Introduction
Bread wheat (Triticum aestivum L.) is hexaploid (2n=6x=42)
comprising of A, B and D genomes which has largest genome of 17 Gb with 80%
repeats (Kumar et al., 2016). Nowadays 95% hexaploid
wheat is grown in Pakistan which contributes 10% to the value added in
agriculture and 2% to GDP, whereas national yield average is 2.5 t/ha (Anonymous 2018). The common yield of wheat is pretty low due to increase in population and
also drastic changes in climatic conditions. Though, there is still need
to improvement and genetic manipulation is the best
tool to increase the production. Therefore, induced new genetic variation is the key factor and mode of inheritance in
altered plant traits to initiate constructive wheat breeding programs for
sustainable agriculture (Kharestani
et
al. 2016). Hence, induced mutation is applied as a successful
tool to increase genetic variability while physical and chemical mutagens
induce different mutation spectra and induction of new alleles in crop
species.
Molecular characterization of wheat genotypes is also beneficial to
assess the loss of genetic polymorphism and detect more variability (Kumar et al. 2016).
Simple
sequence repeat (SSR) markers for genome analysis
have many additional properties that evenly disbursed within whole genome,
co-dominant and impartial. SSR markers are used effectively to study genetic
variation in wheat germplasm (Abbasov et al. 2018). In the present
study, SSR markers were used to assess the genetic variation among thirty
promising wheat mutants, which may possibly help for the development of new
variety with wide range of genetic base in wheat breeding.
Materials and
Methods
We used 50 g pure basic seed of each variety i.e.,
Sarsabz, Kiran and TD1 for each treatment/dose for induced mutation by gamma
rays (50, 100,
150, 200, 250 and 300 Gy), EMS (0. 4, 0. 8, 1.2, 1.6 and 2.0%) and combined treatment from NIA, Tando Jam and ARI, Tando Jam due
to their yield stability and adaptability in different climatic conditions.
Control was used as non-mutagenized seeds of each variety and raised
the M1, M2, M3, M4 and M5
generation. Finally, thirty mutants were selected on the basis of improved
agronomical traits, phenotypic diversity and higher yield. Fresh young leaves were collected from field
at seedling stage from thirty mutants and DNA was isolated and quantified by
using modified CTAB method (Bibi et al.
2012).
Forty SSR primers (Table 1)
have been used to amplify thirty mutants and three parents. The
cocktail was prepared in 10 μL containing 1 μM SSR
forward and reverse primer (Gene link),1X Taq buffer, 0.1 u/μL of
Taq enzyme, 2.5 mM of MgCl2, 0.2 mM of dNTPs and 0.8
ng/μL of DNA template for PCR amplification. PCR was programmed for first
denaturation for 5 min at 95°C, followed by thirty five repeats for 1 min at 95°C, 1 min at 55°C, 1.30 min at
72°C and one last step of extension at 72°C for 07 min. PCR amplification DNA
segment were resolved by 3% agarose gel. Subsequently, gel photograph was
documented via gel documentation system of Vilber Lourmat, France.
Table 1: Simple sequence repeats (SSR)
primers for characterization of the wheat mutants
|
S. # |
Primers |
Sequence (5` to 3`) |
Temp. (°C) |
%GC |
|
1 |
WMS508 |
F: GTTATAGTAGCATATAATGGCC R: GTGCTGCCATGATATTT |
55 48 |
36 41 |
|
2 |
WMS361 |
F: GTAACTTGTTGCCAAAGGGG R: ACAAAGTGGCAAAAGGAGACA |
57 56 |
50 43 |
|
3 |
WMS193 |
F: CTTTGTGCACCTCTCTCTCC R: AATTGTGTTGATGATTTGGGG |
59 54 |
55 38 |
|
4 |
WMS644 |
F: GTGGGTCAAGGCCAAGG R: AGGAGTAGCGTGAGGGGC |
58 61 |
65 68 |
|
5 |
WMS-71 |
F: GGCAGAGCAGCGAGACTC R: CAAGTGGAGCATTAGGTACACG |
61 60 |
67 50 |
|
6 |
WMS-319 |
F: GGTTGCTGTACAAGTGTTCACG R: CGGGTGCTGTGTGTAATGAC |
60 59 |
50 55 |
|
7 |
WMS-429 |
F: TTGTACATTAAGTTCCCATTA R: TTTAAGGACCTACATGACAC |
50 53 |
29 40 |
|
8 |
Gwm361 |
GTAACTTGTTGCCAAAGGGG ACAAAGTGGCAAAAGGAGACA |
52 50 |
50 43 |
|
9 |
Gwm219 |
GATGAGCGACACCTAGCCTC GGGGTCCGAGTCCACAAC |
56 55 |
60 67 |
|
10 |
Wmc221 |
ACGATAATGCAGCGGGGAAT GCTGGGATCAAGGGATCAAT |
65 63 |
50 50 |
|
11 |
Wmc121 |
GGCTGTGGTCTCCCGATCATTC ACTGGACTTGAGGAGGCTGGCA |
69 69 |
59 59 |
|
12 |
Xcfd68 |
TTTGCAGCATCACACGTTTT AAAATTGTATCCCCCGTGGT |
60 55 |
40 45 |
|
13 |
Gwm325 |
TTTCTTCTGTCGTTCTCTTCCC TTTTTACGCGTCAACGACG |
55 63 |
45 47 |
|
14 |
Gwm179 |
AAGTTGAGTTGATGCGGGAG CCATGACCAGCATCCACTC |
52 53 |
50 58 |
|
15 |
Gwm335 |
CGTACTCCACTCCACACGG CGGTCCAAGTGCTACCTTTC |
55 54 |
63 55 |
|
16 |
Xgwm46 |
GCA CGT GAA TGG ATT GGA C TGA CCC AAT AGT GGT CA |
51 45 |
53 47 |
|
17 |
Xgwm2 |
CTG CAA GCC TGT GAT CAA CT CAT TCT CAA ATC GAA CA |
52 40 |
50 35 |
|
18 |
Xgwm18 |
TGG CGC CAT GAT TGC ATT ATC ATC TTC GGT TGC TGA AGA ACC TTA TTT AGG |
58 54 |
44 42 |
|
19 |
Xgwm33 |
GGA GTC ACA CTT GTT TGT GCA CAC TGC ACA CCT AAC TAC GTG C |
52 57 |
48 55 |
|
20 |
Xgwm5 |
GCC AGC TAC CTC GAT ACA ACT C AGA AAG GGC CAG GCT AGT AGT |
57 54 |
55 52 |
|
21 |
Xgwm44 |
GTT GAG CTT TTC AGT TCG GC ACT GGC ATC CAC TGA GCT G |
52 53 |
50 58 |
|
22 |
Xpsp2999 |
TCC CGC CAT GAG TCA ATC TTG GGA GAC ACA TTG GCC |
50 50 |
56 56 |
|
23 |
Xpsp3000 |
GCA GAC CTG TGT CAT TGG TC GAT ATA GTG GCA GCA
GGA TAC |
54 52 |
55 48 |
|
24 |
Xcn15 |
GGT GAT GAG TGG CAC AGG CCC AAC AGT TGC AGA AAA TTA G |
53 51 |
61 41 |
|
25 |
Xcn13 |
AGA ACA
GTC TTC TAG GTT AG CGA GGG ACA GAC GAA TC |
48 49 |
40 59 |
|
26 |
DuPw004 |
GGTCTGGTCGGAGAAGAAGC TGGGAGCGTACGTTGTATCC |
56 54 |
60 55 |
|
27 |
DuPw023 |
ATTAGACACGACCAAACGGG TCAAACAAACAACAGCCAGC |
52 50 |
50 45 |
|
28 |
DuPw043 |
TTTGAACGGAATTTGAGAATTT AGGGTGTGAACATGGAGGAG |
46 54 |
27 55 |
|
29 |
DuPw108a |
TGAAGAGTGCGATGTGAAGG TGTGACAGAAACTACTAACATTGCG |
52 54 |
50 40 |
|
30 |
DuPw108b |
TGTTTCTTCCTCGCGTAACC CCTCGAATCTCCCAGTTATCG |
52 54 |
50 52 |
|
31 |
DuPw123 |
CAACGAGAACCAGAAGACCG CCCGTTACACTTGGATGCC |
54 53 |
55 58 |
|
32 |
DuPw217 |
CGAATTACACTTCCTTCTTCCG CGAGCGTGTCTAACAAGTGC |
53 54 |
45 55 |
|
33 |
DuPw216 |
ACAAACCTCTCCCTCTCACG ATGATGATTCAGCGAGTCGG |
54 52 |
55 50 |
|
34 |
DuPw210 |
CGATTTGGATTCTTCCGC AGAGCCTTTGAAGAGCAGGG |
48 54 |
50 55 |
|
35 |
DuPw207 |
GAGAGTATCAATAAAGCTAGATGCCC GCATTTGGAAGGAGATGTGG |
56 52 |
42 50 |
|
36 |
DuPw205 |
ATCCAGATCACACCAAACGG CTTCCGCTTCATCTTCTTGC |
52 52 |
50 50 |
|
37 |
DuPw238 |
TTCATAGACGCAACTAGCCG GACTTTGGTTGTTAAAGGCG |
52 50 |
50 45 |
|
38 |
DuPw398 |
CTGAGCCCTCTTTGCTATGC TCGGTGAGATTGAAAGGTCC |
54 52 |
55 50 |
|
39 |
DuPw254 |
TTAACCATGCAGCAACTTCG GTGTGTACTAACGGCTACGGC |
50 56 |
45 57 |
|
40 |
DuPw165 |
TAGGTCTCGACAACAAGCCG TCACCACTTGGAGGTTACTGC |
54 54 |
55 52 |
Data were recorded as
presence of allele and absence of allele through UVi Band Map software. The
genetic attributes were created by software of population genetic structure
named “POPGENE” (Yeh et al. 1997). Genetic
kinship among the populations was calculated by the Nei’s formula and also used
to find phylogenetic relationship through un-weight pair group method with the
arithmetic averages (UPGMA) (Nei and Li 1979).
Results
Estimation
of genetic variability among promising mutants
Out of 40 primers, fourteen alleles produced polymorphic
amplification from the genomic DNA of wheat mutants with parents. The total
number of the amplified alleles was 269 across the set of 33 mutants with
parent. The share of the polymorphic alleles with a mean was 75.46% (Table 2). The individual
genotype of 33 mutants and parents created polymorphism and among these few
monomorphic alleles were also ascertained (Fig. 1). Primer WMS-644 amplified
six DNA fragments, in which five were polymorphic and varied from 200 bp to
1.25 kb.
Genetic variation
within population
Genetic variation between the mutants and parents is given in Table
1. In individual mutants along with parent, the percentage of P allele per
population varied from 66.7–87%,
with a mean of 78.96%. Number of alleles (Na) ranged from 1.3 to 2.0, while
number of effective alleles (Ne) ranged from 1.325 to 1.925. Heterozygosity (H) varied from 0.165 to 0.479 to with a mean of 0.415. Shanon Index (I) showed a range of
0.23 to 0.672, with an
average of 0.598. In 30 mutants and three
parents of bread wheat, various levels of genetic dissimilarity were observed.
The maximum dissimilarity was observed in mutant SE4/12-1, while the minimum
was detected in mutant SG1/12-41 (Table 3).
Dendrogram based on UPGMA (Fig. 2), the varieties were classified into three
groups and nine clusters A to I.
Table 2: Genetic variation
statistics for all alleles of mutants and their parents
|
S. # |
Mutants |
No of P alleles |
% of P alleles |
Na |
Ne |
H |
I |
|
1 |
SE4/12-1-1 |
9 |
77.8 |
2.0000 |
1.9252 |
0.4794 |
0.6722 |
|
2 |
SE4/12-1-2 |
4 |
66.7 |
2.0000 |
1.7333 |
0.4213 |
0.6118 |
|
3 |
SE4/12-3 |
7 |
77.8 |
2.0000 |
1.8667 |
0.4630 |
0.6554 |
|
4 |
SE4/12-4 |
10 |
83.3 |
2.0000 |
1.8394 |
0.4529 |
0.6445 |
|
5 |
SE4/12-5 |
6 |
75 |
2.0000 |
1.8218 |
0.4488 |
0.6406 |
|
6 |
SE4/12-6 |
8 |
80 |
2.0000 |
1.5509 |
0.3450 |
0.5254 |
|
7 |
SE5/12-7 |
11 |
85 |
2.0000 |
1.6687 |
0.3773 |
0.5561 |
|
8 |
SE5/12-8 |
9 |
82 |
2.0000 |
1.8218 |
0.4488 |
0.6406 |
|
9 |
SE5/12-9 |
9 |
82 |
2.0000 |
1.8218 |
0.4488 |
0.6406 |
|
10 |
SE5/12-10 |
9 |
82 |
2.0000 |
1.7000 |
0.3944 |
0.5779 |
|
11 |
TCT4/12-1 |
10 |
83 |
1.6667 |
1.5551 |
0.3007 |
0.4284 |
|
12 |
TCT4/12-2 |
10 |
83 |
2.0000 |
1.8218 |
0.4488 |
0.6406 |
|
13 |
SE5/12-12 |
10 |
83 |
2.0000 |
1.7628 |
0.4266 |
0.6164 |
|
14 |
SE5/12-13 |
11 |
85 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
|
15 |
SE5/12-15 |
8 |
80 |
2.0000 |
1.8533 |
0.4596 |
0.6520 |
|
16 |
SE5/12-17 |
5 |
71 |
2.0000 |
1.8533 |
0.4596 |
0.6520 |
|
17 |
SE5/12-19 |
4 |
66.7 |
2.0000 |
1.6727 |
0.3994 |
0.5882 |
|
18 |
SG3/12-20 |
8 |
80 |
2.0000 |
1.9119 |
0.4760 |
0.6688 |
|
19 |
SG3/12-21 |
10 |
83.3 |
2.0000 |
1.8218 |
0.4448 |
0.6406 |
|
20 |
SG3/12-23 |
7 |
77.8 |
2.0000 |
1.9119 |
0.4760 |
0.6688 |
|
21 |
SG3/12-25 |
6 |
75 |
2.0000 |
1.8218 |
0.4488 |
0.6406 |
|
22 |
SG2/12-26 |
9 |
77.8 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
|
23 |
SG2/12-27 |
6 |
75 |
2.0000 |
1.8533 |
0.4596 |
0.6520 |
|
24 |
SE2/12-29 |
4 |
67 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
|
25 |
SG4/12-35 |
8 |
80 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
|
26 |
SG1/12-38 |
12 |
86 |
2.0000 |
1.5509 |
0.3450 |
0.5254 |
|
27 |
SG1/12-41 |
6 |
75 |
1.3333 |
1.3252 |
0.1646 |
0.2290 |
|
28 |
SG1/12-43 |
13 |
87 |
1.6667 |
1.4060 |
0.2513 |
0.3760 |
|
29 |
KCT7/12-44 |
8 |
80 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
|
30 |
SCT6/9- |
10 |
83 |
2.0000 |
1.8533 |
0.4596 |
0.6520 |
|
31 |
Sarsabz |
7 |
77.8 |
2.0000 |
1.5509 |
0.3450 |
0.5254 |
|
32 |
Kiran-95 |
7 |
77.8 |
2.0000 |
1.8533 |
0.4596 |
0.6520 |
|
33 |
TD-1 |
8 |
80 |
2.0000 |
1.7632 |
0.4324 |
0.6238 |
Abbreviations: P: Polymorphic allele; Na: Observed
number of alleles; Ne: Effective number of alleles; h: Nei's
gene diversity; I: Shannon's index
Fig. 1: Amplification profile of 33 wheat genotypes
with primer WMS-644 by SSR makers (Number are correspondent to names of the
genotypes presented in Table 1).
Population genetic
structure and differentiation
Wheat
mutants and their parent exhibited different levels of genetic variation among
the populations in Table 2. The total genetic diversity (HT) and observed
genetic diversity (Hs) within the populations were
estimated about 0.50
and 0.42, respectively.
The genetic diversity within populations (Ds) was recorded as
16.39% of the whole diversity which showed that high genetic diversity was observed among the populations. The Nm (gene flow) value was 2.55 showing that number of genes migrating between the populations
was maximum (Table 4).
Discussion
In Pakistan, wheat genotypes such as Sarsabz, kiran-91
and TD1 are high yielding popular varieties but due to climate change these
varieties are susceptible to biotic and abiotic stress. To address this issue,
we developed mutants to create new genetic variation for the improvement of
these varieties. This genotypic variation is useful for the parental selection,
breeder rights, and varietal development (Abbasov et al 2018). Our results revealed that the genetic variability
appeared in all the mutants/parents which produced 75.46% polymorphic
fragments. Our promising mutants exhibited the genetic polymorphism through
their banding pattern. SSR markers confirmed that the polymorphism might be a
result of variations in nucleotides because of addition or deletion between two
priming positions (Kumar et
al 2016).
Table 3: Nei's Original Measures of Genetic Identity and
Genetic distance
|
Pop ID |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 |
22 |
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 |
31 |
32 |
33 |
|
1 |
**** |
0.9559 |
0.9223 |
0.9425 |
0.9027 |
0.8069 |
0.8203 |
0.9027 |
0.9027 |
0.9271 |
0.7400 |
0.8950 |
0.8625 |
0.8744 |
0.9659 |
0.9892 |
0.8795 |
0.9180 |
0.8950 |
0.9967 |
0.9027 |
0.9652 |
0.9892 |
0.9652 |
0.8971 |
0.8704 |
0.6771 |
0.6954 |
0.8971 |
0.8961 |
0.8704 |
0.9194 |
0.8744 |
|
2 |
0.045 |
**** |
0.8969 |
0.8101 |
0.9267 |
0.8286 |
0.7499 |
0.8002 |
0.8002 |
0.7858 |
0.6522 |
0.7783 |
0.8030 |
0.7554 |
0.9154 |
0.9816 |
0.9296 |
0.8848 |
0.7783 |
0.9312 |
0.9267 |
0.8747 |
0.9390 |
0.9993 |
0.8101 |
0.6954 |
0.7679 |
0.5741 |
0.8101 |
0.8066 |
0.8888 |
0.8728 |
0.8701 |
|
3 |
0.081 |
0.1088 |
**** |
0.8385 |
0.9644 |
0.8222 |
0.8219 |
0.9644 |
0.9644 |
0.8551 |
0.9166 |
0.9417 |
0.8788 |
0.9056 |
0.8594 |
0.9281 |
0.9674 |
0.9658 |
0.9417 |
0.9193 |
0.9644 |
0.9056 |
0.9281 |
0.9056 |
0.9727 |
0.7598 |
0.8386 |
0.8275 |
0.9727 |
0.9281 |
0.7598 |
0.9968 |
0.9056 |
|
4 |
0.059 |
0.2106 |
0.1761 |
**** |
0.7950 |
0.7566 |
0.8653 |
0.9045 |
0.9045 |
0.9608 |
0.7442 |
0.9265 |
0.8802 |
0.9348 |
0.9512 |
0.8843 |
0.7193 |
0.8755 |
0.9265 |
0.9581 |
0.7950 |
0.9348 |
0.9212 |
0.8269 |
0.8635 |
0.9847 |
0.5327 |
0.7760 |
0.8695 |
0.9212 |
0.8173 |
0.8543 |
0.8269 |
|
5 |
0.102 |
0.0761 |
0.0363 |
0.2294 |
**** |
0.9308 |
0.8779 |
0.8889 |
0.8889 |
0.7486 |
0.8488 |
0.8815 |
0.9222 |
0.8612 |
0.8896 |
0.9122 |
0.9723 |
0.9800 |
0.8815 |
0.8808 |
1.0000 |
0.8171 |
0.8748 |
0.9165 |
0.8833 |
0.6993 |
0.9270 |
0.8011 |
0.8833 |
0.9200 |
0.8691 |
0.9427 |
0.9707 |
|
6 |
0.215 |
0.1880 |
0.1957 |
0.2790 |
0.0718 |
**** |
0.9513 |
0.7609 |
0.7609 |
0.6131 |
0.7303 |
0.7952 |
0.9578 |
0.8173 |
0.8897 |
0.7860 |
0.8261 |
0.9317 |
0.7952 |
0.7753 |
0.9308 |
0.6553 |
0.7288 |
0.8227 |
0.7161 |
0.6838 |
0.8981 |
0.7939 |
0.7161 |
0.8948 |
0.9435 |
0.7911 |
0.9847 |
|
7 |
0.198 |
0.2878 |
0.1961 |
0.1446 |
0.1303 |
0.0500 |
**** |
0.8430 |
0.8430 |
0.7329 |
0.8046 |
0.8922 |
0.9940 |
0.9276 |
0.9103 |
0.7613 |
0.7468 |
0.9389 |
0.8922 |
0.8117 |
0.8779 |
0.7200 |
0.7496 |
0.7543 |
0.7823 |
0.8400 |
0.7833 |
0.9041 |
0.7823 |
0.9624 |
0.8933 |
0.8134 |
0.9616 |
|
8 |
0.102 |
0.2229 |
0.0363 |
0.1004 |
0.1177 |
0.2732 |
0.1707 |
**** |
1.0000 |
0.9252 |
0.9474 |
0.9925 |
0.8859 |
0.9707 |
0.8522 |
0.8748 |
0.8659 |
0.9420 |
0.9925 |
0.9187 |
0.8889 |
0.9265 |
0.9122 |
0.8171 |
0.9928 |
0.8621 |
0.7165 |
0.8964 |
0.9928 |
0.9574 |
0.6993 |
0.9800 |
0.8612 |
|
9 |
0.102 |
0.2229 |
0.0363 |
0.1004 |
0.1177 |
0.2732 |
0.1707 |
0.0000 |
**** |
0.9252 |
0.9474 |
0.9925 |
0.8859 |
0.9707 |
0.8522 |
0.8748 |
0.8659 |
0.9420 |
0.9925 |
0.9187 |
0.8889 |
0.9265 |
0.9122 |
0.8171 |
0.9928 |
0.8621 |
0.7165 |
0.8964 |
0.9928 |
0.9574 |
0.6993 |
0.9800 |
0.8612 |
|
10 |
0.076 |
0.2411 |
0.1565 |
0.0399 |
0.2895 |
0.4892 |
0.3108 |
0.0777 |
0.0777 |
**** |
0.7645 |
0.9181 |
0.7708 |
0.8977 |
0.8658 |
0.8874 |
0.7308 |
0.8209 |
0.9181 |
0.9543 |
0.7486 |
0.9819 |
0.9469 |
0.8079 |
0.9187 |
0.9420 |
0.4604 |
0.7205 |
0.9187 |
0.8605 |
0.6719 |
0.8821 |
0.7236 |
|
11 |
0.301 |
0.4274 |
0.0871 |
0.2955 |
0.1639 |
0.3143 |
0.2175 |
0.0540 |
0.0540 |
0.2685 |
**** |
0.9408 |
0.8450 |
0.9205 |
0.6963 |
0.7164 |
0.8259 |
0.8975 |
0.9408 |
0.7545 |
0.8488 |
0.7638 |
0.7496 |
0.6666 |
0.9401 |
0.7169 |
0.7815 |
0.9494 |
0.9401 |
0.9102 |
0.5661 |
0.9303 |
0.8234 |
|
12 |
0.111 |
0.2506 |
0.0601 |
0.0763 |
0.1262 |
0.2292 |
0.1141 |
0.0075 |
0.0075 |
0.0854 |
0.0610 |
**** |
0.9225 |
0.9928 |
0.8748 |
0.8522 |
0.8301 |
0.9497 |
1.0000 |
0.9111 |
0.8815 |
0.9045 |
0.8896 |
0.7950 |
0.9707 |
0.9034 |
0.7104 |
0.9285 |
0.9707 |
0.9800 |
0.7335 |
0.9574 |
0.8833 |
|
13 |
0.148 |
0.2195 |
0.1292 |
0.1276 |
0.0810 |
0.0431 |
0.0061 |
0.1211 |
0.1211 |
0.2604 |
0.1684 |
0.0807 |
**** |
0.9451 |
0.9265 |
0.8156 |
0.8134 |
0.9710 |
0.9225 |
0.8535 |
0.9222 |
0.7720 |
0.8034 |
0.8078 |
0.8369 |
0.8419 |
0.8223 |
0.9109 |
0.8369 |
0.9808 |
0.8974 |
0.8699 |
0.9809 |
|
14 |
0.134 |
0.2938 |
0.0992 |
0.0675 |
0.1494 |
0.2017 |
0.0751 |
0.0298 |
0.0298 |
0.1079 |
0.0828 |
0.0073 |
0.0565 |
**** |
0.8843 |
0.8174 |
0.7828 |
0.9434 |
0.9928 |
0.8902 |
0.8612 |
0.8695 |
0.8543 |
0.7617 |
0.9348 |
0.9239 |
0.6941 |
0.9465 |
0.9348 |
0.9880 |
0.7566 |
0.9212 |
0.8921 |
|
15 |
0.035 |
0.0884 |
0.1516 |
0.0501 |
0.1170 |
0.1168 |
0.0940 |
0.1600 |
0.1600 |
0.1441 |
0.3620 |
0.1338 |
0.0763 |
0.1230 |
**** |
0.9315 |
0.8089 |
0.9228 |
0.8748 |
0.9564 |
0.8896 |
0.8843 |
0.9189 |
0.9212 |
0.8174 |
0.8948 |
0.6877 |
0.7312 |
0.8174 |
0.9189 |
0.9520 |
0.8503 |
0.9212 |
|
16 |
0.011 |
0.0186 |
0.0746 |
0.1230 |
0.0919 |
0.2408 |
0.2727 |
0.1338 |
0.1338 |
0.1194 |
0.3335 |
0.1600 |
0.2038 |
0.2016 |
0.0710 |
**** |
0.9172 |
0.8996 |
0.8522 |
0.9796 |
0.9122 |
0.9512 |
0.9874 |
0.9880 |
0.8843 |
0.7911 |
0.7061 |
0.6341 |
0.8843 |
0.8503 |
0.8482 |
0.9189 |
0.8543 |
|
17 |
0.128 |
0.0730 |
0.0332 |
0.3294 |
0.0281 |
0.1911 |
0.2920 |
0.1440 |
0.1440 |
0.3136 |
0.1913 |
0.1862 |
0.2066 |
0.2449 |
0.2121 |
0.0864 |
**** |
0.9242 |
0.8301 |
0.8585 |
0.9723 |
0.8251 |
0.8814 |
0.9299 |
0.8885 |
0.6043 |
0.8997 |
0.7061 |
0.8885 |
0.8381 |
0.7670 |
0.9464 |
0.8876 |
|
18 |
0.086 |
0.1224 |
0.0384 |
0.1330 |
0.0202 |
0.0708 |
0.0631 |
0.0597 |
0.0597 |
0.1974 |
0.1081 |
0.0516 |
0.0294 |
0.0583 |
0.0804 |
0.1058 |
0.0789 |
**** |
0.9497 |
0.9085 |
0.9800 |
0.8528 |
0.8868 |
0.8902 |
0.9207 |
0.8104 |
0.8726 |
0.8871 |
0.9207 |
0.9796 |
0.8685 |
0.9564 |
0.9808 |
|
19 |
0.111 |
0.2506 |
0.0601 |
0.0763 |
0.1262 |
0.2292 |
0.1141 |
0.0075 |
0.0075 |
0.0854 |
0.0610 |
0.0000 |
0.0807 |
0.0073 |
0.1338 |
0.1600 |
0.1862 |
0.0516 |
**** |
0.9111 |
0.8815 |
0.9045 |
0.8896 |
0.7950 |
0.9707 |
0.9034 |
0.7104 |
0.9285 |
0.9707 |
0.9800 |
0.7335 |
0.9574 |
0.8833 |
|
20 |
0.003 |
0.0713 |
0.0842 |
0.0428 |
0.1270 |
0.2545 |
0.2086 |
0.0847 |
0.0847 |
0.0468 |
0.2818 |
0.0931 |
0.1585 |
0.1163 |
0.0446 |
0.0206 |
0.1526 |
0.0960 |
0.0931 |
**** |
0.8808 |
0.9808 |
0.9924 |
0.9434 |
0.9129 |
0.8966 |
0.6389 |
0.7094 |
0.9129 |
0.8996 |
0.8385 |
0.9228 |
0.8528 |
|
21 |
0.102 |
0.0761 |
0.0363 |
0.2294 |
0.0000 |
0.0718 |
0.1303 |
0.1177 |
0.1177 |
0.2895 |
0.1639 |
0.1262 |
0.0810 |
0.1494 |
0.1170 |
0.0919 |
0.0281 |
0.0202 |
0.1262 |
0.1270 |
**** |
0.8171 |
0.8748 |
0.9165 |
0.8833 |
0.6993 |
0.9270 |
0.8011 |
0.8833 |
0.9200 |
0.8691 |
0.9427 |
0.9707 |
|
22 |
0.035 |
0.1339 |
0.0992 |
0.0675 |
0.2020 |
0.4226 |
0.3285 |
0.0763 |
0.0763 |
0.0183 |
0.2695 |
0.1004 |
0.2588 |
0.1398 |
0.1230 |
0.0501 |
0.1923 |
0.1592 |
0.1004 |
0.0194 |
0.2020 |
**** |
0.9880 |
0.8921 |
0.9348 |
0.8834 |
0.5506 |
0.6814 |
0.9348 |
0.8543 |
0.7161 |
0.9212 |
0.7617 |
|
23 |
0.011 |
0.0629 |
0.0746 |
0.0821 |
0.1338 |
0.3164 |
0.2882 |
0.0919 |
0.0919 |
0.0546 |
0.2882 |
0.1170 |
0.2189 |
0.1575 |
0.0846 |
0.0127 |
0.1262 |
0.1201 |
0.1170 |
0.0076 |
0.1338 |
0.0121 |
**** |
0.9512 |
0.9212 |
0.8482 |
0.6352 |
0.6662 |
0.9212 |
0.8629 |
0.7911 |
0.9315 |
0.8174 |
|
24 |
0.035 |
0.0007 |
0.0992 |
0.1901 |
0.0763 |
0.1952 |
0.2820 |
0.2020 |
0.2020 |
0.2134 |
0.4056 |
0.2294 |
0.2135 |
0.2723 |
0.0821 |
0.0121 |
0.0727 |
0.1163 |
0.2294 |
0.0583 |
0.0763 |
0.1141 |
0.0501 |
**** |
0.8269 |
0.7161 |
0.7581 |
0.5874 |
0.8269 |
0.8174 |
0.8834 |
0.8843 |
0.8695 |
|
25 |
0.109 |
0.2106 |
0.0277 |
0.1398 |
0.1241 |
0.3340 |
0.2456 |
0.0073 |
0.0073 |
0.0848 |
0.0617 |
0.0298 |
0.1780 |
0.0675 |
0.2016 |
0.1230 |
0.1182 |
0.0826 |
0.0298 |
0.0912 |
0.1241 |
0.0675 |
0.0821 |
0.1901 |
**** |
0.8227 |
0.7120 |
0.8518 |
1.0000 |
0.9212 |
0.6553 |
0.9880 |
0.8269 |
|
26 |
0.139 |
0.3633 |
0.2747 |
0.0155 |
0.3577 |
0.3801 |
0.1743 |
0.1403 |
0.1403 |
0.0598 |
0.3229 |
0.1016 |
0.1721 |
0.0791 |
0.1112 |
0.2344 |
0.5037 |
0.2102 |
0.1016 |
0.1092 |
0.3577 |
0.1240 |
0.1646 |
0.3340 |
0.1952 |
**** |
0.4260 |
0.7810 |
0.8227 |
0.8897 |
0.7404 |
0.7860 |
0.7566 |
|
27 |
0.390 |
0.2640 |
0.1760 |
0.6298 |
0.0758 |
0.1075 |
0.2443 |
0.3334 |
0.3334 |
0.7757 |
0.2466 |
0.3419 |
0.1957 |
0.3652 |
0.3744 |
0.3480 |
0.1057 |
0.1363 |
0.3419 |
0.4480 |
0.0758 |
0.5967 |
0.4538 |
0.2769 |
0.3397 |
0.8533 |
**** |
0.7396 |
0.7120 |
0.7822 |
0.7479 |
0.8006 |
0.9015 |
|
28 |
0.363 |
0.5550 |
0.1894 |
0.2536 |
0.2217 |
0.2308 |
0.1008 |
0.1093 |
0.1093 |
0.3278 |
0.0519 |
0.0742 |
0.0934 |
0.0550 |
0.3131 |
0.4555 |
0.3480 |
0.1198 |
0.0742 |
0.3433 |
0.2217 |
0.3837 |
0.4061 |
0.5320 |
0.1604 |
0.2472 |
0.3016 |
**** |
0.8518 |
0.9379 |
0.6353 |
0.8409 |
0.8526 |
|
29 |
0.109 |
0.2106 |
0.0277 |
0.1398 |
0.1241 |
0.3340 |
0.2456 |
0.0073 |
0.0073 |
0.0848 |
0.0617 |
0.0298 |
0.1780 |
0.0675 |
0.2016 |
0.1230 |
0.1182 |
0.0826 |
0.0298 |
0.0912 |
0.1241 |
0.0675 |
0.0821 |
0.1901 |
0.0000 |
0.1952 |
0.3397 |
0.1604 |
**** |
0.9212 |
0.6553 |
0.9880 |
0.8269 |
|
30 |
0.110 |
0.2150 |
0.0746 |
0.0821 |
0.0833 |
0.1112 |
0.0383 |
0.0435 |
0.0435 |
0.1502 |
0.0941 |
0.0202 |
0.0194 |
0.0121 |
0.0846 |
0.1621 |
0.1767 |
0.0206 |
0.0202 |
0.1058 |
0.0833 |
0.1575 |
0.1474 |
0.2016 |
0.0821 |
0.1168 |
0.2457 |
0.0641 |
0.0821 |
**** |
0.8326 |
0.9315 |
0.9512 |
|
31 |
0.139 |
0.1179 |
0.2747 |
0.2017 |
0.1403 |
0.0582 |
0.1129 |
0.3577 |
0.3577 |
0.3976 |
0.5690 |
0.3099 |
0.1083 |
0.2790 |
0.0492 |
0.1646 |
0.2653 |
0.1410 |
0.3099 |
0.1761 |
0.1403 |
0.334 |
0.2344 |
0.1240 |
0.4226 |
0.3005 |
0.2905 |
0.4537 |
0.4226 |
0.1833 |
**** |
0.7288 |
0.9239 |
|
32 |
0.084 |
0.1361 |
0.0032 |
0.1575 |
0.0591 |
0.2344 |
0.2065 |
0.0202 |
0.0202 |
0.1254 |
0.0722 |
0.0435 |
0.1393 |
0.0821 |
0.1621 |
0.0846 |
0.0551 |
0.0446 |
0.0435 |
0.0804 |
0.0591 |
0.0821 |
0.0710 |
0.1230 |
0.0121 |
0.2408 |
0.2224 |
0.1733 |
0.0121 |
0.0710 |
0.3164 |
**** |
0.8843 |
|
33 |
0.134 |
0.1392 |
0.0992 |
0.1901 |
0.0298 |
0.0155 |
0.0388 |
0.1494 |
0.1494 |
0.3235 |
0.1944 |
0.1241 |
0.0193 |
0.1141 |
0.0821 |
0.1575 |
0.1192 |
0.0194 |
0.1241 |
0.1592 |
0.0298 |
0.2723 |
0.2016 |
0.1398 |
0.1901 |
0.2790 |
0.1037 |
0.1595 |
0.1901 |
0.0501 |
0.0791 |
0.1230 |
**** |
Nei's genetic identity (above
diagonal) and genetic distance (below diagonal)
Table 4: Nei's Analysis of Gene Diversity in Subdivided
Populations
|
Locus |
Sample Size |
Ht |
Hs |
Gst |
Nm* |
|
Mean |
210 |
0.4959 |
0.4146 |
0.1639 |
2.5506 |
|
St. Dev |
|
0.0000 |
0.0004 |
|
|
* Nm = estimate of gene flow from Gst or Gcs. E.g., Nm =
0.5(1 - Gst)/Gst
See McDermott and McDonald, Ann. Rev. Phytopathol. 31:353-373 (1993)
Fig 2: Dendrogram
showing Nei's genetic distance by UPGMA method
The present, results showed large differentiation, based
on the Nei's analysis of gene diversity and a significant degree of genetic
differences was exhibited among all the wheat genotypes. It
is the correlation of gametes in subpopulations relative to gametes
moved at indiscriminately from the
complete population and studies the overall genetic divergence among subpopulations (Aboughadareh et al.
2018). It describes expected degree of heterozygosity within a population. Results
showed that the gene flow among the mutants was high enough. The migration of
genes in distinct populations is high in comparison to those two populations
which have the same or less genetic diversity. The population divergence may be
explained in terms of genetic drift when one migrant per generation is received
(Aboughadareh et al. 2018). It could be one of the reasons that gene flow
constraints phylogeny by combining the gene pools of the populations and
accordingly prevents the event of differences in genetic diversity. Moreover,
high genotypic variations are recognized to control gene flow.
Results showed genetic relationship among the promising
mutants with their parents and proved that mutation is valuable technique to
create the new alleles in bread wheat. Previously, Bibi et al. (2012) recorded that crop plant improvement depends on the
data about the genetic kinships among plants within or between crop species.
The information regarding the genetic similarity is useful to prevent any
possible risk of elite genotypes developing genetically uniform. It was also
reported that breeders usually use the exotic material from ICARDA/CIMMYT
crossed with indigenous cultivars to develop the variety which may cause the
narrow genetic stock for wheat (Sundeep et
al. 2016). Thus, conscious struggles have to be generated to expand the
parental genetic makeup to create assured high genetic variability among the
genotypes of the crop plants. In the present study, among 30 mutants, ten
mutants were grouped together in one group (71%). Though, eleven mutants and a
single parent Kiran-95 in group two was observed the most distinguishable one
and these eleven mutants in the same group showed the sharing of the same blood
among the mutants (70%). However, nine mutants and two parents Sarsabz and TD1
formed another distinguished group which exhibited the 37% distinctness among
the mutants. Phylogenetic relationship not only gives the information regarding
genetic similarity but also provides a chance to find new and helpful genes
(Sajjad et al. 2018). Thus, conscious
struggles have to be generated to expand the parental genetic makeup to create
assured high genetic variability among the genotypes of the crop plants.
Conclusion
Our mutants manifested significant degree of genetic
differences among the genotypes with 16.4% of the total variation among the
mutants whereas heterozygosity Hs and Ht was recorded 0.4146 and 0.4959,
respectively while gene flow among the mutants was high enough (2.55). It also
provides a better gene flow of wheat mutants and a source of variation for the
selection of the parents to speed up the breeding program.
Acknowledgement
I am very thankful to PAEC for providing me funds for
this research work. It is the part of my Ph.D. thesis submitted to University
of Sindh, Jamshoro (Higher Education Commission), Pakistan.
Author Contributions
Sajida bibi as
a first author contribution is 70% and second author rubina has 30%
contribution in this research paper. I tried to write in a correction grid but
I could not write on it.
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