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Material and Methods
3.1 Material
3.1.1. Plant materials
The plant material used in the present study comprised of a total of 15 important
accessions of genus Ocimum placed within five species. The seed material for all the
accessions was obtained from National gene bank of CIMAP, as described in (Table 3.1).
Table: 3.1. Description of planting materials employed in the present study
Accession Source of seeds Ocimum species Germplasm OCS1 CIMAP, Lucknow Ocimum tenuiflorum CIM-Ayu/HYDOCS2 CIMAP, RC, Hyderabad O. tenuiflorum CIM-Kanchan/HydOCS3 CIMAP, RC, Hyderabad O. tenuiflorum OTP-Tall/HydOCS4 CIMAP, Lucknow O. tenuiflorum CIM-Angana/LkoOCS5 CIMAP, Lucknow O. tenuiflorum CIM-Amrit/LkoOCS6 CIMAP, Lucknow O. tenuiflorum Nasik/LkoOCB7 CIMAP, RC, Hyderabad O. basilicum LO/HydOCB8 CIMAP,RC, Hyderabad O. basilicum LM-IV/HydOCB9 CIMAP,RC, Hyderabad O. basilicum OB5/OBT/HydOCB10 CIMAP, RC, Hyderabad O. basilicum OB4/EXO/HydOCB11 CIMAP,RC, Hyderabad O. basilicum Soumya/HydOCA12 CIMAP,RC, Hyderabad O. americanam CTRL-II/HydOCG13 CIMAP,RC, Hyderabad O. gratissimum OGO/HydOCG14 CIMAP,RC, Hyderabad O. gratissimum OGM/HydOCK15 CIMAP,RC, Hyderabad O. kilimandscharicum OK/Hyd
Among the various genotypes, six accessions were from O. tenuiflorum L.f. (syn.
O. sanctum L.), five accessions were of O. basilicum L., two accessions were from O.
gratissimum L. and a single accession each was from O. kilimandscharicum Baker ex.
Guerke and Ocimum canam Sim. syn. O. amricanam were selected for the study. Initially,
most of these accessions were maintained at CIMAP Resource Centre, Hyderabad (AP),
except few accessions that were maintained at CIMAP Lucknow (UP). The G x E
interaction and phenotypic stability analysis was performed for important characters of
Ocimum accessions grown in the field conditions across two consecutive years (2007 to
2009) and three locations (Lucknow, Hyderabad and Bangalore) as shown in the (Figure
3.1). The average data from the observations were used for morphological characterization,
statistical derivation and interpretation in the study.
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Material and Methods
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Figure: 3.1. Three different Agro-environment (Lucknow,Hyderabad and Bangalore) of study for Ocimum species germplasm accessions.
Agro-ecological locations of India
Material and Methods
3.1.2. Chemicals and reagents
Chemicals and reagents used in the present study were procured from various
manufacturing companies viz., Sigma Aldrich (USA), High media (India), Difco (USA),
Merk (Germany), BioLabs (India), Thermo Scientific (India) etc.
CTAB, Tris-HCl, NaCl, chloroform, phenol, isopropanol, TAE, ethanol, high salt
TE, sterilized-H2O, PVP (Sigma), EDTA, dNTPs (Fermentas), Taq polymerase, PCR buffer
(Fermentas), RAPD Primer (Operon technologies, USA), ISSR primers (Ocimum
Biosolution, Hyderabad), 100 bp DNA molecular marker (BioLabs), Agarose (High Media),
Loading dye and Ethidium bromide, SDS, Dimethylsulfoxide, Anhydrous sodium sulphate,
HPLC grade H2O, culture media for bacterial and fungal growth and standard antibiotic discs
were purchased from Hi-Media. McFarland Standard solution, Nutrient agar and broth,
Mueller-Hinton agar, Sabaroud’s dextrose agar etc. were used in the study.
Buffers, solutions and media
The stock solutions of various buffers and other solutions used for DNA isolation
and gel electrophoresis were prepared as given in the (Table 3.2).
Table: 3.2. Buffers, solutions and media used in the study.
Sr. No. Buffer/Solution Composition/preparation1 Tris-HCL (1M, pH-8) 12.11g Tris base was dissolved in 100 ml deionized H2O2 EDTA(0.5M, pH-8) 18.612 g EDTA was dissolved in 100 ml deionized H2O3 NaCL(5M) 29.22 g NaCL was dissolved in 100 ml deionized H2O4 CTAB (20%) 20 g CTAB was dissolved in 100 ml deionized H2O5 EtBr (5mg/ml) 10 mg EtBr was dissolved in 1ml deionized H2O6 Loading dye (6 x) 0.25% bromophenol blue, 0.25% Xylene cianol, 30%
Glycerol (dissolved in sterile H2O)7 DNA extraction
Buffer2.5% CTAB,100 mM Tris-HCL(pH-8), 25 mM EDTA (pH-8), 1.4 mM NaCL,1% PVP,0.2% β -mercaptoethanol
8 High salt TE 10 mM Tris-HCL (pH-8),1mM EDTA (pH-8)1M NaCL9 50 x TAE 242 g Tris-base,57.1ml glacial acetic acid,100 ml
0.5M EDTA (pH-8) dissolved in sterile H2O to make 1 liter final volume.
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Material and Methods
3.1.3. DNA Primers for genotyping of Ocimum germplasm
Random primers for RAPD analysis
Seventeen random decamer primers which were used for the characterization and
RAPD profiling of germplasm in this study are described in the (Table 3.3)
Table: 3.3. List of RAPD primers used for germplasm characterization and phylogenetic analysis.
Sr. No Primer Sequence 5’-----3’1 OPO-6 CCA CGG GAA G2 OPO-9 TCC CAC GCA A3 0PO-11 GAC AGG AGG T4 OPO-13 GTC AGA GTC C5 OPO-14 AGC ATG GCT C6 OPO-15 TGG CGT CCT T7 OPO-16 TCG GCG GTT C8 OPO-17 GGC TTA TGC C9 OPO-18 CTC GCT ATC C10 OPO-19 GGT GCA CGT T11 OPO-20 ACA CAC GCT G12 OPT-2 GGA GAG ACT C13 OPT-12 GGG TGT GTA G14 OPT-13 AGG ACT GCC A15 OPT-16 GGT GAA CGC T16 OPT-17 CCA ACG TCG T17 OPT-18 GAT GCC AGA C
ISSR Primers
ISSR markers have unique efficiency in distinguishing even closely related
germplasm accessions. In this study, eight ISSR primers which were used for genotyping
of Ocimum germplasm are described in (Table 3.4).
Table: 3.4. List of ISSR primers for phylogenetic analysis
Sr. No Primer Sequence5’-----3’
Primer length Tm
1 Micro-8 (CAA)5 15 40.62 Micro-13 (GTGA)4 16 40.03 Micro-16 (GAAT)4 16 40.04 Micro-19 (GACA)4 16 44.85 G-13 (GTG)5 15 48.06 G-18 (ACG)5 15 57.07 M-6 (GTC)5 15 61.48 C-12 (GAC)5 15 56.0
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Material and Methods
3.1.4. Bacterial strains
Eight bacteria comprising gram positive and gram negative strains which were used
for determination of antibacterial activity of the essential oils from various Ocimum species
and their accessions are described in (Table 3.5).
Table: 3.5. Bacterial strains used in the antibacterial bioassay of Ocimum essential oils.
Sr. No Bacterial strain Type of bacteriaName MTCC No.
1 Staphylococcus aureus MTCC96 Gram positiveGram positiveGram positiveGram positive
2 Streptococcus mutans MTCC4973 Bacillus subtilis MTCC1214 Salmonella typhi MTCC7335 Klebsiella pneumonia MTCC109 Gram negative
Gram negativeGram negativeGram negative
6 Escherichia coli MTCC7237 Micrococcus luteus MTCC24708 Raoultella planticola MTCCRP530
The above bacterial cultures were procured from the Microbial Type Culture
Collection Centre (MTCC), CSIR-Institute of Microbial Technology (CSIR-IMTech),
Chandigarh and are currently maintained in the Bio Safety Division of CSIR-CIMAP,
Lucknow, India.
3.1.5. Instruments
The instruments used for different analysis during the study are as follows. Laminar
air flow (Newton), Ultra low Freezer-80 (NBS), Electronic Balance (Sartorius, India),
Water bath (RMS Lauda), Labtherm shaker (Kuhner), Autoclave (Tomy, USA), High
speed refrigerated centrifuge (Sigma), PCR Machine (Bio-Rad), UV Trans-illuminator
(Fotodyne), Gel documentation system (Pharmacia Biotech), Spectrophotometer
(Beckman), Nanodrop (ND1000), Clevenger apparatus (Borosil glass), Gas
Chromatography-Perkin-Elmer (8700), Mortar and Pestle, cooler freezer (Geni), deep
freezer -20 (vest frost), submarine electrophoresis units (Bio Labs), pH meter (Lab India),
Digital Camera (Nicon).
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Material and Methods
3.1.6. Glasswares and plasticwares
All the glasswares used in the present study viz., spreading glass rod,
culture tubes, oil collection tubes, conical flask, beakers, measuring cylinder, schott duran
bottles etc. were purchased from Borosil, India. The plasticwares such as digital micro
pipettes (Nichipet ECO, USA) and micro tips (Genaxy, India), Micro-centrifuge tubes
(Eppendorf, USA), Powder free latex gloves and 90 mm plastic petri plates (Genaxy, India)
were used in the study.
3.1.7. Statistical software and databases
CIMAP Ver. 1.0 for D2 and other statistical analysis, SPSS ver. 17, free tree
software, treeview 32, and Sigma plot ver. 14, Microsoft office word 2007 and Microsoft
office excel 2007 were used for the statistical analysis of the experimental data.
3.2. Methods
3.2.1. Experimental location and edapho-climatic conditions
In order to evaluate the G x E interaction, genetic diversity among different Ocimum
germplasm and stability of genotypes, field experiments were conducted at three locations of
the India, namely (1). CIMAP Research Centre, Hyderabad [altitude 542 m above mean sea
level -(MSL), latitude 17o25’ N, and longitude 78o33’ E); mean annual rainfall as 764 mm
(80% of which is received between June and September); winter season characterized by
mild, cool, dry weather. (2). CIMAP Lucknow (Semiarid-to Subtropical climate, located at
128 m above MSL, latitude 26’8’N and longitude 80.9 E); mean annual rainfall as 800 mm
(80-85% of which is received between July to September) and (3). CIMAP Research Centre,
Bangalore (altitude 930 m above mean sea above MSL, latitude 13o05’ N and longitude
77o55’ E). The area receives a mean annual rainfall of 870 mm, between May and October.
The field view (Figure 3.2) and climatic variation of all the three locations during the
experimentation are summarized in (Table 3.6)
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Material and Methods
3.2.2. Experimental field design
Figure: 3.2. Experimental field view of Ocimum crop at full blooming stage at Hyderabad (A and
B), Lucknow (C and D) and Bangalore (E and F) locations.
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BA
C D
E F
Material and Methods
The experiment was laid out in Randomized Block Design (RBD) on well-drained
soil at all the locations by using the method as prescribed by Sidney, A. (1970). All the
fifteen Ocimum germplasm accessions were transplanted in the respective locations with
three replications for two consecutive cropping years (2007-2008 and 2008-2009).
Individual plot size was 2.5 × 3 m (7.5 m2). Each plot received vermin-compost (1-2 t/ha),
single superphosphate (P2O5 40 kg.ha-1), and muriate of potash (K2O 40 kg.ha-1) prior to
planting. Seeds of all the fifteen Ocimum germpalsm were sown in nursery beds in June
first week at all the three experimental locations. Six weeks old, uniformly growing,
healthy seedlings were transplanted at 60 cm row-to-row and 45 cm plant-to-plant spacing
(Rao et al., 2011) during third week of July at all the experimental locations. The fields
were irrigated after planting and thereafter, at 10 to 12 day intervals. Nitrogen (as urea) was
given at 50 kg/ha for each harvest. Weeds were manually removed 25 and 45 days after
transplanting seedlings, and after each harvest.
Metric observations
The morphometric data for the following growth and yield contributing traits were
recorded on 10 randomly selected plants from all the blocks for characterization and
evaluation of G x E interaction and phenotypic stability in Ocimum germplasm accessions
over the years. All the traits were measured at full flowering stage of crop emphasizing the
morphological characters viz., plant height (cm), plant canopy (cm), No. of primary
branches, leaf length (cm), leaf width (cm), leaf area (cm2), leaf stem ratio (fresh wt basis),
essential oil content (% w/ v), fresh herb yield (t/ha) and essential oil yield (lit/ha).
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Material and Methods
Harvesting of crop
Crop was harvested manually 10-15 cm above the ground level (Singh, et al., 2013)
during each growing season at all the respective locations having full bloom stage (Gupta
et al.,1996).
3.2.3. Morphological characterization of Ocimum germplasm
The important qualitative morphological characters for characterization of germplasm
accessions were observed before harvesting of crops. Quantitative morphological data viz.,
plant height, plant canopy, number of primary branches, fresh biomass/plant, leaf length,
leaf width, leaf area, essential oil content, 1000 seeds weight, spike length and number of
whorls/spike recorded for Hyderabad experimental location were subjected for
phenotyping and used to calculate Euclidean distance (Ed) for all pairs of accessions.
Euclidean distance obtained from 11 morphological traits was used to construct
hierarchical dendogram with the help of SPSS statistical software ver.17. Principal
Component Analysis (PCA) was performed to demonstrate the relationship among Ocimum
germplasm accessions in terms of their position relative to two coordinate axes. PCA using
correlated data matrix among phenotypic traits was used to determine differences between
accessions.
3.2.4. Chemotypic characterization and diversity analysis
For the extraction of essential oil, after every harvest aerial shoots biomass (leaf,
branches and inflorescence) weighing approximately 300 g was collected from every block
at each location and hydro distilled using Clevenger’s apparatus (Clevenger, 1928) for 3 h.
Essential oil concentration (%) was estimated using formula-
EOC = Essential oil (ml) recovered/ weight of biomass (g) ×100.
Essential oil yield per unit area was calculated by multiplying the biomass yield
with essential oil concentration (Clevenger’s data) and density of the essential oil. Essential
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Material and Methods
oil samples were dried over anhydrous sodium sulphate and stored in sealed glass vials at
40C till further analysis.
Gas chromatography
Gas chromatography (GC) usually represents the method of choice nowadays to
scrutinize the complex mixture of an essential oil (Cserháti, 2010). Volatile oil samples were
analyzed on a PerkinElmer (PerkinElmer Life and Analytical Sciences, Milano, Italy) Gas
Chromatography (auto system XLGC) machine having flame ionization detector (FID), an
electronic integrator, and bonded phased fused silica capillary column (60 meters x 0.32 mm
id x 0.25 mm film thickness); Capillary column coated with polydimethylsiloxane was
employed as the stationary phase. Hydrogen was the carrier gas with 0.4 ml/min flow rate
and 10 psi column pressure. The oven temperature was programmed initially at 700C (2min)
and increased at 250oC at 3oC/min (hold time 2 min) and 290oC at 6oC /min ramp rate and 10
min hold time. Injector and detector were set at 280oC and 350oC, respectively. Samples
(0.06 mL) were injected neat with split ratio 1:80.
Identification of essential oil constituents
Essential oil components were identified by comparing their retention times with
authentic compounds run under identical conditions, by comparison of retention indices
(Kováts, 1965) computed from gas chromatograms by logarithmic interpolation between n-
alkanes. Homologous series of n-alkanes C8-C22, Poly Science Inc. Niles, USA, were used
as standard) with literature data and comparison of peaks’ mass spectra with standard
compounds reported in literature and stored on NIST and Wiley MS libraries. Quantitative
data was obtained by electronic integration of peak areas without the use of response
correction factors:
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Material and Methods
3.2.5. Chemotypic diversity analysis through Euclidean distance model
Quantitative data of the major essential oil components obtained from GC analysis
were used to calculate the Euclidean distance (Ed) for all pairs of samples.
Where i and j represent two cases (samples) of the data matrix, k represents the variable
(chemical compound) and the total number of variables is n. Cluster analysis was used to
classify and group all the germplasm according to their main components. Cluster analysis
based on selected components and was calculated using the Euclidean distance measure.
For the grouping of the chemotypes, the agglomerative and hierarchical method was
applied (Ozdemir, 2002) using the single linkage method. Principal Component Analysis
(PCA) was performed to display the relationship among Ocimum germplasm accessions in
terms of their position relative to two dimensional coordinate system. PCA using correlated
data matrix among essential oil constituents was used to determine differences between
accessions.The computations were performed using SPSS package software (Version 17).
3.2.6. DNA fingerprinting for molecular genetic diversity analysis
The diversity due to genetic constitution of germplasm accessions was analyzed by
employing PCR amplification profile of DNA with random primers and microsatellite
primers ISSR.
Isolation of genomic DNA
Total genomic DNA was isolated from the young apical leaves of all 15 Ocimum
germplasm accessions grown at Lucknow location, using CTAB method described by
Porebsky et al. (1997). Well cleaned and fine tissue powder of leaves grounded in liquid
nitrogen was extracted using CTAB buffer (2.5% CTAB, 100 mM Tris-Hcl (pH 8), 25 mM
EDTA (pH8), 1.5M NaCl, 1% PVP, 0.3% b-mercapto-ethanol) and incubated at 650C for
one hour. Chloroform: iso-amylalcohol (24:1) was added to the incubated sample and
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Material and Methods
mixed by inversion up to 15 min. to form an emulsion, centrifuged at 10,000 rpm for 10
minutes at room temperature. The upper layer (supernatant) was transferred and
precipitated by adding 5 M NaCl (1/25th vol.) and 0.6 volume of isopropanol. DNA was
pelleted at 10,000 rpm for 10 min, washed with 80% ethanol, dried and resuspended in
high salt TE buffer (10 mM, Tris-HCl (pH-8.0) and 1M-NaCl). This DNA was again
extracted with chloroform/ isoamyl alcohol (24:1), and then the supernatant was transferred
and precipitated with ice-cold absolute alcohol. This was followed by a centrifugation at
12,000 rpm for 10 min at 40C and washing with 70% ethanol. The dried pellets were
dissolved in 50µl sterile distilled water. The quality and concentration of DNA were
estimated using a DNA ladder of known concentration and absorbance at 260 nm and
working stocks of DNA were prepared based on both the estimates.
PCR and amplification of Genomic DNA
DNA fingerprinting by RAPD
Decamer arbitrary oligonucleotides (Operon technologies, USA) were used for PCR
amplification following the procedure of Williams et al. (1990) and Khanuja et al. (1998)
with a few modifications. Amplification were performed in 25 µl volume of PCR reaction
mixture containing 25-50 ng genomic DNA as template, 0.6 unit of Taq polymerase, 100
µM of each of the dNTPs, 2.0 µl 10 x buffer, 1 µl of MgCl2 (15mM) and 5 pmol of primers.
The PCR were performed in a thermo- cycler programmed as follows.
Step 1: Initial denaturation at 940C for 5min
Step 2: Denaturation at 940C for 1.0 min
Annealing at 38.40C for 1.0 min
Extension at 720C for 2.0 min
Step 3: Final extension at 720C for 5 min
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45 cycles
Material and Methods
DNA fingerprinting by ISSR
Inter simple sequence repeat (ISSR) are the primers anchored at the 5' or 3' end of a
repeat region and extend into the flanking region. This technique allows amplification of
the genomic segments between inversely oriented repeats (ISSRs). Generally, a series of
single primers are used to generate series of fragments that are size-separated on either an
agarose gel or polyacrylamide. The same volume and concentration of reaction mixture
(mentioned for RAPD) was employed for ISSR amplifications. The PCR programming for
amplification is as follows:
Step 1: Initial denaturation at 940C for 5 min
Step 2: Denaturation at 940C for 1min
Annealing at 420C* for 2 min
Extension at 720C for 1 min
Step 3: Final extension at 720C for 5 min.
The annealing temperatures were optimized for each primer used. A rough estimate of the
annealing temperature (Tm) for the primer was calculated by using the following formula
(Suggs et al., 1981)
Tm = (G + C) ×4°C + (A + T) ×2°C
Where G, C, A and T are the numbers of these nucleotides in the primer, respectively.
(*Annealing temperature was set according to Tm value of used primers).
Gel electrophoresis
The amplification products (RAPD and ISSR) were differentiated by horizontal gel
electrophoresis using 1.4 % agarose in 0.5X TAE buffer and 20 μl amplicon product of
each sample with 2μl loading dye (1 times concentrated) was loaded on the agarose gel.
DNA marker of 100 bp was used to estimate molecular weight and size of the fragments.
Gel Electrophoresis was run for 3 hours at 100 V. The DNA was stained with (0.5mg/ml)
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40 cycles
Material and Methods
ethidum bromide (EtBr) which was mixed with warmed agarose gel before pouring in
casting tray. The gels were photographed with Image master VDS (Pharmacia).
3.2.7. Statistical analysis of RAPD and ISSR profile for genetic diversity
RAPD and ISSR profiles/bands were scored manually for each individual accession
from the gel photograph. The bands were recorded as discrete characters, presence ‘1’ or
absence ‘0’ and ‘?’for missing and ambiguous data. The resulting presence/absence data
matrix of the RAPD and ISSR were analyzed by using FreeTree software to estimate
genetic diversity parameters such as; the percentage of polymorphic bands (PPB) and the
genetic standard distance (Ds) between populations were computed using the formula
given by (Nei's, 1972).
To examine the genetic relationship among populations, a dendrogram was also
constructed by an un-weighted paired group method of cluster analysis using arithmetic
averages (UPGMA). Jaccard’s coefficient was calculated by using FREETREE software,
common estimator of genetic identity and similarities were calculated as follows:
Jaccard’s coefficient = NAB / (NAB+ NA+ NB)
Where NAB is the number of bands shared by samples, NA represents fragments in
sample B. Similarity matrices based on these indices were calculated. Similarity matrices
were utilized to construct the UPGMA (un-weighted pair group method to construct
arithmetic average) dendogram. Statistical stability of the branches in the cluster was
estimated by bootstrap analysis with 1,000 replicates, using the Tree-Free software
program.
3.2.8. Antibacterial bioassay
Disk-diffusion assay (Bauer et al., 1996) was conducted to get an idea about the
antibacterial activity of essential oil of Ocimum germplasm collections against some
selected pathogenic microbes. The inoculums of the test microbes were prepared up to a
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Material and Methods
density equivalent to the McFarland Standard 0.5. Uniform lawn of each of the test bacteria
(Gram positive and Gram negative bacteria) were prepared using 100 ml inoculum of the
specified bacteria on nutrient agar plate. The filter paper (What man No.6 were soaked with
neat essential oil (8 µl) and placed over the seeded plates. The plates were incubated at
37oC for 24 hrs. The activity was measured in terms of zone of microbial growth inhibition.
The net zone of inhibition was determined by subtracting the disc diameter (6.0 mm) from
the total zone of inhibition shown by the test disc in terms of clear zone around the disc.
3.2.9. Statistical analysis of agro-morphological traits for genetic divergence study
The statistical analysis of 10 economic traits was performed with the help of
analysis of variance (ANOVA) technique (statistical software CIMAP ver.1. based on
(Singh and Chaudhary, 1979) as applicable to Randomized Block Design (Panse and
Sukhatme, 1978). Variance (F) ratio was applied to test the significance of treatment
variance versus error variance. Least significant difference (LSD) values at 5% and 1%
probability level (5% and 1%) were computed by multiplying standard error of difference
(SED) values with tabulated t values, for assessing the significance of differences between
any two treatment means.
Analysis of variance (ANOVA)
Xijk =m+gi + rj + eijk
Where, Xijk is kth observation in jth replication of ith treatment (genotypes) , m is the grand
mean gi is the effect of ith genotype (treatment), rj is the effect of block jth replication. eijk is
the environmental effect associated with ijk th observation.
Source Main effect Degree of freedom Mean squre F (expected)
Block (Replications) r-1 Rms Rms /Ems
Treatment (Genotypes) g-1 Gms Ems (Gms-Ems)/r
Error (r-1)(g-1)
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Material and Methods
Correlation coefficient:
Correlation coefficient between independent (x) and dependent (y) variables were
calculated at phenotypic (rp), genotypic (rg) and environmental (re) levels using variance
and covariance from sub-section A as under.
rp = Cov (p) xy/ . ) 1/2, rg = Cov (g) xy/ . ) 1/2, re = Cov (e) xy/ . ) ½
Multivariate analysis
The genetic divergence among 15 Ocimum accessions was determined on the basis
of ten characteristics using Mahalanobis (1936) D2 statistic and canonical analysis (Rao,
1952) as follows;
D2 statistic
The Mahalanobi’s generalized distance (D2) between any two populations is defined
as:
D2= ∑ ∑ λij di dj
Where,ij-The matrix reciprocal to the common dispersion matrix (error variance and
covariance) and di and dj were the difference between the mean values of the two
populations for ith and jth characters, respectively. The analyses were involved as following
steps:
Assessment of component of variance among the accessions
Variance due to genotypes, i.e. the genotypic variance
was calculated as the difference (MSG-MSE) over number of replications, i.e. =
(MSG-MSE)
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Material and Methods
Phenotypic variance ):
Phenotypic variance being the sum total of the variation among genotypes and the
environmental variation was calculated as = +
Heritability in broad sense ( 2 (bs)
It was calculated as the ratio of total genotypic variance to the phenotypic variance in
broad sense ( 2 (bs)) as 2
(bs) = + (Lush, 1948).
Genetic advance (GA)
It was calculated by following formula (Robinson, 1949).
GA = i x 2 (bs) x
Where, i is the standardized selection differential (i.e. the difference in mean of a
genotype from the base population means at 1% selection intensity, i.e. I =2.64),) 2 (bs) and
are the usual notations described above.
Computation of D2 values
The D2 values representing divergence between two accessions were obtained as the
sum of squares of differences in the value of y’s associated with the two genotypes for each
component.
Group constellation:
(i) The grouping by visual observation of the relative size of D2 between any two accessions,
and (ii) Final grouping confirmed by Tocher’s (Rao, 1952)
Intra and inter cluster divergence:
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Material and Methods
After final grouping, the intra-cluster divergence was obtained from the last column
of Tocher’s table itself. The inter-cluster divergence was calculated by averaging the total
D2 values between any two accessions included in different clusters. The cluster means for
each character were calculated by averaging each of the x’s values for all genotypes
included in each cluster.
Canonical analysis
The spatial distances among individual accession could also be determined by
canonical analysis. For this the uncorrelated y-variables obtained under D2 statics were
further exploited. λ1- λ2 chart were prepared by the first two canonical vectors supplied
the best two linear functions Z1 and Z2. Since λ1+ λ2 accounted for more than 70 % of the
total variance, λ1- λ2 was found to be adequate. Further, the y-values and then mean value
of Z1 and Z2 were calculated. The two dimensional representation was depicted having the
canonical variation λ1 and λ2 as the coordinate axes.
3.2.10. G x E interaction and stability analysis
In order to analyses the Gene x Environment interaction, the comparative
expression patterns of distinct characters were studied across the locations. The stability
model proposed by Eberhart and Russell (1966) was adopted to analyze the data over three
environments. The model involves the estimation of three stability parameters like mean
(µi), regression coefficient (bi) and deviation from regression (S2di), which are defined by
the following mathematical formula.
Yij = µi + βiIj + ij
Yij: Mean of ith genotype at the jth environment (i=1, 2, 3, 4, 5….22, j = 1, 2, 3)
μi: The mean of ith genotype over all the environments
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Material and Methods
∑i: The regression coefficient that measures the response of i th genotype to varying
environment
∑ij: The deviation from regression of the ith genotype of jth environment
Ij: The environmental index obtained by subtracting the regression of the ith
Stability parameters
The mean (µi), the regression coefficient (bi) and mean square deviation from linear
regression line (S2di) are the three stability parameters proposed by Eberhart and Russell
(1966) in their stability model. These parameters were computed by using the following
formula.
a) Genotype the grand mean from the mean of all genotype at jth environment.
µi (mean) = ∑jYij / s
b) Environmental index Ij
Ij =
Where, t = Number of varieties, s = Number of environments with jIj = 0
c) The regression coefficient for each germplasm.
bi =
(d) Deviation from regression S2di =
Where, S2e/r = Mean square for (estimate of) pooled error
(e) Predicted performance of the genotypes
Yij = + biIj
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Material and Methods
Where, is the estimate of bi
In this model the variance due to environments and Genotype × Environment (G x E) are
partitioned into environment (linear), genotype × environment (linear) and deviations from
the regression coefficient.)
ANOVA for stability
Source of variation D.F SS MS F valueGenotypes (G) (g-1) SiSjY2ij -CF MS1 MS1/MS3Environment (E) (n-1) 1/nSjY2i – 1/n SjY2iG x E (g-1)(n-1) SiSj Y2ij – 1/nSjY2iEnvironment (linear) 1 1/g [(SjYj - Ij)2 SjIj2 ]Genotype x Environment (Linear)
(g-1) Sj [(S Yij Ij)2 / Sj Ij2]- 1/g [(SjYj - Ij)2 SjIj2]
MS2 MS2/MS3
Pooled deviation g (n-2) Si Sj d2ij2 MS3 MS3/MS4Pooled error n(g-1)(r-1) MS4Total (ng – 1) Si Sj Y Ij2 – CF TSSWhere,
n = Number of environments, r = Number of replications
g = Number of genotypes, CF = Correction factor
(a) To test the significance of pooled deviation mean square against the pooled error mean
square
F = MS3/ MS4
If pooled deviation mean square is found significant then it is the appropriate denominator
to test the significance of all components including genotypes, genotype G x E
environment (linear). Otherwise pooled error mean square is appropriate denominator.
(b) To test the significance of the differences among the means of genotypes.
F = MS1/ MS3
(c) To test that the genotypes do not differ for their regression on environmental index
F = MS2/ MS3
Further, t’ test was used to test the significance of deviation of ‘bi’ from unity
t =1-bi / S.E (bi)
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Material and Methods
Where ,
SE (bi) =
ij is the deviation of ith variety in jth environment from regression
d) To test individual deviation from linear regression
F = / Pooled error mean square
A joint consideration of three parameters, that is;
(i) The mean performance of the genotype over environments (location),
(ii) Regression coefficient bi and
(iii) The deviation from linear regression S2di, are used to define stability of genotype
(variety).
The estimate of deviation from regression suggests the degree of reliance that should be put
to linear regression in interpretation of data. If these values are significantly deviating from
zero, the expected phenotype cannot be predicted satisfactorily. When deviations are non
significant, the conclusions, may be drawn by joint consideration of mean yield and
regression values (Finlay and Wilkinson (1963) and Eberhart and Russell (1966) as
described below;
Regression Stability Mean yield
Remarks
bi = 1 Average High Well adapted to all the environments
bi = 1 Average Low Poorly adapted to all environments
bi > 1 Below average High Specifically adopted to favourableenvironments
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