Genetic diversity and population structure analysis of Ethiopian bread wheat (Triticum eastivum L.) germplasm using SSR markers

doi:10.21203/rs.3.rs-4186694/v1

Background: Bread wheat, belonging to the most diverse and important family Poaceae in the plant kingdom, produces crucial edible grains. Ethiopia has been considered as a center of diversity and the second center of bread wheat domestication. Genetic diversity and population structure analyses in the Ethiopian bread wheat germplasm have enormous importance in enhancing breeding and sustainable conservation.

Methodology: 96 bread wheat germplasm were gathered from Kulumsa and Adet Research Centers, Ethiopia. The samples were taken to the ICARDA-BIGMP, Cairo, Egypt and grown at the green house, after two weeks leaf samples were collected per plant, and taken to the laboratory for DNA extraction. Data were analyzed using PowerMarker ver. 3.25, NJ, UPGMA, Structure, ver.2.3.4, and AMOVA.

Results: Genetic diversity and population structure were estimated across 96 germplasm using 7 polymorphic and informative SSRs. Varied values of diversity indices were observed across chromosomes and genomes. Higher mean values of MAF (0.67), PIC (0.34), and Nei's gene diversity (Gd) (0.36), and values of Gd (0.41) and PPL (87.28%) were signifying the presence of high genetic diversity within and among populations, respectively. AMOVA showed highly significant population differentiation for 98% variation within population letting only 2% significant variation among populations. The Structure analysis showed four populations (COV, EBWNVT, EBWYT, and EBWAT), while the UPGMA revealed 3 main population clusters, in which the EBWYT and EBWAT were the 2 sub-clusters. The NJ analysis and PCoA across 96 germplasm revealed three main clusters in each the germplasm were found inter-mixed irrespective of their breeding history and evolution likely to the Clumpak result, signifying the presence of higher admixture due to the existence of historical exchanges of seeds through informal system involving regional and nationwide farming communities in Ethiopia.

Conclusions: Sustainable utilization and conservation of rich Ethiopian bread wheat genetic resource is an irreplaceable means to cope up the recurrent climate changes and biotic stresses happening wide, and thereby able to meet the demand of bread wheat productivity for the ever-growing human population.

Bread wheat

Genetic diversity

Germplasm

Population structure

SSRs

Wheat cultivars generally refer to two species: hexaploid wheat, Triticum aestivum (2n = 6x = 42, AABBDD), and tetraploid wheat, Triticum durum (2n = 4x = 28, AABB). T. aestivum belonging to the family Poaceae is considered the most diverse and important family of the plant kingdom, and produces one of the most important edible grains that provides about one-half of humans’ food calories and a large part of their nutrient requirements [1]. In Ethiopia, wheat is the second most widely produced cereal crop after corn and the third most important staple food behind corn and sorghum [2]. Hexaploid wheat accounts for about 75–80% of the national production, while tetraploid makes up roughly 10–15% [2]. Additionally, wheat’s straw is commonly used as a roof thatching material and as animal feed in most wheat-growing rural areas of Ethiopia. Hence, increasing wheat production has been a national goal to decrease the gap between production and human consumption especially in view of the fastest-growing population as compared to production.

Ethiopia is one of the few countries that have been served as the center of primary gene pool for various crops [4, 5, and 6]. In Ethiopia, the Institute of biodiversity conservation (IBC), maintained more than 60,000 accessions of different crops in its gene bank and of these, 13,000 are hexaploid wheat varietals accounting 15% from the total [7, 8]. Besides, up to recent time, agricultural research centers and institutions have been involved in collecting and conserving Ethiopian bread wheat varietals in the country. Ethiopian bread wheat has been served as a center of focus for genetic studies and the source of novel alleles [9–14]. Vavilov [4] and Zohary [15] reported the presence of high genetic diversity in Ethiopian bread wheat and recent studies specified uniqueness of Ethiopian hexaploid landraces form the Fertile Crescent collections (primary center of domestication) and considered as the possible second center of domestication for the crop [3].

Plant genetic diversity is changed by evolution and by breeding history during which intensive selection often reduces genetic diversity in the elite germplasm pool [16; 17]. Genetic diversity information among the germplasm is useful to (i) classify lines for desirable traits [18; 1], (ii) determine the genetic diversity reduction due to long term plant breeding programs [80], and (iii) evaluate genetic differentiation by different breeding programs [19]. The availability of genetic variability in wheat material is a pre-requisite for any breeding program aimed towards the improvement of wheat productivity. Wheat breeding through hybridization also requires the selection of diverse germplasm, irrespective of whether the end product is a pure line or a hybrid variety [20]. Loss of genetic diversity has become a problem, not only of the natural plant and animal population, but also agriculturally important species. Ancient cultivars or landraces and wild relatives of domesticated species are being lost as modern varieties become adopted by farmers. This had led to calls for genetic conservation of crop germplasm [21].

Simple sequence repeats (SSRs) markers have been used for genome analyses and plant breeding studies such as genetic evolution, quantitative trait loci (QTL) mapping, gene tagging based on map position, cultivar identification, and genetic diversity analysis in germplasm [22]. It has high level of polymorphisms, co-dominant inheritance and equal distribution in wheat genome without genetic effects like epistasis or pleiotropic [23]. Simple sequence repeats have the ability to discriminate among closely related individuals for diversity and allelic variation across a wide range of germplasm, and have the advantage over other markers to trace pedigrees in plants [24]. However, genetic diversity and population structure of Ethiopian bread wheat germplasm has not been extensively investigated with SSR markers. Hence, the present study aimed to assess the extent and pattern of genetic diversity and population structure of 96 Ethiopian bread wheat germplasm using SSR marker system, and to suggest for the proper implementation of genetic resources, and provide prior information for the improvement and conservation activities.

Plant material

In this study, a total of 96 bread wheat germplasm (45 released/commercial varieties (COV) and 51 pipe/inbreed lines) collected from Kulumsa and Adet Research Centers, Ethiopia was used. The 51-germplasm included 21 from a set of Ethiopian bread wheat for yield improvement trial (EBWYT), 7 were from Ethiopian bread wheat for adaptation trial (EBWAT), and 23 were from the Ethiopian bread wheat for national verification trial (EBWNVT), the detail is available in Additional file; Table S1.

Genomic DNA extraction

Healthy seed samples were taken to the ICARDA Biodiversity and Integrated Gene Management Program (BIGMP), Cairo, Egypt. After 2 weeks of growth in a green-house the pooled leaf samples from five plants per line were used for DNA extraction and analysis following Cetyle Tri-methyl Ammonium Bromide (CTAB) protocol by [68], with slight modifications; where, 850µl CTAB solution was added to the leaf powder (about 0.5gm) in the microtube and incubated at 65^oC in water bath for 30min to 1h. Then the mixture was cooled on ice for 5min and chloroform-iso-amyl was added in the proportion of 24:1. Acetate and iso-propanol solution was added to precipitate the DNA pallet and the supernatant/clear solution removed. Finally, the collected pallet/residue was washed with 70% ethanol and dissolved in 1x TE buffer, and made ready for later gel electrophoresis, after the quantity and quality of extracted DNA was checked with Nano drop spectrometry and on 1% agarose gel, respectively.

SSR genotyping and PCR amplification

In total, 15 SSR markers from (vivantisl: www.vivantechnologies.com) (Table 1) were tested for polymorphism based on reports by [26] and [27]. Under optimized PCR conditions 7 primer pairs (Table 2) consistently amplified their targets were selected for use on the whole samples in the following manner. The PCR amplification reaction was carried out using Gene Amp PCR system 9700 thermo cycler, in a total volume of 20µL containing, 2.5µL of 10 x PCR buffer, 2.5µL MgCl₂ (25mM), 0.5µL dNTP mixture (10mM), 0.5µL primer (15pmole/µL), 0.3µL Taq DNA polymerase (1U/µL), 2µL of template DNA (80ng/µL), and 11.7µL double distilled water. The PCR amplification was programed with an initial denaturation at 94ºC for 5min, followed by 40 cycles of denaturation at 94ºC for 1min. PCR annealing temperature for each primer was optimized using primer digital software (primerdigital.com) at (45–52°C for 1 min), and 72ºC for 2min and 10min for primer extension and final extension, respectively. The pooled amplicon products were fractionated by loading 10µl per amplification product with 2µl 6X loading dye on 1.67% agarose gel electrophoresis supplemented with Ethidium Bromide in 1x TBE buffer at 120V for 2hr using an ABI3730 DNA genomic analyzer (Applied Bio systems, Foster City, CA) and denatured with Hi-Di Formamides at 95^oC for 3min were mixed with a 100 base pair DNA ladder was used to estimate the sizes of amplicons (GeneScan-500 Internal LIZ and 1200 Internal LIZ Size Standards) and capillary electrophoresis was conducted. Finally, the gel was visualized under UV light, and subsequently photographed using a BIO-RAD Gel Doc TM EZ Imaging System.

Data scoring

The discrete statistics using a binary matrix; “0” coded for absence, “?” for ambiguity, and “1” for presence of each band from the fragments of 7 SSR primers for each locus with highly polymorphic, clearly distinguishable, and reproducible bands across the germplasm were used for data scoring for genetic diversity and population structure analyses.

Statistical analysis

SSR markers distribution analysis

The data points produced by genotyping 96 bread wheat germplasm, from 15 SSR markers tested, 7 primers with a high-quality genotyping were selected for polymorphisms detection, while those failed to generate clear genotyping were excluded. Sizes of the SSR fragments were checked using ABI3730 system, and the size of alleles was analyzed using GENEMAPPER V 4.1 software (Applied Bio systems). Each primer pair was assumed to amplify a single genetic locus where bands of different molecular weight were considered to be different alleles of a particular locus. The scored data on SSR marker to be treated as dominant marker for each locus was considered as a bi-allelic locus with one amplifiable and one null allele was analyzed using Kluster Caller.

Genetic diversity within and among population’s analyses

The genotypic data were subjected to various statistical tools. Accordingly, Power Marker version 3.25 [28] was used to measure genetic diversity indices at each SSR locus, including the total number of alleles (NA), major allele frequency (MAF), accession-specific alleles, observed heterozygosity (Ho), Shannon’s information index (I), polymorphism information content (PIC) and expected heterozygosity (He). GenAlEx version 6.5 [67] was used to compute the numbers of rare alleles (RA), common alleles (CA) and abundant alleles (AA), partitioning of total genetic variation into within and among pre-grouped populations through molecular analysis of variance (AMOVA), pairwise Fst and gene flow (Nm). Genetic distances between each pair of within and among populations was measured based on both shared allele frequencies and Nei's genetic distance [25] using Power Marker [28] Genetic distance matrices for each locus were summed across loci assuming statistical independence. Pair-wise genetic frequency-based dissimilarity or distance matrix between individuals was calculated according to [25] as implemented in Power Marker. The resulting dissimilarity matrix was subjected to tree construction using the Un-weighted Pair Group Method with Arithmetic mean (UPGMA) analysis using the using FigTree ver. 0.9.1.5 Software [30] to compare individual germplasm and evaluate patterns of genetic diversity.

Population structure and pattern of admixture analyses

Bayesian model-based software called Structure 2.3.4 [29; 32] was used to infer the population structure of the sampled germplasm set using a burn-in of 10,000, a run length of 100,000, and a model allowing for admixture and correlated allele frequencies. At least five runs of Structure were conducted by setting the number of populations (K) from 1 to 20. The model choice criterion to detect the most probable value of K was, both the LnP(D) value for each given K and ΔK, an ad hoc quantity related to the second-order change of the log probability of data with respect to the number of clusters inferred by Structure [34]. Once the best K was found, the analysis was re-run in the same software using a burn-in of 10,000, a run length of 500,000 with the same aforementioned model. CLUMPAK: "a program for identifying clustering modes and packaging population structure inferences across K" (CLUMPAK server) was used. A bar plot for the optimum K was determined using Clumpak beta version [96]. Principal coordinate analysis (PCoA) [94] for the genetic relationships among individuals was calculated using a package “SSRrelate” [69] in R studio [97].

Table 1 Characteristic of the 15 SSRs primers tested for genotyping in the current study

S.N.	Primer code	Forward primer sequence	Reverse primer sequence	Annealing T^o
1	Xgwm_3*	GCAGCGGCACTGGTACATTT	AATATCGCATCACTATCCCA	55
2	Xgwm_129*	TCAGTGGGCAAGCTACACAG	AAAACTTAGTAGCCGCGT	50
3	Xgwm_165*	TGCAGTGGTCAGATGTTTCC	CTTTTCTTTCAGATTGCGCC	60
4	Xgwm285*	ATGACCCTTCTGCCAAACAC	ATCGACCGGGATCTAGCC	60
5	Xgwm456	TCTGAACATTACACAACCCTGA	TGCTCTCTCTGAACCTGAAGC	55
6	Xgwm458	AATGGCAATTGGAAGACATAGC	TTCGCAATGTTGATTTGGC	60
7	Xgwm459	ATGGAGTGGTCACACTTTGAA	AGCTTCTCTGACCAACTTCTCG	55
8	Xgwm471	CGGCCCTATCATGGCTG	GCTTGCAAGTTCCATTTTGC	60
9	Xgwm642	ACGGCGAGAAGGTGCTC	CATGAAAGGCAAGTTCGTCA	60
10	WMC_24*	GTGAGCAATTTTGATTATACTG	TACCCTGATGCTGTAATATGTG	51
11	WMC25	TCTGGCCAGGATCAATATTACT	TAAGATACATAGATCCAACACC	51
12	WMC_44*	GGTCTTCTGGGCTTTGATCCTG	TGTTGCTAGGGACCCGTAGTGG	61
13	WMC_216*	ACGTATCCAGACACTGTGGTAA	TAATGGTGGATCCATGATAGCC	51
14	WMC243	CGTCATTTCCTCAAACACACCT	ACCGGCAGATGTTGACAATAGT	61
15	WMC256	CCAAATCTTCGAACAAGAACCC	ACCGATCGATGGTGTATACTGA	61

Where, Seven markers (* labeled) showed highly polymorphic bands

SSRs marker distribution and polymorphism

From the total of 15 SSR markers tested (Table 1) for the present study, 7 markers (46.7%) (Table 2) were highly polymorphic with a total of 65 alleles revealed at an average of 1.89 across the 96 germplasm, and from the 2170 SSRs loci showed a known position, 1010 (87.07%) were polymorphic, while 150 (12.93%) were monomorphic (Table 3) across four populations. However, reduced number of SSRs were observed in the nucleotide diversity between the genomes, with relatively lower value (3.00) in the B genome, (3.30) in the D genome, and relatively higher in the A genome (3.33 SSRs).

Table 2 List of transferred and functional 7 SSR primers with forward and reverse sequences, annealing temperature (^oC), and fragment length used for current bread wheat study

S.N.	Primer code	Forward primer sequence	Reverse primer sequence	Annealing T^o	Expected size (bp)
1	Xgwm_3	GCAGCGGCACTGGTACATTT	AATATCGCATCACTATCCCA	55	170
2	Xgwm_129	TCAGTGGGCAAGCTACACAG	AAAACTTAGTAGCCGCGT	50	200
3	Xgwm_165	TGCAGTGGTCAGATGTTTCC	CTT TTCTTTCAGATTGCGCC	60	250
4	Xgwm285	ATGACCCTTCTGCCAAACAC	ATCGACCGGGATCTAGCC	60	500
5	WMC_24	GTGAGCAATTTTGATTATACTG	TACCCTGATGCTGTAATATGTG	51	152
6	WMC_44	GGTCTTCTGGGCTTTGATCCTG	TGTTGCTAGGGACCCGTAGTGG	61	242
7	WMC_216	ACGTATCCAGACACTGTGGTAA	TAATGGTGGATCCATGATAGCC	51	223

Table 3 Allele frequency distribution across the four populations

Locus	WMC_216			Xgwm_3		XgwM_129		Xgwm_165		WMC_44		Xgwm_285		WMC_24		Total	Ave.	Property of allele
Allele	X	Y		X	Y	X	Y	X	Y	X	Y	X	Y	X	Y			Mono	Poly
Number	125	135	35		50	110	120	200	210	210	280	200	230	130	135	2170	140	150	1010
Freq,	0.72	0.28	0.47		0.53	0.41	0.59	0.26	0.74	0.73	0.27	0.91	0.09	0.36	0.47		0.01	0.17	6.97

Where, X & Y are alleles per locus revealed by each SSRs primers; total polymorphic bands produced by the alleles (Poly); total number of monomorphic bands (mono) produced by the SSRs; average (Ave), and frequency of the allele (Freq.)

The SSR markers exhibited a wide range in most of the diversity indices within populations; MAF from 0.47 (WMC_24) to 0.91(Xgwm_285) with an average of 0.67, PIC from 0.15 (Xgwm_285) to 0.55 (WMC_24) with an average of 0.34, the He with an average of 0.34 ranged from 0.06 (Xgwm_285) to 0.48 (WMC_24), I from 0.10 (Xgwm_285) to 0.78 (WMC_24) at an average of 0.50, Gd ranged from 0.07 (Xgwm_285) to 0.49 (WMC_24) at the average of 0.36, and Na (number of specific alleles) range from 1.2 (xgwn_285) to 2.6 (WMC_24) with an average of 1.89 alleles per locus across the chromosomes and genomes of 96 Ethiopian bread wheat germplasm (Table 4). The highest level of PIC, He, Gd, I, and Na was detected by the primer WMC_24, while the lowest PIC, He, Gd, I, and Na values were observed at Xgwm_285 locus, but the values of these two loci were vice versa for the value of MAF, while Ho value was zero for all the loci (Table 4), indicated polymorphic property of the markers and level of diversity within population.

Table 4 Mean diversity indices across the seven SSR loci used in the 96-bread wheat germplasm

Locus	MAF	NA	Na	Ne	I	Ho	He	Gd	PIC	Fst	Nm	F
WMC_216	0.71	3.00	2.20	1.82	0.65	0.00	0.44	0.49	0.34	0.05	5.09	1.00
XGWM_3	0.53	2.00	1.80	1.77	0.55	0.00	0.39	0.41	0.37	0.20	1.02	1.00
XgwM_129	0.59	2.00	1.80	1.66	0.51	0.00	0.36	0.37	0.37	0.15	1.45	1.00
Xgwm_165	0.74	2.00	1.80	1.55	0.46	0.00	0.31	0.33	0.31	0.37	0.44	1.00
WMC_44	0.73	2.00	1.80	1.55	0.46	0.00	0.32	0.33	0.32	0.15	1.42	1.00
Xgwm_285	0.91	2.00	1.20	1.09	0.10	0.00	0.06	0.07	0.15	0.17	1.25	1.00
WMC_24	0.47	3.00	2.60	2.22	0.78	0.00	0.48	0.49	0.55	0.27	0.67	1.00
Mean	0.67	2.29	1.89	1.67	0.50	0.00	0.34	0.36	0.34	0.19	1.62	1.00

Where, Major allele frequency (MAF); observed total number of alleles (NA); observed number of specific alleles (Na); Shannon’s information index (I); observed heterozygosity (Ho); number of effective alleles (Ne); Nei’s gene diversity (Gd); expected heterozygosity (He); polymorphic information content (PIC); and inbreeding coefficient (F); and over all gene flow (Nm)

Table 5 Summary of different population diversity indices averaged over the seven loci

Population	Na	Ne	I	Ho	He	Gd	F	PPL
COV	2.29	1.71	0.60	0.00	0.40	0.40	1.00	100.00
EBWYT	2.00	1.79	0.59	0.00	0.40	0.43	1.00	85.71
EBWAT	2.00	1.86	0.61	0.00	0.41	0.44	1.00	85.71
EBWNVT	2.00	1.82	0.59	0.00	0.40	0.41	1.00	85.71
Mean	2.07	1.77	0.59	0.00	0.40	0.41	1.00	89.28

Where, Percent polymorphic loci (PPL)

Genetic variations within and among populations

The frequency distribution of SSRs among the populations for PPL with a mean value of 89.28% varied from 85.71% for (all the three) to 100% for (COV), indicated a relatively higher genetic diversity for the current study (Table 5). The overall mean value for I (0.59) was varied from 0.59 (EBWNVT) to 0.61 (EBWAT), and the overall mean value for Gd was 0.41 varied from 0.40 (COV) to 0.44 (EBWAT), while the mean for He was 0.40 ranged from 0.40 (for all other three) to 0.41 (EBWAT) (Table 5). The study of variation among the four populations was highest for the values of Ne (1.86), I (0.61), He (0.41), and Gd (0.44) via the same population (EBWAT). However, the values for Ne (1.71), I (0.59), He (0.40), and Gd (0.40) were relatively lower via the rest of the three (COV, EBWYT and EBWNVT) populations that showed narrow ranges and exhibited low level of genetic variation among the populations tested. The mean value for Ho across all populations was 0.00, while the highest value of PPL (100%) was detected in the (CO-V) population than the rest of the three populations 85.71% with an average of 87.28% (Table 5). The diversity indices I and He based on SSR marker and pedigree information indicated that all the four populations showed higher gene diversity among the four populations.

Table 6 Allelic richness across the four populations and seven loci used in the study

Population	WMC_216	XGWM_3	XgwM_129	Xgwm_165	WMC_44	Xgwm_285	WMC_24	LA
COV	2.28	2.00	2.00	1.85	2.00	1.97	2.31	2.06
EBWYT	2.00	2.00	2.00	2.00	2.00	1.00	3.00	2.00
EBWAT	2.00	2.00	2.00	2.00	2.00	1.00	3.00	2.00
EBWNVT	2.00	2.00	2.00	2.00	1.95	1.00	2.98	1.99
PA	2.07	2.00	2.00	1.96	1.99	1.24	2.82

Where, Loci average (LA); Population average (PA)

Table 7 Private allelic richness across the four populations and seven loci studied

Population	WMC_216	XGWM_3	XgwM_129	Xgwm_165	WMC_44	Xgwm_285	WMC_24	LA
COV	0.33	0.00	0.00	0.00	0.00	0.97	0.00	0.19
EBWYT	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
EBWAT	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
EBWNVT	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
PA	0.07	0.00	0.00	0.00	0.00	0.24	0.00

Regardless of the low Ho, all the studied populations with almost equal proportions revealed highest allelic richness across the entire loci, with average values of allelic richness across the four populations was (2.82 for WMC_24), (2.07 for WMC_216), and each 2.00 for (XGWM_3 and XGWM_129) in that order (Table 6). While, the private allelic richness was null for all the populations, except for (COV) with the highest values at two loci (0.97 for XGWM_285) and (0.33 for WMC_216), this indicated a considerable private allele was observed for both commercial varieties and (COV) population, and all the alleles except one (rare) are common alleles (with a frequency > 5%) (Table 7). Similarly, the current marker detected a wide range of genetic variability among populations; moreover, the PIC values of the loci across populations look wider (0.15–0.55). However, the values for Ne, He, Gd, and I showed relatively higher values for within populations (Table 4) than among populations (Table 5).

Table 8 Analysis of Molecular Variance (AMOVA) in Ethiopian bread wheat within and among populations

	SV	DF	SS	MS	EV	PV	Fst		P-value
Among pops		3.00	17.56	4.39	0.11	2%	0.023	P < 0.001
Within pops		92.00	377.80	4.15	2.08	98%	0.003	P < 0.001
Total		95.00	395.35		2.19	100%

Where, Degree of freedom (DF); Sum of squares (SS); Mean square (MS); Estimated Variation (EV); Percent of variation (PV); Source of variation (SV); and Fixation index (Fst)

Genetic differentiation within and among populations

In the present study, AMOVA was used to observe genetic variations/differentiation among and within populations. Accordingly, a total of 1010 bands were screened using 7SSR primers, and all of the four populations were created with a higher (98%) of total genetic variation attributed to the within population, leaving only 2% for the (genetic variations) among populations (Table 8). Similarly, the Nei's heterozygosity [54] showed a weak genetic differentiation within than among populations with a very low overall Fst of 0.003 than 0.023 (Table 8), respectively, and the pairwise population differentiation (0.01 to 0.12) was relatively wider (Table 9). Moreover, maximum overall within population (Fis = 1.00) and total (Fit = 1.00) genetic differentiation values have also been evidenced as revealing fixation of the alleles within population, as was also evident for higher (Nm = 1.62) overall gene flow (Table 4).

Table 9 Nei’s population pairwise genetic distance (Pix) (above) and genetic identity (F_ST) (below diagonal)

Population	COV	EBWYT	EBWAT	EBWNVT
COV	***	0.08	0.12	0.01
EBWYT	0.03	***	0.03	0.05
EBWAT	0.01	0.08	***	0.06
EBWNVT	0.12	0.06	0.05	***

Where, Commercial variety (COV); Ethiopian bread wheat for yield improvement trial (EBWYT); Ethiopian bread wheat for adaptation trial (EBWAT); and Ethiopian bread wheat for national variety trial (EBWNVT)

The pair-wise Nei’s genetic distance (Pix) (Table 9) between the four populations ranged from 0.01 (COV vs. EBWNVT) to 0.12 (COV vs EBWAT), where the highest pair-wise genetic distance (0.12) was observed between (COV vs EBWAT), followed by (COV vs EBWYT) for 0.08, EBWNVT vs EBWAT for (0.06), and EBWNVT vs. EBWYT (0.05) in that order, indicated that they were the most genetically distinct populations as the pair wise genetic distance getting with higher values. Similarly, all pair-wise genetic identity (F_ST) between the populations revealed a wide range from 0.01 to 0.12; however, in the contrary highest similarity was observed by the pair wise similarity (F_ST) between (COV vs. EBWNVT) for 0.01, followed by 0.03 between (EBWYT and EBWAT), indicated these pairs were the most similar populations in that order (Table 9). This condition indicated the Pix and the Fst were inversely proportional to each other.

The (EBWAT) revealed relatively higher value 0.44 for Gd (Table 4). The gene distances were calculated for the germplasm as in the same manner as in the populations, based on the shared-allele distance, i.e., the value of Gd between germplasm (in which they were derived from that population) with the same result of Gd as in the results found among the populations using the pair wise analysis. Accordingly, the highest (Pix) was (0.12) was obtained from the germplasm derived from a pair wise analysis between the populations (COV and EBWAT), followed by the germplasm derived from the populations (COV and EBWYT) with GD value of 0.08. However, the most related germplasm were observed from the germplasm derived from the populations (COV and EBWNVT) as in the minimal Pix was (0.01), followed by (EBWYT and EBWYT) with the value of 0.03 for Pix, indicated that these populations were the most related that they were resulted for the most related germplasm, while the highest Pix (least relatedness) was observed in the germplasm derived from the (COV and EBWAT) populations (0.12) (Table 9) as a result, the germplasm derived from (COV vs. EBWAT) population showed the highest (0.12) F_ST, i.e., the average number of pairwise differences (Pix) was within than among populations (Table 9). In terms of the loci, some of them were represented by excess heterozygosity (negative Fis).

Genetic clustering within and among populations

The neighbor-joining analyses from SSR data found three clusters with strong relationships among Ethiopian bread wheat germplasm/within populations (Figure 1), in which all the three main clusters revealed sub-clusters and sub-sub-clusters, whereby a mixture of germplasm were found in each cluster (C-I. C-II and CIII) consisted of germplasm gathered from all of the four COV, EBWNVT, EBWYT, and EBWAT populations/sources, indicated the germplasm were found intermixed, in each cluster without their breeding information in all the populations, while few of the germplasm were found based on their breeding history without spreading all over the clusters forming the strict grouping. The complex varietal distribution of the germplasm without clear cut grouping to their sources; and the revealed three main clusters (C-II, C-I, and C-III) comprised of (8, 12, and 76 germplasm) respectively, in which C-I consisted of 12 germplasm in admixture from different populations; 6 germplasm in serial numbers (52, 53, 68, 71, 79, and 87) from the COV; 3 germplasm (35, 40 , and 45) from the EBWNVT, and 3 germplasm (27, 28, and 51) from the EBWAT. While the C-II; composed of 8 germplasm from various populations, a set of 4 germplasm (56, 66, 73 and 89) from COV, 1 germplasm (9) from EBWAT, 1 germplasm (3) from EBWYT, and 2 germplasm (49 and 50) from EBWNVT. The C- III consisted of 76 germplasm consisted of 36 germplasm from COV, 17 from EBWNVT, 16 from EBWAT, and 7 from EBWYT. The result indicated that the commercial and the pipeline germplasm were found distributed at every cluster without being differentiated by their being released or pipeline consortium. None of the clusters was composed of exclusively isolates from a particular population background, confirming the existence of considerable intermixing of the genotypes.

Accordingly, the UPGMA based on Nei’s genetic distance matrix (Figure 2), the constructed dendrogram categorized the four populations into three main clusters (C-I for COV, C-II for EBWNVT, (C-III) was divided in to two sub-clusters (C-III-I for EBWYT and C-III-II for EBWAT). This result identified all the three main clusters, where C-I consisted of 46.89% assigned genotypes from COV, and C-II with 31.25% assigned genotypes from EBWNVT where both were originated from the primary origin of the dendrogram, indicated these populations were more similarity in their evolutionary history. While the third cluster (C-III) showed 29.17% of the assigned genotypes for further sub-divided in to two sub-clusters, where (C-III-I ) with 7.29% the lowest number of assigned genotypes from EBWYT, and (C-III-II) with 21.88% assigned genotypes from EBWAT.

Population genetic structure and principal coordinate analyses (PCoA)

Testing for population genetic structure is important while conducting association studies and identifying association between markers and the traits of interest. As wheat germplasm can be efficiently categorized using population structure analyses, using the maximum membership probability of the germplasm in populations based on Structure analysis. The Bayesian model-based population structure of the 96-wheat germplasm was inferred using Structure ver. 2.3.4, [34] software and the K value were used to estimate the number of clusters of the isolates. Output of the Structure harvester revealed that the delta K (ΔK) values reached a sharp peak at K=2 (Figure 3a), confirming that the studied wheat germplasm can be clustered into two subpopulations. The Clumpak result (bar plot) detected a greater degree of genetic admixture between the four populations (Figure 3b), which means the analyses revealed the presence of higher genetic distance between the genotypes among populations than within populations.

The PCoA is a technique frequently used in multivariate statistics to picture the pattern of population genetic structure, and similarly to determine the amounts of variance described per component and cumulatively [69], In the current study PCoA explained 50.90% of the total genetic variations and the first three (1, 2 and 3) axes (Figure 4) accounted for 20%, 13.04%, and 17.86%, respectively. It clustered the entire populations into three subgroups with high genetic admixture.

The scatter plot of PCoA (Figure 4) revealed three population clusters the COV, EBWNVT, and (EBWAT together with EBWYT in one) together that showed nearly a uniform distribution on the two-dimensional coordinate plane of the germplasm with deprived population clustering. None of the clusters were composed of entirely germplasm from a particular population confirming the existence of significant intermixing of genetic backgrounds of various populations. Thus, the results of NJ clustering, UPGMA relationship, Bayesian based genetic structuring, and the PCoA based individual germplasm distribution along the three axis, all were failed to show a close relationship (weak sub-division) among the four populations, indicated the presence of high genetic intermixing of the germplasm/found lumped/gathered from different genetic backgrounds in each population cluster; in the sense that the pattern of their genetic background/parental information was not maintained.

Genetic diversity in wheat has been increasingly narrowed down, due to different reasons like modern breeding practices [99]. Therefore, for improvement of existing germplasm, diversity studies are always important [100]. This work studies the genetic diversity and population structure of 96 bread wheat germplasm, so that they can be used efficiently in selection of varied parents for the forthcoming breeding programs and also for conservation purposes.

SSRs markers distribution and polymorphism

Microsatellite marker can be competently used to study the genetic diversity of bread wheat germplasm to find out genetic relationship among germplasm, which is vital component in germplasm conservation and improvement through breeding, as it can be used for genetic variability study of genotypes for the purpose of identification as it has been used in the current study. The current study on the use of microsatellite marker has been confirmed as a powerful tool for identification and characterization of variations in intra specific and among the populations, due to the following reasons; (i) the SSRs markers are multi-allelic, co-dominantly heritable, relatively abundant and with extensive genome coverage [33; 35], (ii) they are useful and popular for different applications in wheat breeding in addition to their high level of polymorphism and easy handling [33; 36], (iii) they have been used to evaluate genetic diversity in bread wheat [33; 37] and (iv) they have succeeded in screening, genetic diversity, evaluation, and molecular mapping studies in bread wheat germplasm. Thus the previous studies were congruent with the current findings that showed higher polymorphisms among the studied germplasm and populations using SSRs markers, which might be due to the fact that the diversity measurements applied with higher resolution property in the study of germplasm and populations can also be transformed to genetic similarity; hence, the diversity parameters used may be implemented for further selection study of bread wheat breeding and conservation programs, and the germplasm that showed higher genetic distance can be used for this purpose.

The higher polymorphic wheat SSR markers applied in the current work could be used for efficient screening of the germplasm due to saturating ability in the regions of wheat genome, as in the previous studies who employed 20 genic SSRs markers for molecular characterization of 16 genotypes with a mean value of 0.50 gene diversity ranged from 0.11 (Xgwm-247) to 0.70 (Xgwm-257) [47]. A similar approach was employed in the study of molecular characterization of 16 durum wheat varietals analyzed using 9 microsatellite markers amplified each one single locus [81]. The present 7SSRs based result with a mean and total values of alleles per locus (6.97 and 1010) across populations (Table 3) and (1.89 and 65) across germplasm (Table 4) respectively, were relatively higher than the numerous previous reports; [48] employed eleven microsatellites generating 44 alleles with an average of 4 alleles per locus, [49] found a total of 80 allele with an average of 3.2, that ranged from 1 to 5 alleles per locus, [13] found an average value 4.6 with a range from 2 to 9 alleles per locus at a total of 115 alleles, and [82] was also found a total of 93 alleles with the range from 2 to 6 at an average of 3.72 per primer.

Among the 7 SSR loci in the current study for each genome, the number of specific alleles was 3.33 in A genome, 3.00 in B genome, and 3.30 in D genome showed medium level of polymorphism, as in the previous works of [38] and [39]. In addition, [31] likely in his study on French bread wheat accessions, found the three genomes were ranked as A > D > B based on SSR alleles/locus. Likewise, the reason for reduced nucleotide diversity can be by 30 to 50% in the A- and B-genomes, depending on previous study of diversity measures used by [74,75] showed reduced level of diversity as a consequence of the polyploidy bottleneck resulting from hexaploid wheat speciation [75,76], and different rates of gene flow from the ancestors of hexaploid wheat, for the A-and B-genomes and Aegilops tauschii for the D-genome [77,78] resulted during different diversity studies in hexaploid wheat genomes [76], while diversity levels were relatively similar in the A- and D- genomes with slight reduction in the B-genome was due to the fact that higher levels of linkage disequilibrium (LD) was found when compared with the A-and D-genomes [76,79]. Due to the number of alleles per marker depends on the relative distance of the locus from the centromere (in which high genetic variation occurs in the non-centromeric regions than the centromeric regions of chromosomes), and the factors for the formation of related motifs and repeated number of allele frequencies were the result for the genome values reduction [50]. However, the level of the three genomes showed an insignificant difference in diversity indices, indicating the presence of similar evolutionary history in the Ethiopian bread wheat germplasm for the current study [8].

In the present work, the mean value of MAF (0.67) per marker ranged from 0.47 (WMC_24) to 0.91 (Xgwm_285), and the mean value of PIC (0.34) ranged from 0.15 (Xgwm-285) to 0.55 (WMC_24), coincides with the previous [22] and [82] findings for the frequency of the allele (an allele with the highest frequency in the one locus) with an average of 0.56 was varied from 0.33 (Xgwm129) to 0.75 (Xgwm540) in their genetic diversity study. So, the polymorphic index showed discriminatory power for every SSR in the present study with respect to the number and relative frequency of each allele, though some of the SSRs were equally similar alleles, due to different frequency of same alleles, and suggests application of different polymorphic indices. Overall, PIC values increased commensurate with increasing heterozygosis at a locus. Anyway, because scarce alleles have less effect on the PIC values than common alleles, this trend was not consistent with diversity study [83]. Hence, the primers applied for the current work were informative and can be utilized for genetic diversity and population structure study likely for future breeding and conservation activity in bread wheat as [40] reported the PIC value range for marker between (0.25 and 0.5; where PIC ≤ 0.25) is a slightly informative, if PIC was (0.5 > PIC > 0.25) is an informative, and if PIC was > 0.5 it is highly informative for the study of genetic diversity. Higher polymorphism among individual germplasm was observed in the current work due to the variation in the DNA sequences present in the chromosomes, as higher polymorphic bands of the primers were efficient to study genetic diversity and discrimination of genotypes in genetic conservation [33; 51], and also [62] evaluated 16 durum wheat cultivars using 7 SSR markers with high PIC values. Thus, the SSRs were efficient in a lot of previous researches as in the current work due to the fact that they are locus specific, ease of use, co-dominant nature, and highly polymorphic [41; 42; 52]. Then they are appropriate for marker-assisted selection, identifying quantitative trait loci, genetic diversity, and labeling of stress-tolerant genes in wheat or wild relatives [42; 43]. The PIC values in the previous study of Bulgarian winter wheat as in the present study showed a range between 0.10-0.81 [42], and [44] similarly revealed the mean value of PIC (0.51) to study the 49 SSR primer pairs isolated from bread wheat genome.

Though a maximum of two alleles are expected per individual plant at a single microsatellite locus in diploid species, in the present study the observed heterozygosity was zero for all the loci, while the average value 0.34 of expected heterozyosity showed a wider range from 0.06 (xgwm_285) to 0.48 (WMC_24) (Table 4). The average allelic richness across the four populations in the current study was 2.82 highest for WMC_24, followed by 2.07 for WMC_216, and 2.00 for each of Xgwm_3 and Xgwm_129, with frequencies in that order (Table 6). Similarly, two loci Xgwm_285 and WMC_216 with scores of (0.97) and (0.33), respectively, revealed a considerable higher private allele for both commercial germplasm as well as for COV population besides that all the alleles (rare) are common alleles with the frequency of greater than 5% (Table 7). In this regard, all the studied populations showed highest allelic richness, and hence are more interesting in terms of genetic and evolutionary studies for future bread wheat breeding and conservation [73]. Moreover, the COV population showed higher proportion of private alleles that indicates certain level of independent evolution of their gene pools that allowed maintenance of private alleles at a population level [33; 84]. Inaddition, evaluation of populations allelic richness (allelic diversity) and private allelic richness (private allelic diversity) is an alternative criterion to detect the extent of genetic diversity particularly in populations with different size, and hence, their long-run evolutionary potential that especially targets conservation and management programs [85; 46], since the effects of selection is limited to the initial allelic composition than allelic frequencies or levels of heterozygosity [45]. Moreover, the measure of allelic richness is powerful for inferring the evolutionary histories of populations [58] and to test reductions in population size [92] as it is more sensitive to the presence of rare alleles [93] which is prominent in this study, and population bottlenecks (the current status of the crop) compared to expected heterozygosity. Similarly, [57] reported 83.5% polymorphisms generated by 80 SSRs on genetic diversity and population structure of F3:6 Nebraska winter wheat genotypes using genotyping-by-sequencing; as, [88] reported a total of 86 bands using 10 ISSR primers, in which the percentage of polymorphic bands ranged between 60 and 100, with an average value of 80.2% (much lower than the current result for PPL was 89.28% using 7 SSR markers) indicated high level of genetic diversity among four population of bread wheat. This might be due to the germplasm were derived from hybridization from different history and/or the local farmers may be responsible for the allele exchange during breeding, besides that the germplasm could be explained by the broader spectrum of alleles initially acquired from subsequent genetic recombination and this could practically have broadened the genetic base of the national breeding programs via introduction of new allele to the genotypes derived from hybridization.

The presence of relatively higher average Nei’s gene diversity (0.36) and PIC (0.34) in the current study of germplasm using SSR markers (Table 4) was an indication for higher resolution property. Genetic diversity (Nei’s gene diversity) was higher in the current study compared to the previous studies of [3] obtained a mean value of PIC (0.12) in the study of 337 durum wheat accessions collected from more than 30 countries, and [57] reported the mean values of gene diversity (0.30) and PIC (0.23) in 250 winter wheat accessions study by sequencing platform. The higher values of gene diversity and PIC in most of the germplasm currently studied, as was a higher level of heterozygosity range from 0.06 to 0.44 (Table 4), might be due to the out-crossing character of the crop, and hence it shows that the materials studied were segregating, and can also strengthen the unresolved and ongoing argument of Ethiopia as the center of origin or domestication of bread wheat [3].

Currently studied loci revealed differences between Ho and He in which all of them showed excess heterozygosity that led to a significant departure from HWE across germplasm as well as populations (Table 4&5). Such excess heterozygosity is expected in historically outcrossing species that can maintain their heterozygosity through reproduction, or if other factors such as natural and artificial selection pressure favors heterozygosity or minor genotyping errors like null alleles as in the current study detected might have been contributed [33; 89]. In addition, Fisher’s exact test, assuming HWE and collapsing less frequent alleles revealed a significant (p<0.05)) and relatively higher (12%) pairwise genotypic LD compared to other cereal crops like maize (Zea mays L.) (9.7%) [95]. If the loci are not linked, the observed higher LD could be an effect of currently declining population size, a low recombination rate or natural selection [53] or genetic isolation between populations because of the usually practiced reproductive methods unlike outcrossing populations that are assumed to have relatively low LD [86]. Hence, the SSRs markers in general and the developed loci in particular are powerful in detecting the breeding nature of wheat which is the key for further breeding programs and conservation measure.

The SSR marker detected a wide range of genetic variation among the entire populations studied currently, and the PPL range (87.71 to 100) across populations was more or less wider (Table 5), this range indicates, the presence of considerable number of loci for the SSR at the informative level as in the [40] suggestions. Thus, a thorough screening procedure was applied to identify highly variable polymorphic loci suitable to group the crop genetic resources into certain classes for efficient conservation, genetic study, and breeding programs. In addition to the extent and pattern of genetic variability indices showed a vast diversity among most of the germplasm currently studied, the I and He based on SSR marker and pedigree information showed higher gene diversity among the four populations. This could be due to the presence of recombination nature of the hybridized and breeding lines. Most of all, the reason why genetic diversity is larger in hybridized cultivars than in pipe line germplasm may be due to breeding strategy and breeders’ efforts made during variety development. Generally, the current results suggest the SSR marker to be utilized to detect genetic variation and varietal identification, and also the germplasm and lines identified by can further be used to develop sergeant materials, in addition the SSRs markers can be utilized to follow inheritance.

Patterns of genetic diversity within and among populations

Information on the extent and amount of genetic diversity that exist in crop plants plays a significant role in the development of breeding strategies and designing future conservation practices for agricultural crops. Maintained genetic diversity in crop plants could also help the crops populations to evolve and cope up themselves with the current environmental changes by maintaining the sustainability of crops species in agricultural production system. Genetic diversity serves as the foundation for adaptation and speciation, serving as the "brick" of evolution [71], if there is little or no genetic diversity with cultivated crops, the probability of the crops to cope up with the changing environment and susceptibility to wide spread disease will aggravate. In line with these, efforts have been made by many scientists to investigate crop genetic diversity using different markers system and generated considerable amount of information about genetic diversity that existed in conserved or actively utilized genotypes [72]. Genetic diversity in cultivated crops is essential for successful breeding and creation of new cultivars. Estimating the genetic diversity of wheat germplasm can help in identifying diverse parental combinations and creating segregating progeny with high genetic variability for selection [33; 47]. A narrowed gene pool increases the development of risk factors as an increase in yield and disease resistance could be provided by expanding the genetic diversity of bread wheat [87].

Though very narrow ranges of within and among populations was for Ne, He, Gd and I in the present study, it was relatively higher for EBWAT population, This could be due to a relatively narrow genetic basis of the populations that resulted from limited germplasm resources accessible to farmers, or due to reduction in population size due to natural and/or human factors. The PPL highest value (89.28%) among populations (Table 5) was by far greater than the previous results of PPL by [56&55] was (19%) and (81%) among populations, respectively. Though, all the currently studied populations showed highest allelic richness across the entire loci (Table 6), the private allelic richness was zeros for all populations, except for COV population with (0.97) and (0.33) recorded only at two loci XGWM_285 and WMC_216 (Table 7), respectively. Overall, the genetic diversity indices in the current study showed narrow ranges owing to the populations' genetic foundation and intense selection pressure for the COV, EBWYT, EBWAT, and EBWNVT in that order might be suggested as a suitable selection for breeding materials. Thus, the coupling of higher PPL with the PIC provides a powerful discriminatory power of a locus [33; 88], and the allelic diversity suggest great potential of the SSR marker for use in future genetic diversity studies. Therefore, SSR marker approved the presence of higher genetic variation among Ethiopian bread wheat germplasm (Table 4). That is, 98% allelic diversity was contributed by the within populations (Table 8) for molecular diversity among populations due to the sources of collection depicting shared alleles among them. The reason could be due to high sexual recombination within the population, and high gene flow among populations, and the lower proportions of among population genetic variations, conversely higher within population genetic variations were reported in previous studies by [55]. Genetic diversity is considerably influenced by gene flow, which encompasses several mechanisms of gene exchange among populations [33; 84]. It signifies that there was no prior significant variation in molecular diversity among populations based on the sources of collection depicting shared alleles among them as [55] observed highest proportion of 81% within population variation though was insignificantly 19% variation among populations. Thus, highest within genetic variation was observed than among populations indicated that the populations were constituted by genetically distinct individual Ethiopian bread wheat germplasm, due to the partially allogamous nature of bread wheat for the presence of high genetic variation for within populations. The other reason could be due to the fact that the pollen of bread wheat can easily move by insect pollinators like bees and beetles causing outcrossing of the genotypes [3; 33]. The alternative possible reason may be due to the inbreeding history of the cultivars with which primarily experienced artificial selection and secondarily natural selection for some desirable traits. Hence, the current work helps breeders to accelerate bread wheat improvement by addressing the patterns of genetic variation within bread wheat germplasm and maximize the level of variations present in segregating populations by crossing germplasm with greater gene distance.

Patterns of genetic differentiation within and among populations

The current AMOVA (Table 8) showed a relatively higher (2.08) within populations variation than among populations (0.11), may be attributed to the presence of germplasm collected from diverse’ geographic locations of 14 zones that can be grouped in to three major bread wheat producer regions (Amhara, Oromia, and Tigray) of Ethiopia. Similarly, highly significant (98%) variation was observed within population, having only (2%) among populations variation (Table 8), due to highly sexual recombination property of within population. Similarly, higher gene flow was observed within than among populations, as a result of highly significant (p< 0.001 with low Fst=0.003) values of genetic differentiation was observed within population than among population in which a very low significant (P < 0.001 with higher Fst = 0.023), hence higher genetic diversity resulted within than among population, due to the fact that gene flow increased with lower Fst was for within population than among populations as it is inversely proportional to the genetic differentiation [63].

The present AMOVA result showed a higher pair-wise combination (Fst) averaged across all loci (Fst=0.023) among populations, and the Nei’s gene distance as [59] pair-wise F_STfor all pairs of populations (ranged from 0.03 to 0.12) (Table 9), revealed moderate as in the [60] and [61] suggestions that the levels were low for the range (0.00-0.05), moderate (0.05-0.15), and high (for > 0.15). Thus, genetic differentiation among populations was moderate for the value of F_ST [60]. This could be if gene flow partly attained, which is a powerful force to decrease differentiation among populations, is low (Nm<1) [84] or if genetic drift removes rare/scarce alleles, and it increases private alleles within populations [89]. Hence, the present study showed that Ethiopian bread wheat has very little population sub-structuring. The presence of low genetic differentiation among population was supported by high gene flow (mean Nm=1.62) (Table 4) owing to hsitorical step-wise pollen movement across populations, contemporary germplasm exchange largely in the form of seeds through sharing common markets among several of the adjacent areas where different populations were collected. Similarly, this study also showed the minimal effects of their sources or origins of populations on genetic variation in Ethiopian bread wheat. This could be partly explained by the extensive exchange of seeds as planting materials among farmers (gene flow), common origin of the populations, the reproductive nature of the crop in which only a limited number of individuals contribute seeds to the next generation, which gradually leads to recent or old population bottlenecks and hence facilitate genetic drift [33; 55] to serve as potential sources of new genetic variation of important traits that can be used in breeding programs.

Accordingly, EBWAT population showed relatively higher (0.12) pairwise Nei’s gene diversity with COV (Table 9), thus the two populations showed the most genetically distinct populations. This can be partly due to the EBWAT and COV populations were collected from a relatively distant genetic background, and it was supported by UPGMA dendrogram cuerrently revealed (Figure 2) in which they were derived each alone from the other populations with a relatively wider distance that probably may be due to restricted from recent seed exchange. Hence, these populations may serve as a potential sources of new genetic variation for important traits that can be used in further breeding programs and as a potential parental sources. Inaddition to that the germplasm were derived from a pair wise differentiation between EBWAT and COV populations, thus it could likely to result with relatively higher value of pairwise differences (Pix), and such similarly might be due to higher within population differntiation attributed to the larger number of sources, and hence, the result imply a large amount of genetic diversity of the crop in this population to be preserved.

Patterns of genetic clustering

The NJ cluster analysis revealed a complex varietal distribution pattern with no clear grouping of the 96 germplasm studied based on their pedigree and source. Thus, the germplasm were divided into 3 clusters each containing 76, 12, and 8 germplasm on the basis of NJ genetic distance matrix (Figure 1), inferred the relatedness among the studied germplasm were gathered (from all source populations) and found in each cluster lumped together without being differentiated across their sources. Likely, the previous literature, have been reported for diversity analysis and genetic variability determination [33; 91]. Thus, the NJ analysis showed the germplasm with more similar microsatellite loci were found/placed (mixed with the pipeline and commercial germplasm) in each cluster (Figure 1). Unlikely, the germplasm from similar source/genetic background were expected to exist in a given cluster as in the previous work of [49] using NJ found the cultivars with more similar microsatellite loci (closely related germplasm) were placed in the same cluster, and also some of the germplasm that showed higher dissimilarity in microsatellite loci were placed in different clusters. However, the presence of germplasm in a given cluster is a means of representing similarity in the pedigree of microsatellite loci and this uniformity was expected to help the researchers as an indication for the relative genetic similarity of the germplasm. In fact, the type of growth cannot be a factor for the difference between the germplasm to avail in one cluster because the region that controls the type of growth is a small portion of the whole genome and, hence germplasm with different growth type is not acceptable to be placed in one group; nevertheless, such NJ clustering did not indicate any clear divisions among the bread wheat germplasm based on their sources. Thus, in the current work, the distribution of germplasm from similar source into different clusters might indicate the existence of varietal diversity within populations. As a result, the distribution of commercial genotypes and pipelines found mixed in each cluster might indicate that the germplasm gathered from their source/pedigree were more diverse. Hence, the distribution and pattern of germplasm, over all the clusters different from their source, would suggest future collections of the germplasm out of their source/pedigree, in agreement with the SSR and DArT work on Ethiopian lupine for unique gene pool as [26] this signifies that the Ethiopian bread wheat germplasm were very distinct and with separate grouping/gene pool than others.

The patterns of relationships among the four populations using UPGMA dendrogram generated on Nei’s genetic distance matrix, showed two major clusters (Figure 2) the C-I (COV) and C-II (EBWNVT) were originated from the primary branch showed no significant admixture of germplasm due to the absence of gene flow between the studied populations. While, the third cluster (C-III) was further subdivided in to two sub-clusters C-III-I for (EBWYT) and C-III-II for (EBWAT) which was originated from the sub branch but not from the primary branch, thus resulted in the intermixture of the germplasm. As a matter of fact, the germplasm used for the current study were selected according to the nature/source where they were (Supplementary material S1), thus the germplasm derived from the COV population were under the released types, while the other three populations were from the pipeline types, letting the COV and EBWNVT population showed no admixture of genotypes; whereas, for the sub cluster were the derivatives, hence it could result in the intermixture of germplasm. Generally, there was a good correspondence between the population genetic clustering and the population structure identified. Similarly, the current NJ (Figure 1) three clustering and UPGMA based genetic distance (Figure 2), for the sub clusters showed a strong relationships among bread wheat germplasm in which most of the germplasm were found mixed in each cluster without considering their prior breeding information or source populations. The possible reason for grouping of these germplasm from different populations into the same cluster could be due to the breeding objectives designed by the breeders in Ethiopia, and their breeding objectives of bread wheat where ultimately designed to improve the germplasm for their yield, resistance for biotic and abiotic factors, and recently for seed size [46]. Therefore, these common objectives could make the materials to carry similar gene responsible for yield, resistance to biotic and abiotic, and seed size. The other reason could be due the common ancestral genetic base existed among populations each other. Hence, the clustering pattern once again showed a low genetic differentiation among populations. However, few of the germplasm were remained within their source populations without spreading all over the clusters forming the strict grouping. This might be due to out-crossing nature of the floral biology of bread wheat that has its own impact on the intermixing of germplasm from different genetic information into similar cluster. Similar results were reported by authors [33] and [63-65].

The NJ tree-based analyses (Figure 1), the genetic distance based UPGMA population dendrogram pattern (Figure 2) was maintained among the four major populations as in the population structure analysis (Figure 3b). Thus, all the three analytical results found majority of the germplasm mixed in each cluster without following their genetic background/parental information. In this regard, cluster three (C-III) (Figure 1) in the NJ result found mixed germplasm in each sub clusters; suggest selection of parental lines from different sub-populations might be an effective way for making hybrid combinations.

Patterns of population genetic structure

The results of simple matching dissimilarity coefficient (NJ) tree over the 96 genotype clusters, the genetic distance between four population clusters using UPGMA dendrogram generated, and the Structure analysis confirmed the presence of high genetic relationships among the studied wheat populations. Similarly, the Bayesian based genetic structure proved the presence of optimally two distinct and clear clusters (Figure 3b), with higher admixture of different gemplasm collections in each cluster due to the presence of higher gene flow. The current PCoA also confirmed the presence of higher genetic variation within populations than among populations, where the individuals of different populations failed to form distinct clusters [100]; rather they were mixed up along the three axes. Thus, the PCoA revealed three clusters (Figure 4), where none of the clusters were composed of entirely germplasm from a particular population, indicating the existence of significant mixture of germplasm gathered in each clusters from different genetic background within populations than among populations. Likewise, the results of NJ, and Structure analysis supported the PCoA, confirming the presence of high genetic relationships within the studied wheat populations, might be due to the presence of higher gene flow [55]. The Bayesian model statistics (ΔK) developed by [34], a sharp peak in ΔK at K = 2 was observed, and found two sub groups (Figure 3a), indicated the analysis of K = 2 populations consisted of individual germplasm gathered from the four source collections distributed between the two populations. The Clumpak result (bar plot) (Figure 3b) detected a greater degree of genetic admixture between the four populations, the pattern of the model-based grouping revealed a significant admixture among the four populations which was somehow congruent with NJ tree of the clusters, UPGMA dendrogram of the populations, and the PCoA results (Figure 4). Similarly, the genetic relationship in the populations structural analysis showed a close relationship (weak subdivision) among the samples from the four populations, and in general, two inferred population clusters, for (K=2), with a potential admixtures of genotypes have been observed. It is interesting to indicate that all individual plants have alleles originated from the four population clusters, which supports the presence of no gene flow that led to good population differentiation. The materials used in the present study showed a certain degree of admixture indicating the introduction of chromosomes from different ancestry and allele frequency. Therefore, the possible factors for such admixture could be differential selection, mutation effect, and an out crossing nature of the crop. Furthermore, this could give a clue for the Ethiopian bread wheat germplasm/pipelines could be a significant factor for genetic variation, and hence this plays a vital role for the development of improved varieties that can withstand the ever-changing environmental factors. Similarly, [27], [33] and [49] reported the lines within a group or sub-group showed a low level of genetic differentiation, and hence the crosses between genetically divergent lines selected from different populations or sub-populations can be suggested to produce better-performing heterotic germplasm than the closely related parents.

Implication of the study for bread wheat improvement

The study of genetic diversity is an important practice for designing relevant breeding program. The presence of SSRs based study indicated high genetic diversity among the Ethiopian bread wheat germplasm, especially with germplasm derived via hybridization. Therefore, utilization of these materials in variety development scheme will provide a sound result for selection of individuals with different important characters. The diversity parameters like gene diversity and genetic distances observed in the present study showed high genetic variability among the bread wheat germplasm and considering these parameters in a breeding program could be valuable approach. Another point that should be given due attention is the impact of released varieties for specific purpose on genetic diversity of Ethiopian bread wheat. Number of bread wheat germplasm collected from the trials at the nation or preliminary level so far showed significant amount of genetic diversity relative to the released types; however, care should be taken while popularizing and pushing these germplasm towards farming system known to have pipelines with unique features. Replacement of the local germplasm by improved once could result in narrowing down the genetic bases of bread wheat in Ethiopia. Therefore, this problem can be solved by awaking farmers by providing different options of germplasm, and the Ethiopian gen bank should give due attention for conserving the germplasm to maintain the genetic diversity of germplasm.

Out of the 15 SSRs generated or examined, a total of 7 SSRs markers showed reproducible polymorphisms. The degree and distribution of genetic variation among the 96-bread wheat (45 released and 54 pipelines) germplasm were estimated. As a result, the mean value of PPL was 89.28% in all the studied germplasm. The average Nei's genetic diversity (0.36) and PIC (0.34) were both quite high, according to SSR markers. Overall, the SSR markers utilized in this study showed significant genetic variation among the Ethiopian bread wheat germplasm. In particular, the SSR marker demonstrated the presence of highly genetic diversity among the many bread wheat kinds farmed in Ethiopia. Hence, the availability of genetic diversity within population is crucial for developing better breeding strategies for bread wheat that will improve their genetic makeup and meet the producers' ultimate goals while also benefiting both current and future generations. Sustainable conservation and utilization of Ethiopian bread wheat genetic resource is key for future breeding strategies in Ethiopia and worldwide. Clustering analysis showed a higher genetic admixture between all the germplasm as well as the populations studied, despite their breeding history resulted from the existence of higher rate of historical seed exchange throughout the country. The SSR markers showed a high level of polymorphism and considered as enough informative marker in bread wheat genetic diversity and population structure study, thus these primers suggested in the studies of genetic diversity, genomics and evolutionary studies, genome mapping and gene tagging of more bread wheat types. The genetic diversity levels observed in Ethiopian bread wheat would be useful indicators with the genetic capacity to tolerate various stresses could be used as a source of unique alleles in the enhancement of bread wheat breeding through marker assisted selection or marker assisted backcrossing. This makes genetic diversity estimates as a potentially valuable predicting source for selecting diverse parent germplasm for favorable heterotic combinations in wheat improvement program. Hence, sustainable conservation and utilization of Ethiopian bread wheat genetic resource is key for future breeding strategies in Ethiopia and worldwide.

AMOVA: Analysis of molecular variance; BIGMP: biodiversity and integrated gene management program; COV: commercial varieties; EBWAT: Ethiopian bread wheat preliminary verification adaptation trial; EBWNVT: Ethiopian bread wheat national verification trial; EBWYT: Ethiopian bread wheat preliminary verification yield trial; ICARDA: International center for agricultural research in dry land areas; MAF: major allele frequency: NJ: Neighbor joining; PCoA: principal coordinate analysis; PIC: polymorphic information content; SSRs: Simple sequence repeats; and UPGMA: Unweighted pair group method with arithmetic mean.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

No, I don't have any research data outside the submitted manuscript file.

Competing Interests

The authors certify that the publishing of this paper does not involve any conflicts of interest.

Funding

The research was supported by Addis Ababa and Gondar Universities research and community service engagement office through the universities postgraduate program, and the ICARDA was providing the internship to accomplish both the green house to grow the samples to the whole DNA laboratory work.

Authors' contributions

AT, TF and KD conceived and designed the study. KD and AT assembled the panel and participated in genotyping. AT prepared the manuscript and carried out the data analysis. All co-authors participated in interpreting the data, revising and editing the manuscript and approved the final version of the manuscript.

Acknowledgements

The authors would like to acknowledge the Adet and Kulumsa national agricultural research centers for providing the germplasm. To Dr. Kifle Dagne, who was my one of fatherly advisor deceased two years ago, I am very sorry. He was exhausting his time from the day of proposal development to the accomplishment of this work.

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No competing interests reported.

Supplementaryinformation.docx

Genetic diversity and population structure analysis of Ethiopian bread wheat (Triticum eastivum L.) germplasm using SSR markers

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Abstract

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Materials and Methods

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Supplementary Files

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