD). Principal component analysis (PCA) was performed using GenAlex6.503 and PAST 4.11 software [57,58]. For population structure analysis, the Bayesian model-based clustering was performed in STRUCTURE 2.3.4 software [59] to infer the appropriate cluster (K) using 5,00,000 burn-in with Markov Chain Monte Carlo (MCMC) replicate of 5,00,000 assuming admixture model and correlated allele frequencies. Ten runs of STRUCTURE were performed by setting the number of populations (K) from 1 to 10. The most probable value of K was selected by ∆K methodology using the web-based software STRUCTURE HARVESTER [60].

2.4. Analysis of Molecular Variance (AMOVA)

AMOVA was calculated for the entire population based on indigenous and exotic accessions and for the core collection with a model-based population using GenAlex 6.503 [57] software.

2.5. Establishment and Evaluation of Core Collection

To establish a core collection, we used a multipurpose core selection method to assemble the diverse core subset from the large germplasm collections with SSR genotypes, implemented in the R package ‘core hunter’ [61]. Five different core sets were developed with different intensities, i.e., 5%, 10%, 15%, 20% and 25% using average entry to nearest entry method. The efficient core set was selected and evaluated based on different genetic diversity parameters.

3. Results

3.1. Genetic Diversity Analysis

A total of 3115 accessions from the Indian National Gene Bank (ICAR–NBPGR) were used to estimate the genetic diversity that exists within the germplasm. Although the species is a low-out breeder (26.6%), in order to get consistent analysis and proper utilization, the germplasm accessions were grown and individual flowers were bagged to get genetically pure seeds. All the SSR markers showed polymorphism of above 95%. Among the markers, CAT-43 showed higher gene diversity, whereas CAT-6 and CAT-57 showed lower gene diversity, with a mean gene diversity of 0.964 (Table 3). Among the markers, CAT-43 generated the maximum number of alleles and CAT-64 generated the minimum number of alleles, with an average allele number of 71 in the germplasm. Among the markers, the major allele frequency was less than or equal to 10%. The highest among them was generated by CAT-5 and the lowest was generated by CAT-46, with an average MAF of 8%.

3.2. Population Structure Analysis

The SSR genotyping results were used to perform population structure analysis of 3115 accessions under an admixture model using the STRUCTURE program. The estimated likelihood was found to be greater when K = 2, suggesting the population used in the study can be divided into two clusters (Figure 2). In the first cluster we have 1023 accessions (Figure S1A) and the remaining 2092 were in the second cluster (Figure S1B). The second highest likelihood value was found in K = 3, with the second cluster having two subclusters representing pure population and one admixture population. The pure population was represented in 1236 accessions and the admixture population in 856 accessions (Figure S1C). These admixture accessions were classified as mixed populations.
To better illustrate the pairwise relationship across the 3115 safflower accessions, we performed principal component analysis (PCA) (Figure 3), where the accession did not fall into any pattern of grouping but was rather distributed across a whole spectrum.

3.3. Analysis of Molecular Variance

Analysis of molecular variance among the germplasm was conducted to find out the variation and association between the indigenous and the exotic collection (Figure S2A). The sum of the square and the estimated variation between the collections was less than within the whole germplasm (Table 4).
An analysis was drawn of molecular variation between populations, as per the likelihood value through a STRUCTURE analysis, and a trend similar to the source of germplasm was found, where the variation between a model-based population was much less than among the individuals of the population (Figure S2B). However, the amount of variation was much less than the collection-based populations (Table 5).

3.4. Development of Core Collection

To assemble the core set of safflower germplasm, core hunter software was utilized by setting the weights and different sampling proportions from 5% to 25% with increments of 5%. The outcome of each core set was analyzed for different genetic diversity parameters, to arrive at a conclusion on the number of accessions to be selected for the core set. At the 5% level, the number of alleles drastically reduced, which resulted in an increase in MAF. At 10%, the number of alleles increased along with the gene diversity and PIC values. After 15%, the number of alleles stabilizes and the gene diversity, MAF and PIC remained stable. With the historic notion and the effective handling of germplasm, the 10% level of the core set was considered to be representative of the entire collection without major variation between them. This 10% of the core set accounted for 311 accessions (Table 6) and with the addition of selected varieties (Table S3), the total core germplasm included 351 accessions. The entries of core germplasm are listed in Supplementary table (Table S2).

3.5. Evaluation of Core Collection

To evaluate the reliability of the core set, we compared the diversity between the core set and the entire collection. We found that the core set contributes 80% of the allele and more than 75% of the major allele frequency; however, gene diversity and PIC values are significantly more than the entire collection (Table S1). The principal component analysis showed that the core collections were as evenly distributed as that of the entire collection. The comparative studies analyzing the molecular variation of the core set with the sourcing of the accessions from the indigenous and the exotic collections revealed that there was no discrete variation between the populations based on the sourcing of safflower accessions.

4. Discussion

Information about the genetic diversity of PGRs provides useful alleles or genomic regions associated with the development and improvement of crops. This is essential for conservation as well as the utilization of germplasm conserved in gene banks [62,63]. The development of molecular marker technology provides useful information for the analysis of genetic diversity, genetic relationship, population structure and core collection in the germplasm of many crop species [64,65,66].
In safflower, the development of SSR markers started with EST-based genic SSR markers [24] and, subsequently, the markers were mined with the development of sequencing information in safflower and related species [67,68,69]. These SSR markers were extensively used in genetic diversity analysis for breeding populations of cultivated varieties and in the natural population for estimating the level of diversity that exists in the species. Naturally, this species was grown in the Middle East and was later introduced to Asia, Europe and North America. Although the outcrossing percentage is meagre, the development of open-pollinated germplasm produces offspring with varied genetic diversity, leading to an increase in the adaptation of this species into new environments as a crop [70]. Although the area of cultivation in India and globally is restricted to very few regions, the diversity that exists in the crop is enormous. Accordingly, the representation in the national and international gene bank has been increased in order to conserve the variable germplasm.
There are 7135 accessions of safflower being conserved in the Indian National Gene Bank. Although variation exists within the conserved germplasm, the utilization of these germplasm for a crop improvement program is important. As a crop, safflower has been extensively utilized for its seed oil and natural dye production from petals [71]. In India, the varietal development program has gone quiet, as there have been only few varieties released for the past seven years, at national level. The reason behind this is that the crop improvement program did not have any promising starting material to be used as donor for any traits of interest. The germplasm diversity is the key for the utilizing the varietal improvement program. However, the number of accessions in the germplasm is too large to do evaluation and allele mining [72], hence, the core development approach comes in handy, providing a reasonable number of accessions representing maximum diversity in the germplasm.
In this study, we have used SSR markers to estimate the genetic diversity and draw population structure in order to identify the entries considered to be representative of whole variation. Compared to morphological diversity, where limited numbers of descriptors are used, SSR-based genetic diversity analysis provided meaningful genetic diversity analysis and core development [31]. The selected markers were known for distinguishing varieties in detail [28]. In the present study, all the used markers produced high polymorphism, with an average PIC value of 0.96, whereas the frequency of major alleles was very low, with an average of 0.08. This may be due to multiple alleles being generated from all the primers (mean no. of allele = 71); against a large sample size of 3115, the frequency of major allele would reduce. However, the average gene diversity (He) was more than 96%, showing that the markers used for genetic diversity analysis are highly reliable for population structure and core development.
A hierarchical analysis through the analysis of molecular variance (AMOVA) was carried out on the entire collection, with the distinction based on the sourcing of accessions. The variation between exotic and indigenous collections was meagre (6%) compared to the variation that existed between the accessions of all the populations. This may be due to the expansion of cultivation of this crop to different countries that happened after the domestication process, as the same gene pool was represented in the collections sourced from different countries [14]. The Bayesian-based population structure analysis was able to differentiate the entire collection into two main groups, with the second group divided into two subgroups based on the extent of admixture. The first group and one of the subgroups in the second group consisted of pure accessions and the admixture group consisted of about 25% of the whole population. Earlier studies on the global core of safflower with AFLP markers also revealed two distinct populations based on STRUCTURE analysis [73], with 25% of them as admixture populations. This may be due to the recent domestication process in the species, and thus does not show the structured population based on geographical location.
Based on the above analysis, it is imperative that core development should not be based on geographical distribution or morphological descriptors. Rather, marker-based genetic diversity analysis should be used. In the present study, the genetic structure based on SSR markers was used to develop a core set using core hunter software. The advantage of using core hunter is that the number of representatives of a core set can be determined by the user [51]. However, in the present case, we have developed different levels of entries to be included in the core sets (5% to 25%). Comparison of these entries, based on the genetic diversity parameters, clearly showed that the 10% core set had a higher gene diversity and PIC than the entire collection and that the number of alleles with major allele frequency were optimum when compared to other core sets, as well as to the entire collection, and it was considered to be an effective core set with 311 accessions, excluding cultivars (Table S2).
The developed core set was evaluated with different genetic parameters and was found to represent a similar pattern in PIC, He and major allele frequency, and to significantly correlate with the entire core collection (Table S4). The distribution of the accessions of the core set in relation to the entire range of diversity that existed in the entire collection was enumerated in the PCA plot, where both of the data sets were evenly distributed. The diversity of released varieties of safflower in India was compared with the entire collection and core collection. In spite of its narrow genetic base, as shown in previous studies, the selected varieties showed significant diversity between themselves and displayed enormous diversity in the PCA plot. This showed that the existing varietal development program was on the right path and that the resources from the germplasm will add value to the existing program by providing trait-specific accessions for high seed yield, high oil content and tolerance to biotic and abiotic stresses.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/agriculture13040836/s1, Table S1: Complete details of safflower accession used for genetic diversity analysis and core development (Color coded according to geographic locations). Table S2: List of core set entries of safflower accession developed by core hunter (Color coded according to geographic locations). Table S3. List of 43 Selected varieties. Table S4. Genetic diversity parameters between entire and different core collections. Figure S1. (A) Bar plot of cluster1. (B). Bar plot of cluster 2. Bar plot of admixture (Color coded according to geographic locations). Figure S2. (A) AMOVA of entire collection. (B) AMOVA of core collection.

Author Contributions

Conceptualization, S.R., R.P. and P.K.; methodology, G.P.K., P.P., N.G. (Nitu Goyal) and N.G. (Nishu Gupta); software, G.P.K. and P.P.; validation, S.R.; formal analysis, G.P.K.; resources, J.R. and S.S.G.; data curation, S.R.; writing—original draft preparation, G.P.K., P.P.; writing—review and editing, S.R.; funding acquisition, S.R., P.K. and R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Department of Biotechnology, Government of India, grant number BT/Ag/Network/Safflower/2019-20”. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data are available in the manuscript.

Acknowledgments

We are thankful to the Director, ICAR- National Bureau of Plant Genetic Resources for providing facilities to carry out this research work.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 13 00836 g001 550
Figure 1. Details of the sources and number of safflower accessions used in the study.
Figure 1. Details of the sources and number of safflower accessions used in the study.
Agriculture 13 00836 g001
Agriculture 13 00836 g002 550
Figure 2. Population structure of safflower germplasm. (A) ∆K reached maximum when K = 2 following the ad-hoc method. (B) Two population clusters with two subclusters in the second population inferred by STRUCTURE.
Figure 2. Population structure of safflower germplasm. (A) ∆K reached maximum when K = 2 following the ad-hoc method. (B) Two population clusters with two subclusters in the second population inferred by STRUCTURE.
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Agriculture 13 00836 g003 550
Figure 3. Principle component analysis (PCA). (A) PCA of entire collection with selected varieties. (B) PCA of core collection (10%) with selected varieties.
Figure 3. Principle component analysis (PCA). (A) PCA of entire collection with selected varieties. (B) PCA of core collection (10%) with selected varieties.
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Table
Table 1. Details of the 3115 safflower accessions used in the study.
Table 1. Details of the 3115 safflower accessions used in the study.
Entire Collection (3115)
(IC = 2107, EC = 1008)
CountryNo. of Accessions
India (Andhra Pradesh)90
India (Bihar)3
India (Delhi)4
India (Gujarat)1
India (Haryana)1
India (Karnataka)37
India (Maharashtra)1022
India (Madhya Pradesh)78
India (Odisha)8
India (Punjab)4
India (Rajasthan)1
India (Tamil Nadu)2
India (Telangana)6
India (Uttar Pradesh)254
India (West Bengal)1
India (Others)595
Ethiopia5
Hungary10
Israel1
Italy8
Mexico10
Singapore103
USA871
Table
Table 2. SSR Primers and sequence information.
Table 2. SSR Primers and sequence information.
SSR Marker NameForward (5’ to 3’)Reverse (5’to 3’)
CAT-3CAATTAGAAAAACCCTTGTAGAGACATCAACTTCCACTGCTG
CAT-5GGATATGGGTTGAGGGACGAGTCCAGCCATCGCCACACTC
CAT-6AGGTTGGAGAAAGCTGGTTAGGCTAACCTATAGCTTACCACC
CAT-8GATCAGATGAAAATACAACGTAAAGATAACTTGCCTTC
CAT-15GAATGACATAGAGGTAGACGTTCAGGGTCAGGAGAAATATCAACAG
CAT-23CAAATGAGTGTTGAGAGTTGTCTAAATAGTGGCAAACTCA
CAT-29TAGTATAAAGAGACACTTCCCAAACGGCTATGATTGGCTTGTA
CAT-38GAGGAAGCTAGCTAATGAAATGATGATGATATCCTTGCGGAATC
CAT-43AGCTTGGTCTAGATGAACACGCAGTAGTAACCGATATGCTA
CAT-46CAAATAGGTGCTAGAAAACACACTCAATCCTCATAGCAATTG
CAT-48GAAATCCGATGGTAGCCGGACTTCAACCTTCATCCCTCCC
CAT-52GAAACCCTAGATTCATTCACGCATGATTACAGTCTGAG
CAT-57GTTGGCCGAATAATCCTTCACTATGCGTATATATGGAGAGATG
CAT-58CATATGATAAAATATCACTAACATAAGATGATGCCATTGTGAC
CAT-64CTAAAGCAATCCTAAGCAAATCCCTAGGGTTCTTACCAAATTGGGA
CAT-65AGAAGGTAAATCCATTGTGGAAGTGCAAGAGTCCCTCAAGAGTC
CAT-91GAAGGTGTGTAGCCCAGATACGTAATGATTCACACGATAAACAG
CAT-96CATGCAATCATCAAGGGGTGGTGCTCAAGTGTGTTTAATCA
Table
Table 3. Genetic parameters based on SSR markers.
Table 3. Genetic parameters based on SSR markers.
Marker NameMAFNaHePIC
CAT-30.084500.9520.95
CAT-50.100670.9660.97
CAT-60.093480.9490.95
CAT-80.085800.9690.97
CAT-150.062860.9750.98
CAT-230.081840.9700.97
CAT-290.066660.9650.97
CAT-380.071680.9650.96
CAT-430.080930.9760.98
CAT-460.054790.9720.97
CAT-480.082680.9650.96
CAT-520.092780.9680.97
CAT-570.085690.9490.95
CAT-580.089880.9720.97
CAT-640.081450.9470.95
CAT-650.064620.9600.96
CAT-910.083590.9550.96
CAT-960.080780.9570.96
MEAN0.080710.9640.96
Note: MAF: major allele frequency; Na: Number of alleles; He: Gene Diversity; PIC: Polymorphic Information Content.
Table
Table 4. AMOVA statistics of entire safflower accessions based on indigenous collections and exotic collections.
Table 4. AMOVA statistics of entire safflower accessions based on indigenous collections and exotic collections.
SourcedfSSMSEst. Var.
Among collections11518.1991518.1990.550
Among accessions311354,000.97917.3478.673
Within Indiv31150.0000.0000.000
Total622955,519.178 9.224
Note: df: degree of freedom; SS: sum of square; MS: mean sum of square; Est.Var.: Estimated variations.
Table
Table 5. AMOVA statistics of 10% core safflower accessions from model-based (43 varieties) populations.
Table 5. AMOVA statistics of 10% core safflower accessions from model-based (43 varieties) populations.
SourcedfSSMSEst. Var.%
Among model-based populations21596.746798.3730.3804%
Among accessions311253,791.39117.2858.64396%
Within Indiv31150.0000.0000.0000%
Note: df: degree of freedom; SS: sum of square; MS: mean sum of square; Est.Var.: Estimated variations.
Table
Table 6. Details of 311 safflower core accessions.
Table 6. Details of 311 safflower core accessions.
Core Collection 311
(IC = 200, EC = 111)
CountryNo. of Accessions
India (Andhra Pradesh)10
India (Karnataka)4
India (Maharashtra)10
India (Madhya Pradesh)81
India (Tamil Nadu)1
India (Telangana)1
India (Uttar Pradesh)29
India (West Bengal)1
India (Others)63
Hungary1
Mexico1
Singapore16
USA93
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Kumar, G.P.; Pathania, P.; Goyal, N.; Gupta, N.; Parimalan, R.; Radhamani, J.; Gomashe, S.S.; Kadirvel, P.; Rajkumar, S. Genetic Diversity and Population Structure Analysis to Construct a Core Collection from Safflower (Carthamus tinctorius L.) Germplasm through SSR Markers. Agriculture 2023, 13, 836. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13040836

AMA Style

Kumar GP, Pathania P, Goyal N, Gupta N, Parimalan R, Radhamani J, Gomashe SS, Kadirvel P, Rajkumar S. Genetic Diversity and Population Structure Analysis to Construct a Core Collection from Safflower (Carthamus tinctorius L.) Germplasm through SSR Markers. Agriculture. 2023; 13(4):836. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13040836

Chicago/Turabian Style

Kumar, Gaddam Prasanna, Pooja Pathania, Nitu Goyal, Nishu Gupta, R. Parimalan, J. Radhamani, Sunil Shriram Gomashe, Palchamy Kadirvel, and S. Rajkumar. 2023. "Genetic Diversity and Population Structure Analysis to Construct a Core Collection from Safflower (Carthamus tinctorius L.) Germplasm through SSR Markers" Agriculture 13, no. 4: 836. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13040836

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