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International journal of Advanced Biological and Biomedical Research
Volume 2, Issue 4(2), 2014: 86-97
Genetic diversity of genotypes of durum wheat (Triticum turgidum L.) genotypes based on cluster and principal component analyses
Masoomeh Rajabi Hashjin1, Mohammad Hossein Fotokian1*, Mostafa Agahee Sarbrzeh2, Mohsen Mohammadi2, Daryush Talei3
1Department of Agricultural Biotechnology, Shahed University, Tehran, Iran
2Research Institute of Seed and Plant Breeding, Karaj, Iran
3Medicinal plant research center, Shahed University, Tehran 3319118651, Iran
ABSTRACT
Genetic diversity is the basis of the natural evolution of plant breeding and biological system are important components of sustainability. The aim of this study was to evaluate 116 genotypes of Triticum turgidum from seven countries in terms of morphological traits. The results showed that high significant differences among the genotypes. The correlation between grain yield and weight per spike was significant and positive, while the correlation between days to heading, length of peduncle and plant height was significant and negative. The factor analysis classified four main groups which accounted for 74.4% of the total variability. The results indicated the presence of high genetic diversity among the genotypes. The tested genotypes were classified in three groups according to the linkage distance analysis. Our findings suggest that the parents from divergent clusters can be used for hybridization to isolate useful recombinants in the segregating generations, the genetics and breeding programs for improvement of durum wheat.
Keyword: Cluster analysis, Durum wheat, Genetic diversity, Morphological trait, Factor analysis
Abbreviations: Days to heading; DHE, Flag leaf length; FLL, Flag leaf wide; FLW, Genomic score; GS, Grain weight per spike; GWS, 1000 Grain weight; 1000GW, Grain yield; GY, Number of grain per spike;
NGS, Number of spikelet per spike; NSS, Peduncle length; PL, Peduncle projection length; PPL, Physiological maturity; PM, Plant height; PH, Spike length; SL, Spike weight; SW
INTRODUCTION
Evolution of sepytoneg with high yield potential accompanied with desirable combination of traits has always been the major objective of wheat breeding programs. Genetic diversity of wheat has been well evaluated using morphological traits, protein pattern and molecular markers. The genetic variation coefficient, which expresses the amount of existing genetic variation as a percentage of the general mean,
are of great importance for genetic improvement programs. The genetic variation coefficient shows the range of genetic variation for a trait, in view of its improvement potential (Mohammadi et al. 2011).
However, morphological traits have a number of limitations, including low polymorphism, low heritability, late expression, and may be controlled by epistasis and pleiotropic gene effects (Nakamura 2001), While molecular markers such as seed storage proteins reflect the genotype more directly, independent of environmental influences (Brown and Weir 1983). Protein markers are useful tools to identify cultivar, registration of new varieties and classification of crop species to study genetic diversity, thereby improving the efficiency of wheat breeding programs in cultivar development (Gianibelli et al.
2001). Proteins are grouped into four classes according to their solubility: albumins, globulins, prolamins and glutenins (Godon and Willm 1991). According to the proteins quantity and quality, mainly gluten, wheat varieties are regrouped in different classes (Godon and Willm 1991). Gluten, comprising roughly 78 - 85% of total wheat endosperm protein, is a very large complex composed mainly of polymeric and proteins known respectively as glutenins and gliadins. Gliadins and glutenins have been extensively studied and the genetics and biochemistry are relatively well known (Shuaib et al. 2007). Therefore the aim of this study was to determine genetic diversity in landraces and cultivars of wheat durum using morphological traits. This information will be useful to improve techniques for sampling wheat genetic variation, which might increase efficiency of conservation of germplasm.
MATERIALS AND METHODS
The experiment was conducted at Seed and Plant Improvement Research Institute in the field of Karaj with 35 degrees and 48 minutes north latitude and 51 degrees 10 minutes east longitude and altitude of 1321 meters above sea level and has a clay loam soils during crop year 2011-2012. A total of 116 genotypes of durum wheat (Triticum turgidum) from seven countries (Iran, Portuguese, Italy, Yugoslavia, Iraq and Afghanistan) were provided by the National Plant Gene Bank of Iran (Table 1). Each genotype was sown in one m long row. After eight months all plants were harvested and data on morphological (days to heading, leaf length, leaf width , length of peduncle, length of peduncle projection, spike length, plant height, Physiological maturity, number of spikelet /spike, grain weight /spike, 1000 grain weight) were measured from three plants of each row randomly. The SPSS software version 20 was used for all statistical analyses including analysis of variance, factor analysis, multiple regression analysis and cluster analysis.
RESULTS
Analysis of variance showed significant differences among the genotypes in terms of all studied morphological traits except days to heading (P≤0.01) (Table 2). The correlation between most measured traits was significant at P≤0.01 (Table 3). The highest correlation was between grain weight per spike and spike weight (0.947**) and peduncle projection length and peduncle length (0.809**). There was a significant correlation between grain yield and the number of grains per spikelet (0.411*). There was a significant correlation between grain yield and weight per spike (0.441**) and negative correlation between day to heading (-0.464**), length of peduncle (-0.337**) and plant height (-0.327**). Factor analysis of the 14 morphological studied traits produced four main groups that were accounted for 74.4%
of the total variability in the dependent structure (Table 3). The first factor (group) included spike weight, number of grain per spike, and weight of grains/spike which accounted for 24.85% of the total variability in the dependent structure. The suggested name for the first factor is grain yield. The second factor contained plant height, spike length and length of peduncle which accounted for 22.4% of the total
weight which accounted for 15.62% of the total variability in the dependence structure. The suggested name for this factor is 1000 grain weight factor. The forth factor included leaf length and leaf wide which accounted for 11.39% of the total variability in the dependence structure and it was named leaf factor.
The multiple regression analysis using stepwise method indicated that grain weight per spike (GWS) was the most related trait to grain yield, since it explained 36% of yield variation. The model fitted for yield was fallow as:
Y= -188.32 + 0.36 (GWS) – 0.29 (NSS) + 0.38 (PM) – 0.39 (PH)
Number of spikelet per spike (NSS), Physiological maturity (PM) and plant height (PH) entered in the equation in second, third and fourth position respectively, accounting jointly with grain yield approximately 32.7% of yield variations. The total variance explained by factors is indicated in Table 4, only the first 5 factors which account for 79.13% of the total variance are important. A principal factor matrix after orthogonal rotation for these 5 factors given in Table 5. The values in the table, or loadings, indicate the contribution of each variable to the factors. The first factor (group) included spike weight, number of grain per spike, and weight of grains/spike which accounted for 29.85% of the total variability in the dependent structure. The suggested name for this factor is grain. The second factor included Peduncle length, Peduncle projection which accounted for 21.98% of the total variability in the dependent structure and it was named peduncle factor. These results are consistent with the reported large reduction of leaf area (Ludlow and Muchow 1990, Giunta et al. 1995, Royo et al. 2004), and peduncle length (Krishnawat and Shar-ma 1998) with low water supply. The third factor included Day to reach which accounted for 11.85% of the total variability in the dependence structure. The suggested name for this factor is Day to reach factor. The forth factor included leaf Length and leaf Wide which accounted for 8.73% of the total variability in the dependence structure and it was named leaf factor and the fifth factor accounted for 7.17% of the total variability and included Grain Color. The tested genotypes were classified in three groups according to the linkage distance analysis. The first group of 62 genotypes, 53.44% of the total genotypes won. The average values of genotypes in this cluster for peduncle projection, peduncle length and plant height, is higher than the mean of all genotypes (Table 7). Standard deviation for all traits in this cluster is less than the total standard deviation. The numbers of 37 genotypes were classified in second cluster including 31.89% of total genotypes. The average value of genotypes in this cluster for day to reach, flag leaf length, flag leaf width, spike length, plant height, peduncle projection, peduncle length, physiological maturity, number of spike per spike, spike weight, grain weight per spike, number of grain per spike and, grain weight per spike and 1000 grain weight is higher than the mean of all genotypes (Table 6). Standard deviation for all traits in this cluster for day to reach and physiological maturity was higher than the total standard deviation. The third group of 17 genotypes, 14.65% of total genotypes was the smallest cluster. The average of all genotypes for grain yield, the number of grain per spike, physiological maturity, is higher than the mean of all genotypes. Standard deviation for all traits in this cluster for flag leaf length and grain yield was higher than the total standard.
The first cluster contained 14 genotypes, indicating the close similarity among most accessions, the second cluster contained 11 genotypes, the third cluster contained eight genotypes, the fourth cluster contained 63 genotypes, indicating the close similarity among most genotypes, the fifth and sixth clusters contained only one genotype and the seventh cluster comprised of 11 genotypes. The alleles 21 and 22 were only found in genotypes Wc-45648 and Kc-1478, respectively. These genotypes were located separately in two different clusters. The distance between leader and joiner genotype in cluster analysis based on the protein profiles was 14.72.
For each of the traits evaluated, descriptive statistics, including range mean, standard deviation, are summarized in Table 4. Among traits, grain yield (g) ranged from 24 to 226 with a mean value of 105.40 g. High differences between the maximum and minimum mean values were found for all other traits. The results showed a high diversity of genotypes was studied. Grain yeild and flag leaf length by 35.23% and 36.26%, respectively, had the highest coefficient of variation. Grain weight per spike had the lowest coefficient of variation. The tested genotypes were classified in three groups according to the linkage distance analysis. The first group of 62 genotypes, 53.44% of the total genotypes won. The average values of genotypes in this cluster for peduncle projection, peduncle length and plant height, is higher than the mean of all genotypes (Table 6). Standard deviation for all traits in this cluster is less than the total standard deviation. The numbers of 37 genotypes were classified in second cluster including 31.89% of total genotypes. The average value of genotypes in this cluster for day to reach, flag leaf length, flag leaf width, , spike length, plant height, peduncle projection, peduncle length, physiological maturity, number of spike per spike, spike weight, grain weight per spike, number of grain per spike and, grain weight per spike and 1000 grain weight is higher than the mean of all genotypes (Table 6). Standard deviation for all traits in this cluster for day to reach and physiological maturity was higher than the total standard deviation. The third group of 17 genotypes, 14.65% of total genotypes was the smallest cluster.
The average of all genotypes for grain yield, the number of grain per spike, physiological maturity, is higher than the mean of all genotypes. Standard deviation for all traits in this cluster for flag leaf length and grain yield was higher than the total standard deviation. Pantwan et al. (2003) reported that plant height related negatively with grain yield under moisture deficit and long- legged genotypes weren't suited in region, where drought stress occur during grain filling stage.
CONCLUSION
The results of the present study indicate the presence of genetic diversity among the tested durum wheat genotypes. Parents from divergent clusters can be used for hybridization in order to isolate useful recombinants in the segregating generations. This information might be used in the genetics and breeding programmes for improvement of durum wheat. This study indicated that selection of variables in the productivity per plant factor (factor 1) could enable breeders to better realize the desired increment in seed yield of wheat. Efficient utilization of the genetic potential held in the germplasm collections requires, among other things, better knowledge of the genetic diversity present in the collected material.
Knowledge of the extent of plant trait variability and the association of specific traits with geographic origin not only facilitates breeding programmes but also helps to define the needs and locations for future collections of germplasm. The factor loadings refer to the coefficients in each principle component or the correlation between the component and the variables. A high correlation between PC1 and a variable indicates that the variable is associated with the direction of the maximum amount of variation in the data set. Similar results were reported by Yin et al. (2002) who stated that the grain yield was divided into three components, namely number of spikes/m2, number of grain/spike, and 1000 grain weight. Efficiency of choice to this attribute can increase by choosing morphological traits related to grain which have positive and direct relation with grain yield and measure rapidly and easily (Blum, 1997). Grain yield is an important economic trait. The genotypes of third cluster had average more than total average of grain yield. Many reports indicate that there is a high diversity of the samples in the gene bank (Goolabady and Arzani, 2003; Farahani abd Arzani, 2006; Aghaee-Sarbarzeh and Rostaie, 2008).
Grains per spike and grain yield in durum wheat traits that could be the basis for selection in breeding programs used. This trait showed high variability.
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Figure 1. Dendrogram of genotypes for 15 studied variables using hierarchical cluster analysis (ward’s method and squared Euclidean distance)
