Evaluation of genetic divergence in wheat (Triticum aestivum L.) germplasm

Abstract

In the present study, twenty seven genotypes of wheat genotypes were evaluated for assessing genetic divergence for 14 different characters. The genetic diversity analysis revealed the formation of five clusters suggested the presence of wide genetic diversity among the 27 genotypes studied. The clustering pattern indicated that geographic diversity was not associated with genetic diversity. The maximum inter-cluster distance (D) was observed between clusters II and IV (D=43.62) followed by clusters IV and V (D=35.49) and II and III (D=34.68). The minimum inter-cluster distance (D=23.10) was found between clusters III and IV. Grains per spike, plant height, biological yield per plant, harvest index, days to heading and test weight contributed maximum towards total genetic divergence. Based on the maximum genetic distance, it is advisable to attempt crossing of the genotypes from cluster II with the genotypes of cluster IV, which may lead to the generation of broad spectrum of favorable genetic variability for yield improvement in bread wheat. Noteworthy is that cluster IV and V reflected high cluster means for days to heading, days to 50 % flowering, days to maturity, grains per spike, grain yield per plant, biological yield per plant, grain filling period, tiller per plant and plant height these clusters can be successfully utilized in hybridization programmes to get desirable transgressive segregants.

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Electronic Journal of Plant Breeding, 9(1) : 361- 367 (Mar 2018)
ISSN 0975-928X
361
DOI:10.5958/0975-928X.2018.00040.6
Research Note
Evaluation of genetic divergence in wheat (Triticum aestivum L.)
germplasm
Deshraj Gurjar and Shailesh Marker
Department of Genetics and Plant Breeding, Sam Higginbottom Institute of Agriculture, Technology and Sciences,
Allahabad - 211 007.
E-mail: deshraj.agri@gmail.com
(Received: 8 Jun 2017; Revised: 9 Mar 2018; Accepted: 15 Mar 2018)
Abstract
In the present study, twenty seven genotypes of wheat genotypes were evaluated for assessing genetic divergence for
14 different characters. The genetic diversity analysis revealed the formation of five clusters suggested the presence
of wide genetic diversity among the 27 genotypes studied. The clustering pattern indicated that geographic diversity
was not associated with genetic diversity. The maximum inter-cluster distance (D) was observed between clusters II
and IV (D=43.62) followed by clusters IV and V (D=35.49) and II and III (D=34.68). The minimum inter-cluster
distance (D=23.10) was found between clusters III and IV. Grains per spike, plant height, biological yield per plant,
harvest index, days to heading and test weight contributed maximum towards total genetic divergence. Based on the
maximum genetic distance, it is advisable to attempt crossing of the genotypes from cluster II with the genotypes of
cluster IV, which may lead to the generation of broad spectrum of favorable genetic variability for yield
improvement in bread wheat. Noteworthy is that cluster IV and V reflected high cluster means for days to heading,
days to 50 % flowering, days to maturity, grains per spike, grain yield per plant, biological yield per plant, grain
filling period, tiller per plant and plant height these clusters can be successfully utilized in hybridization programmes
to get desirable transgressive segregants.
Keywords
Wheat, Genetic divergence, Inter-cluster and Intra-cluster distance
Wheat (Triticum aestivum L.) is considered as
king of cereals and contributing 30% of food
basket of the country. It is an important staple
food of many countries in the world and
occupies a unique position as used for the
preparation of a wide range of food stuffs. It is
agronomically and nutritionally most important
cereal essential for food security, poverty
alleviation and improved livelihoods. To feed
the growing population, the country wheat
requirement by 2030 has been estimated at 100
million metric tons. To achieve this target, the
wheat production has to be increased at the rate
of <1m.mt per annum (Sharma et al., 2011) and
this can be achieved by enhancing the
production of wheat by developing improved
varieties through heterosis breeding among
parents having high genetic divergent. It is
important that variability for economic traits
must exist in the working germplasm for
profitable exploitation following recombination
breeding and selection. The presence of genetic
diversity and genetic relationship among
genotypes is a prerequisite and paramount
important for successful wheat breeding
programme. Precise information on the nature
and degree of genetic diversity helps the plant
breeder in choosing the diverse parents for
purposeful hybridization (Samsuddin, 1985).
Several genetic diversity studies have been
conducted on different crop species based on
quantitative and qualitative traits in order to
select genetically distant parents for
hybridization (Shekhawat et al., 2001). Jagadev
et al. (1991) reported that the character
contributing maximum to the divergence should
be given greater emphasis for deciding the type
of cluster for purpose of further selection and
the choice of parents for hybridization. Hence,
characterization of genotypes should be based
on statistical procedure such as D2 statistics and
on hierarchical ecludean cluster analysis.
In view of these facts, twenty-seven wheat
genotypes were evaluated in this study: - i) To
determine the grouping pattern of genotypes in
different cluster. ii) To identify genetically
diverse and agronomically desirable genotypes
for exploitation in a breeding programme aimed
at improving grain yield potential of wheat.
The experimental material comprised of twenty
five lines of wheat genotypes along with two
checks viz., K 9162 and Raj 4037. The test
genotypes obtained from Rajasthan Agriculture
Research Institute, Durgapura, exhibited wide

Electronic Journal of Plant Breeding, 9(1) : 361- 367 (Mar 2018)
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spectrum of variation for various agronomic and
morphological characters. These were selected
on the basis of their diverse geographical origin,
wide variation and genotype adaptability for
different agro-climatic zones of India. The 27
entries (25 test genotype + 2 checks) were
evaluated in Randomized Block Design with 3
replications under normal soil during rabi
season of 2012-2013 at Field experimental
centre of Department of genetics and plant
breeding, SHIATS, Allahabad. Each genotype
was sown in a two row plots of 2 meter length
following inter-row and intra-row spacing of 23
cm and 5 cm, respectively. Thinning was done
to maintain a plant to plant spacing. The
recommended doses of N: P: K was applied. All
the recommended agronomic practices were
followed to raise a healthy crop.
Five competitive plants were randomly selected
from each genotype in each replication for data
collection. However, traits like days to 50%
flowering and days to maturity were recorded
on whole plot basis. Observations on fourteen
quantitative and morphological traits viz., days
to heading, days to 50 % flowering, tillers per
plant, plant height, flag leaf length, flag leaf
width, spike length, grains per spike, grain
filling period, days to maturity, biological yield,
harvest index, test weight and grain yield were
recorded from each replication. The data were
subjected to statistical analysis of genetic
divergence using Mahalanobis’s D2 (1936)
statistics as described by Rao (1952).
Genetic divergence (D2) is the basis of
variability and helps to craft the designed
genotypes as per the requirement. The
significant mean squares due to genotypes
suggested the preface of ample variability. The
D2-values between all possible pairs, which
indicated the presence of greater diversity
among the genotypes for all the traits. Grouping
of the genotype was carried-out by following
Tocher’s Method (Rao, 1952) with the
assumption that the genotypes within the cluster
have smaller D2-values among themselves than
those from groups belonging to different
clusters.
Based on Mahalanobis’ D2 analysis, twenty-
seven genotypes were grouped into five clusters
with variable number of genotypes (Table 2)
suggesting considerable amount of genetic
diversity is present in the material. The cluster I
had maximum of 15 genotypes followed by II
and V having 8 and 2 genotypes, respectively.
The clusters having maximum number of
genotypes, reflecting narrow genetic diversity.
Two clusters (III & V) possessed 1 genotype
each. Only two genotypes (GW 2010-288 and K
9162) formed a separate cluster. Similarly
twenty seven germplasms were dispersed in five
clusters. The possible reason for grouping of
genotypes of different places into one cluster
could be free exchange of genotypes among the
breeder of different region or unidirectional
selection practiced by breeder in tailoring the
promising cultivar for selection of different
region (Verma and Mehta, 1976). The intra-
cluster D2 value ranged from 12.92 to 21.08
while, inter-cluster D2 value ranged from 23.10
to 43.61 (Table 3). The maximum intra cluster
distance was exhibited by the genotype of
cluster II (21.08) followed by cluster I (19.02)
and cluster IV (12.92). The maximum inter-
cluster D2 value was observed between II and
IV (D2 = 43.16) followed by cluster IV and V
(D2 = 35.49) and cluster II and III (D2 = 34.68)
suggesting wide diversity between them and
genotypes in these clusters could be used as
parents in hybridization programme to develop
desirable type because crosses between
genetically divergent lines will generate
heterotic segregants. As heterosis can be best
exploited and chances of getting transgressive
segregants are maximum when generating
diverse lines are crossed (Zaman et al., 2005,
Saxena et al., 2013).
The comparison of cluster means revealed
considerable differences among the clusters of
different quantitative characters (Table 4).
Cluster V had highest cluster mean for plant
height (103.87) and second highest cluster for
tillers per plant (13.87) grain filling period
(35.00), biological yield per plant (44.13) and
test weight (15.63). Cluster III had high mean
value for flag leaf length (31.20), flag leaf width
(2.47), spike length (14.37), grain yield per
plant (39.04) and harvest index (45.27). Cluster
IV had highest values for days to heading
(83.17), days to 50 % flowering (88.17), days to
maturity (120.67), grains per spike (80.53).
The varieties of same geographical region
clustered with the varieties of other
geographical region due to selection pressure
and genetic drift. This indicates that there is no
parallelism between genetic diversity and
geographical region except in some cases.
Hybridization between the genotypes of
different clusters can give high amount of
hybrid vigour and good recombination. Grains
per spike, plant height, biological yield per
plant, harvest index, days to heading and test
weight were important components and these
should be taken into account while breeding in
wheat.

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Genetic improvement mainly depends upon the
amount of genetic variability present in the
population. The use of Mahalanobis’s D2
statistics for estimating genetic divergence have
been emphasized by many workers (Murthy and
Arunachalam, 1966) because it permits precise
comparison among all the population given in
any group before effecting actual crosses. So,
for improving the grain yield, selection of
parents based on number of characters having
quantitative divergence is required which can be
assessed by D2 statistic developed by
Mahalanobis (1936). The clustering pattern
could be utilized in selecting the parents and
deciding the cross combinations which may
generate the highest possible variability for
various traits. The genotypes with high values of
any cluster can be used in hybridization
programme for further selection and
improvement.
It has been well-established fact that more the
genetically diverse parents used in hybridization
programme, greater will be the chances of
obtaining high heterotic hybrids and broad
spectrum variability in segregating generations
(Arunachalm, 1981). Therefore, based on the
maximum genetic distance, it is advisable to
attempt crossing of the genotypes from cluster II
with the genotypes of cluster IV, which may
lead to the generation of broad spectrum of
favourable genetic variability for yield
improvement in bread wheat.
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Table 1. List of wheat genotypes.
S. No.
Genotype name
Pedigree
Origin
1.
AKAW-4731
WAWSN 12, KAUZ//STAR
Akola, Maharashtra
2.
AKAW-4739
II SSN 98.99/DF298.99
Akola, Maharashtra
3.
DL 1012
SFW/VAISHALI//UP2425
New Delhi
4.
GW 09-211
J96-1/K9533
Vijapur, Gujarat
5.
GW-2010-272
TUSN(NS)NIAW835/CPAN-11931/WH147
Vijapur Gujarat
6.
GW-2010-275
SORA/2PLATA12//GW 1102
Vijapur Gujarat
7.
GW-2010-282
GW 1193/ SULA
Vijapur Gujarat
8.
GW-2010-287
GW 336/HW 1042//KAZU
Vijapur Gujarat
9.
GW-2010-288
WR196/CMH 83-2578
Vijapur Gujarat
10.
GW-2010-289
GW 273/GW 353
Vijapur Gujarat
11.
GW-2010-290
STAR//KAZU/STAR/3/GW 241
Vijapur Gujarat
12.
GW-2010-291
W 462//NEE/KOEL/3/PEG
Vijapur Gujarat
13.
HPW 355
CMH79;1384/4/AGA/3/SN64/CN067//INIA 66/5
Palampur, Himachal Pradesh
14.
J 07- 40
GW 273/MACS 2496
Junagadh, Gujarat
15.
KLY-1090
HUW 468/PBW 343
Kanpur, Uttar Pradesh
16.
LBPY 2010-10
BAE 923//SOURAY/ K 8801
Karnal, Haryana
17.
LBPY 2010-24
NL 887//NL888//BL 2037
Karnal, Haryana
18.
NWL 9-11
NW=2036/HD 2733
Faizabad, UP
19.
VW 921
VL 830/BUDIFEN//VL829
Almora, Uttaranchal
20.
VW 20107
RAJ3765/CHINA 84-4000022
Almora, Uttaranchal
21.
VW 20143
VL 832/Druchamp/PHR 1010
Almora, Uttaranchal
22.
WSM-30
DWR1331/GREEN 3
Washim, Maharashtra
23.
WSM-55
EDULT 51.DWR 192
Washim, Maharashtra
24.
GW-2011-347
CMH 84-3379/PBW 475
Vijapur Gujarat
25.
JS 6-4
RAJ 4014 X HUW 510
Madhya Pradesh
26.
RAJ-4037
CHECK 1
Durgapura, Rajasthan
27.
K 9162
CHECK 2
Kanpur, Uttar Pradesh

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Table 2. Grouping of 27 bread wheat genotypes on the basis of D2 statistic
Cluster
No. of Genotypes
Name of the genotypes
I
15
GW-2010-289, J 07- 40, VW 20143, WSM-30, LBPY 2010-10, LBPY
2010-24, VW 20107, GW-2010-275, GW-2010-290, GW 09-211, GW-
2010-287, GW-2010-291, VW 921, WSM-55, JS 6-4
II
8
AKAW-4731, AKAW-4739, DL 1012, GW-2010-272, GW-2010-282,
NWL 9-11, GW-2011-347
RAJ-4037
III
1
GW-2010-288
IV
2
HPW 355, KLY-1090
V
1
K 9162
Table 3. Average inter and intra-cluster distance values of bread wheat genotypes
I
II
III
IV
V
I
19.020
25.906
25.940
29.416
29.321
II
21.085
34.688
43.614
31.086
III
0.000
23.104
27.328
IV
12.918
35.941
V
0.000

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DOI:10.5958/0975-928X.2018.00040.6
Table 4. Cluster mean for 14 different characters in 27 genotypes of bread wheat
Clusters
Day to
heading
Days to
50%
flowering
Plant
height
(cm)
Tillers
per plant
Flag leaf
length
(cm)
Flag leaf
width
(cm)
Spike
length
(cm)
Days to
maturity
Grain
Filling
Period
Grains per
spike
Biologic
al yield
per
plant (g)
Test
weight
(g)
Harvest
index (%)
Grain
yield per
plant (g)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
I
80.333
85.733
96.156
8.467
26.060
2.018
12.007
116.222
30.600
56.769
24.223
35.759
32.242
8.030
II
75.500
81.125
88.579
7.929
24.909
1.993
11.475
113.042
31.542
44.895
27.880
36.410
37.479
10.052
III
80.333
84.333
98.567
4.667
31.207
2.477
14.377
117.667
33.333
71.800
27.473
39.040
45.267
10.467
IV
83.167
88.167
99.950
8.483
24.183
1.947
13.010
120.667
31.667
80.533
26.362
36.753
27.767
9.668
V
75.333
78.000
103.407
13.867
27.630
1.783
13.460
114.000
35.000
58.533
44.133
35.487
38.167
15.633
Percentage contribution of characters towards total divergence
1
2
3
4
5
6
7
8
9
10
11
12
13
14
No. of
times
appearing
first
11
2
77
0
2
0
0
6
4
161
45
11
32
0
%
contributio
n
3.13
0.57
21.94
0.00
0.57
0.00
0.00
1.71
1.14
45.87
12.82
3.13
9.12
0.00