Four kinds of cotton are cultivated to supply worlds’ textile fiber and are vital sources of oil and cottonseed meal  . Cultivated species include two diploids G. herbaceum and G. arboreum, and two New World tetraploids species, G. hirsutum and G. barbadense. Genetic diversity in cultivated cotton, though, is generally considered limited  -  . Coyle and Smith  studied evaluate traits for within-boll lint yield components among a group of cotton genotypes, which were diverse by date of release, type of release, originating program and fiber quality parameters. Within-boll yield components were determined by direct measurement or through calculations. Tatineni et al.  and Kumar et al.  studied genetic variation among cotton genotypes resulting from interspecific hybridization at the DNA level with the random amplified polymorphic DNA (RAPD) method. Pathak and Singh  used six populations of different cotton cultivars (P1, P2, F1, F2, BC1 & BC2). Even though the primitive upland cotton accessions contain extremely useful genetic variability for varietal improvement  , breeders have been utilizing very closely related ancestors with few economic characters in their breeding programs with less gain in productivity. It included cross of five upland cotton cultivars (G. hirsutum L.) to evaluate genetic dominance bases for fiber traits. The results indicated the substantial potential for improving fiber properties. In this study, therefore Principal Component Analysis (PCA) and analysis of heritability were carried out; this may help in choosing parents for a successful breeding goal. Keeping all this in view the current study was conducted to access genetic diversity in germplasm of upland cotton (G. hirsutum L.) in Pakistan.
2. Materials and Methods
Regionally adapted 50 cotton genotypes (Table 1) were evaluated under field conditions during summer (2012). Delinted seeds of all genotypes were grown in a twice replicated randomized complete block design at the experimental area of the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad (UAF), Pakistan. Row to row and plant to plant distances were maintained 75 and 30 cm respectively. Recommended agronomic practices were followed throughout the crop growth and developmental period. Genotypes were evaluated for number of bolls per plant (NB), number of seed per boll (S/B), seed cotton yield (SCY), seed index (SI), lint index (LI), boll size (BS), ginning out turn (GOT) from five randomly selected plants from each replication. Fiber parameters including fiber length (FL), fiber fineness (FF), fiber uniformity (FU) and fiber elongation (FE) were measured by Spin lab HVI-900 from the Department of Fiber Technology, UAF. Standard descriptors for cotton were used to measure the traits at appropriate growth stages.
3. Data Analysis
Data were analyzed using PCA  . Two data matrixes (10 × 50) for combined 10 × 25 for 1st group and 10 ×
Table 1. Comparison of traits.
NB = Number of Bolls per Plant; S/B = Number of Seed per Boll; BS = Boll Size; SCY = Seed Cotton Yield; GOT = Ginning Out Turn; LI = Lint Index; SI = Seed Index; FU = Fiber Uniformity; FL = Fiber Length; FE = Fiber Elongation; FF = Fiber Fineness.
25 for 2nd group were prepared for the analysis. The data matrices were standardized to make the variable traits unit less for computing PCA. The character loading was used to calculate the accession component scores. The first two components were extracted for a two dimensional ordinations of accessions (Figure 1 and Figure 2).
4. Results and Discussion
To discern patterns of variation, PCA was performed on all variables simultaneously. Eigen values well representing the variation accounted for principal components and eigenvectors indicating the correlation among principal components and original data sets have been presented in Table 2. Out of 10, four PCs exhibited more than 1 Eigen value (Table 2). The PC1 have 23.8%, PC2 showed 16.8%, PC3 exhibited 12.3% and PC4 exhibited 11.9% variability among the genotypes for the traits under study. Fiber fineness, number of bolls per plant, seed cotton yield and ginning out turn were noted as the characteristics for variability. The first principal component exhibited positive effects for GOT, number of bolls per plant, seed cotton yield and fiber length and negative for fiber fineness. The second component has positive effect for GOT% but negative effects for fiber fineness, seed cotton yield and number of bolls, which showed variation among cotton genotypes for these traits. The third
Figure 1. Two dimensional ordinations of 50 germplasm lines of cotton.
Figure 2. Principal component’s biplot of 50 germplasm lines of cotton.
Table 2. Principal components (PCs) for twelve characters in 50 germplasm lines of cotton (5 - 10).
*PC = Principle component.
principal component exhibited positive effects for GOT, number of bolls per plant but negative effect for seed cotton yield. Among all the PCS: fiber fineness exhibited as the weighted average of the characters (Table 2). Among the genotypes in PC1 are poor in fiber strength. Fiber elongation and fiber fineness were affected due to effect of rain. Number of bolls, seed cotton yield, lint index, seed index, GOT%, fiber strength and fiber have positive effects, these results are in the line of  . Yield parameters in PC1 have positive effects. The traits exhibited low variability ranges from 1% to 36%. These results strengthen that the genotypes belong the same genus. In the PC2 the genotypes are low in yield components i.e. number of bolls, seed cotton yield whereas Ginning out turn percentage is in desired direction. Genotypes belonging to PC2 are erect type and must be given weight, fiber fineness and ginning out turn. This suggests that there will be more lint percentage (Table 2). PC3 has variation for seed cotton yield and number of bolls per plant which are desired yield parameter, fiber fineness and fiber strength also in desired direction. All PCs exhibited low level of dissimilarity except 1st three PCs. From this study it is clear that a good hybridization breeding program can be initiated by the selection of genotypes from the PC1 and PC2 respectively.
4.1. Score Plot
A principle component scatter plot of the cotton accessions depicts that the accessions that are close together are perceived as being similar when rated on the 10 variables; accessions which are further apart are more different. Thus accessions 32 - 25, 29 - 4, 22 - 18, are very close to each other on both PC1 and PC2. The accessions 23, 31, 28, 27 and 50 are rather separated from other accessions. Thus accession 50 is opposite to 31 because one lies in positive region and second lies in negative region (Figure 1).
A principal component biplot shows that variables wre super imposed on the plot as vectors; relative length of the vector represents the relative proportion of the variability in each variable represented. The variety which was far away from origin showed more variation and less similarity with other varieties. In PC1 and PC2 together number of bolls, seed cotton yield, GOT% and fiber uniformity are well represented in the plot but lint index, fiber strength and seed index have difference in PC1 and PC2 together. Number of bolls and seed cotton yield both are closely related to each other (Figure 2).
4.3. Analysis of Heritability
Genotypic and phenotypic variances, genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and broad sense heritability (h2b) of 17 yield and yield related traits of fifteen cotton genotypes are presented in Table 1. GCV ranged from 8.55 to 383.21 among all the traits which were studied. The highest value of GCV (383.21) was observed for seed cotton yield followed by number of bolls per plant (231.38) and the lowest GCV was found in fiber strength (1.82). PCV ranged from 387.47 to 231.98 among all the traits was studied. Number of bolls, seed per boll, seed cotton yield and ginning out turn showed more variation at phenotypic level. Zahid et al.  narrated similar types of findings while studying genetic variability for yield and its components in fourteen genotypes of upland cotton. Rana and Bhat  studied 59 cotton genotypes, 36% genetic diversity was detected. In 41 G. hirsutum cultivars, the average genetic resemblance was 74%. Percy et al.  examined the genetic deviation and heritability of agronomic and fiber traits among cotton genotypes. Genotype coefficients of variance (CV) were maximum for boll size and seed cotton yield. Most traits showed high broad sense heritability, ranging from 0.69 for lint yield to 0.97 for seed cotton yield. Heritability estimates in broad sense were relatively higher (more than 90%) for all the characters except boll size (0.70). High heritability estimates have been found to be useful in making selection of superior genotypes on the basis of phenotypic performance. Genetic advance value was the highest for seed cotton yield (46.64) preceded by seed per boll (16.37) and number of bolls (15.62), which showed that due to high heritability value and genetic advance, the trait which could be further improved was seed cotton yield, number of bolls, seed per boll and GOT.
4.4. Ward’s Linkage Cluster Analysis
The letters 1 - 50 correspond to the genotypes as exhibited in the dendrogram (Figure 3). Two major clusters, i.e., I and II, are formed by using the Wards linkage. May et al.  reported that cluster analysis identified groups of cotton cultivars that were more closely related. Menezes-Sobrinho et al.  conducted a study to characterize 89 garlic germplasm of Brazil and found 13 clusters. Similarly  also evaluated 65 garlic accessions and found six clusters on the basis of morphological characters. In the current study cluster-I consisted of two sub clusters i.e, ia and ib respectively. Sub cluster ia is further portioned into ic and id, sub cluster ic consisted of 15 genotypes whereas sub-cluster id exhibited 12 genotypes and sub cluster ib consisted of 5 genotypes, whereas Cluster-II is partitioned into two sub-clusters iia, iib. Sub-cluster iia is composed of 11 cotton genotypes whereas sub-cluster iib consists of 6 genotypes.
Accessions DPL-6 and QUALANDARI showed 92.02% similarity in sub-cluster iia. Whereas genotypes COKER-307 and FH-113 showed 87.15% similarity whereas BH-160 75.34% level of similarity with both COKER-307 and FH-113 in sub cluster ib. Similarly in sub cluster ia FH-87 and LRA-5166 showed 86.68% and with other varieties i.e. STONEVILLE, VH-61, VH-141 showed 61.82% similarity (Figure 3). Rana and Bhat
Figure 3. Dendrogram of 50 genotypes of G. hirsutum L.
 observed that average genetic similarity was 74% in 41 G. hirsutum cultivars. Aliyu et al.  reported that cluster analysis had the singular efficacy and ability to identify crop accessions with the highest level of similarity using dendrogram. Ghafoor et al.  showed multivariate analyses to be a valid system to deal with germplasm collection. Pillay and Myers  and Abdukarimov et al.  reported that cotton had low genetic diversity. Agronomical traits are expected to provide a general representation of variety relationship according to their growing environment.
The whole discussion can be concluded that variety performance of cotton germplasm did not necessarily depend on the geographical origin or even pedigree relationship. Varieties that display high phenotypic similarity need not be genetically similar because the environment can manipulate phenotypic expression.
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