3. materials and methods - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/77654/9/09-chapter...
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3. MATERIALS AND METHODS
The present investigation on ―Gene action for agromorphological and quality
traits in Linseed (Linum usitatissimum L.)‖ was carried out at the Experimental Farm of
the Department of Crop Improvement, CSK HPKV, Palampur and Rice and Wheat
Research Centre (RWRC), Malan of CSK HPKV, Palampur during rabi season 2012-13.
The crossing work was done during off season at Hill Agricultural Research and
Extension Centre (HAREC), Kukumseri (Lahaul & Spiti). Molecular studies were
conducted in the Department of Agricultural Biotechnology of CSK HPKV, Palampur.
The details of materials used and methods employed in the present study to understand
the nature of combining ability, type of gene action governing the inheritance of
economic characters and the nature and extent of heterosis in linseed are described under
the following sub-heads:
3.1 General description of the experimental sites
The present investigation was carried out at the experimental farm of the
Department of Crop Improvement, CSK HPKV, Palampur, situated at an elevation of
1290.8 m above mean sea level with 32°80' N latitude and 76°33' E longitude,
representing mid-hill zone of Himachal Pradesh, characterized by humid sub-temperate
climate with annual rainfall 2500 mm, having acidic soil with pH ranging between 5.0 to
5.6. The Rice and Wheat Research Centre, (RWRC, Malan) Malan is situated at an
elevation of 950 m above mean sea level with 32°07' N latitude and 76°23' E longitude
representing sub humid, mid-hill conditions having annual rainfall of 1234 mm, having
silty clay loam soil with pH ranging between 5.8 to 6.0. The Hill Agricultural Research
and Extension Centre (HAREC), Kukumseri is situated at an elevation of 2672 m above
mean sea level with 32o44'55"N latitude and 76
o41'23"E longitude representing high hills
temperate dry zone with average rainfall of 250 mm per annum. The soil is sandy to sandy
loam in texture, neutral in reaction, and low to medium in fertility. The monthly
meterological data during the crop period i.e. rabi 2012-2013 at Palampur and Malan are
given in Appendix-I.
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Location Altitude
(m)
Latitude Longitude Annual
rainfall (mm)
Palampur 1290.8 32°80´ 76°33´ 2500
Malan 950 32°07´ 76°23´ 1234
Kukumseri 2672 32o44'55" 76
o41'23" 250
3.2 Experimental materials and Layout plan
The experimental material for the present study comprised of 26 linseed (Linum
usitatissimum L.) genotypes, amongst which Nagarkot, Him Alsi -1 and Jeewan were
used as standard checks. Two agronomically superior and genetically diverse lines,
Belinka-60 and Surbhi and F1 (Belinka-60 × Surbhi) between them, were used as testers
‗L1‘, ‗L2‘ and ‗L3‘, respectively. These three testers were used as male parents for
crossing with 26 lines (females) to develop 78 TTC progenies in the form of 52 single
crosses and 26 three way crosses. The parentage of linseed genotypes used in the present
investigation is given in Table 3.1.
3.2.1 Methods
3.2.2 Crossing Plan
The crosses were attempted as per triple test cross design proposed by Kearsey
and Jinks (1968) and Jinks et al. (1969). During summer, 2011 i.e. off season, F1
(Belinka-60 × Surbhi) crosses were attempted at HAREC, Kumkumseri (Lahaul & Spiti)
and sufficient F1 seed produced. In the next rabi season (2011-12), the aforementioned
linseed twenty six lines were crossed to three testers, viz., Belinka-60 (L1), Surbhi (L2)
and their F1 (L3), to generate 3n progenies (78 TTC crosses) at the Experimental Farm of
Department of Crop Improvement. In order to increase the seed of 3n progenies, crosses
were again attempted at HAREC, Kukumseri (Lahaul & Spiti), during summer, 2012.
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(A)
(B)
Plate 3.1 (A and B) View of crossing block
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Table 3.1 List of linseed accessions/lines and their parentage/source used in the
study
S.
No.
Lines/Testers Parentage/Source Type
Lines
1. Belinka Exotic flax material Fibre type
2. Flak-1 Exotic flax material Fibre type
3. Lauro Exotic flax material Fibre type
4. Ariane Exotic flax material Fibre type
5. Giza-5 Exotic flax material Fibre type
6. Giza-6 Exotic flax material Fibre type
7. Giza-7 Exotic flax material Fibre type
8. Giza-8 Exotic flax material Fibre type
9. KL-236 Jeewan × Janaki Dual purpose
10. KL-240 RLC-29 × NL-93 Seed type
11. KL-244 (RLC-29 ×Jeewan) × RLC-29 Seed type
12. KL-254 KL-223 × KL-221 Seed type
13. KL-255 KL-210 × KL-224 Seed type
14. KL-256 KL-210 × Flak-1 Seed type
15. KL-257 LC-2323 × KLS-1 Seed type
16. KL-263 KL-223 × KL-224 Seed type
17. Nagarkot New River × LC-216 Dual purpose
18. T-397 T491 × T1193-2 Seed type
19. Chambal Local Chambal × RR-45 Seed type
20. Binwa Flak-1 × SPS, 47/ 7-10-3 Seed type
21. Him Alsi-1 K2 × TLP-1 Seed type
22. Him Alsi-2 EC-21741 × LC-216 Dual purpose
23. Jeewan Sumit × LC-216 Dual purpose
24. Baner EC-21741 × LC-214 Seed type
25. Bhagsu RL-50-3 × Surbhi Seed type
26. Himani DPL-20 × KLS-1 Seed type
Testers
1. Belinka-60 (L1) Exotic flax material Fibre type
2. Surbhi (L2) LC-216 × LC-185 Seed type
3 Belinka-60 × Surbhi (L3) F1
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3.2.3 Experimental design and layout
All the 78 crosses (L1i, L2i and L3i) and the parents were grown in a completely
randomized block design with three replications during rabi 2012-13 at two locations
viz., Palampur (E1) on 7 Nov., 2012 and Malan (E2) on 24th
Oct., 2012. Each cross/ parent
was raised in single row, 1.5 m long with row to row and plant to plant spacings of 30 cm
and 5 cm, respectively. Plot size was 0.5 m2 (1 row, 1.5 m long and 30 cm row spacing).
A pre-sowing irrigation was given to ensure proper germination. The experimental field
was well prepared and FYM was added before sowing. The recommended dose of
fertilizer (50 Kg N, 40 Kg P2O5 and 20 Kg K2O/ha) was applied. Half dose of nitrogen
and full dose of phosphorous and potash was applied as basal and the remaining half
nitrogen was top dressed after 2 months of sowing. Irrigation was given whenever
required and regular weeding was done to keep the trial free from weeds.
3.3 Recording of observations
The data were recorded on ten randomly selected competitive plants in each of
seventy eight TTC progenies and parents in each of three replications for all the
characters except for days to 50 per cent flowering and days to 75 per cent maturity,
which were observed on plot basis at both the locations. Observations were recorded for
the following characters:
3.3.1 Morphological and yield contributing characters
1. Days to 50 per cent flowering
The number of days taken from the date of sowing to 50 per cent blooming of
the plants was recorded.
2. Days to 75 per cent maturity
The number of days taken from the date of sowing to 75 per cent maturity of the
capsules was recorded.
3. Plant height (cm)
The height was measured in centimeters from the base upto the end of the main
stem at the time of maturity.
4. Technical height (cm)
The height of the plant from the ground surface to the point from where the
primary branches starts, was recorded in centimeters at the time of maturity.
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5. Primary branches per plant
The numbers of branches emerging directly from the main stem were counted
for each plant.
6. Secondary branches per plant
The numbers of branches emerging from the primary branches were counted.
7. Capsules per plant
Total numbers of capsules were counted from ten randomly selected plants per
plot and the mean value was obtained.
8. Seeds per capsule
Total numbers of seeds in ten randomly selected capsules per plant of each
randomly sampled plant per plot were worked out and then averaged.
9. Seed yield per plant (g)
The average yield of ten randomly selected plants were recorded after threshing.
10. 1000-seed weight (g)
Randomly one thousand seeds of each genotype from each plot were obtained
and weighed in grams.
11. Straw yield per plant (g)
Weight of each selected plant individually was recorded in grams after
threshing.
12. Biological yield per plant (g)
Ten randomly selected sun dried plants were weighed (g) and average weight
per plant was calculated.
13. Harvest index (%)
It was calculated as
Seed yield per plant (g)
× 100
Biological yield per plant (g)
3.3.2 Fibre and quality characters
14. Retted-straw weight per plant (g)
The stalks obtained from selected plants from each genotype were subjected to
retting in controlled retting tank at a temperature of 28 ±20C. This temperature in
retting tank was maintained by adding warm water. After retting the stalks for 5-6 days,
these were sun dried and weight was taken in grams.
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15. Fibre yield per plant (g)
The straw after retting was scutched with the help of power driven scutching
machine and weight of fibre was recorded.
16. Oil content (%)
Oil content of each genotype was determined by nuclear magnetic resonance (NMR)
at Chandar Shekhar Azad University of Agriculture and Technology, Kanpur.
3.4 Reaction to diseases
1.) Reaction to powdery mildew (Oidium lini)
2.) Reaction to rust (Melampsora lini)
3.) Reaction to wilt (Fusarium oxysporum f. sp. lini)
All the parents and F1‘s were screened for reaction to these diseases under natural
conditions and observations on disease severity were recorded on the basis of visual
observations.
3.4.1 Disease assessment
Data on disease severity of powdery mildew as well as rust on leaves was
recorded on 100 days after sowing on 10 plants sampled randomly from each plot and
disease scoring was done as per the scale of AICRP (1991). The level of disease
resistance/susceptibility of the parents and their crosses was determined by percentage
disease index (PDI) following the formula of McKinney (1923);
Total sum of all numerical rating
PDI = × 100
Number of observations taken × maximum disease score
Observations on the incidence of fusarium wilt disease were recorded at weekly
intervals, starting from one month after the sowing as per the scale of Snyder and Hansen
(1940).
Number of plants infested
Incidence of wilt (%) = –––––––––––––––––––––––– × 100
Total number of plants
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Table 3.2 Scale (0-5) for rating of parents and F1’s for reaction to powdery mildew
Score Disease intensity
(% area of leaves/plant infected)
Rating
0 Free from disease Highly resistant HR
1 1 to 10 Resistant R
2 11 to 25 Moderately resistant MR
3 26 to 50 Moderately susceptible MS
4 51 to 75 Susceptible S
5 Above 75 Highly susceptible HS
Table 3.3 Scale (0-5) for rating of parents and F1’s for reaction to rust
Grade Disease
intensity (%)
Description for rust Rating
0 Free from
disease
No pustule formation Highly resistant HR
1 1 to 10 Few scattered and scanty
pustules seen after careful
searching
Resistant R
2 11 to 25 Pustules common and seen
early on planting
Moderately
resistant
MR
3 26 to 50 Pustules very common Moderately
susceptible
MS
4 51 to 75 Pustules extensively present
on whole plant ,defoliation
and drying of leaves
Susceptible S
5 Above 75 Pustules extensively present
on whole plant, defoliation,
drying of leaves and branches
and ultimately complete
drying of plant
Highly
susceptible
HS
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Table 3.4 Scale (0-9) for rating of parents and F1’s for reaction to fusarium wilt
Scale Wilt (%) Category
0 No symptoms of wilt Highly resistant
1 1 % or less plants wilted Resistant
3 1-10 % plants wilted Moderately resistant
5 11-20 % plants wilted Moderately susceptible
7 21-50 % plants wilted Susceptible
9 51 % or more plants wilted Highly susceptible
3.5 Statistical analysis
3.5.1 Analysis of variance
Data was statistically analysed as per the procedure given by Panse and Sukhatme
(1984). The analysis of variance was based on the following linear model.
where,
= Phenotypic observation of ith
genotype grown in jth
replication
= General population mean
= Effect of ith
genotype
= Effect of jth
replication
= Error component of ith
genotype in jth
replication
3.5.2 On the basis of this model, the analysis of variance was done as follows:
Source of
variation
Degree of
freedom
Sum of
squares
Mean sum of
squares
F-ratio
Expected
mean sum of
squares
Replications (r) (r-1) Sr Mr = Sr/(r-1) Mr/Me σ2e + gσ
2r
Genotypes (g) (g-1) Sg Mg = Sg/(g-1) Mg/Me σ2e + rσ
2g
Error (e) (r-1) (g-1) Se Me = Se/(r-1) (g-1) σ2e
Total (rg-1) -
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where,
r = Number of replications
g = Number of genotypes
σ2r = Variance due to replication = Mr
σ2g = Variance due to genotypes = Mg
σ2e = Error variance = Me
The replications and genotypes mean sum of squares were tested against error
mean squares by ‗F‘ test for (r-1), (r-1)(g-1) and (g-1), (r-1)(g-1) degrees of freedom at
P=0.05 and P=0.01.
3.5.3 Analysis of variance for pooled over the environments was done as per following;
Source of variation Degree of
freedom
Mean sum
of squares
Expected mean sum of
squares
Replications (within
environments)
E(r-1) Mr -----
Environments (E-1) ME
Genotypes (g-1) Mg 2e + r
2g × E + rE
2g
Genotypes × Environment (g-1)(E-1) Mg × E 2e + r
2g × E
Pooled error E(g-1)(r-1) Me(C) 2e
Where,
r = Number of replications,
E = Number of environments,
g = Number of genotypes,
2e = Error variance,
2g × e = Variance due to genotype × environment interaction, and
2g = Variance due to genotypes.
(Error SS at E1 + Error SS at E2)
Me(C) = ––––––––––––––––––––––––––––––
(df at E1 + df at E2)
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The replications (within environments), environments, genotypes, genotype ×
environment mean squares were tested against pooled error mean squares by ‗F‘ test for
E (r-1), E (g-1) (r-1); (E-1), E(g-1) (r-1); (g-1), E(g-1) (r-1) and (g-1) (E-1), E (g-1) (r-1)
degrees of freedom at P = 0.05 per cent level of significance (P=0.05) and 0.01 per cent
level of significance (P=0.01).
From these analysis, the following standard errors were calculated where the ‗F‘
test was significant.
Standard error for the entry mean:
SE (m) for Individual environment = + (Me/r)1/2
SE (m) for Pooled environment = + (Me(C)/rE)1/2
Standard error for the difference of entry mean:
SE (d) for individual environment = + (2Me/r)1/2
SE (d) for pooled environment = + (2Me(C)/rE) 1/2
The critical difference (CD) at 5 per cent level of significance was obtained by
multiplying SE(d) by the table value of ‗t‘ at error degree of freedom and P = 0.05.
CD = SE (d) × ‗t‘ value at error degree of freedom and P = 0.05
Coefficient of variation (CV) % = (Me1/2
or Me(C)1/2
/general mean) × 100
3.6 Triple test cross analysis
The information on the genetic architecture of the material under investigation
was gathered through triple test cross design. The analysis of this design is divided into
two parts:
(i) Test for epistasis and the adequacy of the model, and
(ii) estimation of additive and dominance components of variation
3.6.1 Test for the detection of epistasis
3.6.1.1 Test for the detection of epistasis in individual environments
The presence of non-allelic interaction can be determined by using the model
proposed by Kearsey and Jinks (1968). This test is based on the following comparison:
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Test Comparison Reference
i1L + i2L - 2 i3L 1 1 -2 Kearsey and Jinks (1968)
The test i1L + i2L - 2 i3L is unambiguous and always test the presence of
epistasis for non-common loci between the L1 and L2 testers. i1L , i2L and i3L are mean
of the ith
family with respect to the tester concerned.
The analysis of variance to detect the presence/absence of epistasis has been
performed with the following partitioning.
3.6.1.1.1 Analysis of variance to detect the presence of epistasis and its further
partitioning
Source of variation Degrees of freedom
Epistasis n
(i) type 1
(j+l) type (n–1)
Epistasis × replication (r–1)n
(i) type × replication (r–1)
(j+l) type × replication (n–1)(r–1)
Error (within family) 3nr(m–1)
where,
n = number of lines/males/TTC families,
m = average number of plants and
r = number of replications.
The epistasis sum of squares for ‗n‘ degrees of freedom was further
partitioned into (i) type (homozygote × homozygote) of epistatic interaction having ‗l‘
degrees of freedom and (j+l) type of epistatic interaction, i.e. the homozygote x
heterozygote and heterozygote × heterozygote interactions, having (n–1) (r-1) degrees of
freedom. Similarly, the sum of squares due to replication × epistasis for (r–1) n degrees
of freedom was divided into replication × epistasis (i type) and replication × epistasis (j
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and l type) with (r–1) and (r–1) (n–1) degrees of freedom, respectively. Each of the three
types of epistasis was tested against their respective interaction with replications using
‗F‘ test at 5 per cent level of significance.
3.6.1.2 Test for the detection of epistasis in pooled over the environments
The interaction between the progeny families of a triple test cross and the
environments of their testing was also calculated. If m (5) individuals, of each of the L1i,
L2i and L3i families of a triple test cross, where i= 1 to n, were raised in each of the
environments, the following items from the analysis of variance can be extracted;
3.6.1.2.1 Analysis of variance of triple test cross in varying environments
Source of variation Degrees of freedom
Epistasis n (e-1)
Epistasis (i) type 1
Epistasis (j and l) type (n–1)
Environment (e-1)
Epistasis × Environment n(e-1)
Epistasis (i) × Environment (e-1)
Epistasis (j and l) × Environment (n–1)(e–1)
Block within environment e(n-1)(r-1)
3.6.2 Estimation of additive and dominance components of variation
In the absence of epistasis the estimation of additive and dominance
components of variation now proceeds. In the present study, the additive (sums) and
dominance (differences) components of variation have been computed irrespective of the
presence or absence of epistasis for the characters under study in order to determine their
relative magnitude for various interactions.
The additive (D) and dominance (H) components of genetic variation were
estimated from the following orthogonal comparisons (Kearsey and Jinks 1968);
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Comparison Testing for
Sums 1 1 1 Additive Component
Differences 1 -1 0 Dominance Component
Where all the three kinds of crosses are made, an alternative analysis is
possible in which all comparisons among the three kinds of progeny means i.e.
, and are orthogonal to one another (Jinks and Perkins 1970). These are;
Comparison Testing for
1 1 1 1 Additive component
2 1 -1 0 Dominance component
3 1 1 -2 Epistasis component
For testing the significance, the analysis of variance will be as under:
(i) Analysis of variance for sums and differences in individual environments
Source of variation Degrees of
freedom
Mean sum of
squares
Expected mean sum of
squares
Analysis of sums
Replication r–1
Sums n–1 MS3
m
1e
2+sr
2+3rs
2
Sum × replication (n–1)(r–1) MS2
m
1e
2+sr
2
Error (within family) 3nr (m–1) MS1
m
1e
2
Analysis of differences
Replication r–1
Difference ) n–1 MS3
m
1e
2+dr
2+2rd
2
Difference × replication (n–1)(r–1) MS2
m
1e
2+dr
2
Error (within family) 2nr(m–1) MS1
m
1e
2
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where,
n = number of males,
m = average number of plants per progeny,
e2
= variance due to error,
s2 = variance due to sums and
d2 = variance due to differences.
(ii) Analysis of variance for sums and differences in pooled over the
environments
Source of variation Degrees of
freedom
Mean sum
of squares
Expected mean sum of
squares
Analysis of sums
Sums (n-1) MS3
Environment (e-1)
Sums × Environment (n-1)(e-1) MS2
Block within environment e(n-1)(r-1) MS1
Analysis of difference
Source of variation Degrees of
freedom
Mean sum
of squares
Expected mean sum of
squares
Differences (n–1) MS3
Environment (e-1)
Differences ×
Environment
(n-1)(e-1) MS2
Block within environment e(n-1)(r-1) MS1
3.6.3 Average degree of dominance
On a simple additive-dominance model, the additive and dominance
components of variation were estimated as;
s2 =
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D = 8n
1=i
∑uvdi2 = 8s
2
d2
=
H = 8n
1=i
∑uvhi2 = 8d
2
The average degree of dominance was computed from the estimated
components of D and H as bellows:
Average degree of dominance (
)
where,
H = Dominance genetic variances
D = Additive genetic variances
3.6.4 Covariance (sums/differences)
In the absence of epistasis and correlated gene distribution, this covariance has
the expectation;
Cov sums/differences = -n
1=i
∑uvdihi = -¼ F
F, therefore, has the same coefficient as D and H, but measures the sum of
products of the d and h terms. Both the magnitude and the sign of covariance provide
information about the magnitude and direction of dominance, which supplements that
obtained from d2.
F may be estimated as covariance of sums and differences and its significance determined
as the correlation of sums and differences (Jinks et al. 1969).
3.6.5 Estimation of correlation coefficient
To determine whether the covariance is significant, it can be converted into a
correlation coefficient with (n–3) degrees of freedom.
√
A number of situations can occur in practise each of which has its own
interpretation. These are:
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(a) d2 is significant and r(sums/differences) is also significant
This means that there is a dominance contribution to the variation and the
dominance is predominantly in one direction. By examining the sign of ‗F‘ (which is the
opposite of the sign of covariance), the pre-dominant direction of the dominance effects
can be determined. If F is positive, then the increasing alleles are dominant more often
than the decreasing alleles, if F is negative, the decreasing alleles are predominant more
often than the increasing alleles.
(b) d2 is significant and r(sums/differences) is non-significant
This means that there is a dominance contribution to the variation but the
dominance is ambidirectional, increasing and decreasing alleles being dominant and
recessive to the same extent.
(c) d2 is non-significant and r(sums/differences) is non-significant
This means that there is no evidence of dominance contribution to the
variation.
(d) d2 is non-significant and r(sums/differences) is significant
This is trivial and could only arise as a result of sampling error.
3.7 Line x Tester analysis
The replication wise mean values of F1‘s generation of 52 crosses for each trait
were subjected to statistical analysis using the following model suggested by Kempthorne
(1957) after excluding the L3i families and the F1 tester.
Yijk = µ + gi + gj + Sij + eijk
Where,
Yijk = value of the ijkth
observation of the cross involving ith
line and jth
tester in kth
replication,
µ = general mean (an effect common to all crosses in all replications),
gi = general combining ability (GCA) effect of ith
line,
gj = general combining ability (GCA) effect of jth
tester,
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Sij = specific combining ability (SCA) effect of the cross involving
ith
line and jth
tester,
eijk = error associated with ijkth
observation,
i = ith
line (1, 2, 3………28),
j = jth
tester (1, 2), and
k = kth
replication (1, 2 and 3)
3.7.1 Analysis of variance for combining ability
(Partitioning crosses sum of squares)
Source of
variation
Degree of
freedom
Sum of squares Mean sum
of squares
Expected mean
sum of squares
Replications (r-1)
fmr
...x -
fm
(x..k) 2r
1=k
2
∑ - -
Crosses (fm-1)
fmr
...x -
r
.x 2fm
1=ij
ij2
∑ - -
Lines (f-1)
fmr
...x _
mr
..x 2f
1=i
2i∑ Mf
2e+r
2fm+rm
2f
Testers (m-1)
fmr
...x
fr
.x 2m
1=j
2j∑ Mm
2e+r
2fm+rf
2m
Lines x
Testers
(f-1) (m-1)
-r
.xfm
1=ij
2ij∑ -
mr
..xf
1=i
2i∑
fmr
...x
fr
..x 2m
1=j
2j∑
Mfm 2e+r
2fm
Error (fm-1) (r-1) By difference Me
2e
Total (fmr-1)
∑f
1=i
∑m
1j=
∑r
1=k
x2ijk –
fmr
...x
2
- -
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Where,
f = number of lines/ females,
m = number of testers/ males,
x..k = sum of kth
replication of crosses,
x… = sum of all crosses of all lines and testers over all
replications,
xij. = sum of ijth
hybrid combination over all replications,
xi.. = sum of ith
line over all testers and replications,
xj.. = sum of jth
tester over all lines and replications,
xijk = ijth
observation in kth
replication,
Mf = mean squares due to lines,
Mm = mean squares due to testers,
Mf × m = mean squares due to line x tester interactions,
Me = error mean squares,
2
f = variance due to lines/ progeny variance arising from
differences among female parents/lines,
2
m = variance due to testers/ progeny variance arising from differences
among male parents/testers,
2
f × m = variance due to lines x testers / progeny variance arising from the
interaction of the contribution of female and male parents, and
2
e = environmental variance / error variance among individuals from
same mating
3.7.2 Estimation of general and specific combining ability effects
The gca and sca effects were obtained from the two way table of female parents
vs. male parents in which each figure was total over replication. The individual effects
were estimated as follow:
(i) GCA effects of ith
line
Xi.. X…
gi = –––– — –––––
mr fmr
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Where,
X… = sum total of all crosses,
Xi.. = total of ith
female parent over all males and replications,
r = number of replications,
f = number of lines/female parents, and
m = number of testers/male parents
(ii) GCA effects of jth
tester
Xj.. X…
gj = –––– — –––––
fr fmr
Where,
Xj.. = total of jth
male parent over all females and replications
(iii) SCA effects of ijth
cross
Xij. Xi.. Xj.. X…
ijs = –––– — ––––– — ––––– + ––––
r mr fr fmr
Where,
Xij. = ijth
combination total over all replications
(iv) Standard errors for different combining ability effects
(a) SE (gi) lines = mr
Me±
(b) SE (gj) testers = fr
Me±
(c) SE ( ijs ) crosses = rMe±
(d) SE ( ig - jg ) lines = mr2Me± = SE (D1)
(e) SE ( ig - jg ) testers = fr2Me± = SE (D2)
(f) SE ( ijs -Skl) crosses = r2Me± = SE (D3)
Where,
Me = mean sum of squares due to error
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3.7.3 Pooled analysis of variance for combining ability
Source of
Variation
Degree
of
freedo
m
Sum of squares Mean
sum of
squares
Expected
mean sum of
squares
Environments (E-1)
E
1n
22
mfrE
....x
mfr
....nx --- ---
Testers (m-1)
m
1j
22
j
mfrE
....x
mrE
....x
M1 2e+r
2fmE+
rE2
fm+rEf2
m
Lines (f-1)
f
1i
22
i
mfrE
....x
mrE
....x M2
2e+r
2fmE+
rE2
mf+rEm2
f
Lines ×
Testers
(f-1)
(m-1)
mf
1ij
2
ij
mrE
....x
m
1j
2
j
mrE
....x
f
1i
22
i
mfrE
....x
mrE
....x M3 σ2
e+ rσ2
fmE
+rE2
mf
Testers × Env. (m-1)
(E-1)
mE
1jn
m
1j
22
jE
1n
..n22
j.nA
mfrE
...x
frE
...x
mfr
x
fr
...x M4 σ2e+ rσ
2fmE +
rfσ2
Me
Lines × Env. (f-1)
(E-1)
fE
1in
f
1i
22
iE
1n
..n22
i.n BmfrE
...x
frE
...x
mfr
x
mr
...x M5 σ2e+ rσ
2fmE +
rmσ2
fE
Lines ×
Testers × Env.
(m-1)
(f-1)
(E-1)
BAmfrE
...x
mfr
..nx
rE
x
r
...xmfE
1ijn
E
1n
22mf
1i
ij22
ij.n
j
M6 σ2e+ rσ
2fmE
Pooled error E (mf-1)
(r-1)
Error as at environment-I +
Error as at environment-II +
Me σ2
e
Where,
m = number of males,
f = number of females,
E = number of environments,
r = number of replications at each environment,
x… = sum of all crosses of all lines, testers, replications and over all
environments,
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x…n = sum of all crosses of all lines and testers over replications,
x.j... = sum of jth
testers over all lines, replications and environments,
xi… = sum of ith
lines over all testers, replications and environments,
xij… = sum of ijth
cross over all replications and environments,
xj.n = sum of jth
tester over all lines and replications at nth
environment,
xij.n = sum of ijth
cross over replications at nth
environment, and
Me = Pooled error mean square.
Combined general and specific combing ability effects were estimated as follows:
(i) Estimation of general mean
μ = mfre
x...
Where,
x… = total of all crosses over all replications in all environments
(ii) gca effects of ith
line
xi... x…
gi = ––––– – ––––––
mrE mfrE
Where,
xi… = sum of ith
lines over all testers, replications and environments
E = number of environments
(iii) gca effects of jth
tester
xj.. x….
gj = ––––– – –––––
frE mfrE
Where,
xj… = sum of jth
testers over all lines, replications and environments
(iv) SCA effects of ijth
cross
xij xi xj x…
Sij = –––– – ––––– – ––––– - –––––––
rE mrE frE mfrE
Where,
xij.. = ijth
cross total over all replications and environments
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(v) Standard error for pooled cobinedcombining ability effects
(a) SE pooled (gi) lines = (Me/rmE)1/2
(b) SE pooled (gj) testers = (Me/rfE)1/2
(c) SE pooled (Sij) crosses = (Me/rE)1/2
(d) SE (gi– gj) lines = (2Me/mrE)1/2
= SE (D1a)
(e) SE (gi - gj) testers = (2Me/frE)1/2
= SE (D2a)
(f) SE (Sij - Skl) crosses = (2Me/rE)1/2
= SE (D3a)
(vi) Test of significance for GCA and SCA effects
There are two methods
Method-I
GCA and SCA effects > [(SEgi/SEgj/SEsij) ×‗t‘ tab at error degree of freedom and
P = 0.05] were marked significant (*).
Method-II
(a) ti (cal) for GCA of lines (females) = (gi – 0)/SE (gi)
(b) tj (cal) for GCA of testers (males) = (gj – 0)/SE (gj)
(c) tij (cal) for SCA of crosses = (Sij – 0)/SE (Sij)
where,
ti (cal), tj (cal) and tij (cal) are the calculated ‗t‘ values,
gi = GCA effect of ith
line,
gj = GCA effect of jth
tester, and
sij = SCA effect of ijth
cross
The gca effects of lines and testers and sca effects of crosses were marked
significant (*) when the values of ti (cal), tj (cal) and tij (cal) were >‗t‘ tabulated value at
error degree of freedom of individual environment or pooled over environment and P =
0.05.
(vii) Critical differences (CD) for comparing GCA effects of lines/testers and SCA
effect of crosses
(a) CD for GCA (lines) = SE (D1a) ×‗t‘ tab (error df, p=0.05)
(b) CD for GCA (testers) = SE (D2a) ×‗t‘ tab (error df, p=0.05)
(c) CD for SCA (crosses) = SE (D3a) ×‗t‘ tab (error df, p=0.05)
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The difference between GCA of any two lines/testers and SCA of any two crosses
were considered significant when the differences were > respective CD values.
3.7.4 Estimation of variance components
1. Individual environment
Cov (HS) = 2
f (females) = (Mf - Mfm) / mr = 2
GCA (lines)
Cov (HS) = 2
m (males) = (Mm - Mfm) / fr = 2
GCA (testers)
Cov HS (average) = 1/r (2fm-f-m) [(f-1) (Mf) + (m-1) (Mm)/1 + m-2-Mfm]
2
fm (females × males) = (Mfm - Me)/ r = 2
sca
(i) Estimation of Cov HS (average) and Cov (FS)
These were calculated as:
Cov HS (average) = (m2
f + f2
m)/ (f + m)
Cov (FS) = 2
fm + 2 Cov (HS)
These can also be calculated from the expectations of mean squares as:
Cov HS (average) = (Mf + Mm – 2 Mfm)/ r (f + m)
Cov FS = [Mf + Mm + Mfm – 3 Me + 6r Cov (HS) – r (f + m) Cov (HS)]/ 3r
2. Combined over the environments
Cov (HS) = 2
f (females) = (Mf - Mfm)/ mrE
= 2
f × E (females × environments) = (Mf E – Mfm E)/mr
Cov (HS) = 2
m (males) = (Mm - Mfm)/ frE
= 2
m × E (males × environments) = (Mm E – Mfm E)/fr
2
fm × E [(females × males) × Environment] = Mfm E – Me/r = 2
sca × E
(i) Estimation of Cov HS (average) and Cov (FS)
These were calculated as:
Cov HS (average) = (m2
f + f2
m)/ (f + m)
Cov HS (average) × environment = (m2
fE + f 2mE)/ (f + m)
Cov FS = 2fm + 2 Cov HS
Cov FS × environment = 2
fmE + 2 Cov HS × environments
These can also be calculated from the expectation of mean squares as:
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66
Cov HS (average) = (Mf + Mm – 2Mfm)/ rE(f + m)
Cov HS (average) × environment = (MfE + MmE – 2MfmE)/ r(f + m)
These can also be calculated from the expectation of mean squares as:
Cov (FS) = [Mf + Mm +Mfm – 3Me + 6rE Cov (HS) – rE(f + m) Cov
(HS)]/ 3rE
Cov (FS) = [MfE + MmE + MfmE – 3Me + 6rCov(HS) × E – r (f + m)
Cov(HS) × E]/ 3r
Where E= Environments
(ii) Estimation of GCA and SCA variances
From the estimates of Cov (HS) and Cov (FS), variances due to
general combining and specific combining ability were calculated as:
2
gca = Cov (HS) = (Mf + Mm – 2Mfm)/rE(f + m)
2
gca × Environment = Cov (HS) × Environments
= (MfE + MmE – 2MgmE)/r(f + m)
2
sca = Cov (FS) – 2 Cov (HS) = (Mfm - Me)/ rE
2
sca × Environments = Cov (FS) × Environments – 2Cov(HS) ×
Environments = (MfmE – Me)/ r
3.7.5 Estimation of additive (2
A) and dominance (2
D) component of variances
For computing the additive and dominance components of variances following
formulae have been used by Singh and Chaudhary (1979) and Dabholkar (1992).
2
gca = [(1 + F) / 4] 2
A = ½ 2
A
So, 2
A = 2 2
gca
2
sca = [(1+F) / 2]2
2D =
2D
So, 2
D = 2
sca
Where, F (Inbreeding coefficient) =1.0, since linseed being self-pollinated crop, it does
not suffer from inbreeding depression.
2
A = additive variance, and
2
D = dominance variance
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3.7.6 Per cent contribution of lines, testers and their interactions
These were computed as per the formulae suggested by Singh and Chaudhary
(1979).
(i) Per cent contribution of lines = SS (lines)/SS (crosses) × 100
(ii) Per cent contribution to testers = SS (testers)/SS (crosses) × 100
(iii) Per cent contribution of lines x testers = SS (lines x testers)/SS (crosses) × 100
3.7.8 Estimation of Heterosis
The estimates of heterosis were calculated as the deviation of F1 mean ( 1) from
the mean values of better parent ( ) and standard check ( )) Nagarkot.
1. Heterosis over better parent/ heterobeltiosis (%) =
2. Heterosis over standard check/economic heterosis (%) =
100X]SC)/SCF[( 1
1. Calculation of standard errors
(i) SE for testing heterosis over batter parent:
Individual environment = + (2Me/r)½= SE (H1)
Pooled environment = + (2Me/rE)½ = SE (H1)
(ii) SE for testing heterosis over standard check:
Individual environment = + (2Me/r)½ = SE (H2)
Pooled environment = + (2Me/rE)½ = SE (H2)
2. Test of significance for heterosis
There are two methods:
Method-I
The difference of )(HSEor)[SE(H)SCF()orBPF( 2111 × ‗t‘ tab, at error
degree of freedom of individual environment analysis of variance or at error degree of
100 X ] BP )/ BP F [( 1
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freedom of pooled over environments analysis and P=0.05] were considered significant
and the asterisk (*) was put on the per cent values only. This method is relatively less
time consuming.
Method-II
‗t‘ calculated values were worked out as follow;
1. ‗t‘ calculated values for heterosis over BP = )11 )/SE(HBPF(
2. ‗t‘ calculated value for heterosis over SC = )21 )/SE(HSCF(
The ‗t‘ calculated values for heterosis over better parent (BP) and standard check
(SC) were compared with ‗t‘ tabulated values at error degree of freedom and P = 0.05.
‗t‘ calculated values > ‗t‘ tabulated values were marked as significant and asterisk
was put on per cent values only (Dabholkar 1992).
3.8 Genetics of resistance to powdery mildew in linseed
3.8.1 Plant materials
Genetics of resistance to powdery mildew was studied using different generations
of nine crosses of linseed. Five parents comprising two susceptible varieties T-397 and
Chambal and three resistant lines, Nagarkot, Janaki and Jeewan were used to develop six
susceptible × resistant (T-397 × Nagarkot, T-397 × Janaki, T-397 × Jeewan, Chambal ×
Nagarkot, Chambal × Janaki, Chambal × Jeewan) and three resistant × resistant crosses
(Nagarkot × Janaki, Nagarkot × Jeewan and Janaki × Jeewan) and back crosses and F2
generations were developed. The detailed information of linseed genotypes used in the
study is given below:
Genotypes Pedigree Disease
score Disease reaction Type
T-397 T-491 × T-1193-2 5 Highly susceptible Seed type
Chambal Local Chambal × RR-45 5 Highly susceptible Seed type
Nagarkot New river × LC-216 0 Highly resistant Dual purpose
Janaki New river × LC-216 1 Resistant Seed type
Jeewan Sumit × LC-216 0 Highly resistant Dual purpose
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All the five parents were sown in crop season 2011-12 at the experimental farm of
the Department of Crop Improvement, CSK HPKV, Palampur (H.P.), India, and crosses
among the parents were attempted to develop nine F1s involving six susceptible (S) ×
resistant (R) crosses and three resistant (R) × resistant (R) crosses. The off-season nursery
(summer 2012) at HAREC, Kukumseri (Lahaul & Spiti ), H.P., India, were used to
advance F1 population of nine crosses and their single plants were harvested, separately
and also used to develop the BC1s of six crosses.
The different generations of all the crosses were sown in the powdery mildew
screening nursery in the crop season of rabi 2012-13 at the Experimental Farm of the
Department of Crop Improvement, one row of each parent, two rows of F1s, three rows of
BC1 and 40 rows of F2 of each of the nine crosses were sown. Each cross/ parent was
raised in a 2 m long row with row-to-row and plant-to-plant spacings of 25 cm and 10
cm, respectively. The check variety T-397 was planted as an indicator-cum-infestor row
after every 8 rows of test material. The recommended package of practices was followed
to raise the crop.
3.8.2 Screening of the material
The field screening technique was used to evaluation of different generations for
disease reaction. All the parents and crosses were exposed to natural epiphytotic
conditions under field conditions at Experimental Farm of Department of Crop
Improvement and , Rice and Wheat Research Centre (RWRC), Malan.
3.8.3 Data collection and analysis
The individual plants were scored for disease reaction on 0-5 scale where, 0-
highly resistance, 1- resistance, 2- moderately resistance, 3- moderately susceptible, 4-
susceptible and 5- highly susceptible (AICRP 1991). The assessment of the disease per
plant was obtained by observing the intensity of lesions present on the leaves. The plants
with disease rating < 2 were considered as resistant and above 2 as susceptible. Based on
disease reaction, plants of each cross were classified into two classes i.e. resistant and
susceptible. Data was fit into different genetic ratios to find out the best fit ratio in order
to know the genetics of resistance to powdery mildew. Chi- square (χ2) test was applied
to fit the appropriate genetic ratio for the estimation of number of gene (s) governing
resistance and also to find out allelic relationship among resistance genes.
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3.9 Molecular analysis
The present investigation was carried out in the Department of Agricultural
Biotechnology, College of Agriculture, CSK HPKV, Palampur. The material used and
the methodology adopted to achieve the objectives of the investigation is given here
under:
3.9.1 Plant materials
The plant material used for present investigation consisted of 28 genotypes. All
the genotypes were grown in field at the Experimental Farm of Department of Crop
Improvement during crop season 2013-14. The details of these genotypes are presented in
Table 3.1. All the genotypes were subjected to RAPD and ISSR assay as per the
following procedure (Table 3.5).
3.9.2 Methodology
3.9.2.1 Extraction of plant genomic DNA
Genomic DNA was isolated from young leaf tissue (0.5-1g) of the individual
genotype using CTAB method (Murray and Thompson 1980). The leaf tissues were
rinsed in deionized water, dried on tissue paper discs and ground to fine powder in liquid
nitrogen in autoclaved pre-cooled pestles and mortars. The ground tissue was transferred
to a separate 2 ml eppendorf tubes containing 800 µl of extraction buffer (2% CTAB,
100 mMTris, 20 mM EDTA, 1.4 mMNaCl and 1% PVP, pH 8.0) maintained at 60oC in
water bath and mixed vigorously. The mixture was incubated at 60oC for 1 h with
occasional mixing. An equal volume of chloroform-isoamyl alcohol (24:1) was added to
the tubes followed by gentle mixing. The mixture was centrifuged at 10,000 rpm for 10
minutes at 4oC. The aqueous phase was transferred to fresh tube, followed by addition of
500 µl of pre-chilled isopropanol. The contents of the tubes were mixed gently and the
mixture was incubated at -20oC for 1 h. DNA was precipitated by centrifugation at
10,000 rpm for 10 minutes using centrifuge (SIGMA, Laborzentrifugen, Germany).
The supernatant was drained and the resulting pellet was washed twice with 1 ml
of 70 per cent chilled ethanol. The pellet was dried in a stream of sterile air in a laminar
air flow cabinet for 3-4 h. Dried DNA pellet was dissolved in 1 ml TE buffer (10
mMTris-HCl, 0.1 mM EDTA, pH 8.0). The dissolved DNA was treated with 1 µl of
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RNase (10 mg/ml). The quantity and quality of DNA was estimated through
electrophoresis using 1 per cent agarose gel (HIMEDIA).
3.9.2.2 Purification of DNA
100 µl of phenol: chloroform: isoamyl alcohol (25:24:1) was added to the tubes
followed by gentle mixing. The mixture was centrifuged at 10,000 rpm for 10 minutes at
4˚C.The aqueous phase was transferred to fresh tube, followed by addition of 200 µl of
pre-chilled isopropanol. The contents of the tubes were mixed gently and the mixture was
incubated at -20˚C for 30 min. DNA was precipitated by centrifugation at 10,000 rpm for
10 minutes using centrifuge.
The supernatant was drained and the resulting pellet was washed twice with 70
per cent chilled ethanol. The pellet was dried in a stream of sterile air in laminar airflow
cabinet. Dried DNA pellet was dissolved in 80 µl TAE buffer. The quantity and quality
of DNA was estimated through electrophoresis using 0.8 per cent agarose gel.
3.9.2.3 PCR amplification of DNA
Polymerase chain reaction was performed in final volume of 12.5 µl containing
7.15µl of sterilized distilled water, 1.0µl template DNA (25ng/µl), 1.0 µl of primer
(5µM), 1.0µl MgCl2 (25mM), 1.25µl 10 X PCR buffer, 1.0µl dNTP mix (0.2mM each of
dATP, dGTP, dCTP and dTTP) and 0.1µl Taq polymerase (5U/µl).
The amplifications were carried out in eppendorf tubes in the thermocycler. The
PCR conditions for RAPD were optimized with 5 minute initial denaturation at 94˚C
followed by 36 cycles of 94˚C for 1 minute, with the annealing temperature of 37˚C for 1
minute, extension at 72˚C for 2 minutes and final extension step at 72˚C for 10 minutes
before cooling at 4˚C. Similarly, for ISSR the following cycling program was applied: at
94˚C denaturation for 5 min. 40 cycles of at 94˚C for 45 s, at 52˚C annealing for 45 s and
72˚C for 1 min; and a final elongation step at 72˚C for 10 min and holding at 20˚C
3.9.2.4 Analysis of PCR product
10 µl of each PCR product was mixed with 3 µl of 6X gel loading dye and
electrophoresed in 1.8% (RAPD) and 2% (ISSR) agarose gel in 1X Tris acetate-EDTA
(TAE) buffer. The gels were run at a constant voltage of 120 V for 1.5h. The ethidium
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bromide stained (3 µl/100 ml) gels were observed and images were taken using Gel
Documentation system (Biovis).
3.9.3 Data acquisition and statistical analyses
The RAPD and ISSR data were scored as ―1‖ (presence of fragments) and ―0‖
(absence of fragments). Changes in band intensity were not considered as polymorphism.
The data matrix of 1‘s and 0‘s was prepared from the scorable bands and was entered into
data analysis package. The binary data were used to generate a similarity matrix using
Jaccard‘s coefficient, Jij = Cij/ (ni + nj - cij), where ‗Cij‘ is the number of positive matches
between two genotypes, while ni and nj is the total number of band in genotype i and j
respectively, in SIMQUAL programme of NTSYS-PC-2.0 (Rohlf 1998).
Genetic distances (GD) were calculated as GD = 1 – [Cij/(ni+nj- Cij)]. The data
were subsequently used to construct a dendrogram using the unweighted pair group
method with arithmetical averages (UPGMA) in SAHN program of NTSYS–PC package
(version 2.0). The genotypic data were used to calculate different parameters such as
polymorphic information content (PIC), marker index (MI) and resolving power (RP).
The PIC for each primer combinations was calculated according to Roldan-Ruiz et al.
2000 formula: PICi = 2fi(1−fi), where, PICi is the polymorphic information content of
marker i, fi is the frequency of the fragments which were present and 1−fi is the
frequency of the fragments which were absent. PIC was averaged over the fragments for
each primer combination. Marker index (MI) was calculated following Powell et al.
(1996) as: = PIC × EMR, where EMR (effective multiple ratio, EMR = n × β) is defined
as the product of the fraction of polymorphic loci (β) and the number of polymorphic loci
(n).
1.) Effective multiplex ratio (EMR): The number of loci polymorphic in the
germplasm set of interest, analyzed per experiment, called effective multiplex ratio
(EMR) is estimated as:
EMR = n × β
Where, β is the fraction of polymorphic markers and is estimated after considering
the polymorphic loci (pl) and non-polymorphic loci (npl) as
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β
2.) Marker index (MI): The utility of a given marker system is a balance between
the level of polymorphism detected and the extent to which an assay can identify
multiple polymorphisms. A product of information content, as measured by PIC,
and effective multiplex ratio (EMR), called as marker index provide a convenient
estimate of marker utility (Powell et al. 1996). MI can be calculated as;
MI = PIC × EMR
or MI = n × β × PIC
Table 3.5 List of RAPD and ISSR primers used in molecular analysis
S. No. Primer Name Sequence (5'-3') No. of Bases
1 OPG-02 GGCACTGAGG 10
2 OPG-05 CTGAGACGGA 10
3 OPI-02 GGAGGAGAGG 10
4 OPM-10 TCTGGCGCAC 10
5 OPM-13 GGTGGTCAAG 10
6 OPO-03 CTGTTGCTAC 10
7 OPO-07 CAGCACTGAC 10
8 OPO-12 CAGTGCTGTG 10
9 OPS-03 CAGAGGTCCC 10
10 OPS-07 TCCGATGCTG 10
11 OPS-11 AGTCGGGTGG 10
12 OPS-12 CTGGGTGAGT 10
13 OPU-01 ACGGACGTCA 10
14 OPZ-01 TCTGTGCCAC 10
15 OPZ-03 CAGCACCGCA 10
16 OPZ-05 TCCCATGCTG 10
17 UBC- 810 GAGAGAGAGAGAGAGAT 17
18 UBC- 815 CTCTCTCTCTCTCTCTG 17
19 UBC- 818 CACACACACACACACAG 17
20 UBC- 819 GTGTGTGTGTGTGTGTA 17
21 UBC- 825 ACACACACACACACACT 17
22 UBC- 840 GAGAGAGAGAGAGAG AYT 18
23 UBC- 850 GTGTGTGTGTGTGTG TYC 18
24 UBC- 855 ACACACACACACACA CYT 18
25 UBC- 858 TGTGTGTGTGTGTGT GRT 18
26 UBC- 861 ACCACCACCACCACCACC 18
27 UBC- 868 GAAGAAGAAGAAGAAGAA 18