Population structure analysis of emmer wheat (Triticum dicoccum Schrank) germplasm conserved in National Genebank of India using SSR markers

doi:10.21203/rs.3.rs-4296283/v1

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Population structure analysis of emmer wheat (Triticum dicoccum Schrank) germplasm conserved in National Genebank of India using SSR markers

https://doi.org/10.21203/rs.3.rs-4296283/v1

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Knowledge of genetic diversity of crop germplasm is essential for their utilization in breeding programme. Emmer wheat germplasm received less attention for exploring its genetic diversity towards enhancing utilization. Agro-morphological characterization was done for 192 emmer wheat accessions during rabi season 2019-20 and subsequently, 96 accessions were selected for morphological and molecular diversity analysis during 2020-21. We estimated genetic diversity and population structure of 96 diverse dicoccum genotypes conserved in national genebank of India using 56 microsatellite (SSR) markers. The number of alleles per locus ranged from 1 to 6 with an average of 1.68 alleles. A total of 93 alleles were detected with the highest PIC value (0.9912) observed for Xcfd20 marker. Based on ‘STRUCTURE’ analysis, 96 dicoccum accession were divided into two sub-populations. The analysis of molecular variance (AMOVA) revealed that genetic differentiation among subpopulations was low and within subpopulations was high. A cluster analysis based on the Jaccard’s dissimilarity index identified two clusters which was in congruence with population structure. Indigenous and exotic collections were categorized into distinct sub-clusters within the same cluster with some overlapping suggesting limited genomic differentiation between these collections from different geographical regions. Based on the allelic information and cluster analysis, cultivated emmer wheat showed low genetic diversity and narrow genetic base. The low genetic diversity in emmer wheat accessions may be due to limited cultivation in small pockets that emphasized a need to broaden the genetic base of emmer wheat genetic resources for enhancing its utilization.

Emmer wheat

Germplasm

Genetic diversity

STRUCTURE

SSR markers

AMOVA

Emmer wheat (Triticum turgidum ssp. Dicoccum, 2n = 4x = 28, AABB), also known as khapli wheat, is recognized as the cultivated variant of wild emmer (Triticum dicoccoides). Presently, emmer wheat cultivation encompasses only about 1% of the global wheat cultivation area. This includes regions such as Ethiopia, Iran, Eastern Turkey, Transcaucasia, Former Yugoslavia, Central Europe, Italy, Spain, India, and the Volga Basin [1]. In India, khapli wheat, is cultivated on limited lands primarily in Gujarat, Maharashtra, and Karnataka states. Emmer wheat exhibits valuable traits of economic significance, including nutrient density, resilience against pests and diseases, and the ability to withstand environmental stresses. Hence, it serves as a valuable genetic reservoir for enhancing both durum and bread wheat varieties. Emmer wheat is recognized for its significant content of iron, zinc, fibre, antioxidants, high protein digestibility, and abundant resistant starch [2,3]. Studies have reported that emmer wheat contains higher levels of crude fibre, lysine, and minerals compared to bread wheat and durum wheat [4]. Cultivated emmer wheat, serves as the direct ancestor of durum wheat and is recognized as a minor crop on a global scale [5]. The domestication of cultivated emmer wheat took place in the Fertile Crescent region [5,6] through the selection of mutations in the Br loci originating from wild emmer [Triticum turgidum ssp. dicoccoides (Körn.) Thell (2n = 4x = 28, AABB)]. As a consequence, cultivated emmer wheat evolved to possess a non-brittle rachis, yet its seeds retained their non-free threshing and hulled characteristics [6]. Subsequently, the transition from cultivated emmer wheat to durum wheat occurred due to mutations in the Q and tenacious glume (Tg) genes, leading to the development of free-threshing plants [7,8]. Despite the enormous potential of emmer wheat, there has been a lack of substantial efforts towards its improvement. Analysis of the genetic diversity among unexplored emmer wheat accessions of different geographical regions is required for the utilization of diverse emmer genetic resources in wheat improvement, particularly in the creation of synthetic wheat [4]. Molecular markers offer distinct advantages in genetic diversity studies as they are not influenced by environmental factors and require only a small amount of DNA to estimate genetic variation accurately [9]. Molecular characterisation and diversity analysis of different cereal crops including wheat accessions have used various molecular markers like RAPD, SSR, AFLP, ISSR and others [10]. Molecular markers have been used in wheat for detecting genetic variation, genotype identification, and genetic mapping by several workers [10,11,12,13,14]. SSR and SNP markers are the most promising markers [15] due to their co-dominant nature and broad genome coverage. Despite the increasing adoption of SNP markers over the past decade, microsatellites are still commonly used due to their ease of use, relatively low cost, and high level of polymorphism resulting from their multiallelic nature. Several studies have reported the usefulness of SSR markers in determining population structure, genetic diversity, and relationships between wheat genotypes, which is crucial for formulating effective breeding plans [11,16].

Molecular characterization and genetic diversity study of emmer wheat accessions has been conducted in different countries such as India [17], Ethiopia [18], Italy [19,20], Europe [21], Iran [22]. However, these studies were limited to smaller set of genotypes of specific ecogeographical region. The dicoccum wheat germplasm conserved in the Indian national genebank need to be explored for their population structure and genetic diversity analysis. Thus, the present study aimed to investigate the genetic diversity and population structure of diverse set of emmer accessions representing India as well as different geographical regions of the world with the objective to identify untapped and potentially valuable sources of genetic diversity in emmer wheat.

Plant material

The experiment was carried out during the rabi season of years 2019–20 and 2020–21 at ICAR-NBPGR Pusa Farm, New Delhi. In the first year, agro-morphological characterization of 192 emmer wheat germplasm (135 indigenous and 57 exotic collection) conserved in the National Genebank of India was carried out using 33 agro-morphological descriptors. A diverse set of 96 dicoccum accessions were selected using PowerCore Software. Subsequently, in the second year, agro-morphological characterization as well as molecular diversity analysis was conducted using SSR markers for 96 emmer wheat accessions (including 69 indigenous and 27 exotic accessions). Details of the used material is listed in Supplementary Table 1.

Molecular Markers and analysis

A total of 56 SSR markers, based on their distribution across the 14 chromosomes, with at least 4 SSRs per chromosome were chosen for diversity analysis. Primer sequences for majority of SSR markers were obtained from the graingenes website (http://www.wheat.pw.usda.gov/ggpages/SSR). Details of the used markers, their chromosomal position, forward/reverse primer sequences is given in supplementary Table 2. Genomic DNA was extracted from 96 selected diverse dicoccum accessions using CTAB method [23]. DNA samples were quantified using a NanoDrop™ spectrophotometer. Subsequently, the DNA samples were diluted with Millipore water to achieve concentrations of 25 ng/µl as working stock and were stored at -20°C until they were utilized for PCR amplification. PCR reactions were conducted using SSRs in a reaction volume of 10 µl. Each reaction included 4 µl of 2x EmeraldAmp GT PCR Master Mix (Takara Bio), 1 µl of each primer, 2 µl of nuclease-free water, and 2 µl of DNA at a concentration of 25 ng/µl. The reactions were set up in 96-well PCR plates with thermal seals and were performed in an Eppendorf thermal cycler. The thermal profile consisted of an initial denaturation step at 94°C for 4 minutes, followed by 35 cycles of denaturation at 94°C for 30 seconds, primer extension at 72°C for 1 minute at the specific annealing temperature of the primers, and a final extension at 72°C for 5 minutes. The samples were then stored at 4°C. The amplified products were separated on a 2% agarose gel using gel electrophoresis assembly with 1X TBE Buffer, stained with ethidium bromide, and visualized on a UV transilluminator Gel Documentation System (G:Box, Syngene). The allelic size was estimated visually for all 96 dicoccum accessions. To determine the band size of the amplified products, they were compared with a 100 bp DNA ladder. The bands were then scored as either present (1) or absent (0) for dominant markers, and for codominant markers, they were scored as upper band (2) or lower band (1).

Statistical Analysis

Polymorphic information content (PIC) values were determined for each SSR locus using the formula PIC = 1 − Σ (pi²), where pi represents the frequency of the i^th allele. The population structure of the 96 dicoccum accessions was established through Bayesian clustering analysis using STRUCTURE version 2.3.4 [24]. The analysis involved setting the number of sub-populations (K-values) from 1 to 6, with each run repeated five times. The program was configured with a burn-in iteration of 150,000, followed by 300,000 Markov chain Monte Carlo (MCMC) replications after burning, utilizing the admixture model. To determine the optimal number of sub-populations, the STRUCTURE HARVESTER web version v0.6.94 was employed, which utilizes a modified delta K (ΔK) method following Evanno et al. 2005 [25] to ascertain the true number of clusters.

The level of genetic variation among accessions and within populations was assessed through Analysis of Molecular Variance (AMOVA) using the software program GenAlEx6.5 [26]. The genetic distance matrix was employed for conducting AMOVA, with the number of sub-populations determined via STRUCTURE. Various statistical parameters, including the total number of alleles per locus (Na), number of effective alleles per locus (Ne), Shannon’s information index (I), observed heterozygosity (Ho), gene diversity (He), and unbiased expected heterozygosity (uHe), were estimated. Additionally, a Jaccard dissimilarity matrix was computed based on binary data for cluster analysis. A Neighbor-Joining tree was constructed using an unweighted neighbor-joining (UNJ) method [27], with 1000 permutations to determine bootstrap values for each clustering step.

Molecular diversity analysis study using DNA-based markers is an efficient strategy to estimate genetic diversity and population structure. In the current study, SSR marker techniques served to investigate genetic diversity among khapli or emmer germplasm comprising Indian landraces, local collection, indigenous as well as exotic cultivars. A total of 56 Simple Sequence Repeat (SSR) markers were employed to characterize 96 selected diverse dicoccum wheat accessions. Out of these, 28 markers located on different 13 chromosomes, showed polymorphism and detected a total of 93 alleles. Of 28, 13 SSRs were located on group A, and 15 were located on group B. Out of 4 SSRs, for each chromosome the number of polymorphic SSRs varied from 1 (1A, 1B, 4A, and 7B) to 3 (2A, 3A, 4B, 5B, 6B, and 7A). Polymorphism Information Content (PIC) was observed highest for Xcfd20 marker (0.9912) followed by Xcfd13 (0.9853), Xgwm636 (0.9658), Xcfd5 (0.9149) and the lowest PIC value (0.1365) for Xgwm2 marker (Table 1). The molecular profile of emmer germplasm using simple sequence repeat (SSR) marker namely, Xwmc737 is shown in Fig. 1. Among all SSR markers, Xwmc737, Xwmc617, Xwmc783, Xcfd39, Xbarc182, Xcfa2040, Xcfa2134, Xcfa2219, Xcfa2147 showed more length variation among different accessions and hence were considered the best markers for the study of genetic diversity and characterization of dicoccum accessions. The number of alleles per locus ranged from 1 to 6 with an average of 1.68 alleles per locus which was found to be lower compared to the majority of previous studies conducted on emmer wheat genetic resources. In one study, researchers detected 2–8 alleles per locus (with an average of 4) in a collection of 39 Italian emmer accessions, using 6 EST-SSR markers [28]. In another analysis of 194 emmer accessions with 15 SSR markers, an average of 7.7 alleles per locus was found [29]. Similarly, in a diversity analysis of 34 Ethiopian emmer landraces using 29 microsatellite markers, an average of 6.95 alleles per locus was reported [18]. Additionally, a study reported a total of 148 bands ranging from 3 to 18, with an average of 10.21 bands per primer (95.42% polymorphism rate) [30].

In addition, a total of 6 private alleles were identified with frequency of one unique band in 96 accessions. Some of them are Xcfa2040 (7A) with band size 320bp in accession EC519491, Xcfd39 (4B) with band size 180bp in IC35097, Xgwm2 (3A) with two different band sizes 130bp, 220bp in IC128425, Xwmc617 (4B) with band size 225bp in IC443709, Xwmc505 (3A) with band size 120bp in IC128425, and Xwmc737 (6B) with band size 160 bp in IC138418.

Table 1

List of 28 polymorphic SSR markers along with their chromosomal position and PIC.
S. No	SSR marker	Chromosomal position	PIC		SSR marker	Chromosomal Position	PIC
1	Xcfa2219	1A	0.33496	15	Xwmc617	4B	0.69889
2	Xcfa2147	1B	0.22352	16	Xwmc238	4B	0.2143
3	Xcfd86	2A	0.31749	17	Xcfa2190	5A	0.41786
4	Xgwm636	2A	0.96582	18	Xcfa2250	5A	0.15441
5	Xgwm497	2A	0.50597	19	Xcfd5	5B	0.91493
6	Xbarc167	2B	0.62283	20	Xcfd20	5B	0.99121
7	xcfd73	2B	0.33811	21	Xwmc783	5B	0.66005
8	Xcfa2076	3A	0.2577	22	Xcfd13	6B	0.98535
9	Xgwm2	3A	0.1365	23	Xbarc178	6B	0.26975
10	Xwmc505	3A	0.50987	24	Xwmc737	6B	0.74273
11	Xbarc180	3B	0.23893	25	Xbarc174	7A	0.28722
12	Xcfa2134	3B	0.66385	26	Xcfa2040	7A	0.342
13	Xbarc170	4A	0.71094	27	Xwmc596	7A	0.72374
14	Xcfd39	4B	0.25792	28	Xbarc182	7B	0.33301

Population structure and genetic relationships

Based on STRUCTURE analysis, 96 accessions were divided in two sub-populations namely P1, and P2, consisting of 33 and 63 accessions respectively (Table 2). Sub-population 1 consisted of 21 Indigenous Collections and 12 Exotic Collections while Sub-population 2 consisted of 43 Indigenous Collections and 20 Exotic Collections germplasm. A total of nine accessions were found to show admixture. Within a group, accessions with affiliation probabilities (inferred ancestry) > 80% were assigned to a distinct group, and those with < 80% were treated as admixture, i.e., these accessions seem to have mixed ancestry from parents belonging to different gene-pools or geographical origin (Fig. 2). A significant genetic divergence was observed among the two subpopulations from each other. A total of 93 polymorphic alleles were scored in 96 dicoccum accessions. In sub-population P1, a total of 71 polymorphic alleles were recorded, whereas in sub-population P2, 88 alleles were present. Several private alleles (i.e. alleles/markers present only in one accession) were found in the analysed material which may distinguish populations as well as accessions. In sub-population 1, 5 Private/unique alleles and in sub-population 2, 22 Private/unique alleles were observed which can be used for germplasm identification.

The presence of private alleles specific for the accessions deriving from the same region is important to assign a fixed allele discriminating the origin site, for preservation of genetic pool of the region and, as a source of new variation. Hence, these results confirm that this germplasm, of which cultivation was tremendously reduced, should be conserved to avoid the loss of important alleles. In order to gain insights into the genetic structure of emmer wheat and to identify potentially valuable accessions, a molecular characterization study was conducted on Italian emmer wheat accessions [29]. Distinctive molecular traits and considerable diversity were observed both within and among varieties of 39 Italian ecotypes and cultivars of emmer wheat, as characterized through the use of agro-morphological and molecular tools [28]. The genetic diversity of tetraploid native wheats and emmer wheat cultivated in Ethiopia was investigated using microsatellites markers [18, 31]. Similarly, 47 microsatellite (SSR) markers were utilized to evaluate the genetic variability of 48 emmer wheat accessions from India, which included 28 accessions collected locally and 20 accessions obtained from CIMMYT, Mexico [17]. In an investigation into the relationship between the eco-geographic and genomic diversity of emmer wheat and its performance under Mediterranean climate conditions, it was observed that the eco-geographic grouping of emmer wheat accessions aligned with their genomic grouping [32]. In general, reported studies covered mostly smaller set of emmer germplasm. However, we analysed relatively larger set of 96 genotypes in comparison to previous studies to estimate molecular diversity and to discover novel allele variations within the set and the unique alleles identified here may be used for molecular differentiation of germplasm.

Table 2

List of germplasm accession divided under two sub-populations.
Population	Accessions
Sub-Population 1	IC28596, IC32513, IC35097, IC47545, IC138450, EC577960, IC78699, IC32502, EC519491, EC540809, EC540812, IC78706, EC609395, EC577402, EC540813, EC299211, EC299157, EC299111, IC118765, EC578111, IC551400, EC577932, IC35174, IC35170, IC47026, IC47034, IC47037, IC35171, IC35119, IC47021, IC551396, IC566241, IC535086
Sub-Population 2	IC212168, IC535071, IC212164, IC212165, EC06900, EC12565, IC402012, IC402020, IC593664, EC11072, IC551399, IC551398, IC113725, EC590345, IC402045, IC535130, IC584049, IC591073, IC534018, IC47548, IC138371, IC128425, IC535079, IC138900, IC118729, IC534587, IC534016, IC533783, IC138331, EC577400, EC06845, EC06839, EC11389, EC08572, EC06908, EC08479, EC6839, IC535116, IC443709, IC539302, IC416358, IC138474, IC535142, IC128392, IC530555, IC535301, IC531559, IC47048, IC47800, IC118774, EC6838, IC47022B, IC277713, IC547564, IC252504, IC252503, IC551397, IC138472, IC112083, IC138418, EC11073, EC11232, IC448026

Population Genetic Differentiation Assessment through Analysis of Molecular Variance (AMOVA)

AMOVA was conducted to assess population differentiation among 96 dicoccum wheat accessions. Allelic information from two subpopulations identified by STRUCTURE analysis was utilized for the AMOVA and calculation of genetic diversity indices. The results of the AMOVA indicated that 1% of the total variation was found among or between subpopulations, while the remaining 99% of the variation was due to individuals or accessions within subpopulations. Genetic variation within the two populations, including sample size, number of different alleles (Na), number of effective alleles (Ne), Shannon’s index (I), expected heterozygosity (He), and unbiased expected heterozygosity (uHe), are presented in Table 3. Number of effective alleles (Ne) was 1.321 and 1.352 in subpopulation 1 and 2 respectively. Similarly, Shannon’s index was more i.e 0.353 in subpopulation 2 than in subpopulation 1 i.e. 0.302. The percentage of polymorphic loci per population was higher in subpopulation II (94.62%) than subpopulation I (70.97%). Populations had average values of 0.327, 0.208 and 0.211 for I, h, and uh, respectively. Thus, it is evident by the result that the Subpopulation II is genetically more diverse than subpopulation I. It comprised mostly indigenous collections such as IC277713, Local Sadak from Karnataka; IC138418, Kenphed from Delhi; IC448026, Goduma vadlu (from Andhra Pradesh), and several local collections from Karnataka.

The AMOVA analysis carried out in this study offered valuable insights into the population differentiation among 96 dicoccum wheat accessions. The findings indicated a low level of genetic differentiation among subpopulations and a high level of differentiation within subpopulations. Furthermore, these results validate the previously reported high genetic variability in emmer wheat [20, 28, 31].

Table 3

Genetic diversity parameters including the number of different alleles (Na), number of effective allele (Ne), information index (I), expected heterozygosity (He), and unbiased heterozygosity (UHe) in two sub-population of 96 *dicoccum* accessions
Pop	No. of Bands	Bands Freq. >= 5%	No. of Private Bands	Sample size	Number of alleles	Number of effective alleles Ne	Information index, I	Mean Expected heterozygosity, He	Mean Unbiased Heterozygosity uHe	%P
Pop1	71	63	5	21.731	1.473	1.321	0.302	0.195 ± 0.019	0.200 ± 0.020	70.97%
Pop2	88	77	22	72.989	1.892	1.352	0.353	0.222 ± 0.017	0.223 ± 0.017	94.62%
Mean				47.36	1.682	1.336	0.327	0.208 ± 0.018	0.211 ± 0.019	82.795%

Cluster analysis of 96 dicoccum wheat accessions based on SSR markers

The SSRs profiling data was used to study the genetic diversity among 96 dicoccum accessions by hierarchical clustering approach employing Jaccard’s dissimilarity index and neighbour joining distance using GenAlex 6.5 software (Fig. 3). Dicoccum accessions were classified into two main groups/clusters. Cluster I comprised 33 accessions, consisting of 21 indigenous and 12 exotic collections whereas cluster II comprised 63 accessions, consisting of 43 indigenous and 20 exotic accessions. Accessions, IC138472 (NP163) and IC112083 (Khapli, local collection) were very closely related as evident by clustering. Similarly, IC402020 and IC402012 augmented from Haryana state were similar and overlapped together. Also, EC11386 and EC08572 introduced from USA grouped together with close proximity. This may be due to high genetic similarity between them sharing same pedigree. Although indigenous and exotic collection grouped in a mixed fashion, without clear-cut separation, it seems that there is not much differentiation at genomic level in emmer genotypes from different geographical region. Salunkhe et al. (2013) [17] also reported that Indian emmer wheats are not very diverse. Consequently, there is a need to increase the diversity within the Indian emmer wheat eco-geographic group, by introducing diversity from other eco-geographic groups, or even from other wheat species. The low genetic diversity in emmer accessions may be due to decreased cultivation area during the past years and limited cultivation in small pockets that emphasized a need to broaden the genetic base of emmer wheat genetic resources in India towards increasing its genetic diversity, breeding and conservation.

This study concluded that the SSR markers are cost effective techniques for the evaluation of the genetic diversity in wild relatives of wheat. We could identify and select polymorphic SSR markers, that can be used in future research work. The unique alleles identified may serve as diagnostic tools for particular region of the genome of respective genotypes. Based on the allelic information and cluster analysis, it was revealed that cultivated emmer wheat specifically from Indian origin had relatively low genetic diversity and narrow genetic base. Therefore, we should target for introducing diversity by crossing Indian genepool with other exotic material from different eco-geographic group or with other wheat species.

Ethics approval and consent to participate

The material used in the study was augmented in the National Genebank in the past following local or national guidelines with due permission of competent authority.

Conflicts of interest/Competing interests:

The authors declare no conflicts of interest or competing interests associated with this work.

Funding

The Graduate School, ICAR-IARI, New Delhi

Author Contribution

JK,SKJ- Planning of experiment, material; JT, ST, SKJ, SS- DNA extraction and data generation, ST, JK, JT, PJ, GK- data analysis and figures preparation; All authors-manuscript editing and reviewing

Acknowledgement

The first author would like to acknowledge the Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi for providing Fellowship during masters’ programme. We further extend our thanks to the director, ICAR-ICAR-Indian Agricultural Research Institute, New Delhi and ICAR-National Bureau of Plant Genetic Resources, New Delhi, India for providing infrastructure and support.

Data Availability

Data is provided within the manuscript or supplementary information files

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No competing interests reported.

SupplementaryMaterialSN.docx
Supplementary Table 1 List of 96 selected dicoccum accessions along with their geographical origin. Supplementary Table 2 Details of the SSR markers, their chromosomal position and forward/reverse primer sequences
SupplementaryMaterialGel.docx
Fig 1 Gel electrophoresis profile of SSR marker Xwmc737 on 96 dicoccum accessions showing length polymorphism = 100 base pair ladder, (1-96 dicoccum accessions)

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Editorial decision: Revision requested
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Population structure analysis of emmer wheat (Triticum dicoccum Schrank) germplasm conserved in National Genebank of India using SSR markers

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Plant material

Molecular Markers and analysis

Statistical Analysis

RESULTS AND DISCUSSION

Population structure and genetic relationships

Population Genetic Differentiation Assessment through Analysis of Molecular Variance (AMOVA)

Cluster analysis of 96 dicoccum wheat accessions based on SSR markers

Declarations

Ethics approval and consent to participate

Conflicts of interest/Competing interests:

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1