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The 9th
Plant Breeding international Egypt. J. Plant Breed. 19 (3):57-70. 2015
Conference September 2015 Special Issue
UTILIZATION OF TRIPLE TEST CROSS IN BREAD
WHEAT F2 POPULATIONS. 2- GENETIC DIVERSITY
OF TRIPLE TEST CROSS FAMILIES BASED ON
PRINCIPAL COMPONENT AND CLUSTER
ANALYSES
H. A. Dawwam
1, F. A .Hendawy
1, M. A. Abo Shereif
2 and
E. L. Elmassry 2*
1- Crop Science Department, Faculty of Agriculture, Minufiya University
2- Wheat Research Department, Field Crops Research Institute, A.R.C.
*Corresponding author. E-mail: [email protected]
ABSTRACT Sixty TTC families derived from cross (Gemmeiza 9 x Misr 1 ) were used in a
randomized complete block design with three replications at the Experimental Farm of
Gemmeiza Agriculture Research Station, Agriculture Research Center, Egypt during
four successive growing seasons i.e. 2009 / 2010, 2010 / 2011, 2011 / 2012 and
2012/2013. Highly significant differences were recorded for all the traits studied among
triple test cross (T.T.C) families, indicating the presence of high segregations in F2. The
results indicated that overall epistasis was found to be highly significant for most traits
studied. Partitioning of total epistasis to its component parts revealed that (i) type of
epistasis was significant and highly significant for most traits studied. Also, the (J and L
type) epistasis was highly significant for most traits studied except main spike length and
no. of spikelets per main spike. The mean squares due to sums were found to be
significant for most traits studied. Also, the mean square estimates due to differences
were found to be highly significant for most traits studied. The first four principal
components, PC axes accounted for about 73.2 % of total variance of all traits. PCA1
accounted for about 31.2 % of the variation; PCA2 for 15.7 %; PCA3 for 14.1 % and
PCA4 for 12.2 %. The dendrogram for clustering pattern of TTC families were grouped
into nineteen clusters. Most members and families of selected clusters exhibited higher
values for most agronomic characteristics than TTC families mean. Families (48, 53 and
55) which formed single cell clusters by themselves surpassed all families in the studied
cross, having the highest values of all studied characteristics.
Key words: Wheat, Triple test cross, epistasis, additive, dominance, principal components
analysis and cluster analysis.
INTRODUCTION
Wheat is the most important cereal crop in Egypt. It is the major
crop in winter season, increasing wheat production to narrowing the gap
between production and consumption is considered the main goal in Egypt
as well as in most countries all over the world (Shehab El-Din, 1993). Egypt
imports about 45% of its wheat requirements. This reflects the size of the
problem and the efforts needed to increase wheat production. Thus,
increasing production per unit area appears to be one of the important
85
factors for narrowing the wheat production gap. This can be achieved by
breeding for high yielding cultivars which is considered as ongoing process
of the national wheat research program.
For a successful wheat breeding program, the presence of genetic
diversity plays a vital role which is essential to meet the diversified goals of
plant improvement. The characterization of genetic variability and estimate
of the genetic relationship among varieties are essential to any breeding
program because artificial crossing among less similar parents allows a large
segregation and combination of different favorable alleles (Bered et al.,
2002).
Estimation of genetic diversity is an important step for any breeding
program, but not the last one – Another helpful issue to be evaluated is the
relative importance of the characters, though plant breeders often measure
several characters simultaneously in wheat breeding program, then it is
possible to estimate the genetic divergence by using multivariate methods.
The use of multivariate statistical algorithms is an important strategy
for classification of germplasm and analysis of genetic relationships among
breeding material (Mohammadi and Prasanna, 2003). The efficacy of the
genetic divergence as a criterion for choosing parents to be crossed has been
reported by Cox et al. (1985) and Menshawy (2008 ) .
Therefore, the objectives of the present study were: 1) To existence
of epistasis and to determine the additive and dominance variances using the
triple test cross analysis. 2) To interpret the extent of genetic variation and
relationships among TTC families based on quantitative traits using
multivariate analysis and to identify the set of morpho-agronomic attributes
which could be further utilized in breeding programs.
MATERIALS AND METHODS
This experiment was carried out at the Experimental Farm of
El-Gemmeiza Agriculture Research Station, Agriculture Research Center,
Egypt during four successive growing seasons i.e. 2009 / 2010, 2010 / 2011,
2011 / 2012 and 2012/2013.
In the first season (2009/2010), two genotypes of bread wheat,
differ in most of their agronomic traits namely Gemmeiza 9 and Misr 1,
were crossed to obtain their F1 progeny (Gemmeiza 9 × Misr 1). The
pedigree of the aforementioned genotypes are illustrated in the Table (1).
Table (1). The names, pedigree and origin of the parental genotypes.
Name Pedigree Origin
Gemmeiza 9 ALD "S" /HUAC // CMH 74 A. 630/SX
CGM 4583 - 5 GM - 1GM - OGM Egypt
Misr 1
OASIS/KAUZ//4*BCN/3/2*PASTOR
CMSS00Y01881T-050M-030Y-030M-030WGY-00M-0Y-0S Egypt
85
In the second season (2010/2011), the F1 plants were selfed to
produce F2 grains. In 2011/2012 growing seasons, the obtained materials F1,
F2 and the parental genotypes were sown. Twenty random F2 plants were
crossed, as males, back to its respective parents P1, P2 and F1 (P1 × P2) to
produce L1i (P1 × F2i), L2i (P2 × F2i) and L3i (F1 × F2i), respectively. In
2012/2013 growing season, the sixty families (L1 (20) + L2 (20) + L3 (20))
were sown in a randomized complete block design with three replicates.
The progenies were raised in single rows; 2 meters long with 30 cm.
apart rows and plants within rows were 10 cm. Data were recorded using
fifteen random plants from each family for number of days to heading, Flag
leaf area, number of days to maturity, number of spikes per plant, plant
height, main spike length, number of spikelets per main spike, number of
kernels per main spike, main spike yield, grain yield per plant and 1000-
kernel weight.
Biometrical analysis
Before proceeding to analysis, the families subjected firstly to the
conventional one way analysis of variance for the L1i, L2i, L3i sets of
families for every trait separately outlined by Kearsey and Pooni (1996).
This analysis provides a test for the significance between families terms.
Test of epistasis were carried out according to Kearsey and Jinks
(1968), Jinks et al. (1969) and Jinks and Perkins (1970).
The mean squares for deviations (1i
L + 2i
L – 23i
L ) was used
for detection of epistasis. The overall epistasis was partitioned into (i) type
of epistasis (additive x additive) and (i + j) type due to additive x dominance
and dominance x dominance gene interactions.
The estimation of additive (D) and dominance (H) genetic
components and the correlation coefficient (r) between sums (1i
L +
2iL +
3iL ) and differences (
1i L -
2iL ) were obtained to detect
the direction of dominance, according to Jinks and Perkins (1970). Average
degree of dominance was calculated as the formula (H/D) 1/2
, where H and D
are the dominance and additive variance components respectively. Also, the
F value was computed from the covariance of sums / differences which
equal to (-1/8F), where F is the association dispersion of dominant alleles in
the parental lines, having a maximum value of 1 if all the dominant alleles
are associated in P1 and having a minimum value -1 if all dominant genes
are in P2.
Multivariate technique was used to assess the similarities among
varied groups and to evaluate morphological parameters contributing to the
variation in each genotype. For this purpose, principal components analysis
was performed, on the correlation matrix of contributed characters for all
genotypes. The principle components were expressed as eigen value, latent
06
root, and manifested in eigen vector for all studied traits in each principal
component axis (Hair et al. 1987).
Hierarchical clustering procedure using ward's minimum variance
method, which minimize within group sum of squares across all partitions,
was applied to determine the genetic diversity and distance as outlined by
Anderberg (1973) and developed by Johnson and Wichern (1988). All
computations were performed using Minitab (version 15) and SPSS (version
19) computer procedures.
RESULTS AND DISCUSSION
The analysis of variance for all the traits studied shown in Table (2).
Highly significant differences were recorded for all the traits studied among
triple test cross (T.T.C) families, indicating the presence of high
segregations in F2. Likewise, the results indicated that L1, L2 and L3 TTC
families were highly significant different from each other , revealing of high
amount of genetic variability which could be assessed by means of triple
test cross analysis. Current results were in conformity to findings of many
researchers. Menshawy (2008), El-Nahas, Marwa(2010), Koumber (2011)
and Morad (2012)
Test of epistasis:
Epistasis is the interaction between alleles of different genes, i.e.
non-allelic interaction. In general, the mating designs usually adopted in the
breeding programs assume the absence of epistasis. Thus, ignoring such
effect led to loss information about epistasis as well as estimates of genetic
components would be biased. Thus, the triple test cross analysis indicates
their relative importance in the inheritance of a particular traits and help the
breeder to follow alternative breeding procedures.
The analysis of variance for testing the presence of epistasis is
presented in Table (3). The results indicated that overall epistasis was found
to be highly significant for all traits studied except main spike length and
no. of spikelets per main spike indicated the important role of epistasis in
the control of these traits. Similar results were reported by Hendawy et al.
(2009), Koumber (2011) and Morad (2012).
Partitioning of total epistasis to its component parts revealed that (i)
type of epistasis was significant and highly significant for most traits studied
except main spike length, no. of spikelets per main spike and no. of kernels
per main spike. Also, the (J and L type) epistasis was highly significant for
most traits studied except main spike length and no. of spikelets per main
spike indicate that (J+ L) types are not fixable by selection and not favorable
for developing pure lines for these traits.
06
Table (2). Analysis of variance of TTC families for all traits studied in cross (Gemmeiza 9 × Misr 1).
S.O.V. D.F. No. of days to
heading
Flag leaf
area
No. of days to
maturity
No. of spikes
per plant
Plant
height
Main spike
length
No. of spikelets
per main
spike
No. of kernels per main
spike
Main spike
yield
Grain yield per
plant 1000- kernels weight
Between L1, L2, L3
families 59 24.98** 1221.47** 28.11** 76.99** 293.84** 10.07** 19.79** 407.00** 1.10** 514.34** 292.83**
Between L1 19 13.62** 876.41** 21.07** 48.44** 92.31** 8.82** 18.09** 537.09** 1.01** 348.52** 111.09**
Between L2 19 29.20** 1420.17** 14.77** 84.21** 109.90** 3.30** 9.81** 147.71** 1.09** 362.92** 198.10**
Between L3 19 22.47** 972.86** 27.32** 89.27** 181.27** 3.46** 4.43** 227.40** 0.95** 669.74** 368.07**
Residual 2 116.52** 4973.68** 229.36** 163.02** 5025.04** 149.10** 276.77** 3340.61** 3.52** 2051.73** 2204.39**
Within families
within replicates 720 0.61 2.15 0.76 2.20 1.81 0.38 0.94 12.21 0.05 5.37 1.95
Between L1, L2
families 39 21.76** 1251.31** 27.93** 68.89** 300.31** 13.51** 27.77** 504.73** 1.15** 358.61** 260.44**
Within families
within replicates 480 0.59 2.10 0.79 1.94 1.75 0.38 0.94 11.64 0.05 5.17 1.81
** Significant at 0.01 levels of probability.
Table (3). Mean squares for test of epistasis for triple test crosses for all traits studied in cross (Gemmeiza 9 × Misr 1).
S.O.V. D.F. No. of days to
heading
Flag leaf
area
No. of days to
maturity
No. of spikes
per
plant
Plant
height
Main spike
length
No. of spikelets
per main spike
No. of
kernels per main
spike
Main
spike yield
Grain
yield per plant
1000- kernels
weight
Overall epistasis 20 4.26 ** 98.95 ** 4.39 ** 11.76 ** 29.42 ** 0.17 0.61 25.59 ** 0.11 ** 94.44 ** 22.68 **
(I) type 1 26.40 ** 637.48 ** 6.71 ** 21.28 ** 290.69 ** 0.19 0.08 1.05 0.28 * 484.66 ** 16.83 **
(J + L) type 19 3.09 ** 70.61 ** 4.27 ** 11.26 ** 15.67 ** 0.17 0.64 26.88 ** 0.11 ** 73.90 ** 22.99 **
Within families
within replicates 720 0.61 2.15 0.76 2.20 1.81 0.38 0.94 12.21 0.05 5.37 1.95
*, ** Significant at 0.05 and 0.01 levels of probability, respectively.
06
Generally, partitioning of epistasis gene effects to its components could
indicate that portion of the epistasis is fixable, the (i) type of epistasis being
fixable and can be exploiting as the additive component. Therefore, standard
hybridization and selection procedures could take advantage of epistasis if it
is of the (I) type. However, (J +L) type of epistasis is non-fixable and
unfavorable by selection procedure and therefore are not useful for
developing pure line cultivars. These may be useful in the development of
hybrids. Therefore, population improvement through pedigree method might
be giving a good response for releasing genotypes. Similar conclusions were
reported by Salama(2007) and Hendawy et al. (2009)
Detection of additive and dominance genetic variance components
Analysis of variance for sums and difference are presented in Table
(4).The mean squares due to sums (1
L + 2
L +3
L ) were found to be significant
and highly significant for all traits studied except no. of days to maturity, no. of
spikelets per main spike and main spike yield. Also, the mean square estimates
due to differences (1
L –2
L ) were found to be highly significant for all traits
studied except no. of days to maturity, no. of spikes per plant, main spike length,
no. of spikelets per main spike and main spike yield .
The estimates of additive (D) and dominance (H) which revealed the
genetic components play an important role in inheritance of all traits studied. In
all cases, the additive genetic components were larger in magnitude than those of
dominance for no. of days to heading, flag leaf area, no. of spikes per plant,
plant height, main spike length and 1000- kernels weight and that resulted in
(H/D)½ to be less than one confirming that these traits were influenced
predominantly by the additivity of the genes and also the role of partial
dominance in the inheritance of these traits. Whereas, the remaining traits the
dominance genetic variance (H) was found to be larger in magnitudes than the
additive genetic variance and that resulted in (H/D)½ to be more than unity
confirming the role of the overdominance in the inheritance of these traits . The
same results were obtained by Esmail (2007), El-Massry (2009), El–Nahas,
Marawa(2010) and Koumber(2011)
The direction of dominance and types of genes exhibiting dominance are
presented in Table (4). The results showed that the (F) value was found to be
significant and negative as (r) values indicated for main spike length and no. of
kernels per main spike revealing that the dominance was unidirectional among
parents. On the other hand, the remaining traits have insignificant (F) values and
positive or negative, reflecting ambidirectional dominance. Salama (2007) and
Morad (2012) obtained similar conclusion.
Consequently, from the previous results it may be concluded that the
additive, dominance and epistatic components are important in wheat but as it is
an autogamous plant, only the additive component is important to develop pure
breeding varieties from any hybridization program. While, additive × additive
epistatic type coupled with additive genetic variance were found to be
06
Table (4). Mean square from analysis of variance for sums and differences and estimates of additive (D), dominance (H)
components, degree of dominance (H/D) 0.5
and covariance between sums and differences (F) of cross
(Gemmeiza 9× Misr 1) for all traits studied.
S.O.V. D.F. No. of days
to heading Flag leaf area
No. of days
to maturity
No. of spikes
per
plant
Plant
height
Main
spike
length
No. of
spikelets per
main
spike
No. of kernels per
main spike
Main spike
yield
Grain yield
per plant
1000-
kernels
weight
Between sums 19 1.63 ** 123.66 ** 0.84 6.54 ** 11.64 ** 0.75 ** 1.30 23.41 * 0.07 30.97 ** 21.16 **
Within families
within replicates 720 0.61 2.15 0.76 2.20 1.81 0.38 0.94 12.21 0.05 5.37 1.95
between differences 19 1.18 ** 58.99 ** 1.24 2.62 6.09 ** 0.20 0.54 23.96 ** 0.08 24.16 ** 12.50 **
Within families
within replicates 480 0.59 2.10 0.79 1.94 1.75 0.38 0.94 11.64 0.05 5.17 1.81
D 0.907 108.016 0.067 3.862 8.735 0.333 0.318 9.955 0.019 22.751 17.073
H 0.784 75.861 0.604 0.911 5.791 -0.248 -0.534 16.434 0.035 25.324 14.248
(H/D)0.5 0.929 0.838 3.000 0.486 0.814 -0.863 -1.296 1.285 1.348 1.055 0.914
F 8.030 -51.960 -5.413 2.767 7.146 -4.055 * -4.885 -281.181** -0.127 -35.018 39.003
r -0.296 0.031 0.271 -0.034 -0.043 0.535 * 0.298 0.606 ** 0.087 0.065 -0.122
*, ** Significant at 0.05 and 0.01 levels of probability, respectively.
06
preponderant for most traits indicating the possible improvement of these traits
through standard hybridization and selection in early generations. If the rest type
of epistasis (J+L) types is predominan the biparental matings may be attempted
in F2 and subsequent generations and selection may be postponed till late
generation to allow sufficient epistasis to get fixed.
Genetic divergence among TTC families
The knowledge about germplasm diversity and genetic relationship
among breeding material could be an individual aid in crop improvement
strategies. Genetic variability is used for detection of genetic diversity in closely
related species.
Morphological traits have been successfully used for estimation of
genetic diversity and cultivar development since they provide a simple way of
quantifying genetic variation (Fufa et al. 2005).
Principal Component analysis (PCA)
The principal component analysis (PCA) is a multivariate statistical
method for exploring and simplifying complex data sets. The (PCA) is known by
the fact that it includes the total variance of variables, describes maximum of
variance within a data set, and is a function of primary traits. This approach is
very helpful in deciding which agronomic traits of crop contributing most to
yield, subsequently, these agronomic traits should be emphasized in the breeding
program.
The relative magnitude of the eigen coefficient of each trait related it to
the first four axes from the components analysis might provide an interpretation
for each component axis. Though no clear guidelines existed to determine the
significance of a trait coefficient, one rule of thumb is to treat coefficients > 0.5
as having a large enough effect to be considered important (Hair et al. 1987).
The first four principal components, PC axes accounted for about 73.2 %
of total variance of all traits. PCA1 accounted for about 31.2 % of the variation;
PCA2 for 15.7 %; PCA3 for 14.1 % and PCA4 for 12.2 % (Table 5). The
principal component analysis showed that the first PCA was related to spike
length, no. of spikelets per main spike, plant height, no. of days to maturity and
no. of kernels per main spike. The traits, which contributed to PCA1, were
suggesting that this component reflected the spike yield potential of each
genotype. Whereas the second PCA was related to no. of spikes per plant and
grain yield per plant suggesting that this component reflected the yield potential
of each genotype. The third PCA was related to flag leaf area and main spike
yield suggesting that this component reflected the spike yield potential of each
genotype. Whereas the forth PCA was related to no. of days to heading and
1000-kernels weight suggesting that this component reflected the earliness
potential of each genotype. Our results are in agreement with Saif et al. (2013)
08
Table (5). Principal Components (PCs) analysis showing eigen
values and eigen vectors of TTC families for the traits
studied of cross (Gemmeiza 9 × Misr 1).
Parameters PC axes
PC1 PC2 PC3 PC4
Eigen value 3.43 1.73 1.55 1.34
Proportion of variance 31.2 15.7 14.1 12.2
Cumulative variance 31.2 46.9 61.0 73.2
Traits studied Eigen vectors
No. of days to heading -0.056 0.023 0.129 -0.885
Flag leaf area -0.321 0.112 0.754 -0.004
No. of days to maturity -0.632 0.008 -0.001 -0.096
No. of spikes per plant -0.106 -0.934 -0.046 0.031
Plant height 0.777 0.027 -0.232 0.035
Main spike length 0.847 -0.147 0.078 -0.215
No. of spikelets per main spike 0.831 -0.122 0.186 -0.311
No. of kernels per main spike 0.558 -0.184 0.559 -0.235
Main spike yield 0.486 -0.055 0.717 0.040
Grain yield per plant 0.329 -0.855 0.028 0.127
1000- kernels weight -0.351 -0.199 0.203 0.576
In the conducted experiment, the strongest discriminatory power was
shown by the main spike length, number of spikelets per main spike, plant
height, number of days to maturity and number of kernels per main spike.
Therefore, they could be considered in the development of desirable progenies in
selection programs of wheat. The hybridization between the divergent genotypes
selected from this study will be highly useful for devising further breeding
strategies. Similar results were obtained by Gulnaz et al. (2012) and Saif et al.
(2013)
The PCA may allow the plant breeder more flexibility in finding the
number of plants to be evaluated and the plant breeder could use the multivariate
methods by first determining the combination of traits that constitute an ideal
plant. (Mohsen et al. 2014).
Cluster analysis:
Estimation of genetic distance is one of appropriate tools for parental
selection in wheat hybridization programs. Appropriate selection of the parents
is essential to be used in crossing nurseries to enhance the genetic recombination
for potential yield increase (Islam, 2004).
The dendrogram of clustering TTC families of the studied cross using
Euclidean distances are illustrated in Figure (1). The clustering pattern of the
sixty families were made on the contributed traits based on Euclidean
dissimilarity lower than (12.5) Euclidean distances.
00
The dendrogram for clustering pattern of TTC families were grouped
into nineteen clusters. The results of the cluster analysis were presented in
groups of genotypes to infer relationships among genotypes. The number of
families per cluster varied from 1 to 11. Cluster 1 contained largest numbers of
families which are 11 families followed by cluster 18 contained 9 families.
Whereas, each of clusters 6, 7 and 14 contained 6 families, cluster 12 had 5
families, cluster 16 had 3 families, each of clusters 4 and 11contained 2 families
and the rest clusters 2,3,5,8,9,10,13,15,17 and 19 had only 1 family.
Results in Table (6) illustrated cluster means of 11 traits studied,
involved in Euclidean clustering analysis for each cluster. The results obtained
showed that highest mean values of grain yield per plant recorded for clusters13
followed by cluster 9 and 10. Cluster 3 had the lowest mean value for no. of
spikes per plant and grain yield per plant and the cluster 19 had the highest value
for no. of spikes per plant.
Number of clusters, with varied means, observed in this study might be
due to the occurrence of some sort of transgressive segregation as a result of
crossing distantly related parents. Establishment of a link between clustering
pattern and transgressive segregation among TTC families of wheat could lead
to more efficient selection procedure in a breeding program. The understanding
of such a relationship within the gene pool might be help in wheat populations
development.
Table (6). Mean values of clusters of 60 TTC families.
Clu
ster
s
No. of days to
heading
Flag
leaf
area
No. of days to
maturity
No. of spikes
per
plant
Plant
height
Mains
pike length
No. of
spikelets per
main
spike
No. of kernels
per main
spike
Main spike
yield
Grain yield
per plant
1000- kernels
weight
1 104.764 39.006 155.515 13.152 110.964 11.364 23.315 68.848 3.393 35.552 53.637
2 106.067 45.534 155.400 10.667 120.600 12.400 24.600 67.800 3.327 31.017 48.652
3 105.333 40.651 155.800 8.800 111.800 11.333 22.200 67.867 3.303 25.745 45.167
4 104.600 33.285 154.433 13.500 120.767 11.833 23.400 67.600 3.299 37.000 59.695
5 101.933 26.908 152.667 18.133 120.800 12.533 24.333 71.800 3.561 42.630 57.190
6 104.200 33.910 154.889 14.678 114.856 12.389 24.489 70.367 3.460 39.899 48.074
7 105.156 27.028 153.933 12.156 115.511 11.900 24.267 69.167 3.308 33.469 45.107
8 104.267 32.282 154.800 15.600 104.267 10.933 22.333 67.667 3.207 33.301 45.323
9 105.200 40.740 154.333 18.067 114.733 12.200 24.333 77.733 3.393 54.034 55.102
10 104.200 35.993 154.067 17.267 112.533 12.067 24.333 76.200 3.439 53.102 46.460
11 105.133 26.984 155.467 12.900 119.933 13.833 26.600 79.067 3.948 41.529 48.678
12 104.507 35.775 154.547 14.040 116.240 12.907 25.133 79.187 3.781 43.529 45.989
13 104.467 26.286 153.933 17.867 118.333 12.000 24.067 76.667 3.399 54.080 48.699
14 104.833 52.284 155.833 14.200 109.844 11.522 23.533 69.222 3.392 34.543 50.833
15 103.867 70.968 155.000 12.667 109.667 11.600 23.800 75.533 4.139 34.659 54.827
16 106.556 43.113 152.156 11.778 113.911 12.756 25.622 83.133 3.693 34.948 46.736
17 105.400 53.592 152.200 11.933 119.867 12.800 26.200 84.400 4.071 39.806 45.425
18 105.156 44.420 154.119 14.148 115.037 12.215 24.363 74.756 3.456 39.369 48.446
19 106.267 45.833 153.000 20.933 108.600 12.267 23.933 68.933 3.519 50.909 49.803
06
Figure (1). Dendogram of TTC families for cross (Gemmeiza 9 x Misr 1 ).
05
To assess the effectiveness of clustering for selection, the cluster means in TTC
families were ranked for their phenotypic values of each contributed trait based
on earliest cluster, high grain yield and its components. In this respect, most
members and families of selected clusters exhibited higher values for most
agronomic characteristics than TTC families mean.
Determination of the members of each selected cluster, might offer an
opportunity to select some TTC families characterized by highest in yield traits.
The selected clusters are illustrated in Table (7). Cluster 5 contained one family
(48); cluster 13 contained one family (53) and cluster 10 contained one family
(55).
It is interesting to note that families (48, 53 and 55) which formed single
cell clusters by themselves surpassed all families in the studied cross, having the
highest values of all studied characteristics. These families possessed high yield
potentials and could be incorporated in wheat improvement program besides
other selected families. Our results are in agreement with Menshawy (2008).
Table (7). Members of selected clusters, their means and average of TTC
families in the cross (Gemmeiza 9 × Misr 1 ).
Selected
clusters
Families
within cluster
No. of days to
heading
Flag leaf
area
No. of days to
maturity
No. of
spikes per
plant
Plant
height
Main spike
length
No. of
spikelets
per
main spike
No. of
kernels per
main spike
Main
spike
yield
Grain yield
per plant
1000-
kernels
weight
5 48 101.933 26.908 152.667 18.133 120.800 12.533 24.333 71.800 3.561 42.630 57.190
13 53 104.467 26.286 153.933 17.867 118.333 12.000 24.067 76.667 3.399 54.080 48.699
10 55 104.200 35.993 154.067 17.267 112.533 12.067 24.333 76.200 3.439 53.102 46.460
Average of TTC
families 104.877 39.262 39.262 13.808 114.046 12.068 24.202 72.599 3.487 38.195 49.561
L.S.D. 0.05 1.246 2.357 2.357 2.448 2.153 1.006 1.575 6.192 0.385 4.279 2.223
These results might prove the relevance and usefulness of clustering
analysis, based on Euclidean distances in plant breeding. Such an analysis would
be useful and effective to breeders for selecting and identifying superior families
within clusters. Furthermore, applying such a method might justify the gain of
selection efficiency in advanced generations.
From this study, genotypes in selected clusters possess desirable
combinations of traits and thus; the genotypes of these clusters hold great
promise as parents to obtain promising heterotic expression and may create
considerable variability in the segregating populations.
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Bered F., J.F. Barbosa-Neto and Fif. De-Carvalho(2002). Genetic variability in common
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66
استخذام التلقح االختبار الثالث ف عشائر الجل الثاو لقمح الخبز
التباعذ الىراث لعائالت التلقح االختباري الثالث اعتمادا عل -2
تحلل المكىوات االولة وتحلل التجمعاتحسان عبذ الجذ دوام
1، فتح احمذ هىذاوي
1، محروس عبذ الغى ابى شرف
2، السذ لطف المصري
2
جاهعح الوفيح –كليح الضساعح –قسن الوحاصيل -6
هشكض الثحز الضساعيح –هعذ تحز الوحاصيل الحقليح –قسن تحز القوح -6
هشكض الثحز الضساعيح رلك ف استعح هاسن –هحطح الثحز الضساعيح تالجويضج هضسعح لثحس ف أجش زا ا
تاسرخذام جيي هي قوح الخثض 6666/6666، 6666/6666، 6666/6666، 6665/6666هرراليح
الرلقيح قح ذحليل الرثاعذ الساش هي خالل طشي زلك. رلك تذف ذقذيش الرثايي الساش ك(6هصش × 5)جويضج
عذد االيام هي –هساحح سقح العلن –عذد االيام هي الضساعح حر طشد الساتل للصفاخ الراليح :االخرثاس الصالش
عذد السيثالخ ف سثلح –طل السثلح الشئيسيح -طل الثاخ –عذد الساتل عل الثاخ –الضساعح حر الضج
هحصل الثاخ –هحصل سثلح الساق الشئيسيح –ب ف سثلح الساق الشئيسيح عذد الحث –الساق الشئيسيح
صى األلف حثح. –الفشد
ومكه تلخض الىتائج المتحصل علها ف ات :
اظش ذحليل الرثايي جود اخرالفواخ هعيوح لاول الصوفاخ الوذسسوح تويي عوائالخ الرلقويح الشجعو
الصالش هوا يؤكذ عل جد كويح كافيح هي االخرالفاخ الساشي.
اظش اخرثاس الرفاعل غيش االليل جد اخرالفاخ هعيح لوعظون الصوفاخ الوذسسوح كاود اوا
السوياد × الوضويف الطشاصالوضويف × يوح لاول هوي الطوشاص الوضويف الرفاعل غيش االليلو هع
السياد لوعظن الصفاخ الوذسسح.× تاالضافح ال السياد
اظشذحليل الرثايي للرأشيشاخ الجييح الوضيفح هعيح لوعظن الصفاخ الوذسسح . كوا اظشخ ايضوا
ح. هوووا يعاوه اويووح كول هووي الروواشيشاخ الرواشيشاخ الجييووح السوائذج هعيووح لوعظون الصووفاخ الوذسسو
الوضيفح السائذج ف الرحان الساش لرلك الصفاخ .
اد حيوس ياالكصوش اويوح فو هعظون الصوفاخ هووا اعاوه علو دسجوح السوو كاى الواى االضاف
هوووا يووذل علوو جوود سوويادج جضيعيووح يؤكووذ علوو صيووادج الرووأشيشاخ كاوود اقوول هووي الاحووذ الصووحيح
الوضيفح.
هوي الرثوايي 6636اظش ذحليل الوااخ االليح اى االستع هااخ االلو كاود هعيوح ذوصول ٪
68.6٪ ، 66.6ذوصول PC1 , PC2 PC3, , PC4الال الوجد. كاد االستع هااخ األل
٪ هي كل الرثاياخ الوجدج تيي الرشاكية الساشيح عل الرال . ٪66.6 ، ٪66.6 ،
ش الرثاعذ الساش تيي عوائالخ الرجويي الشجعو الصالشو اعرووادا علو عوذم الرنوات السوث الو اظ
.جد ذسعح عنش هجوعح هوا يذل عل جد كويح كثيشج هي االخرالفاخ الساشيح
اهاي تاسرخذام(Clustering) ذحذيذ ارخاب افضل العائالخ داخل افضل الرجوعاخ(clusters)
( هصل وز العوائالخ يواوي ادخالوا فو توشاهج ذشتيوح 88، 86، 65ل العائالخ ) فااد افض
القوح .
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