3-1- The results of analyzes related to population genetics
The AMOVA test was performed to estimate molecular diversity between individuals of different populations and regions. The results showed that of the total diversity, 42% related to intra-population diversity and 58% related to inter-population diversity (Table 3).
Table 3
Analysis of Molecular Variance (AMOVA)
Source
|
df
|
SS
|
MS
|
Est. Var.
|
%
|
Value
|
P(rand > = data)
|
Among Pops
|
19
|
847.517
|
44.606
|
11.996
|
58%
|
|
|
Within Pops
|
40
|
344.667
|
8.617
|
8.617
|
42%
|
|
|
Total
|
59
|
1192.183
|
|
20.613
|
100%
|
|
|
PhiPT
|
|
|
|
|
|
0.582
|
0.010
|
3-2- Evaluation of genetic structure
Analysis of the data obtained from the ISSR indicator
All fragments ranging from 150 bp to 2500 bp were evaluated. Additionally, all primers successfully generated bands. A total of 133 bands were produced, with 68 being polymorphic and 65 being monomorphic. On average, each primer yielded 6.8 polymorphic bands. The average percentage of polymorphism among the 10 primers was 52.92%. The UBC-810 and (GA) 9T primers exhibited the highest level of polymorphism, with 66.66%, while the (GA) 9C primer showed the lowest level at 37.5%. The (AGC) 5GG and (GA) 9A primers produced the highest number of bands (22), whereas the UBC-810 and (GA) 9T primers produced the lowest number (6). The smallest amplified fragment, 150 bp, was associated with the (AGC) 5GG and (GA) 9A primers, while the largest fragment, 2500 bp, was linked to the (GA) 9A primer. The (AGC) 5GG and (GA) 9A primers had the highest number of polymorphic bands (11), while the (GA) 9C primer had the lowest number (3). The primers (GA) 9A, (AGC) 5GA, UBC-834, (AGC) 5GG, and UBC-811 exhibited the highest levels of polymorphic information, with values of 0.98, 0.91, 0.84, 0.75, and 0.57, respectively (Table 4).
Table 4
Results of investigating ISSR marker primers in Artemisia sieberi populations
Primer
|
The range of the reproduced piece
(bp)
|
Length of the largest amplified fragment (bp)
|
Length of the smallest amplified fragment (bp)
|
Total number of bands
|
Number of polymorphic bands Polymorphic
|
Polymorphic percentage
|
Polymorphic Information Content
|
UBC-810
|
200–700
|
700
|
200
|
6
|
4
|
66.6
|
0.01
|
UBC-811
|
200–850
|
850
|
200
|
10
|
4
|
40
|
0.57
|
UBC-834
|
200–750
|
750
|
200
|
12
|
7
|
58.33
|
0.84
|
(AGC)5GG
|
150–2100
|
2100
|
150
|
22
|
11
|
50
|
0.75
|
(AGC)5GC
|
200–2200
|
2200
|
200
|
18
|
9
|
50
|
0.09
|
(AGC)5GT
|
300–1500
|
1500
|
300
|
8
|
5
|
62.5
|
0.01
|
(AGC)5GA
|
210–2500
|
2500
|
210
|
21
|
10
|
47.61
|
0.91
|
(GA)9C
|
200–650
|
650
|
200
|
8
|
3
|
37.5
|
0.15
|
(GA)9T
|
200–700
|
700
|
200
|
6
|
4
|
66.66
|
0.01
|
(GA)9A
|
150–2500
|
2500
|
150
|
22
|
11
|
50
|
0.98
|
|
|
|
|
133
|
68
|
52.92
|
0.43
|
Genetic parameters such as Shannon's index, genetic diversity based on Nei's coefficient, and the number of effective alleles were calculated for each primer. The results indicate that primer (AGC) 5GG had the highest value for Shannon's index (0.185), genetic diversity based on Nei's coefficient (0.124), and number of effective alleles (1.213). On the other hand, primer (AGC) 5GT had the lowest values for Shannon's index (0.026), genetic diversity based on Nei's coefficient (0.018), and number of effective alleles (1.032) (Table 5).
Table 5
Results of studying the genetic diversity in populations of Artemisia sieberi using the primers.
Primer
|
Na
|
Ne
|
I
|
He
|
UHe
|
UBC-810
|
0.992
|
1.112
|
0.090
|
0.062
|
0.075
|
UBC-811
|
0.700
|
1.066
|
0.058
|
0.039
|
0.047
|
UBC-834
|
0.775
|
1.145
|
0.118
|
0.081
|
0.097
|
(AGC)5GG
|
0.857
|
1.213
|
0.185
|
0.124
|
0.149
|
(AGC)5GC
|
0.589
|
1.109
|
0.091
|
0.062
|
0.075
|
(AGC)5GT
|
0.838
|
1.032
|
0.026
|
0.018
|
0.021
|
(AGC)5GA
|
0.688
|
1.102
|
0.089
|
0.060
|
0.072
|
(GA)9C
|
0.938
|
1.197
|
0.150
|
0.105
|
0.126
|
(GA)9T
|
1.158
|
1.146
|
0.107
|
0.076
|
0.091
|
(GA)9A
|
0.739
|
1.148
|
0.126
|
0.085
|
0.102
|
Mean
|
0.827
|
1.127
|
0.104
|
0.070
|
0.085
|
Na: Number of Alleles, Ne: Number of Effective Alleles, I: Shannon's Information Index, He: Expected Heterozygosity, UHe: Unbiased Expected Heterozygosity |
Genetic diversity was assessed in 20 populations. Among these populations, Abbas Abad and Separ Rostam exhibited the highest values of genetic diversity, as indicated by Shannon's index (0.173 ± 0.024 and 0.164 ± 0.023, respectively), genetic diversity based on Nei's coefficient (0.120 ± 0.017 and 0.111 ± 0.016, respectively), observed alleles (0.902 ± 0.071 and 0.910 ± 0.070, respectively), effective alleles (1.193 ± 0.029 and 1.217 ± 0.032, respectively), and percentage of polymorphism (28.57%). On the other hand, Avel and Karkesh populations demonstrated the lowest values for these parameters: Shannon's index (0.048 ± 0.015 and 0.051 ± 0.014, respectively), genetic diversity based on Nei's coefficient (0.034 ± 0.010 and 0.034 ± 0.010, respectively), observed alleles (0.647 ± 0.054 and 0.632 ± 0.056, respectively), effective alleles (1.058 ± 0.017 and 1.064 ± 0.020, respectively), and percentage of polymorphism (7.52% and 9.02%, respectively) (see Table 6).
Table 6
Diversity and genetic parameters of various populations of Artemisia sieberi in Qom Province.
Population
|
Na
|
Ne
|
I
|
He
|
PPL %
|
ACH
|
0.850 ± 0.070
|
1.188 ± 0.030
|
0.155 ± 0.023
|
0.106 ± 0.016
|
26.32
|
SFA
|
0.850 ± 0.072
|
1.202 ± 0.031
|
0.165 ± 0.024
|
0.113 ± 0.016
|
27.82
|
SPR
|
0.910 ± 0.070
|
1.193 ± 0.029
|
0.164 ± 0.023
|
0.111 ± 0.016
|
28.57
|
KAJ
|
0.744 ± 0.060
|
1.097 ± 0.022
|
0.082 ± 0.018
|
0.056 ± 0.012
|
14.29
|
LHP
|
0.812 ± 0.070
|
1.185 ± 0.030
|
0.149 ± 0.023
|
0.103 ± 0.016
|
24.81
|
CHS
|
0.880 ± 0.070
|
1.195 ± 0.030
|
0.160 ± 0.023
|
0.109 ± 0.016
|
27.07
|
BQK
|
0.759 ± 0.064
|
1.133 ± 0.026
|
0.108 ± 0.020
|
0.074 ± 0.014
|
18.05
|
ABA
|
0.902 ± 0.071
|
1.217 ± 0.032
|
0.173 ± 0.024
|
0.120 ± 0.017
|
28.57
|
MNZ
|
0.699 ± 0.059
|
1.094 ± 0.023
|
0.076 ± 0.018
|
0.052 ± 0.012
|
12.78
|
VEN
|
0.872 ± 0.064
|
1.130 ± 0.024
|
0.116 ± 0.020
|
0.077 ± 0.014
|
21.05
|
QLC
|
0.789 ± 0.065
|
1.111 ± 0.022
|
0.104 ± 0.019
|
0.068 ± 0.013
|
19.55
|
MHZ
|
0.850 ± 0.068
|
1.158 ± 0.027
|
0.136 ± 0.022
|
0.092 ± 0.015
|
24.06
|
TJK
|
0.737 ± 0.060
|
1.089 ± 0.021
|
0.079 ± 0.017
|
0.053 ± 0.012
|
14.29
|
TLB
|
0.759 ± 0.072
|
1.177 ± 0.029
|
0.146 ± 0.023
|
0.100 ± 0.016
|
24.81
|
AVL
|
0.647 ± 0.054
|
1.064 ± 0.020
|
0.048 ± 0.015
|
0.034 ± 0.010
|
7.52
|
KRK
|
0.632 ± 0.056
|
1.058 ± 0.017
|
0.051 ± 0.014
|
0.034 ± 0.010
|
9.02
|
MYM
|
0.617 ± 0.057
|
1.066 ± 0.019
|
0.056 ± 0.015
|
0.038 ± 0.010
|
9.77
|
KHK
|
0.865 ± 0.067
|
1.158 ± 0.027
|
0.136 ± 0.022
|
0.092 ± 0.015
|
24.06
|
SRM
|
0.722 ± 0.063
|
1.106 ± 0.023
|
0.93 ± 0.018
|
0.062 ± 0.013
|
16.54
|
ABN
|
0.677 ± 0.056
|
1.058 ± 0.017
|
0.053 ± 0.14
|
0.035 ± 0.010
|
9.77
|
Mean
|
0.779 ± 0.015
|
1.134 ± 0.006
|
0.112 ± 0.005
|
0.076 ± 0.003
|
19.44 ± 1.61
|
Na: No. of Different Alleles, Ne: No. of Effective Alleles, I: Shannon's Information Index, He: Expected |
Cluster analysis of genetic diversity using the Jaccard similarity index
After initializing the zero-one matrix, the similarity matrix of the genotypes was obtained using the Jaccard coefficient (Fig. 2). Based on the degree of similarity, the populations were categorized into three distinct groups. Notably, the highest level of similarity was observed among populations belonging to the third group. The first group consisted of populations ACH, SFA, and SPR, the second group comprised populations KAJ, LHP, CHS, and BQK, while the third group encompassed populations ABA, MNZ, VEN, QLC, MHZ, TJK, TLB, AVL, KRK, MYM, KHK, SRM, and ABN.
Figure 2. Cluster analysis of 20 populations of Artemisia sieberi in various habitats of Qom province, using the Jaccard coefficient.
Cluster analysis of genetic diversity using the UPGMA method
The Cophenetic coefficient was calculated (0.87) and a dendrogram was obtained according to the similarity matrix using the UPGMA method. The genotypes were placed in three separate groups as follows (Fig. 3): The first group comprised populations ACH, SFA, and SPR; the second group comprised populations KAJ, LHP, CHS, and BQK and the third group comprised populations ABA, MNZ, VEN, QLC, MHZ, TJK, TLB, AVL, KRK, MYM, KHK, SRM, and ABN.
According to the diagram, the highest similarity between populations was observed in the third group. Thus, the similarity matrix of the UPGMA method and the Jaccard similarity coefficient showed parallel results. According to the dendrogram of genetic diversity based on the Jaccard similarity coefficient and the UPGMA method, these two populations were categorized in the same group. The lowest similarity (0.607) occurred between two populations Kaj and Sarm, which were categorized in two separate groups, according to the graphs obtained from the similarity coefficient (Fig. 3).
Figure 3. Dendrogram based on a similarity matrix using the UPGMA method for 20 Artemisia sieberi populations in different habitats of Qom province
Genetic similarity matrix by Nei’s method
The genetic similarity matrix, based on Nei's coefficient, demonstrated that populations exhibited similarity values ranging from 0.607 to 0.934. These values reflect the genetic diversity both within and between the populations. Specifically, the similarity matrix revealed that populations Karkesh and Mim exhibited the highest level of similarity, with a coefficient of 0.934 (Table 7).
Table 7
Genetic Similarity Matrix using the Nie method
Pop1
|
Pop2
|
Pop3
|
Pop4
|
Pop5
|
Pop6
|
Pop7
|
Pop8
|
Pop9
|
Pop10
|
Pop11
|
Pop12
|
Pop13
|
Pop14
|
Pop15
|
Pop16
|
Pop17
|
Pop18
|
Pop19
|
Pop20
|
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop1
|
0.890
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop2
|
0.787
|
0.873
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop3
|
0.759
|
0.771
|
0.818
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop4
|
0.714
|
0.757
|
0.792
|
0.799
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop5
|
0.703
|
0.738
|
0.786
|
0.789
|
0.881
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop6
|
0.669
|
0.729
|
0.715
|
0.787
|
0.820
|
0.877
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop7
|
0.762
|
0.790
|
0.751
|
0.722
|
0.697
|
0.701
|
0.709
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
Pop8
|
0.768
|
0.747
|
0.713
|
0.737
|
0.713
|
0.701
|
0.694
|
0.864
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
Pop9
|
0.735
|
0.717
|
0.720
|
0.740
|
0.723
|
0.723
|
0.694
|
0.813
|
0.924
|
1.000
|
|
|
|
|
|
|
|
|
|
|
Pop10
|
0.696
|
0.668
|
0.638
|
0.649
|
0.666
|
0.683
|
0.699
|
0.813
|
0.841
|
0.862
|
1.000
|
|
|
|
|
|
|
|
|
|
Pop11
|
0.788
|
0.757
|
0.718
|
0.719
|
0.722
|
0.726
|
0.669
|
0.834
|
0.884
|
0.892
|
0.878
|
1.000
|
|
|
|
|
|
|
|
|
Pop12
|
0.761
|
0.752
|
0.702
|
0.692
|
0.678
|
0.681
|
0.668
|
0.842
|
0.869
|
0.861
|
0.847
|
0.913
|
1.000
|
|
|
|
|
|
|
|
Pop13
|
0.784
|
0.768
|
0.723
|
0.690
|
0.747
|
0.741
|
0.730
|
0.810
|
0.800
|
0.774
|
0.799
|
0.849
|
0.819
|
1.000
|
|
|
|
|
|
|
Pop14
|
0.669
|
0.617
|
0.627
|
0.637
|
0.635
|
0.674
|
0.661
|
0.722
|
0.770
|
0.787
|
0.808
|
0.778
|
0.755
|
0.734
|
1.000
|
|
|
|
|
|
Pop15
|
0.761
|
0.721
|
0.692
|
0.657
|
0.672
|
0.660
|
0.665
|
0.764
|
0.860
|
0.866
|
0.787
|
0.844
|
0.827
|
0.746
|
0.847
|
1.000
|
|
|
|
|
Pop16
|
0.746
|
0.692
|
0.668
|
0.664
|
0.664
|
0.646
|
0.664
|
0.763
|
0.838
|
0.853
|
0.807
|
0.873
|
0.843
|
0.782
|
0.840
|
0.934
|
1.000
|
|
|
|
Pop17
|
0.753
|
0.741
|
0.704
|
0.665
|
0.671
|
0.671
|
0.695
|
0.809
|
0.849
|
0.830
|
0.791
|
0.847
|
0.833
|
0.808
|
0.763
|
0.889
|
0.881
|
1.000
|
|
|
Pop18
|
0.722
|
0.676
|
0.681
|
0.607
|
0.664
|
0.670
|
0.661
|
0.804
|
0.792
|
0.765
|
0.783
|
0.791
|
0.795
|
0.772
|
0.759
|
0.836
|
0.830
|
0.910
|
1.000
|
|
Pop19
|
0.708
|
0.679
|
0.671
|
0.628
|
0.623
|
0.641
|
0.664
|
0.773
|
0.803
|
0.798
|
0.808
|
0.792
|
0.796
|
0.745
|
0.785
|
0.869
|
0.829
|
0.908
|
0.905
|
1.000
|
Pop20
|
(Pop1: Asgar Abad Cham, Pop2: Safar Abad, Pop3: Seper Rostam, Pop4: Kaj, Pop5: Laf-e Hovz Pool, Pop6: Cheshmeh Shour, Pop7: Baghak, Pop8: Abbas Abad, Pop9: Manzariyeh, Pop10: Venan, Pop11: Qaleh Cham, Pop12: Mehr Zemin, Pop13: Taj Khatun, Pop14: Tarlab, Pop15: Avel, Pop16: Karkesh, Pop17: Meyam, Pop18: Kahak, Pop19: Sarm and Pop20: Ali Abad Nizar) |
Principal Component Analysis (PCA) For genetic diversity
After conducting genetic analysis using the GenAlEx software, a principal component analysis was performed to assess genetic diversity. The results of the principal component analysis (PCA) revealed that the 20 populations of Artemisia sieberi could be categorized into three main groups, as shown in Fig. 4. The first group consisted of populations ACH, SFA, and SPR; the second group included populations KAJ, LHP, CHS, and BQK; and the third group encompassed populations ABA, MNZ, VEN, QLC, MHZ, TJK, TLB, AVL, KRK, MYM, KHK, SRM, and ABN.
Figure 4. Principal Component Analysis (PCA) of genetic data
Principal Coordinate Analysis (PCoA) of genetic diversity
After conducting genetic analyses using the GenAlEx software, the principal coordinate analysis (PCoA) was conducted using genetic diversity data. The PCoA results revealed that the 20 populations were classified into the same three groups as observed in the PCA groupings (Fig. 5). The genotypic similarity matrix, based on the Jaccard coefficient, along with the similarity matrices generated using the UPGMA, PCA, and PCoA methods, produced consistent and validated results.
Figure 5. Principal Coordinate Analysis (PCoA) based on genetic data.