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.
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.
Not applicable.
Not applicable.
All the data are available in the manuscript.
We are thankful to the Director, ICAR- National Bureau of Plant Genetic Resources for providing facilities to carry out this research work.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Details of the sources and number of safflower accessions used in the study.
Enlarge this image.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.
Enlarge this image.Figure 3. Principle component analysis (PCA). (A) PCA of entire collection with selected varieties. (B) PCA of core collection (10%) with selected varieties.
Details of the 3115 safflower accessions used in the study.
| Entire Collection (3115) |
|
|---|---|
| Country | No. 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 |
| Ethiopia | 5 |
| Hungary | 10 |
| Israel | 1 |
| Italy | 8 |
| Mexico | 10 |
| Singapore | 103 |
| USA | 871 |
SSR Primers and sequence information.
| SSR Marker Name | Forward (5’ to 3’) | Reverse (5’to 3’) |
|---|---|---|
| CAT-3 | CAATTAGAAAAACCCTTGTAGA | GACATCAACTTCCACTGCTG |
| CAT-5 | GGATATGGGTTGAGGGACGA | GTCCAGCCATCGCCACACTC |
| CAT-6 | AGGTTGGAGAAAGCTGGTTAG | GCTAACCTATAGCTTACCACC |
| CAT-8 | GATCAGATGAAAATACAAC | GTAAAGATAACTTGCCTTC |
| CAT-15 | GAATGACATAGAGGTAGACGTTC | AGGGTCAGGAGAAATATCAACAG |
| CAT-23 | CAAATGAGTGTTGAGAGTTG | TCTAAATAGTGGCAAACTCA |
| CAT-29 | TAGTATAAAGAGACACTTCCCA | AACGGCTATGATTGGCTTGTA |
| CAT-38 | GAGGAAGCTAGCTAATGAAATG | ATGATGATATCCTTGCGGAATC |
| CAT-43 | AGCTTGGTCTAGATGAACAC | GCAGTAGTAACCGATATGCTA |
| CAT-46 | CAAATAGGTGCTAGAAAACAC | ACTCAATCCTCATAGCAATTG |
| CAT-48 | GAAATCCGATGGTAGCCGGA | CTTCAACCTTCATCCCTCCC |
| CAT-52 | GAAACCCTAGATTCATTCA | CGCATGATTACAGTCTGAG |
| CAT-57 | GTTGGCCGAATAATCCTTCAC | TATGCGTATATATGGAGAGATG |
| CAT-58 | CATATGATAAAATATCACTAACA | TAAGATGATGCCATTGTGAC |
| CAT-64 | CTAAAGCAATCCTAAGCAAATCC | CTAGGGTTCTTACCAAATTGGGA |
| CAT-65 | AGAAGGTAAATCCATTGTGGAAG | TGCAAGAGTCCCTCAAGAGTC |
| CAT-91 | GAAGGTGTGTAGCCCAGATAC | GTAATGATTCACACGATAAACAG |
| CAT-96 | CATGCAATCATCAAGGGGTG | GTGCTCAAGTGTGTTTAATCA |
Genetic parameters based on SSR markers.
| Marker Name | MAF | Na | He | PIC |
|---|---|---|---|---|
| CAT-3 | 0.084 | 50 | 0.952 | 0.95 |
| CAT-5 | 0.100 | 67 | 0.966 | 0.97 |
| CAT-6 | 0.093 | 48 | 0.949 | 0.95 |
| CAT-8 | 0.085 | 80 | 0.969 | 0.97 |
| CAT-15 | 0.062 | 86 | 0.975 | 0.98 |
| CAT-23 | 0.081 | 84 | 0.970 | 0.97 |
| CAT-29 | 0.066 | 66 | 0.965 | 0.97 |
| CAT-38 | 0.071 | 68 | 0.965 | 0.96 |
| CAT-43 | 0.080 | 93 | 0.976 | 0.98 |
| CAT-46 | 0.054 | 79 | 0.972 | 0.97 |
| CAT-48 | 0.082 | 68 | 0.965 | 0.96 |
| CAT-52 | 0.092 | 78 | 0.968 | 0.97 |
| CAT-57 | 0.085 | 69 | 0.949 | 0.95 |
| CAT-58 | 0.089 | 88 | 0.972 | 0.97 |
| CAT-64 | 0.081 | 45 | 0.947 | 0.95 |
| CAT-65 | 0.064 | 62 | 0.960 | 0.96 |
| CAT-91 | 0.083 | 59 | 0.955 | 0.96 |
| CAT-96 | 0.080 | 78 | 0.957 | 0.96 |
| MEAN | 0.080 | 71 | 0.964 | 0.96 |
Note: MAF: major allele frequency; Na: Number of alleles; He: Gene Diversity; PIC: Polymorphic Information Content.
AMOVA statistics of entire safflower accessions based on indigenous collections and exotic collections.
| Source | df | SS | MS | Est. Var. |
|---|---|---|---|---|
| Among collections | 1 | 1518.199 | 1518.199 | 0.550 |
| Among accessions | 3113 | 54,000.979 | 17.347 | 8.673 |
| Within Indiv | 3115 | 0.000 | 0.000 | 0.000 |
| Total | 6229 | 55,519.178 | 9.224 |
Note: df: degree of freedom; SS: sum of square; MS: mean sum of square; Est.Var.: Estimated variations.
AMOVA statistics of 10% core safflower accessions from model-based (43 varieties) populations.
| Source | df | SS | MS | Est. Var. | % |
|---|---|---|---|---|---|
| Among model-based populations | 2 | 1596.746 | 798.373 | 0.380 | 4% |
| Among accessions | 3112 | 53,791.391 | 17.285 | 8.643 | 96% |
| Within Indiv | 3115 | 0.000 | 0.000 | 0.000 | 0% |
Note: df: degree of freedom; SS: sum of square; MS: mean sum of square; Est.Var.: Estimated variations.
Details of 311 safflower core accessions.
| Core Collection 311 |
|
|---|---|
| Country | No. 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 |
| Hungary | 1 |
| Mexico | 1 |
| Singapore | 16 |
| USA | 93 |
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Fernández-Martinez, J.; del Rio, M.; de Haro, A. Survey of safflower (Carthamus tinctorius L.) germplasm for variants in fatty acid composition and other seed characters. Euphytica; 1993; 69, pp. 115-122. [DOI: https://dx.doi.org/10.1007/BF00021734]
2. Jhajharia, S.; Choudhary, P.; Jhajharia, A.; Meena, L.K.; Singh, D. Heterosis and combining ability in Safflower (Carthamus tinctorius) germplasm lines. Bioscan; 2013; 8, pp. 1453-1460.
3. Dajue, L.; Mündel, H.H. Safflower Carthamus tinctorius L. Promoting the Conservation and Use of Underutilized and Neglected Crops; Institute of Plant Genetics and Crop Plant Research, Gatersleben, International Plant Genetic Resources Institute: Rome, Italy, 1996; Volume 7.
4. Flider, F.J. Development and commercialization of GLA Safflower oil. Lipid Tech.; 2013; 25, pp. 227-229. [DOI: https://dx.doi.org/10.1002/lite.201300302]
5. Carlsson, A.S.; Zhu, L.H.; Andersson, M.; Hofvander, P. Platform crops amenable to genetic engineering–a requirement for successful production of bio-industrial oils through genetic engineering. Biocatal. Agric. Biotechnol.; 2014; 3, pp. 58-64. [DOI: https://dx.doi.org/10.1016/j.bcab.2013.12.007]
6. Zhou, X.; Tang, L.; Xu, Y.; Zhou, G.; Wang, Z. Towards a better understanding of medicinal uses of Carthamus tinctorius L. In traditional Chinese medicine: A phytochemical and pharmacological review. J. Ethnopharmacol.; 2014; 151, pp. 27-43. [DOI: https://dx.doi.org/10.1016/j.jep.2013.10.050]
7. Ilkılıç, C.; Aydın, S.; Behcet, R.; Aydin, H. Biodiesel from Safflower oil and its application in a diesel engine. Fuel Process. Technol.; 2011; 92, pp. 356-362. [DOI: https://dx.doi.org/10.1016/j.fuproc.2010.09.028]
8. Kaya, M.D.; Bayramin, S.; Kaya, G.; Uzun, O. Seed vigor and ion toxicity in Safflower (Carthamus tinctorius L.) seedlings produced by various seed sizes under NaCl stress. Arch. Biol. Sci.; 2011; 63, pp. 723-729. [DOI: https://dx.doi.org/10.2298/ABS1103723K]
9. Bahrami, F.; Arzani, A.; Karimi, V. Evaluation of yield-based drought tolerance indices for screening Safflower genotypes. Agron. J.; 2014; 106, pp. 1219-1224. [DOI: https://dx.doi.org/10.2134/agronj13.0387]
10. Yeilaghi, H.; Arzani, A.; Ghaderian, M. Evaluating the contribution of ionic and agronomic components toward salinity tolerance in Safflower. Agron. J.; 2015; 107, pp. 2205-2212. [DOI: https://dx.doi.org/10.2134/agronj15.0202]
11. Ebrahimi, F.; Majidi, M.M.; Arzani, A.; Mohammadi-Nejad, G. Oil and seed yield stability in a worldwide collection of Safflower under arid environments of Iran. Euphytica; 2016; 212, pp. 131-144. [DOI: https://dx.doi.org/10.1007/s10681-016-1779-y]
12. Knowles, P.F. Centers of plant diversity and conservation of crop germplasm: Safflower. Econ. Bot.; 1969; 23, pp. 324-329. [DOI: https://dx.doi.org/10.1007/BF02860678]
13. Ashri, A. Evaluation of the Germ Plasm Collection of Safflower, Carthamus tinctorius L. V. Distribution and Regional Divergence for Morphological Characters. Euphytica; 1975; 24, pp. 651-659. [DOI: https://dx.doi.org/10.1007/BF00132903]
14. Chapman, M.A.; Hvala, J.; Strever, J.; Burke, J.M. Population genetic analysis of Safflower (Carthamus tinctorius; Asteraceae) reveals a near eastern origin and five centres of diversity. Am. J. Bot.; 2010; 97, pp. 831-840. [DOI: https://dx.doi.org/10.3732/ajb.0900137]
15. Food and Agriculture Organization (FAO) of United Nations. The Food and Agriculture Organization Corporate Statistical Database Production Quantities of Safflower Seed and Plantains for 2018. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 7 December 2022).
16. Nimbkar, N. “Issues in Safflower production in India,” In Safflower: Unexploited Potential and World Adaptability. Proceedings of the of the Seventh International Safflower Conference; Wagga Wagga, NSW, Australia, 3–6 November 2008.
17. Varshney, R.K.; Mahendar, T.; Aggarwal, R.K.; Borner, A. Genetic Molecular Markers in Plants: Development and Applications. Genomics-Assisted Crop Improvement; Springer: Berlin/Heidelberg, Germany, 2007; pp. 13-29.
18. Johnson, R.C.; Kisha, T.J.; Evans, M.A. Characterizing Safflower germplasm with AFLP molecular markers. Crop Sci.; 2007; 47, pp. 1728-1736. [DOI: https://dx.doi.org/10.2135/cropsci2006.12.0757]
19. Kumar, S.; Ambreen, H.; Murali, T.V.; Bali, S.; Agarwal, M.; Kumar, A.; Goel, S.; Jagannath, A. Assessment of genetic diversity and population structure in a global reference collection of 531 accessions of Carthamus tinctorius (Safflower) using AFLP markers. Plant Mol. Bio. Report; 2014; 33, pp. 1299-1313. [DOI: https://dx.doi.org/10.1007/s11105-014-0828-8]
20. Khan, M.A.; Witzke-Ehbrecht, S.V.; Maass, B.L.; Becker, H.C. Relationships among different geographical groups, agromorphology, fatty acid composition and RAPD marker diversity in Safflower (Carthamus tinctorius). Genet. Resour. Crop Evol.; 2009; 56, pp. 19-30. [DOI: https://dx.doi.org/10.1007/s10722-008-9338-6]
21. Yang, Y.X.; Wu, W.; Zheng, Y.L.; Chen, L.; Liu, R.J.; Huang, C.Y. Genetic diversity and relationships among Safflower (Carthamus tinctorius L.) analyzed by inter-simple sequence repeats (ISSRs). Genet. Resour. Crop Evol.; 2007; 54, pp. 1043-1051. [DOI: https://dx.doi.org/10.1007/s10722-006-9192-3]
22. Golkar, P.; Arzani, A.; Rezaei, M.A. Genetic variation in Safflower (Carthamus tinctorius L.) for seed quality related traits and inter-simple sequence repeat (ISSR) markers. Int. J. Mol. Sci.; 2011; 12, pp. 2664-2677. [DOI: https://dx.doi.org/10.3390/ijms12042664]
23. Panahi, B.; Neghab, M.G. Genetic characterization of Iranian Safflower (Carthamus tinctorius) using inter-simple sequence repeats (ISSR) markers. Physiol. Mol. Biol. Plants; 2013; 19, pp. 239-243. [DOI: https://dx.doi.org/10.1007/s12298-012-0155-1]
24. Majidi, M.M.; Zadhoush, S. Molecular and morphological variation in a world-wide collection of Safflower. Crop Sci.; 2014; 54, pp. 2109-2119. [DOI: https://dx.doi.org/10.2135/cropsci2013.12.0850]
25. Yaman, H.; Tarıkahya-Hacıoğlu, B.; Arslan, Y.; Subas, I. Molecular characterization of the wild relatives of Safflower (Carthamus tinctorius L.) in Turkey as revealed by ISSRs. Genet. Resour. Crop Evol.; 2014; 61, pp. 595-602. [DOI: https://dx.doi.org/10.1007/s10722-013-0061-6]
26. Talebi, M.; Mokhtari, N.; Rahimmalek, M.; Sahhafi, S.R. Molecular characterization of Carthamus tinctorius and C. oxyacanthus germplasm using sequence related amplified polymorphism (SRAP) markers. Plant Omics J.; 2012; 5, pp. 136-142.
27. Parida, S.K.; Kalia, S.K.; Kaul, S. Informative genomic microsatellite markers for efficient genotyping applications in Sugarcane. Theor. Appl. Genet.; 2009; 118, pp. 327-338. [DOI: https://dx.doi.org/10.1007/s00122-008-0902-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18946655]
28. Chapman, M.A.; Hvala, J.; Strever, J.; Matvienko, M.; Kozik, A.; Michelmore, R.W.; Tang, S.; Knapp, S.J.; Burke, J.M. Development, polymorphism, and cross-taxon utility of EST–SSR markers from Saffower (Carthamus tinctorius L.). Theor. Appl. Genet.; 2009; 120, pp. 85-91. [DOI: https://dx.doi.org/10.1007/s00122-009-1161-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19820913]
29. Hamdan, Y.A.S.; Garcia-Moreno, M.; Redondo-Nevado, J.; Velasco, L.; Perez-Vich, B. Development and characterization of genomic microsatellite markers in Safflower (Carthamus tinctorius L.). Plant Breed.; 2011; 130, pp. 237-241. [DOI: https://dx.doi.org/10.1111/j.1439-0523.2010.01826.x]
30. Yamini, K.N.; Ramesh, K.; Naresh, V.; Rajendrakumar, P.; Anjani, K.; Dinesh, K.V. Development of EST–SSR markers and their utility in revealing cryptic diversity in Safflower (Carthamus tinctorius L.). J. Plant Biochem. Biotechnol.; 2013; 22, pp. 90-102. [DOI: https://dx.doi.org/10.1007/s13562-012-0115-4]
31. Lee, G.A.; Sung, J.S.; Lee, S.Y.; Chung, J.W.; Yi, J.Y.; Kim, Y.Y.; Lee, M.C. Genetic assessment of Safflower (Carthamus tinctorius L.) collection with microsatellite markers acquired via pyrosequencing method. Mol. Ecol. Resour.; 2014; 14, pp. 69-78. [DOI: https://dx.doi.org/10.1111/1755-0998.12146]
32. Barati, M.; Arzani, A. Genetic diversity revealed by EST– SSR markers in cultivated and wild Safflower. Biochem. Syst. Ecol.; 2012; 44, pp. 117-123. [DOI: https://dx.doi.org/10.1016/j.bse.2012.04.013]
33. Derakhshan, E.; Majidi, M.M.; Sharafi, Y.; Mirlohi, A. Discrimination and genetic diversity of cultivated and wild Safflowers (Carthamus spp.) using EST–microsatellites markers. Biochem. Syst. Ecol.; 2014; 54, pp. 130-136. [DOI: https://dx.doi.org/10.1016/j.bse.2014.01.003]
34. van Hintum, T.J.L.; Brown, A.H.D.; Spillane, C.; Hodgkin, T. Core collections of plant genetic resources. IPGRI Tech. Bull.; 2000; 3, pp. 1-49.
35. Belaj, A.; Dominguez-García, M.d.C.; Atienza, S.G. Developing a core collection of Olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet. Genomes; 2012; 8, pp. 365-378. [DOI: https://dx.doi.org/10.1007/s11295-011-0447-6]
36. Frankel, O.H. Genetic Perspectives of Germplasm Conservation. Genetic Manipulation: Impact on Man and Society; Werner, A. Cambridge University Press: Cambridge, UK, 1984; pp. 161-170.
37. Charmet, G.; Balfourier, F.; Monestiez, P. Hierarchical Clustering of Perennial Ryegrass Populations with Geographic Contiguity Constraint. Theor. Appl. Genet.; 1994; 88, pp. 42-48. [DOI: https://dx.doi.org/10.1007/BF00222392]
38. Yonezawa, K.; Nomura, T.; Morishima, H. Sampling strategies for use in stratified germplasm collections. Core Collections of Plant Genetic Resources; Hodgkin, T.; Brown, A.H.D.; van Hintum, T.J.L.; Morales, E.A.V. John Wiley and Sons: Chichester, UK, 1995; pp. 35-53.
39. Noirot, M.; Hamon, S.; Anthony, F. The Principal Component Scoring: A New Method of Constituting a Core Collection Using Quantitative Data. Genet. Resour. Crop Evol.; 1996; 43, pp. 1-6. [DOI: https://dx.doi.org/10.1007/BF00126934]
40. Mongkolporn, O.; Hanyong, S.; Chunwongse, J.; Wasee, S. Establishment of a Core Collection of Chilli Germplasm Using Microsatellite Analysis. Plant Genet. Resour.; 2015; 13, pp. 104-110. [DOI: https://dx.doi.org/10.1017/S1479262114000768]
41. Ebana, K.; Kojima, Y.; Fukuoka, S.; Nagamine, T.; Kawase, M. Development of Mini Core Collection of Japanese Rice Landrace. Breed. Sci.; 2008; 58, pp. 281-291. [DOI: https://dx.doi.org/10.1270/jsbbs.58.281]
42. Kaga, A.; Shimizu, T.; Watanabe, S.; Tsubokura, Y.; Katayose, Y.; Harada, K.; Vaughan, D.A.; Tomooka, N. Evaluation of Soybean Germplasm Conserved in NIAS Genebank and Development of Mini Core Collections. Breed. Sci.; 2012; 61, pp. 566-592. [DOI: https://dx.doi.org/10.1270/jsbbs.61.566]
43. Balfourier, F.; Roussel, V.; Strelchenko, P.; Exbrayat-Vinson, F.; Sourdille, P.; Boutet, G.; Koenig, J.; Ravel, C.; Mitrofanova, O.; Beckert, M. et al. A Worldwide Bread Wheat Core Collection Arrayed in a 384-Well Plate. Theor. Appl. Genet.; 2007; 114, pp. 1265-1275. [DOI: https://dx.doi.org/10.1007/s00122-007-0517-1]
44. Upadhyaya, H.D.; Bramel, P.J.; Singh, S. Development of a Chickpea Core Subset Using Geographic Distribution and Quantitative Traits. Crop Sci.; 2001; 41, pp. 206-210. [DOI: https://dx.doi.org/10.2135/cropsci2001.411206x]
45. Amalraj, V.A.; Balakrishnan, R.; Jebadhas, A.W.; Balasundaram, N. Constituting a Core Collection of Saccharum spontaneum L. and Comparison of Three Stratified Random Sampling Procedures. Genet. Resour. Crop Evol.; 2006; 53, pp. 1563-1572. [DOI: https://dx.doi.org/10.1007/s10722-005-8510-5]
46. Quenouille, J.; Saint-Felix, L.; Moury, B.; Palloix, A. Diversity of Genetic Backgrounds Modulating the Durability of a Major Resistance Gene. Analysis of a Core Collection of Pepper Landraces Resistant to Potato Virus Y. Mol. Plant. Pathol.; 2016; 17, pp. 296-302. [DOI: https://dx.doi.org/10.1111/mpp.12277]
47. Zhang, C.Y.; Chen, X.S.; Zhang, Y.M.; Yuan, Z.H.; Liu, Z.C.; Wang, Y.L.; Lin, Q. A method for constructing core collection of Malus sieversii using molecular markers. Agric. Sci. China; 2009; 42, pp. S1671-S2927. [DOI: https://dx.doi.org/10.1016/S1671-2927(08)60210-2]
48. Liu, W.; Shahid, M.Q.; Bai, L.; Lu, Z.; Chen, Y.; Jiang, L.; Diao, M.; Liu, X.; Lu, Y. Evaluation of Genetic Diversity and Development of a Core Collection of Wild Rice (Oryza rufipogon Griff.) Populations in China. PLoS ONE; 2015; 10, e0145990. [DOI: https://dx.doi.org/10.1371/journal.pone.0145990] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26720755]
49. Escribano, P.; Viruel, M.A.; Hormaza, J.I. Comparison of different methods to sequence repeat markers. A case study in cherimoya (Annona cherimola, Annonaceae), an underutilised subtropical fruit tree species. Ann. Appl. Biol.; 2008; 153, pp. 25-32. [DOI: https://dx.doi.org/10.1111/j.1744-7348.2008.00232.x]
50. Richards, C.M.; Volk, G.M.; Reeves, P.A.; Reilley, A.A.; Henk, A.D.; Forsline, P.L.; Aldwinckle, H.S. Selection of Stratified Core Sets Representing Wild Apple (Malus Sieversii). J. Am. Soc. Hortic. Sci.; 2009; 134, pp. 228-235. [DOI: https://dx.doi.org/10.21273/JASHS.134.2.228]
51. De Beukelaer, H.; Davenport, G.F.; Fack, V. Core Hunter 3: Flexible Core Subset Selection. BMC Bioinform.; 2018; 19, 203. [DOI: https://dx.doi.org/10.1186/s12859-018-2209-z]
52. Soleimani, B.; Lehnert, H.; Keilwagen, J.; Plieske, J.; Ordon, F.; Naseri Rad, S.; Ganal, M.; Beier, S.; Perovic, D. Comparison Between Core Set Selection Methods Using Different Illumina Marker Platforms: A Case Study of Assessment of Diversity in Wheat. Front. Plant Sci.; 2020; 11, 1040. [DOI: https://dx.doi.org/10.3389/fpls.2020.01040]
53. Xu, Q.; Zeng, X.; Lin, B.; Li, Z.; Yuan, H.; Wang, Y.; Tashi, N. A Microsatellite Diversity Analysis and the Development of Core-Set Germplasm in a Large Hulless Barley (Hordeum vulgare L.) Collection. BMC Genet.; 2017; 18, 102. [DOI: https://dx.doi.org/10.1186/s12863-017-0563-x]
54. Dwivedi, S.; Upadhyaya, H.; Hegde, D. Development of Core Collection Using Geographic Information and Morphological Descriptors in Safflower (Carthamus tinctorius L.) Germplasm. Genet. Resour. Crop Evol; 2005; 52, pp. 821-830. [DOI: https://dx.doi.org/10.1007/s10722-003-6111-8]
55. Kumar, S.; Ambreen, H.; Variath, M.T.; Rao, A.R.; Agarwal, M.; Kumar, A.; Goel, S.; Jagannath, A. Utilization of Molecular, Phenotypic, and Geographical Diversity to Develop Compact Composite Core Collection in the Oilseed Crop, Safflower (Carthamus tinctorius L.) through Maximization Strategy. Front. Plant Sci.; 2016; 7, 1554. [DOI: https://dx.doi.org/10.3389/fpls.2016.01554]
56. Liu, K.; Muse, S.V. PowerMarker: An Integrated Analysis Environment for Genetic Marker Analysis. Bioinformatics; 2005; 21, pp. 2128-2129. [DOI: https://dx.doi.org/10.1093/bioinformatics/bti282]
57. Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic Analysis in Excel. Population Genetic Software for Teaching and Research—An Update. Bioinformatics; 2012; 28, pp. 2537-2539. [DOI: https://dx.doi.org/10.1093/bioinformatics/bts460]
58. Hammer, O.; Harper, D.; Ryan, P.D. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontol. Electron.; 2001; 4, pp. 1-9.
59. Pritchard, J.K.; Wen, X.; Falush, D. Documentation for Structure Software; version 2.3 University of Chicago: Chicago, IL, USA, 2010.
60. Earl, D.; Vonholdt, B.; Earl, D.A.; Von Holdt, B.M. Structure Harvester: A Website and Program for Visualizing STRUCTURE Output and Implementing the Evanno Method. Conserv. Genet. Resour.; 2012; 4, pp. 359-361. [DOI: https://dx.doi.org/10.1007/s12686-011-9548-7]
61. Beukelaer, H.D.; Smýkal, P.; Davenport, G.F.; Fack, V. Core Hunter II: Fast core subset selection based on multiple genetic diversity measures using Mixed Replica search. BMC Bioinform.; 2012; 13, 312. [DOI: https://dx.doi.org/10.1186/1471-2105-13-312]
62. D’hoop, B.B.; Paulo, M.J.; Kowitwanich, K.; Sengers, M.; Visser, R.G.F.; van Eck, H.J.; van Eeuwijk, A.F. Population structure and linkage disequilibrium unravelled in tetraploid potato. Theor. Appl. Genet.; 2010; 121, pp. 1151-1170. [DOI: https://dx.doi.org/10.1007/s00122-010-1379-5]
63. Odong, T.L.; van Heerwaarden, J.; Jansen, J.; van Hintum, T.J.L.; van Eeuwijk, F.A. Determination of Genetic Structure of Germplasm Collections: Are Traditional Hierarchical Clustering Methods Appropriate for Molecular Marker Data?. Theor. Appl. Genet.; 2011; 123, pp. 195-205. [DOI: https://dx.doi.org/10.1007/s00122-011-1576-x]
64. Wada, T.; Noguchi, Y.; Isobe, S.; Kunihisa, M.; Sueyoshi, T.; Shimomura, K. Development of a Core Collection of Strawberry Cultivars Based on SSR and CAPS Marker Polymorphisms. Hortic. J.; 2017; 86, pp. 365-378. [DOI: https://dx.doi.org/10.2503/hortj.MI-142]
65. Oliveira, E.J.; Ferreira, C.F.; Santos, V.S.; Oliveira, G.A.F. Development of a Cassava Core Collection Based on Single Nucleotide Polymorphism Markers. Genet. Mol. Res.; 2014; 13, pp. 6472-6485. [DOI: https://dx.doi.org/10.4238/2014.August.25.11]
66. Zhu, Y.; Liang, D.; Song, Z.; Tan, Y.; Guo, X.; Wang, D. Genetic Diversity Analysis and Core Germplasm Collection Construction of Camellia Oleifera Based on Fruit Phenotype and SSR Data. Genes; 2022; 13, 2351. [DOI: https://dx.doi.org/10.3390/genes13122351]
67. Ambreen, H.; Kumar, S.; Variath, M.T.; Joshi, G.; Bali, S.; Agarwal, M.; Kumar, A.; Jagannath, A.; Goel, S. Development of Genomic Microsatellite Markers in Carthamus tinctorius L. (Safflower) Using Next Generation Sequencing and Assessment of Their Cross-Species Transferability and Utility for Diversity Analysis. PLoS ONE; 2015; 10, e0135443. [DOI: https://dx.doi.org/10.1371/journal.pone.0135443]
68. Golkar, P.; Mokhtari, N. Molecular Diversity Assessment of a World Collection of Safflower Genotypes by SRAP and SCoT Molecular Markers. Physiol. Mol. Biol. Plants; 2018; 24, pp. 1261-1271. [DOI: https://dx.doi.org/10.1007/s12298-018-0545-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30425439]
69. Mishra, M.; Dixit, S.; Sahoo, S. In Silico Identification of Microsatelite Markers and Its SSR-FDM Analysis from Ests Sequence in Asteraceae Family. World J. Pharm. Res.; 2017; 6, pp. 977-997. [DOI: https://dx.doi.org/10.20959/wjpr20179-9301]
70. Nabloussi, A.; Velasco, L.; Fernández-Martínez, J. Cross Pollination of Safflower under Moroccan Environmental Conditions. Int. J. Plant Breed.; 2013; 7, pp. 145-147.
71. Gomashe, S.; Ingle, K.; Sarap, Y.; Subramani, R.; Chand, D. Safflower (Carthamus tinctorius L.): An Underutilized Crop with Potential Medicinal Values. Ann. Phytomedicine Int. J.; 2021; 1, pp. 242-248. [DOI: https://dx.doi.org/10.21276/ap.2021.10.1.26]
72. Zhao, W.; Cho, G.-T.; Ma, K.-H.; Chung, J.-W.; Gwag, J.-G.; Park, Y.-J. Development of an Allele-Mining Set in Rice Using a Heuristic Algorithm and SSR Genotype Data with Least Redundancy for the Post-Genomic Era. Mol. Breed.; 2010; 26, pp. 639-651. [DOI: https://dx.doi.org/10.1007/s11032-010-9400-x]
73. Ambreen, H.; Kumar, S.; Kumar, A.; Agarwal, M.; Jagannath, A.; Goel, S. Association Mapping for Important Agronomic Traits in Safflower (Carthamus tinctorius L.) Core Collection Using Microsatellite Markers. Front. Plant Sci.; 2018; 9, 402. [DOI: https://dx.doi.org/10.3389/fpls.2018.00402]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Radhamani, J 1 ; Gomashe, Sunil Shriram 2 ; Kadirvel, Palchamy 3 ; Rajkumar, S 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