Assessment of Different Traits to Evaluate Genetic Divergence in Some Wheat ( Triticum aestivum L . ) Genotypes under Late Sowing Condition

The research work was undertaken to identify the extent of genetic diversity in different parameters of wheat related to heat tolerance mechanism. Performances of currently available 25 spring wheat genotypes were studied at the Regional Wheat Research Institute, Shyampur, Rajshahi during the winter season of 2016/2017. All these genotypes (25) were grouped into five clusters on the basis of non-hierarchical clustering parameters viz. cluster I (G2, G5, G6, G16, G20), II (G4, G7, G9, G11, G12, G17), III (G10, G24), IV (G1, G13, G19, G21) and V (G3, G8, G14, G15, G18, G22, G23, G25). These groups were arranged into five (5) pairs of clusters viz. cluster I and III; II and III; II and V; III and IV; IV and V considering their similar potentiality of different traits. Results revealed that the maximum number of genotypes (8) was found in cluster V while cluster III comprised minimum genotypes (2). The in-ter-cluster distance was higher than intra-cluster distances. The highest and second highest eigenvalues belonged to spike/m 2 (25.23%) (23.8˚C and 24.7˚C). However, results suggested that the genotypes G10 and G24 under the cluster III, and genotypes G01, G13, G19 and G21 under the cluster IV could be considered as parents for future hybridization program, as well as the five pairs of clusters viz. cluster I and III; II and III; II and V; III and IV; IV and V might be matched as considered for getting more heterotic F 1 . The results of the study would help to identify the divergent genotypes associate with heat tolerance and this might be helpful in designing future breeding program.


Introduction
Bread wheat is widely adapted crop that can be grown under variable environments. Although it is best adapted to cool or temperate growing conditions, it is grown in many areas of the world where heat stress is a major yield-limiting factor, especially at the end of the season. Under the changing climatic conditions, heat stress is one of the major challenges for wheat production in Bangladesh.
Genetic diversity for heat tolerance in cultivated spring wheat is well established [2]. The success of breeding depends entirely upon the genetic diversity of desired traits. Genetic diversity is defined as the extent to which heritable materials differ within a group of plants as a result of evolutionary forces, including domestication and plant breeding [3].
One of the important approaches to wheat breeding is hybridization and subsequent selection. Parents' choice is the first step in plant breeding program through hybridization. In order to benefit transgressive segregation, genetic distance between parents is necessary [4]. The higher genetic distance between parents, the higher heterosis in progeny can be observed [5].
Wheat Research Centre, BARI now has a wide range of spring wheat germplasm collection from different sources. Most of these have collection from CIMMYT, Mexico and few from Nepal, India, Pakistan, Australia etc. It is important to evaluate the extent of diversity present in this germplasm collection and identify useful variation associated with heat tolerance.
The multivariate analysis has been established by several investigators for Md. M. Hossain et al. American Journal of Plant Sciences measuring the degree of divergence and for ascertaining the relative contribution of different characters of the total divergence [6]. D 2 cluster and factor analysis have been proved to be useful in selecting genotypes for hybridization.
Mahalanobis's [7] D 2 analysis has been successfully used in measuring the diversity in several crops. An understanding of nature and magnitude of variability among the existing wheat germplasm is a prerequisite for its improvement. Divergence analysis is a useful tool in quantifying the degree of divergence between biological population of geographical level and to access in assessing relative contribution of different components to the total divergence both intra and inter cluster levels [8].
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 [9].
Precise information on the nature and degree of genetic divergence helps the plant breeder in choosing the diverse parent for purposeful hybridization [10].
This study will help to determine the extent of genetic diversity for heat tolerant traits that present in the currently available breeding materials of the Wheat Research Centre, BARI, to classify them into group and to identify the appropriate germplasms.
Results of this study will also help in designing future improvement program for the development of heat tolerant varieties. The development of effective selection and breeding methodology is also important to develop heat tolerant varieties. The analysis of physiological determinants of yield responses to heat may help in breeding for both high yield and greater stability under heat stress conditions.
The objectives of this study were to assess the magnitude of diversity and classify them under different groups based on genetic divergence, identify the character contribution to genetic diversity and identify heat tolerant genotypes for hybridization program expecting to provide superior segregates. Therefore, it would be logical to undertake the present investigation.

Locale of the Experiment
The experiment was carried out at the field of the Regional Wheat Research Centre (RWRC), Shyampur, Rajshahi in the jurisdiction of Bangladesh Agricultural Research Institute (BARI) from the month of December, 2016 to April 2017 and thereafter. The location situated at 24˚22' North latitude and 88˚39' East longitude above 14 m sea level, and belongs to the Agro Ecological Zone of High Ganges River Floodplain (AEZ-11). The field soil of the experiment is silty clay which is Gangetic alluvial type, slightly alkaline with a pH of 7.1 to 8.5. The soil contains low organic matter, deficient in boron but high in iron content and poor fertility level.

Planting Materials
Wheat seeds of 25 different genotypes were used as planting materials in this study collected from the Regional Wheat Research Center (

Lay Out and Experimental Design
The experiment was laid out in Alpha Lattice Design with two replications.

Data Collection: Phonological, Physiological Parameters and Yield
Plant phenology is the scientific study of periodic biological phenomena of plant characters in relation to climatic conditions such as days to heading, days to maturity, days to anthesis, flag leaf senescence days and grain filling duration.
Plant physiology concerned with the plant characters like canopy temperature, chlorophyll content, ground coverage, grain filling rate and biomass production.
Data were collected on different phenological, physiological and yield contributing parameters of selected wheat genotypes. Randomly selection of ten (10) plants from each plot for taking data at growing and post-harvest stages was completed. For statistical analysis the average values of ten plants for each character were used.
The traits such as days to heading, days to physiological maturity, canopy temperature were measured on the basis of whole plot, while spikelet/spike, grains/spike, 1000-grain weight and chlorophyll content of flag leaf were measured on the basis of an average of randomly selected plants. Heading days was calculated by counting the days from seeding to a stage at which 50% of the spikes came out fully from the leaf sheath. Maturity days were counted from the date of seeding to the date when 50% peduncle of 50% plants of each plot became yellow [11]. taken the weight and expressed as biomass in kg/ha. One thousand (1000) sun dried clean grains were randomly counted from each plot after harvest then weighed in gram (g) and finally converted the yield into kg/ha.

Data Analysis through Multivariate Analysis (D 2 Statistics)
The data collected on different yield contributing, phenological and physiologi-

Cluster Analysis (CA)
Cluster analysis was performed by D 2 analysis (originally outlined by Mahalanobis, 1928 [12] and 1936 [13] and extended by Rao, 1952 [14], which divides the genotypes based on the data set into more or less homogenous groups. D 2 is the sum of squares of differences between any two populations for each of the uncorrelated variables (obtained by transforming correlated variables through Pivotal condensation method). Clustering was done by using non-hierarchical and hierarchical classification. D 2 statistic is defined by: where, X = Number of metric in point, P = Number of populations or genotypes, ij λ = The matrix reciprocal to the common dispersion matrix, i j d d = The differences between the mean values of the two genotypes for the i th and j th traits respectively. In simpler form, D 2 statistic is defined by the following formula: where, y = Uncorrelated variable which varies from i = 1 to X. X = Number of traits.
Superscripts j and k to y = a pair of any two genotypes. are discussed from the latent vectors of the first two principal components.

Principal Coordinate Analysis (PCA)
Principal coordinate analysis was used to calculate the inter genotype distance and give the minimum distance between each pair of the N points using similarity matrix through the use of all dimensions of P [15].

Canonical Vector Analysis (CVA)
Canonical vector analysis (CVA) complementary to D 2 statistic is a sort of multivariate analysis where canonical vectors and roots representing different axes of differentiation and the amount of variation accounted for by each of such axes are respectively derived. Canonical vector analysis finds linear combination of original variability that maximize the ratio between groups to within groups variation, thereby giving functions of the original variables that can be used to discriminate between the groups. Thus in this analysis, a series of orthogonal transformation sequentially maximize the ratio among groups to within group variation.

Computation of Average Intra-Cluster Distance
The average intra-cluster distance for each cluster was calculated by taking all possible D 2 values within the members of a cluster obtained from Principal coordinate analysis. The formula used to measure the average intra-cluster distance was: where,

Cluster Diagram
A cluster diagram was drawn using the values of inter and intra cluster distances.
The diagram represented the pattern of diversity among the genotypes and relationships between different genotypes included in the clusters.

Selection of Germplasm for Future Hybridization Program
Divergence analysis is usually performed to identify the diverse genotypes for 2) Selection of particular genotype (s) from the selected cluster (s); 3) Relative combination of the traits to the total divergence; 4) Other important traits of the genotypes (as for performance).

Results
The extent of genetic diversity present in a germplasm collection of a crop plant is an index of its genetic dynamism. The experimental data were collected and analyzed to study the performance of wheat genotype under late sowing condition and clustering the genotypes into several clusters those were similar to each other.

Multivariate Analysis (D 2 Statistics)
This technique is helpful to describe phenotypic variation among the genotypes.
Cluster analysis, principal component analysis (PCA), principal coordinate analysis and canonical vector analysis were used to analyze 12 traits of twenty five (25) wheat genotypes in this study.

Non-Hierarchical Clustering
Non-hierarchical clustering using Mahalanobis D 2 statistics and Tocher's method, grouped 25 wheat genotypes into five different clusters. These results were in conformity with the clustering pattern of the genotypes obtained through principal component analysis. The pattern of distribution of genotypes into various clusters is shown in Table 1. The distribution pattern indicated that the maximum number of genotypes (8) was obtained in cluster V followed by cluster II (6), cluster I (5), cluster IV (4) and cluster III (2).

Canonical Vector Analysis
Canonical vector analysis was done to compute the intra (bold) and inter-cluster distances (D 2 statistics) presented in Table 2. The highest intra-cluster distance was observed in cluster IV (1.17) and the lowest was in cluster III (0.19). The longest inter-cluster distance was obtained in between cluster III and IV (23.06) followed by the distance between clusters II and III (19.06) and then between clusters I and III (15.94). Alternatively, the distance between cluster II and IV was the shortest (4.55) followed by the distance between clusters I and II (5.03).

Principal Component Analysis
The eigenvalues and variance percentage about principal components for twenty five (25) agronomic traits are presented in Table 3. The principal component analysis is used to compress and classify the data.
The main purpose is to reduce the dimentionality of a data set to interpret the data in a more meaningful way. However, the number of variables is reduced to

Intra-Cluster Mean
Intra-cluster means for 12 traits are presented in Table 5

Discussion
The investigation was undertaken to identify the extent of genetic diversity considering 12 traits of 25 wheat genotypes in heat tolerance condition. Upon taking in account non-hierarchical parameters these genotypes were classified into five clusters from which the cluster V comprised maximum eight (8) genotypes. The pattern of distribution of genotypes among various cluster groups reflected the considerable genetic variability existed in the genotypes and this wider genetic variability might be due to the adaptation of these genotypes to specific environmental conditions. In some cases, effect of geographical origin influenced clustering, though geographic distribution was not the sole criterion of genetic diversity. This suggested that it is not necessary to choose diverse parent from diverse geographic regions for hybridization [17]. The results in Table 2 showed that the inter-cluster distances were longer than those of intra-cluster distances. Choudhury et al. [18] obtained similar results getting larger inter-cluster distances than the intra cluster distance in a multivariate analysis in wheat genotypes. The highest inter-cluster distance was observed in between cluster III and IV (23.06) followed by cluster II and III (19.06), which indicated higher degree of genetic diversity and thus it might be recommended to utilize in inter varietal hybridization program. It was also reported that genotypes within the clusters with high degree of divergence would produce more desirable breeding materials for achieving maximum genetic advance [19]. The highest intra cluster distance was found within the genotypes of cluster IV (1.17). The greater inter-cluster and intra-cluster distances indicated greater genetic variability among accession between and within clusters respectively.
The more diversity of parents indicated the greater chance of obtaining higher degree of heterosis [20]. Parent for hybridization could be selected on the basis of large inter-cluster distance and cluster mean for isolating useful recombination in segregating generations [21]. In contrast, the lowest intra cluster distance was observed within the genotypes of cluster III (0.19) followed by cluster V (0.61), which exposed less genetic diversity and thus these might be utilized for population improvement of wheat genotypes.  (Table 3). Therefore, these factors/traits are responsible for major variation toward genetic divergence for 25 wheat genotypes in the experiment.
The results of canonical vector analysis in Table 4  In clustering technique genotypes were splitted into various groups on the basis of their performances which are displayed in Table 5. This table based on III and IV, II and III, I and III, IV and V, II and V considered as a tools of hybridization program.

Conclusions
The potential genotypes for valuable traits found in different clusters. There were high degrees of diversity existed among the studied wheat genotypes which can be utilized in different varietal improvement program in future. Performances of 25 spring wheat genotypes were examined during the winter season of 2016/2017 to identify the extent of genetic diversity in different traits related to heat tolerance mechanism. On the basis of non-hierarchical clustering parameters all these genotypes were classified into five clusters viz. cluster I, II, III, IV and V and these groups were arranged in five (5) pairs of clusters viz. cluster I and III; II and III; II and V; III and IV; IV and V considering their similar potentiality of different traits. Maximum number of genotypes (8) was found in cluster V and minimum in cluster III (2). The longest inter cluster distance (23.06) was observed in between cluster III and IV, and the shortest (4.55) was in between cluster II and IV.
The highest eigenvalue was obtained in the parameter spike/m 2 (25.23%) and the second highest was in spikelets/spike (20.18%). These two parameters exposed positive canonical values both in the vectors 1 and 2 for which these traits can be recommended as major traits for exploring their highest potential toward IV. On the other hand, cluster V was exposed as the lowest producer of 1000-grain weight (30.4 g) and grain yield (2172 kg/ha) along with the highest canopy temperature at both stages (23.8˚C and 24.7˚C).
The traits spike/m 2 and spikelets/spike contributed mostly toward genetic divergence. Selection of parents for these two traits has good scope to get broad spectrum of segregates. Considering magnitude of genetic distance, contribution of different traits toward the total divergence, magnitude of cluster mean, the genotypes G10 and G24 under cluster III and genotypes G01, G13, G19 and G21 under the cluster IV might be considered as parents for future hybridization program. Therefore, this study can help breeders to increase genetic diversity by selecting materials of divergent parentage for crosses, thereby reducing vulnerability to diseases and climate changes.

Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.