Responses of wheat seedling to varying moisture conditions and relationship between morphological and molecular characterization

Dwindling environmental conditions and rapid increase in world’spopulation has created serious threats to world food security. To combat with hunger and diminishing water resources is a greatest challenge being faced by scientists today. Decreasing water resources has created alarming situation to sustainable food production. Wheat is the leading cereal crop being consumed by humans across the globe. Limited water supply may decrease wheat yields upto 90% (Dhanda et al. 2004). Different morpho-physiological traits can be studied to evaluate the performance of plants under limited water conditions (Inou et al. 2004). Understanding of the genomic regions controlling these important traits will contribute in the genetic improvement of wheat to cope with number of stresses particularly low moisture (Frova et al. 1999). Moreover, the association among different plant traits should be determined either it is genetic or phonetics, heritable or non heritable. In the following study wheat genotypes were evaluated under different water regimes. The study showed significant variation among genotypes and treatments (different water levels) which demonstrated the contribution of genetic attributes (Birsin 2005). Heritability values were higher than 70% for all the parameters except SR. Awan et al. (2007) and Haidar et al. (2012) also observed significant differences among genotypes and higher values of heritability. 

The traits SL, RL, SFW, SDW, RFW, RDW, CRshowed greater magnitudes of heritability along with higher values of genetic advance were under the control of additive genetic effects. Heritability also provides the estimation of genetic advance, either the selection under certain environment is heritable or non heritable. Magnitude of heritability determines the simplicity of selection (Khan et al. 2008). To undertake selection in succeeding generation, heritability should accompany substantial amount of genetic advance, which is the indicative of potential to which the trait can be improved under certain environment, therefore higher values of heritability and genetic advance in this study provides an opportunity to breeders to fix these traits with full strength and ease in coherent selection programs (Eid 2009). Lower values for coefficient of variation also demonstrated higher precision levels of the study. Noorka et al. (2007) also observed lower values of coefficient of variation. 

Sardana et al. (2007) demonstrated that high heritability may not always lead to high genetic gain, unless sufficient genetic variability existed in the germplasm. Therefore, to account variation among genotypes principal component analysis was performed (Panthee et al. 2006). As the results indicates that the first PCaccounted maximum variation for the studied traits such as SFW, RDW, CR, SL, RFW, SDW, RLand SRbut other PCs have not played an important role in accounting variation. Mohammadi and Prasanna (2003) explained that if there is high correlation among the data set then first few PCs expresses maximum variation but it decreases with the decresae in correlation among original data set. Gulnaz et al. (2012) observed four significant PCs in a set of seven PCs. Similarly results were also reported by Ahmad et al. (2012). Most of the variation has been accounted by first PC so other PCs were not given due importance in the following study. Eigen values showed continuous decrease, which exhibits that major amount of variation has been accounted by the first few principle components (Leilah and Al-Khateeb 2005). High positive association among root and shoot parameters as depicted by this study provide an opportunity to breeders to breed for these traits at the same time. Furthermore, genetic control of these traits should be identified to enhance breeding accuracy.

To explore diversity at genetic level, 14 most diverse genotypes were selected on the basis of accession component score which were further analyzed with ISSR markers. The PCR results showed characteristics differences among genotypes. Assessment of genetic diversity in wheat has been carried out by different molecular marker systems. Najaphy et al. (2012) observed that for evaluating genetic diversity of wheat genotypes ISSR markers provide sufficient polymorphisms and reproducible fingerprint profiles. Sofalian et al. (2003) reported high level of polymorphism of wheat landraces based on ISSR markers as compared to other markers. The amplified DNA fragments were ranged from 4 (UBC-809) to 11 (UBC-808). Carvalho et al. (2009) observed 12.9 polymorphic bands per primer using 8 ISSR primers in 48 wheat accessions. Nagaoka and Ogihara (1997) found that 3.7 polymorphic bands per ISSR primer. Presence of high polymorphism in wheat genotypes using ISSR markers indicates high efficiency of this marker technique. The lowest level of polymorphisms (72.7%) was represented by ISSR primer UBC-808 (Table 6). Abou-Dief et al. (2013) identified 112 amplified DNA fragments, of which 17 were monomorphic (15.2%) and 95 fragments showed polymorphism (84.8%). PIC values were ranged from 0.32 to 0.81. PIC index has been widely used to explore genetic diversity among genotypes (Tatikonda et al. 2009; Talebi et al. 2010; Thudi et al. 2010).

In self pollinated crops like wheat genetic variation is vital for stress tolerance. Joshi et al. (2004) observed genetic diversity between parents is essential to derive transgressive segregants from a cross. To start a wheat hybridization program in which parents have high heritability along with high molecular diversity, cluster analysis should be carried out to exclude similar parents from the breeding material. Therefore, PCA should be followed by cluster analysis so that genotypes can be grouped in similar and distinct groups (Ahmad et al. 2012). Ayed et al. (2010) demonstrated that cluster analysis is a successful strategy for selection of genotypes to initiate a wheat hybridization programme on the basis of certain morphological traits. Using ward,slinkage clustering method experimental data was analyzed by cluster analysis. Ahmad et al. (2012) identified 2 clusters and 3 subclusters by ward,slinkage clustering method.

Rana and Bhat (2005) estimated 74% genetic similarity by cluster analysis. Similarly, Aliyu and Fawal (2000) highlighted the efficiency of cluster analysis to identify and group crop accessions on the basis of genetic similarity using dendrogram. Multivariate analysis is a valid system to study germplasm collection (Ghafoor et al. 2001; Ahmad et al. 2012). Ijaz and Khan, (2009) classified the 63 genotype into three clusters. Salem et al. (2008) showed the cluster analysis of seven wheat varieties into two major clusters and three sub cluster. The dendrogram represents a number of dissimilar groups. Within the same cluster individuals are similar but there have significant differences with other cluster (Finsten 1996). In the following study some genotypes occupy different clusters under different water conditions which showed expression of different genes under different environmental conditions. Similarly, under T4 100% water conditions genotypes were grouped in 4 clusters except 5 as under different environments which showed variation among genotypes under different water regimes. Moisture stress induces the expression of large number of genes (Shinozaki and Yamaguchi-Shinozaki, 2007). Drought tolerance is a veryd trait which is controlled by many genes and their expressions are influenced by various environmental elements. As these traits are controlled by different QTLs so it may be due to the response of different QTLs to different environments. On the other hand it may be due to the pleiotropic effect by the co-location of QTLs for different traits at a single locus or cluster of closely linked genes (Landjeva et al. 2008).

The following study has depicted the influence of different moisture regimes on the trait expression. Molecular and phenotypic characterization also explored the genetic differences among genotypes. Moreover the genetic diversity dissected in this study using ISSR markers should be explored with SSR or SNP markers to identify QTLs controlling these important traits. Because the seedling growth in wheat is under the control of many loci as concluded by Landjeva et al. (2008) while studying on the International Triticeae Mapping Initiative (ITMI) recombinant inbred population, and find QTLs located on different chromosomes. The results of the following study have demonstrated the involvement of different genetic components which are controlling seedling traits. Traits which showed high heritability and genetic advance should be given due importance to start a breeding program. We conclude that only one level of moisture deficit is not a suitable strategy to breed for drought tolerance. As the study depicted that different plant traits are influenced by different water levels. So, phenotypic evaluation should be done at different water levels to select best genotypes having drought tolerance.