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Research Article
Principal Component and Path Analysis for Trait Selection Based
on the Assessment of Diverse Lentil Populations Developed by
Gamma-Irradiated Physical Mutation
Sandip Debnath ,
1
Abhik Sarkar ,
1
Kahkashan Perveen ,
2
Najat A. Bukhari ,
2
Kavindra Kumar Kesari ,
3
Amit Verma ,
4
Nihar Ranjan Chakraborty ,
1
and Mulugeta Tesema
5
1
Department of Genetics and Plant Breeding, Institute of Agriculture, Visva-Bharati University, Sriniketan,
PIN-731236 West Bengal, India
2
Department of Botany & Microbiology, College of Science, King Saud University, Riyadh 11495, Saudi Arabia
3
Department of Bioproducts and Biosystems, Aalto University, Espoo, P.O. Box 11000, Otakaari 1B, Finland
4
University Centre for Research & Development & Department of Computer Science & Engineering, Chandigarh University,
Gharuan, Mohali, Punjab, India
5
Department of Chemistry (Analytical), College of Natural and Computational Sciences, DambiDollo University, Dambi Dollo,
Oromia Region, Ethiopia
Correspondence should be addressed to Sandip Debnath; sandip.debnath@visva-bharati.ac.in
and Mulugeta Tesema; mulugeta@dadu.edu.et
Received 4 June 2022; Revised 19 June 2022; Accepted 25 June 2022; Published 18 July 2022
Academic Editor: Gaganpreet Kaur
Copyright © 2022 Sandip Debnath et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Lentil is a notable legume crop valued for its high protein, vitamin, mineral, and amino acid (lysine and tryptophan) content. This
crop has a narrow genetic base due to the formation of gene pool barriers during interspecific hybridization within and across
species. Mutagenesis may be seen as a novel and alternative breeding technique for the production of new diversity. For the
identification of new alleles, the creation of mutants followed by selection in subsequent generations would be necessary.
Induction of mutation in lentil cv. Moitree by gamma rays therefore produced high variation for the majority of quantitative
measures examined. Henceforth, principal component analysis (PCA) and path coefficient analysis were conducted to identify
and exclude redundant mutant genotypes with similar traits as the success of breeding is dependent on understanding the
relationship between morpho-agronomic traits and seed yield. As shown by the findings of this research, the total quantity of
pods per mutant plant should be given considerable priority. The identified mutant genotypes, such as lines 24, 43, 28, 33, and
10, may be used as parents in future breeding or released directly following trials.
1. Introduction
In India, lentils (Lens culinaris Medik) are an important
pulse crop. It belongs to the Fabaceae family and contains
14 chromosomes (2n=2x). [1]. It is an annual edible legume
with pods and seeds shaped like lenses and purses [2]. This is
the oldest known legume [3]. It's grains contain necessary
amino acids like lysine and tryptophan [4]. Currently,
pulses, their significance in food and nutrition security,
and sustainable agriculture are receiving more worldwide
attention than ever before. Legumes are included among
high-protein plant foods. It is spectacular to note that
protein-rich meals are advised to consume regularly by
mesothelioma patients whose appetites may be affected by
chemotherapy, and nutritious diets give energy and help
preserve muscular mass. Mesothelioma patients generally
eat too little protein and calories to promote healing, boost
immunity, and fight tiredness. A healthy mesothelioma diet
Hindawi
BioMed Research International
Volume 2022, Article ID 9679181, 14 pages
https://doi.org/10.1155/2022/9679181
may reduce stress, maintain weight and energy, combat
infection, and control treatment-related side effects. Improv-
ing the quality of the high-protein legume “lentil”employing
novel crop improvement techniques might thus play a sig-
nificant role. Systematized evaluation is required for the
genetic and agronomic improvement of lentil. It will remain
the major objective of all breeding initiatives. Frequent use
of conventional breeding techniques has decreased genetic
diversity, which is a primary need for crop improvement
projects. To achieve the aim of increasing genetic variability,
new breeding procedures, such as induced mutagenesis, are
necessary. In addition to genetic diversity, induced mutagen-
esis allows for the improvement of a specific trait without
affecting the genetic composition as a whole [5, 6]. The “day-
length bottleneck”constrained the flow of lentil germplasm
into the Indo-Gangetic plain due to its narrow genetic base,
which is less sensitive to photoperiod and more sensitive to
temperature than landraces from West Asia, despite the fact
that South Asia is home to half of the world’s lentil cultiva-
tion [7]. In addition, the tiny and fragile flowers reduced the
success rate of artificial hybridization by 20–50% due to the
problems of emasculation and pollination, which resulted in
mutant plant damage. As a result, cross-pollination of lentils
has become a time-consuming procedure [8]. In addition,
Lens was known for the formation of cross ability barriers
during interspecific hybridization within and across species
[9]. The interspecific hybridization is a technique of inter-
crossing two different species which have the same genus.
This technique is useful for exploiting useful genes from
raw species to improve the cultivated species. Eventually,
these events resulted in the narrow genetic foundation of
lentils. Several researchers have already recognized this
narrow genetic base as the fundamental constraint on lentil
yield [10]. In addition, adaptive specificity and recurrent fail-
ures to use several unproductive wild germplasms contribute
to the failure of lentil genetic advancement [11]. Given the
aforementioned conditions, mutagenesis may be viewed as
a unique and supplemental breeding approach. It is capable
of producing variants that does not exist in the genetic back-
ground of the organism. Consequently, mutagenesis may be
exploited to improve a multitude of desirable traits in a
mutant plant species [12, 13]. The development of mutants
followed by selection in the next generations would be essen-
tial for the discovery of novel alleles and might be released
directly as varieties or prebreeding material in the near
future if proved to be commercially viable and agronomi-
cally advantageous [14]. Nonetheless, crop yield in general
is a complex process arising from the interplay of several
genes with diverse environmental implications that impact
a wide range of phenotypes [15]. Successful mutant plant
selection during breeding requires knowledge of the link
between morpho-agronomic parameters and seed yield
[16]. A path coefficient analysis was conducted to determine
the nature of the link between yield and other variables, as
well as the direct and indirect effects of different factors on
yield. PCA is a statistical technique used to identify and
eliminate duplicate genotypes with similar characteristics
[17]. It allows for the natural classification of genotypes
and gives an accurate indication of genotypic differences.
The primary benefit of PCA is that each genotype may be
assigned to only one group [18]. In addition, this test is used
to categorize a large number of variables into important
components and assess their contribution to the total vari-
ance [19]. So, PCA was done to determine how the various
attributes were connected and to identify the traits that
cosegregated. This enabled for the identification of the char-
acteristics so that the selected M
4
mutants could be utilized
for crop development. Having in mind the limitations on
lentil yield and the importance of optimizing physical muta-
gen, a multiyear-induced mutagenesis field experiment was
conducted from 2017 to 2021 to increase the genetic diver-
sity and yielding potential of lentil cultivars.
2. Materials and Methods
Approximately 5000 healthy seeds of lentil (Cultivar:
Moitree), were irradiated with 250 Gy of gamma rays
because the GR
50
value for Moitree (published elsewhere)
was calculated to be 217.2 Gy. The irradiation was applied
at the RNARC, BCKV, West Bengal, India, where gamma
chamber (GC-6000) was utilized. Using the mutant plant-
to-progeny method, in 2018-2019, seeds from the M
1
gener-
ation were sown in the next generation to grow the M
2
population in the field mentioned above. To investigate the
mutagenic impact of the previously delivered gamma irradi-
ation doses, individual mutant plants were picked across the
field based on their chlorophyll content and a variety of
morphological traits. After ten days of germination, the
potential mutants were tagged and recorded correctly. In
2019–2020, 433 agro-morphologically superior mutants
with high production potential were chosen and produced
in the M
3
generation using the bulk breeding technique to
reap the benefits of natural selection. The M
4
generation
was made up of 62 types of economically superior mutant
types that were obtained from 433 M
3
populations in
2020–21.
These 62 lentil mutant types were grown at the institu-
tional agricultural farm during winters of 2020-2021, using
a RCBD design with three replications. Apparently healthy
and competitive five mutant plants were randomly selected
from each plot, and data were obtained on fourteen distinct
traits defined by Satpathy and colleagues [20]. Dewey and
Lu’s path coefficient analysis was used to examine the direct
and indirect impacts of a variety of independent factors on
seed yield. [21]. Windostat version 9.2 was applied for statis-
tical studies involving correlation, route analysis, and PCA.
Following the formula presented by Allard (1960), different
genetic parameters were determined [22].
3. Results
Recent research on the genetic diversity of 50 distinct lentil
germplasms, as well as the identification of the best heritable
attributes from our laboratory, was published [20]. Examin-
ing the direct and indirect impacts of numerous characteris-
tics on seed yield, as well as analyzing the pattern of
variation in characters, classifying the traits, and exploring
novel mutants, was the objective of this research.
2 BioMed Research International
3.1. Principal Component Analysis. PCA was used to evalu-
ate the diversity of 14 traits. Table 1 illustrates, based on
the variable factor loadings, that the eight PC aspects
explained 78.21 percent of the total variance.
Each principal component analysis shows the central
variability of all attributes. 14.11 percent of the total varia-
tion was explained by the first main component (PC1).
The factors with the largest loading on PC1 were grain
yield/mutant plant, days to fifty percent blooming, harvest
index, and mutant plant height. 12.54 percent of the varia-
tion was explained by the second main component (PC2).
The variables mutant plant height, days to fifty percent flow-
ering, days to fifty percent pod maturity, seeds/mutant plant,
biomass/mutant plant, and weight of 100 seeds showed high
positive support, whereas the remaining variables revealed
substantial negative involvement. The third main compo-
nent (PC3) accounted for 11.30 percent of the variance.
The remaining five PCs explained just 9.91, 8.88, 7.99,
7.33, and 6.11 percent of the variance, respectively. A scree
plot depicted the proportion of variation attributable to
eigenvalues and principal components for each graphed PC
(Figure 1).
PC 1 had the largest variance with an eigenvalue of 1.97,
whereas the variance of the remaining principal components
gradually declined. After the fourth PC, the line begins to
straighten out, with minor differences across PCs. It is obvi-
ous from the graph that PC1 had the highest variance
compared to the other four PCs; therefore, it may be advan-
tageous to select lines for characters with a positive impact
under PC1. The PC score for each component contains both
positive and negative values. These scores may be employed
to develop precise selection indices whose strength is dic-
tated by the variation explained by each basic component
[23]. A high PC score for a certain genotype in a specific
component suggests that the mutant’s variables have high
values. We acquired the PCA scores (Table 2) for 62 lentil
mutant variants in the first three principal components
and referred to them as axes Xvector (PCA-1), Yvector
(PCA-2), and Zvector (PCA-3).
The squared distance of each genotype from these three
axes was also calculated there. These PCA scores for mutant
lineages were graphed to create a three-dimensional scatter
picture (Figure 2).
Examining these findings indicated eight distinct clusters
by Tocher method (Figure 3). Four of these clusters were
shown to be mono-genotypic. The distribution pattern of
mutants in these clusters was discovered to be random, with
no agro-morphological diversity serving as a reference.
Mutants with a more phenotypic variability were clustered
in both the same and distinct classes.
3.2. Genetic Variability of the Mutant Population. Studies of
the coefficient of variation revealed that the phenotypic coef-
ficient variation (PCV) values were greater than those of the
genotypic coefficient variation (GCV) for the majority of the
traits, indicating that the environment influences these traits
to some extent during both the vegetative and reproductive
stages (Table 3). Secondary branches (number) per plant
had the largest phenotypic coefficient variation (PCV) and
genotypic coefficient variation (GCV) according to the
research (70.85 percent and 60.32 percent, respectively). Bio-
mass/mutant plant (57.21 percent) was followed by number
of major branches (36.69 percent), seeds/pod (36.17 per-
cent), seeds/mutant plant (35.25 percent), and pods/mutant
plant (33.44 percent). Characters with a high GCV include
biomass/mutant plant (24.3 percent), pods/mutant plant
(21.86 percent), seeds/pod (20 percent), and mutant plant
height (21.0 percent). The high values of PCV above GCV
for the aforementioned features imply strong environmental
influences; hence, when establishing a breeding program
using this material, additional attention should be placed
on these traits. The secondary branches (number) exhibited
the highest heritability and genetic advance as a percentage
of the mean (72.47 percent and 105.79 percent, respectively),
and the other traits in which high heritability and high
genetic advance were observed were weight of 100 seeds,
harvest index, and pods/mutant plant, indicating that these
traits are less influenced by the environment and are more
stable and governed by additive gene action. Consequently,
the easy selection process affords these characters a larger
potential for advancement.
3.3. Path Analysis. The correlation coefficients between
genotypic and phenotypic characteristics for fourteen traits
are provided in (Tables 4 and 5). Days for pod initiation
and pods/mutant plant (0.221∗∗ and 0.404∗∗) and number
of main branches (0.155∗), root length (0.149∗), seeds/pod
(0.159∗), and harvest index (0.155∗)are significantly and
positively linked with grain production per plant at both
levels, respectively. Secondary branches (number) (0.43∗∗)
and biomass/mutant plant (0.144∗) were shown to be sub-
stantially and adversely associated to grain production per
plant. The phenotypic analysis indicated only a significant
and positive connection between grain yield and pods/
mutant plant (0.246∗∗), seeds/mutant plant (0.399∗∗), and
biomass/mutant plant (0.306∗∗). The correlation study
demonstrated the link between the features; hence, the rela-
tionship between the numerous lentil traits will establish
their relative importance for yield enhancement.
Path analysis based on phenotypic correlations found
that all variables had direct positive impacts on grain pro-
duction per mutant plant, except for number of main and
secondary branches, days for pod initiation, root length,
and harvest index, which had direct negative effects. The
number of main and secondary branches, pods per plant,
and harvest index demonstrated favorable indirect impacts
of mutant plant height on grain yield (Table 6). The number
of main branches had favorable indirect impacts on the
secondary branches (number), days to 50% blooming and
pod initiation, pods per plant, root length, seeds per pod,
and biomass/mutant plant. The secondary branches (num-
ber) had both direct and indirect negative impacts on
mutant plant height, days to 50% blooming and pod matu-
rity, root length, seeds per pod, and seed weight per 100
seeds. Days to 50% blooming have negative indirect impacts
on grain production via solely mutant plant height, main
branch number, and root length. The days to 50 percent
blooming, the days for pod initiation, the weight of 100
3BioMed Research International
seeds, and the harvest index are all negatively impacted by
the days for pod initiation. Days for 50% pod maturity has
a positive indirect influence on the number of main and sec-
ondary branches, days to 50% blooming and pod maturity,
seeds per plant, and 100 seed weight. Pods per plant, on
the other hand, have a negative indirect influence through
just the number of main and secondary branches, days for
pod initiation and 50% maturity, and seeds per pod.
Through the secondary branches (number), pods per plant,
root length, seeds per pod, biomass/mutant plant, and 100
seed weight, root length has a negative indirect effect. Seeds
per pod have a positive indirect influence on secondary
branch number, days to 50% blooming, root length, seeds
per pod, seeds per plant, and biomass/mutant plant.
Through mutant plant height, secondary branches (num-
ber), days for pod initiation, root length, and harvest index,
seeds per plant have a negative indirect influence. Biomass/
mutant plant has a negative indirect influence on mutant
plant height, the number of main and secondary branches,
the number of days before pod initiation, and pod maturity
at 50 percent. There is a positive indirect impact of 100 seed
weight on all variables except mutant plant height and seeds/
pod. The harvest index has a favorable indirect impact via
the secondary branches (number), days for 50% pod matu-
rity, root length, seeds per pod, and seeds per plant.
Path analysis based on genotypic correlations revealed
direct positive effects of mutant plant height, days to 50 per-
cent pod maturity, pods/mutant plant, seeds/pod, biomass/
mutant plant, and direct negative effects of primary branches
(number), secondary branches (number), and days to 50
percent flowering, days for pod initiation, root length,
seeds/mutant plant, 100 seed weight, and harvest index on
2.5
2
1.5
0.5
0
PC 1 PC 2
Eigenvalue (Root)
Cumulative variability
PC 3 PC 4 PC 5 PC 6 PC 7 PC 8
1
Figure 1: Scree plot of eigenvalues and cumulative variability against different principal components in lentil mutants.
Table 1: Eigenvalues for the first five principal components of various Lentil mutant characteristics.
PC-1 PC-2 PC-3 PC-4 PC-5 PC-6 PC-7 PC-8
Eigenvalue 1.97542 1.75629 1.58303 1.3886 1.2444 1.11867 1.02712 0.85597
% variability explained 14.11013 12.5449 11.30738 9.91859 8.88858 7.99046 7.33658 6.11404
Cumulative variability explained 14.11013 26.65503 37.96241 47.881 56.76958 64.76005 72.09662 78.21066
Characters
Mutant plant height (cm) 0.25048 0.38142 0.23943 0.35319 0.10706 0.23783 0.014 0.05966
Primary branches (number) 0.20003 -0.10371 -0.45645 0.03086 -0.04338 -0.27438 -0.45303 -0.28496
Secondary branches (number) -0.42991 -0.36133 0.12583 0.21243 -0.12712 0.06455 -0.1351 -0.21038
Days for fifty percent flowering 0.33698 0.2377 -0.19927 -0.04982 -0.20773 -0.12079 -0.03962 -0.24344
Days for pod initiation 0.18892 -0.04746 0.05889 0.1501 -0.30677 0.08208 0.66552 -0.53522
Days for fifty percent pod maturity -0.22596 0.21722 -0.18955 -0.00637 0.53665 -0.34055 0.12435 -0.43063
Pods/mutant plant 0.17953 -0.42745 -0.36813 0.02671 -0.3105 -0.13369 0.16883 0.26938
Root length (cm) -0.20586 -0.03981 -0.32296 0.3199 0.24083 -0.27468 0.42932 0.28707
Seeds/pod -0.09768 -0.29664 0.12013 -0.50335 0.28993 0.0991 0.20353 -0.01967
Seeds/mutant plant 0.05965 0.10157 0.36315 -0.22488 -0.21891 -0.64245 0.14612 0.23137
Biomass/mutant plant (g) -0.28123 0.18136 -0.43314 -0.23535 -0.17822 0.42124 0.14436 0.06376
100 seed weight (g) -0.22237 0.53104 -0.20035 -0.23379 -0.22218 -0.04262 0.06298 0.17617
Harvest index (%) 0.28529 -0.06901 -0.00689 -0.52825 0.13598 0.07246 0.05908 -0.13679
Grain yield/mutant plant (g) 0.46312 -0.07071 -0.15954 0.08975 0.39904 0.16212 0.1117 0.27769
4 BioMed Research International
grain yield (Table 7). The mutant plant height had favorable
indirect impacts on grain production via the number of
main branches, the secondary branches (number), the num-
ber of seeds per plant, the weight of 100 seeds, and the har-
vest index. Number of main branches had favorable indirect
impacts via secondary branches (number), days for pod ini-
tiation, days for 50% pod maturity, pods per plant, root
length, biomass/mutant plant, and 100 seed weight. The sec-
ondary branches (number) had negative direct and indirect
impacts on mutant plant height, days to 50 percent pod
maturity, root length, seeds per pod, and seeds per plant.
Days to 50% blooming have indirect favorable benefits on
grain output via mutant plant height, secondary branches
(number), pods per plant, and root length. Days for pod ini-
tiation has a negative indirect influence on grain yield
through days to 50% blooming, seeds per pod, seeds per
plant, and harvest index. The secondary branches (number),
the number of days to 50 percent blooming, the number of
seeds per pod, and the number of seeds per plant have a pos-
itive indirect influence on the days to 50 percent pod matu-
rity. In contrast, pods per plant have a beneficial indirect
benefit through biomass/mutant plant and hundred seed
weight. Root length has a negative indirect influence through
the amount of secondary branches, seeds per pod, seeds per
plant, and weight per 100 seeds. Seeds per pod have a nega-
tive indirect influence on three traits: mutant plant height,
pods per plant, and harvest index. Through pods/mutant
plant, seeds/pod, and biomass/mutant plant, seeds/mutant
plant have a positive indirect impact. The days to 50%
blooming, pods/mutant plant, seeds/pod, and harvest index
indicate a favorable indirect influence of biomass/mutant
plant. 100 seed weight demonstrates a beneficial indirect
impact via the number of main and secondary branches,
days for pod initiation and 50% pod maturity, biomass/
mutant plant, and harvest index. The harvest index demon-
strates a negative indirect influence via mutant plant height,
number of main branches, days to 50% blooming and pod
Table 2: PCA scores of 62 lentil mutants shown as three axes X
vector (PCA-1), Yvector (PCA-2), and Zvector (PCA-3).
PCA I PCA II PCA III
Mutant types X vector Y vector Z vector
1 | 1 -1.253 3.83 -4.81
2 | 2 -2.892 6.217 -4.34
3 | 3 0.173 5.999 -4.008
4 | 4 -0.26 7.368 -4.691
5 | 5 -0.077 8.372 -6.995
6 | 7 0.588 6.867 -5.029
7 | 13 -0.458 8.087 -5.493
8 | 14 -1.032 6.472 -5.597
9 | 23 -1.141 5.997 -5.395
10 | 25 0.223 6.772 -5.717
11 | 28 -1.393 6.023 -4.889
12 | 31 -0.641 6.329 -5.094
13 | 32 -1.49 9.11 -5.856
14 | 33 -0.31 7.795 -5.538
15 | 34 -1.247 7.855 -5.326
16 | 35 -1.05 7.798 -6.284
17 | 36 -1.013 7.083 -5.151
18 | 37 0.758 6.742 -5.443
19 | 38 1.296 6.264 -4.003
20 | 39 0.882 7.196 -5.277
21 | 40 0.117 8.701 -4.767
22 | 42 -0.12 6.11 -6.36
23 | 44 -0.401 6.081 -5.307
24 | 45 2.196 6.896 -4.829
25 | 47 -1.564 6.088 -4.856
26 | 51 -1.386 5.34 -4.572
27 | 52 -2.497 5.039 -5.712
28 | 59 1.275 6.306 -4.928
29 | 61 -1.038 5.948 -5.32
30 | 62 -1.195 5.985 -5.731
31 | 66 -1.172 4.713 -4.153
32 | 67 -1.217 7.502 -6.095
33 | 72 1.036 6.306 -5.251
34 | 76 0.057 5.592 -6.419
35 | 77 -1.16 5.831 -5.019
36 | 82 -0.029 7.121 -5.657
37 | 86 -0.982 6.165 -4.892
38 | 87 0.554 6.387 -6.6
39 | 88 -0.649 6.086 -5.168
40 | 89 -1.567 6.463 -5.268
41 | 91 0.326 4.788 -6.247
42 | 93 -0.109 5.682 -6.327
43 | 96 1.433 6.006 -5.533
44 | 97 0.44 6.565 -4.699
45 | 101 0.192 6.662 -5.335
46 | 103 -0.139 4.931 -3.679
47 | 104 -0.675 4.892 -5.731
Table 2: Continued.
PCA I PCA II PCA III
48 | 105 -0.12 6.756 -6.362
49 | 107 0.069 6.586 -4.807
50 | 111 0.64 5.456 -5.767
51 | 114 -0.04 4.967 -5.511
52 | 115 -0.283 6.154 -6.45
53 | 116 0.934 4.987 -5.037
54 | 117 0.575 4.123 -5.015
55 | 127 -0.26 5.489 -5.362
56 | 128 -0.509 7.381 -5.841
57 | 133 0.819 5.802 -6.377
58 | 137 0.533 5.674 -5.441
59 | 138 -0.278 6.376 -5.838
60 | 142 0.35 6.519 -4.07
61 | 150 -0.303 6.729 -4.883
62 | 152 -0.761 6.008 -5.518
5BioMed Research International
commencement, seeds/mutant plant, and biomass/mutant
plant.
4. Discussion
It is suggested that PCA is useful for selecting agronomically
superior mutant lines for breeding endeavors [24]. In multi-
variate analysis, PCA is the major selection method as well
clustering using the Tocher technique separated the muta-
genic populations in the current research, demonstrating that
mutagenic treatments created diverse populations. Because
analyzing several phenotypic traits is a time-consuming and
error-prone procedure that might hamper selection; conse-
quently, multivariate analysis is essential for accurate selection
[25]. It also facilitates the reduction of complicated data and
improves breeding accuracy. Within the various clusters,
34567
PCA score II
8910
–3
–2
–1
0
1
2
3
PCA score I
Figure 2: 3-D scatter diagram of PCA scores for mutant lines.
5
0
4
0
32.82
3
0
2
0
1
9.27
8
0
7
0
6
0
35.47
41.38
33.26
16.52
12.82
37.76
35.53
19.03
15.52
19.29
21.25
30.99
20.27
24.18
39.66
40.5
31.07
11.38
26.56
16.64
22.4
23.27
21.15
27.52
38.26
37.26
11.84
Figure 3: Distribution pattern of mutants in eight clusters by Tocher method.
6 BioMed Research International
Table 3: Parameters related to genetic variability showing high influence of environment to most of the traits throughout both the vegetative and reproductive phases.
Genetic
parameters
Mutant
plant
height
(cm)
Main
branches
(number)
Secondary
branches
(number)
Days for
50%
flowering
Days for
pod
initiation
Days for
50% pod
maturity
Pods/
mutant
plant
Root
length
(cm)
Seeds/
pod
Seeds/
mutant
plant
Biomass/
mutant
plant (g)
100 seed
weight
(g)
Harvest
index
(%)
Grain yield/
mutant
plant (g)
Heritability (%) 40.133 20.383 72.475 16.04 26.397 40.155 42.762 17.61 33.344 24.311 18.042 71.391 56.285 20.459
Genotypic
coefficient of
variations (GCV)
20.046 16.569 60.323 5.637 2.503 2.76 21.868 12.584 20.887 17.382 24.301 21.431 10.958 19.507
Phenotypic
coefficient of
variations (PCV)
31.642 36.699 70.858 14.075 4.872 4.355 33.441 29.988 36.171 35.253 57.21 25.364 14.607 43.127
Genetic advance 3.607 0.217 2.067 2.362 1.846 3.092 1.354 0.511 0.367 1.036 0.03 0.333 6.321 0.009
Genetic advance
as % means 26.16 15.409 105.79 4.651 2.649 3.602 29.458 10.879 24.845 17.655 21.263 37.301 16.936 18.176
7BioMed Research International
Table 4: Genotypic correlation coefficients between all investigated traits over 62 novel mutant types.
Traits Mutant plant
height (cm)
Main
branches
(number)
Secondary
branches
(number)
Days for
50%
flowering
Days for
pod
initiation
Days for 50%
pod maturity
Pods/
mutant
plant
Root
length
(cm)
Seeds/
pod
Seeds/
mutant
plant
Biomass/
mutant
plant (g)
100 seed
weight (g)
Harvest
index
(%)
Mutant plant
height (cm)
Main branches
(number) -0.392∗∗
Secondary
branches
(number)
-0.213∗∗ -0.118
NS
Days for 50%
flowering 0.162∗0.360∗∗ -0.242∗∗
Days for pod
initiation 0.193∗∗ -0.196∗∗ -0.253∗∗ 0.128
NS
Days for 50%
pod maturity -0.035
NS
0.055
NS
-0.222∗∗ -0.078
NS
0.033
NS
Pods/mutant
plant -0.247∗∗ 0.504∗∗ 0.007
NS
0.266∗∗ 0.195∗∗ -0.383∗∗
Root length
(cm) 0.080
NS
-0.144∗0.172∗-0.021
NS
-0.081
NS
0.508∗∗ 0.244∗∗
Seeds/pod -0.213∗∗ -0.329∗∗ -0.069
NS
-0.078
NS
-0.053
NS
0.111
NS
-0.321∗∗ -0.346∗∗
Seeds/mutant
plant -0.375∗∗ 0.030
NS
0.141
NS
0.003
NS
0.157∗-0.178∗0.441∗∗ 0.153∗0.107
NS
Biomass/
mutant
plant (g)
-0.497∗∗ 0.107
NS
0.108
NS
-0.030
NS
0.130
NS
-0.287∗∗ 0.285∗∗ 0.128
NS
0.259∗∗ 0.114
NS
100 seed
weight (g) -0.052
NS
-0.104
NS
-0.136
NS
0.221∗∗ -0.139
NS
0.164∗-0.108
NS
0.081
NS
-0.095
NS
0.065
NS
0.667∗∗
Harvest
index (%) -0.080
NS
0.126
NS
-0.294∗∗ 0.100
NS
0.101
NS
0.139
NS
0.036
NS
-0.291∗∗ 0.378∗∗ 0.113
NS
-0.043
NS
-0.218∗∗
Grain yield/
plant (g) 0.102
NS
0.155∗-0.430∗∗ 0.049
NS
0.221∗∗ -0.052
NS
0.404∗∗ 0.149∗0.159∗0.075
NS
-0.144∗0.005
NS
0.155∗
* = significant at 5 percent level; ** =significant at 1 percent level; NS = Non-Significant.
8 BioMed Research International
Table 5: Phenotypic correlation coefficients between all investigated traits over 62 novel mutant types.
Traits Mutant plant
height (cm)
Main
branches
(number)
Secondary
branches
(number)
Days for
50%
flowering
Days for
pod
initiation
Days for 50%
pod maturity
Pods/
mutant
plant
Root
length
(cm)
Seeds/
pod
Seeds/
mutant
plant
Biomass/
mutant
plant (g)
100 seed
weight (g)
Harvest
index
(%)
Mutant plant
height (cm)
Main branches
(number) -0.179∗
Secondary
branches
(number)
-0.051
NS
-0.006
NS
Days for 50%
flowering -0.005
NS
0.016
NS
-0.065
NS
Days for pod
initiation 0.067
NS
-0.076
NS
-0.147∗-0.079
NS
Days for 50%
pod maturity -0.019
NS
-0.121
NS
-0.159∗0.028
NS
0.094
NS
Pods/mutant
plant 0.031
NS
0.108
NS
0.026
NS
0.127
NS
0.037
NS
-0.100
NS
Root
length (cm) 0.018
NS
-0.059
NS
0.060
NS
0.105
NS
-0.003
NS
0.111
NS
-0.002
NS
Seeds/pod -0.087
NS
0.011
NS
-0.053
NS
0.014
NS
0.060
NS
-0.024
NS
-0.292∗∗ -0.083
NS
Seeds/mutant
plant -0.069
NS
-0.025
NS
0.115
NS
0.047
NS
0.045
NS
0.009
NS
0.389∗∗ 0.078
NS
0.052
NS
Biomass/
mutant
plant (g)
-0.010
NS
0.012
NS
0.101
NS
0.040
NS
0.030
NS
-0.059
NS
0.297∗∗ -0.125
NS
0.153∗0.462∗∗
100 seed
weight (g) -0.045
NS
-0.077
NS
-0.080
NS
0.089
NS
-0.062
NS
0.077
NS
0.016
NS
-0.055
NS
-0.048
NS
0.111
NS
0.336∗∗
Harvest
index (%) -0.092
NS
0.058
NS
-0.206∗∗ -0.027
NS
0.118
NS
0.190∗∗ -0.008
NS
-0.132
NS
0.164∗0.039
NS
-0.114
NS
-0.166∗
Grain
yield/plant (g) 0.073
NS
-0.046
NS
-0.101
NS
0.045
NS
0.053
NS
0.046
NS
0.246∗∗ -0.036
NS
0.049
NS
0.399∗∗ 0.306∗∗ 0.111
NS
0.023
NS
* = significant at 5 percent level; ** =significant at 1 percent level; NS = Non-Significant.
9BioMed Research International
Table 6: Phenotypic path matrix based on phenotypic correlations for all the variables to reveal direct as well as indirect impacts on grain yield per mutant plant.
Phenotypic
path matrix
Mutant
plant
height
(cm)
Main
branches
(number)
Secondary
branches
(number)
Days for
50%
flowering
Days for
pod
initiation
Days for
50% pod
maturity
Pods/
mutant
plant
Root
length
(cm)
Seeds/
pod
Seeds/
mutant
plant
Biomass/
mutant
plant (g)
100 seed
weight
(g)
Harvest
index
(%)
Grain yield/
plant (g)
correlations
Mutant plant
height (cm) 0.40412 0.17438 0.06296 -0.02478 -0.05976 -0.02712 -0.34366 -0.05008 -0.05916 0.01869 -0.01555 0.00212 0.01956 0.102
NS
Main
branches
(number)
-0.15849 -0.44463 0.03484 -0.05519 0.06077 0.04291 0.7006 0.0905 -0.09137 -0.00148 0.00336 0.00428 -0.03089 0.155 ∗
Secondary
branches
(number)
-0.08597 0.05234 -0.29594 0.03709 0.07828 -0.17306 0.00956 -0.10776 -0.01903 -0.00703 0.00339 0.00563 0.07209 -0.430 ∗∗
Days for 50%
flowering 0.06541 -0.16024 0.07168 -0.15313 -0.03973 -0.061 0.36962 0.01306 -0.02156 -0.00015 -0.00095 -0.00913 -0.02456 0.049
NS
Days for pod
initiation 0.07805 0.08734 0.07488 -0.01967 -0.3094 0.02587 0.27033 0.05086 -0.01482 -0.0078 0.00406 0.00572 -0.02481 0.221 ∗∗
Days for 50%
pod maturity -0.01405 -0.02446 0.06565 0.01198 -0.01026 0.78004 -0.53172 -0.31886 0.03084 0.00888 -0.00898 -0.00674 -0.03401 -0.052
NS
Pods/mutant
plant -0.09994 -0.22417 -0.00204 -0.04073 -0.06019 -0.29847 1.38963 -0.15324 -0.08911 -0.02197 0.00891 0.00445 -0.0089 0.404 ∗∗
Root length
(cm) 0.03222 0.06406 -0.05077 0.00318 0.02505 0.39598 0.33903 -0.62812 -0.09612 -0.00763 0.004 -0.00335 0.07146 0.149 ∗
Seeds/pod -0.08607 0.14627 0.02028 0.01188 0.01651 0.08662 -0.44584 0.21737 0.27776 -0.00532 0.0081 0.00393 -0.09285 0.159 ∗
Seeds/
mutant plant -0.15162 -0.01318 -0.04179 -0.00045 -0.04844 -0.13908 0.61281 -0.09618 0.02966 -0.04982 0.00356 -0.00269 -0.02779 0.075
NS
Biomass/
mutant
plant (g)
-0.20088 -0.04774 -0.03208 0.00463 -0.0402 -0.22381 0.3956 -0.08034 0.07194 -0.00567 0.03128 -0.02748 0.01066 -0.144 ∗
100 seed
weight (g) -0.02082 0.04617 0.04039 -0.0339 0.04291 0.12755 -0.14996 -0.05098 -0.02651 -0.00325 0.02086 -0.04122 0.05343 0.005
NS
Harvest
index (%) -0.03222 -0.05598 0.08695 -0.01533 -0.03129 0.10811 0.05038 0.18296 0.10511 -0.00564 -0.00136 0.00898 -0.24535 0.155 ∗
* = significant at 5 percent level; ** =significant at 1 percent level; NS = Non-Significant.
10 BioMed Research International
Table 7: Genotypic path matrix based on genotypic correlations for all the variables to reveal direct as well as indirect impacts on grain yield per mutant plant.
Genotypic
path matrix
Mutant
plant
height
(cm)
Main
branches
(number)
Secondary
branches
(number)
Days for
50%
flowering
Days for
pod
initiation
Days for
50% pod
maturity
Pods/
mutant
plant
Root
length
(cm)
Seeds/
pod
Seeds/
mutant
plant
Biomass/
mutant
plant (g)
100 seed
weight
(g)
Harvest
index
(%)
Grain yield/
plant (g)
correlations
Mutant plant
height (cm) 0.08526 0.00585 0.007 -0.00002 -0.00012 -0.00081 0.00329 -0.00076 -0.00389 -0.02223 -0.00122 -0.00073 0.00107 0.073
NS
Main
branches
(number)
-0.0153 -0.03263 0.00077 0.00007 0.00014 -0.00527 0.01129 0.00251 0.00051 -0.00809 0.00149 -0.00125 -0.00068 -0.046
NS
Secondary
branches
(number)
-0.00431 0.00018 -0.13852 -0.00027 0.00027 -0.00689 0.00276 -0.00257 -0.00237 0.03698 0.01274 -0.0013 0.00239 -0.101
NS
Days for 50%
flowering -0.00047 -0.00053 0.00901 0.0042 0.00014 0.00122 0.01333 -0.0045 0.00061 0.01513 0.005 0.00145 0.00032 0.045
NS
Days for pod
initiation 0.00574 0.00249 0.02042 -0.00033 -0.0018 0.00409 0.00384 0.00013 0.00269 0.01441 0.00379 -0.00101 -0.00138 0.053
NS
Days for 50%
pod maturity -0.0016 0.00396 0.02196 0.00012 -0.00017 0.04347 -0.01051 -0.00475 -0.00108 0.00287 -0.00741 0.00125 -0.00221 0.046
NS
Pods/mutant
plant 0.00267 -0.00351 -0.00364 0.00053 -0.00007 -0.00435 0.10493 0.00009 -0.01306 0.12471 0.03723 0.00025 0.00009 0.246 ∗∗
Root
length (cm) 0.00151 0.00191 -0.0083 0.00044 0.00001 0.00481 -0.00022 -0.04292 -0.00372 0.02511 -0.01568 -0.00089 0.00153 -0.036
NS
Seeds/pod -0.00741 -0.00037 0.00733 0.00006 -0.00011 -0.00104 -0.03061 0.00357 0.04476 0.01676 0.01923 -0.00077 -0.00191 0.049
NS
Seeds/
mutant plant -0.00591 0.00082 -0.01598 0.0002 -0.00008 0.00039 0.04083 -0.00336 0.00234 0.32051 0.05799 0.0018 -0.00045 0.399 ∗∗
Biomass/
mutant
plant (g)
-0.00083 -0.00039 -0.01405 0.00017 -0.00005 -0.00257 0.03112 0.00536 0.00686 0.14807 0.12553 0.00546 0.00132 0.306 ∗∗
100 seed
weight (g) -0.00383 0.00252 0.01111 0.00038 0.00011 0.00336 0.00165 0.00237 -0.00214 0.03552 0.04222 0.01622 0.00194 0.111
NS
Harvest
index (%) -0.00783 -0.00189 0.02847 -0.00011 -0.00021 0.00826 -0.00081 0.00565 0.00733 0.01236 -0.01426 -0.0027 -0.01165 0.023
NS
* = significant at 5 percent level; ** =significant at 1 percent level; NS = Non-Significant.
11BioMed Research International
genetically distinct groups were categorized. In lentil improve-
ment projects focused at promoting genetic variability,
mutants selected from distinct clusters might thus be
advanced to next generations [26]. Positive correlations were
found between the first principal component and grain
yield/mutant plant, days to fifty percent flowering, harvest
index, mutant plant height, main branches (number), days
for pod initiation, pods/mutant plant,and seeds/mutant plant,
indicating that these variables fluctuate in the same direction.
Therefore, the property of the high-yield mutant plant-1 is
related to yield attribute traits and blooming traits. The PCA
analysis demonstrated that yield contributed the most to pop-
ulation divergence, indicating that yield favorably responded
to mutagenic treatments for the prospective selection of
high-yield mutants. Similar modifications were applied to
the second primary component, an evaluation of the architec-
tural and floral aspects of mutant plants. The third key ele-
ment emphasized the correlation between mutant plant
morphology and its relationship. The first five PCs in our anal-
ysis explained 56.76 percent of the total variance. The pattern
of distribution also suggested that the mutant group exhibited
a high degree of quantitative trait variation [27]. The yield-
related properties of the M
4
populations were characterized
using principal component analysis in order to explain their
phenotypes and identify superior high-yield mutant plants
for multiplication. The number of uncorrelated variables have
decreased due to the linear adjustment of the original vari-
ables. Yield per mutant plant and harvest index are major con-
tributions to genetic divergence; selecting these traits in the
following generation might result in mutants with more dis-
tinctive yield characteristics [23, 24]. The three-dimensional
scatter plot illustrates the second component with the first.
Mutant-24, Mutant-43, and Mutant-28 have high values for
the first component; thus, we presume that they also have high
values for the characteristics that are closely related to it.
Therefore, mutant plants from this diverse blend of mutant
lines associated with certain traits may be used as breeding
parents to generate better lentil varieties [17].
Surprisingly substantial environmental influence was
observed for almost all traits, which is rare when genetic var-
iability parameters are assessed on extant cultivars that are
assumed to be acclimatized and adaptable in their optimal
agro-climatic zones [37]. These results pointed to the genetic
uniqueness of the newly created mutant types, which must
be acclimatized by determining their ideal growth circum-
stances and particular agronomic practices. Yield is a poly-
genic trait with a complicated mode of inheritance, and
direct selection for yield is seldom successful. Therefore, cor-
relations between quantitative characteristics are influenced
by the small cumulative effects of numerous genes governing
trait expression. In fact, in mutant breeding initiatives, cor-
relation analysis between character combinations is crucial
for assessing the impact of yield characteristics on overall
yield [28]. So, it is essential to prioritize indirect selection,
concentrating on yield-altering traits. A thorough under-
standing of the relationship between yield and yield charac-
teristics is effectively understood from path analysis. By
splitting the correlation coefficient into direct and indirect
effects, path analysis provides more comprehensive informa-
tion on the interrelationships of complex traits; hence, it has
been used to define the selection criteria for genetic
improvement. The nature of the relationship between yield
and its associated characteristics would decide which
attribute will be used in indirect selection to increase lentil
production. According to the scale supplied by Lenka and
Mishra, the major direct influences on grain yield were
seeds/mutant plant, followed by biomass/mutant plant and
pods/mutant plant [29]. The major difference in correlation
and path analysis may be attributable to the fact that corre-
lation simply calculates mutual association without address-
ing causes, while path analysis detects causes and evaluates
their relative significance. Therefore, correlation and path
analysis must be investigated to determine the precise rela-
tionship between attributes. Similar patterns were seen in
terms of days to fifty percent blossom [30], pods per mutant
plant [31], branches per mutant plant [32], seeds per mutant
plant [33], and biomass/mutant plant [34]. The disparity
between direct impacts and genotypic correlations of traits
revealed that the relationship was mostly the result of
indirect effects of characteristics through other component
variables. Therefore, indirect selection of the previously indi-
cated feature is typically advantageous. By assigning correla-
tions, previous research [35] differentiates between direct
and indirect effects for a more exact evaluation of the
cause-and-effect association. Several vegetative, yield, and
yield-contributing factors have a high association, according
to a recent study. These parameters have both direct and
indirect impacts on pod yield and its contributing character-
istics due to their interaction [36].
Therefore, it was discovered that mutation induction by
gamma rays was useful for establishing new sources of
variability in lentils and avoiding breeding limitations. The
induction of mutation in cv. Moitree by 250 Gy of gamma
rays resulted in considerable variance for the vast majority
of quantitative parameters examined. Due to its significant
connection with and direct impacts on seed yield per mutant
plant, both PCA and path analysis suggested that selection
should place a significant emphasis on the total number of
pods per mutant plant. The findings of cluster analysis
revealed that there are genetic differences among mutant
families [37]. The found mutant genotypes, such as lines
24, 43, 28, 33, and 10, maybe employed as parents for future
breeding.
5. Conclusion
Selecting superior crop genotypes requires genetic diversity.
Artificial selection has reduced allelic diversity since time
immemorial. So, extending genetic diversity in a crop species
may improve breeding effectiveness. Mutagenesis may be
introduced directly to elite cultivars without being disturbed
by the circumstances of linkage drag. Therefore, a popular
lentil cultivar was irradiated for this study. This approach
uses irradiation to create diversity while retaining fertility.
Consequently, this research helped to the discovery of signif-
icant variables that directly or indirectly affect lentil yields.
The highlighted traits may serve as selection criteria for
hybridization and continuous selection-based lentil yield
12 BioMed Research International
enhancement programs. In addition, the researchers identi-
fied a number of lines with diverse sets of traits that may
be used to create superior kinds. Induced mutations at loci
controlling commercially relevant features in chosen high-
yielding mutants have efficiently contributed to diversifying
the existing lentil genetic base and will be of invaluable use
for future lentil breeding programs. In the coming days, Tar-
geting Induced Local Lesions in Genomes (TILLING) might
be used to confirm the mutants generated by this study at
the genomic level.
Data Availability
The data shall be made available on request to the corre-
sponding authors.
Conflicts of Interest
The authors declare that they have no conflict of interest.
Acknowledgments
This research article is the part of Mr. Abhik Sarkar's M.Sc.
(Ag.) in GPB research program in 2022, which was super-
vised by Sandip Debnath at Visva-Bharati University, West
Bengal, India and the study was self-funded. The authors
would also like to acknowledge Researchers Supporting Pro-
ject Number (RSP-2021/358), King Saud University, Riyadh,
Saudi Arabia.
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