Analysis of Genetic Diversity and Phylogenetic Relationships of Wheat (Triticum aestivum L.) Genotypes Using Phenological, Molecular and DNA Barcoding Markers

Wheat (Triticum aestivum L.) is a key food crop, accounting for approximately 765 million tons produced worldwide. The present study evaluated 16 wheat genotypes using 19 morphological and phenological traits, 16 molecular markers (Inter Simple Sequence Repeats and Start Codon Targeted; ISSR and SCoT) and rbcL and matK plastid gene barcoding. The 16 wheat genotypes showed significant genetic variation using the markers assayed. Cell plot of phenological parameters revealed significant differences among the 16-day-old seedlings of wheat genotypes at Z1.1 growth stage. Collectively, W2 genotype had the lowest shoot length (SL), length of first internodes (LFI) and leaf area (LA) values, while W8 genotype had the highest diameter of first internode (DFI) and LA values. Furthermore, W7 genotype had the maximum plant biomass (PB) and leaf width (LW) values. Geometric models grouped wheat kernels into “rounded” and “nearly elongated”. Estimates of heritability (H2) for these morphological characters ranged from 4.93 to 100%. The highest H2 values were recorded for root number (RN) (100%) followed by SL (88.72%), LFI (88.30%), LA (87.76%) and Feret diameter (86.68%), while the lowest H2 value was recorded for DFI (4.93%). Furthermore, highly significant genotypic and phenotypic correlations were also observed among those traits. Reproducible fingerprinting profiles and high levels of polymorphism (PPB%) of SCoT (95.46%) and ISSR (82.41%) were recorded, indicating that they are effective tools for detecting genetic variation levels among wheat genotypes. The informativeness of markers were measured through estimation of polymorphic information content (PIC), resolving power (RP) and marker index (MI). The RP and PPB% of SCoT were significantly higher compared to those of ISSR. Comparatively, the two molecular markers were effective for studying genetic diversity among wheat genotypes, but SCoT markers were more informative. Moreover, based on the two chloroplast DNA regions (rbcL and matK), MatK was found to be more reliable for differentiating among T. aestivum genotypes. Taken together, using all the studied attributes, a clear taxonomic relationship can be used to identify T. aestivum species and improve their pragmatic production and development.


Introduction
Bread wheat (T. aestivum L.) is among the most important food crops worldwide, used to make bread and straw for animal nutrition [1,2]. Varieties of T. aestivum ssp. aestivum, which are known for having naked kernels and producing the majority of commercial bread sold worldwide, are advantageous for milling in the bread and pasta industries in understand results and provide a foundation for the creation of breeding plans that are more effective. While genotypic correlation is the innate link between features, phenotypic correlation is the documented association between two values or traits. To produce results for range conditions, a plant breeder must understand the relationships between various yield ingredients [16].
Molecular markers enhance plant breeding, boost output, and accelerate time [24]. Effective DNA markers, such as SCoT and ISSR, are utilized to explore plant biodiversity [25]. Among the molecular markers, ISSRs were efficient for determining wheat germplasm intercorrelation [26,27]. Due to the great effectiveness of ISSR markers, just two primers were required to differentiate several of the wheat cultivars under study [28]. Additionally, SCoT polymorphisms are derived from the small, conserved area of plant genes that surrounds the ATG translation start codon [25]. The creation of core collections and the preservation of germplasm depend heavily on genetic markers. Utilizing molecular marker technologies to assess genetic variability, identify genotypes, and create DNA fingerprints, several researchers have identified the species of Triticum [18,19,[29][30][31]. To make it easier to choose genotypes with higher diversity and improved performance, it is crucial to evaluate the germplasm's genetic diversity.
The DNA barcode [32] is a genetic tract of short DNA sequences used in the identification of polymorphic plant species or as a genetic recognition approach that can be accurately identified by similar physical traits and chemical compositions [33]. Plastid sequences have traditionally been preferred to nuclear sequences in molecular plant taxonomy owing to their lower intra-molecular recombination [34]. Apart from traditional PCR-based markers, a short DNA sequence obtained from existing recognition sequences of the chloroplast genome can be utilized to classify plant genera and/or species in relation to orthologous databases [35]. RbcL, matK, psbA-trnH, and ITS barcodes are among the genes that have been studied for the identification and documentation of plant diversity. The chloroplast genome's rbcL and matK gene loci have long been thought to be the standard for plant barcodes [32].
As a result, wheat breeders work hard to gain proper knowledge of the extent and genetic basis of variability throughout crucial characteristics in wheat genotypes. As a result, the current research aimed to assess the genetic variation in 16 Egyptian hexaploid wheat genotypes using morphological, phenological, molecular, and two gene barcoding attributes. This will also assist in the collection of data on the amount and nature of genetic variation that dominates yield expression, as well as the analysis of the related qualities in these wheat genotypes..

Plant Materials
The present research included 16 hexaploid wheat cultivars of T. aestivum L. The genotypes' seeds were provided by Agricultural Research Center in Egypt, and their local names and pedigrees were reported in Table 1. The 16 cultivars were grown and the seedlings were collected for morphological analysis. Additionally, small young leaves were kept at −20 • C for molecular and barcoding analyses.

Experimental Design: Morphometric and Phenotypic Markers
The experiments were conducted in field condition following a randomized complete block design (RCBD) in three replications. The sowing was performed mechanically, using 15-20 grains per pot, depending on the genotype characteristics. A total of 45 16-day-old seedlings (three replications of each with 15 plants; 3 × 15) were taken at Z1.1 stage for each cultivar. According to Zadoks' (Z) classifications of growth stages [36], there was one stage (one leaf (L1) on the main shoot and the second leaf (L2) appeared). Scan images from different genotypes were utilized to get quantitative measurements of the morphological traits. For shoot phenotypic traits: plant biomass (PB); shoot length (SL); length of first internodes (LFI); diameter of first internode (DFI) were measured. For leaf phenotypic traits: Genes 2023, 14, 34 4 of 29 leaf area (LA) and leaf width (LW), and for root phenotypic traits: root number (RN), root maximum length (RL), root width (RW) and tip angle of root (TA) were calculated. For seed phenotypic traits: single seed weight (SW); seed area (SA); seed perimeter (SP); length of seed major axis (L); length of seed minor axis (W); aspect ratio (AR) (equals the ratio L/W); circulatory (circ.); roundness (round) and Feret diameter (Feret) were measured. Least Significant Difference (LSD) at 5% and analysis of variance were employed to compare the genotypes. Table 1. Name, pedigree, selection history and year of release of the wheat genotypes and lines analyzed in the present study.

DNA-Based Molecular Genetic Diversity Analyses: SCoT and ISSR Markers
Young leaves of T. aestivum were utilized to extract DNA using the DNeasy plant mini kit. Later, until it was used for PCR amplification, the extracted DNA was stored at −20 • C. According to Collard and Mackill [25], 10 unique ISSR primers and 6 SCoT primers were utilized, as shown in Table 2. PCR amplifications were performed, according to Williams et al. [37], in a total volume of 30 µL, using 2 µL of each primer, 1 U of Taq polymerase, 2.5 mM of each deoxynucleotide, 25 mM MgCl 2 and 25 ng of template DNA. The automated thermal cycle (Model Techno 512) used to perform the DNA amplifications consisted of 45 cycles, each lasting for 1 min at 94 • C, 1 min at 57 • C, and 2 min at 72 • C. After that, the reaction was kept at 72 • C for 10 min. Utilizing a 1.5% agarose gel, ethidium bromide (5 g/mL), 1X TAE buffer, and 100 bp to 3 kb ladder markers, the amplified PCR products were separated electrophoretically. A tiny submarine gel unit (BioRad, Hercules, CA, USA) ran at 80 volts for approximately 30 min. The magnified bands were seen and captured in photos using a gel documentation technique under UV light (InGenius 3, Syngene, Frederick, MD, USA). Each amplification was repeated three times to ensure consistency of results.
Only strong, clear, distinct and repeatable SCoT and ISSR amplified bands were recorded and converted to binary data. The percentage of polymorphism was calculated. Heterozygosity index (H) was used to evaluate the usefulness of ISSR and SCoT markers in identifying different T. aestivum genotypes. H = 1 − Σ p i 2 [38] (p i is the allele frequency for the i-th allele) and the polymorphic information content (PIC = 1 − Σ p i 2 − Σ Σ p i 2 p j 2 [39], p i and p j are the population frequency of the i-th and j-th alleles) were calculated. Data analysis also included the calculation of the effective multiplex ratio, E = n β [40]; β = n p /(n p + n np ), where np and p denote the nonpolymorphic and polymorphic fractions of the markers, so n p and n np denote their respective counting numbers. Average expected heterozygosity was calculated, H.av = Σ H n /n p [40], where H n is the heterozygosity of the polymorphic fraction of markers, and the summation is over all of the polymorphic loci n p . Marker index was also estimated, MI = E H.av [40]. Additionally, discriminating power was determined, D = 1-C [41], where the confusion probability is C = Σ c i = Σ p i Np i -1, where C is equal to the sum of all c i for all of the patterns generated by the primer. Resolving power was estimated, Rp = Σ I b [42], where I b or band informativeness is defined on a scale of 0-1 and is estimated as where p is the portion of the observed band-containing samples.

DNA Barcoding Analysis: Plastid rbcL and matK Genes
The reaction mixture for the PCR amplification of the two rbcL and matK genes contained 50 µL of ultra-pure water, 1 µL of Taq DNA polymerase, 0.2 mM dNTPs, 40 ng DNA, 15 mM MgCl 2 , 20 pcoml of each primer, and 20 pcoml of each primer. After a first denaturation cycle lasting 5 min at 94 • C, PCR amplification was conducted for 40 cycles. Each cycle consisted of a denaturation phase lasting for 30 s at 94 • C, an annealing step lasting for 30 s at 50 • C, and an elongation step lasting for 1 min at 72 • C. In the last cycle, the primer extension phase was prolonged to 7 min at 72 • C. Table 2 contained a list of the primers applied to the matK and rbcL genes for barcoding. The amplification products were electrophoretically separated in a 1.5% agarose gel comprising ethidium bromide (0.5 g/mL) in 1X TBE buffer at 95 volts. The molecular size reference used was a 100 bp DNA ladder. PCR products were seen and photographed.
All PCR products were purified using an EZ-10 spin column. Three volumes of binding buffer 1 were added into the PCR reaction mixture before the mixed solution was moved into an EZ-10 column and allowed to stand for 2 min at room temperature. After that, 750 L of wash solution were added into the column followed by centrifugation. After repeating the process to remove any remaining wash solution, the centrifuge was spun at 10,000 rpm for another minute. The column was put into a clean 1.5 mL microfuge tube, together with 50 L of elution buffer, and incubated there for 2 min before the pure DNA was kept at −20 • C.
Purified PCR products were sequenced using the Big Dye TM Terminator v3.1 Cycle Sequencing Kit in accordance with the manufacturer's instructions on an automated sequencer, the ABI PRISM 3730XL Analyzer (Microgen Company, Moscow, Russia). DNA barcoding of sequences for the rbcL and matK genes was achieved using bioinformatical analysis.

Statistical Analysis
For phenological and morphological markers, the mean and standard error of descriptive statistics were computed for phenological and morphological indicators of wheat genotypes. Before statistical analysis, the normality using Shapiro-Wilk at 0.05 level of all the phenotypic data was tested, and if necessary, the data were transformed. SPSS ver.22.0 was used to construct a one-way ANOVA. The JMP ® ver.16 cell plot was utilized for the graphical depiction (SAS Institute Inc., Cary, NC, USA, 2020-2021). Using the PLABSTAT program [43], the statistical analysis of phenotypic data was done in order to estimate variance and covariance, with genotypes being treated as fixed effects and the three replications being treated as random effects. By dividing genotypic variance (σ 2 G) by phenotypic variance (σ 2 p) and using the HERTI command of the PLABSAT software, heritability (H 2 ) estimates were generated for each trait. The Genetic Coefficient of Variation (GCV) was also estimated. All tests had a 5% threshold of significance. Utilizing covariance analysis and the GENOT command of the PLABSTAT software, the phenotypic and genotypic correlation coefficient for each trait was calculated. JMP ® ver.16 was used to create a constellation plot of hierarchical clustering using Ward's approach to distribute 16 Egyptian wheat cultivars based on phenological and morphological characteristics.
For molecular marker analysis, the online marker efficiency calculator, or iMEC (https: //irscope.shinyapps.io/iMEC/, accessed on 15 August 2022) was used to determine the ISSR and SCoT polymorphism indices [44]. The genetic diversity and similarity matrix of 16 Egyptian wheat genotypes was expressed by hierarchical co-clustering using Ward's approach and heatmap based on combined marker analysis (ISSR and SCoT) polymorphism using JMP ® ver. 16.
In order to summarize the data and better understand the findings, Principal Component Analysis (PCA) was performed to investigate the contribution of the combination of molecular markers (ISSR and SCoT) as well as individual morphological and molecular features to overall variability. The Guttman-Keiser criterion was used to decide how many components were taken for additional examination. Components with an Eigen value greater than 1 were kept for further examination. Genotypes were grouped using a clustergram based on how closely they shared the features that were put to the test, as well as how closely these traits correlated. By using the Ward method of clustering, the similarity matrix was created. JMP ® ver.16 was used to conduct the two analyses.
The sequences generated by each rbcL and matK marker were aligned in DNA barcoding analysis using multiple sequence alignment (multalin) http://multalin.toulouse.inra. fr/multalin/, accessed on 15 August 2022). A homology search of rbcL and matK sequences was analyzed using the BLAST program (http://www.ncbi.nlm.nih.gov/BLAST, accessed on 15 August 2022). The resolution of each locus was determined by the Maximum Likelihood (ML) and the Neighbor Joining (NJ) trees built using the UPGMA algorithm by MEGAX software. The bootstrap methods with 1000 replications were used to test the reliability branching in the ML and NJ analyses. Tamura-3 parameter was calculated for loci [45]. Using DnaSP 6.10.01, the total alignment length (bp), the total number of matrix cells, the percentage of missing data, the number of parsimony informative sites (PIC), the number of variable sites, and the average G+C contents in each region were used to describe the genetic variability of each marker.

Phenological, Morphological and Geometric Traits Characterization
The interplay of phenotyping and genotyping, as well as their interactions with each other and the environment, affect wheat (T. aestivum L.) crop performance. For example, in order to achieve maximal seed count and size (potential yield), wheat must maintain and improve biomass and flowering during ideal seasonal conditions [46]. Quantitative traits are complicated because of the involvement of multiple genes and their interrelations with one another and the environment. Plant architecture and phenology are two quantitative characteristics that contribute significantly to wheat adaptation [47].
The results of morphological traits showed tremendous variations among Z1.1 stage of 16-day-old wheat genotypes ( Figure 1 and Table 3). These variations are essential for developing new cultivars with distinct morphological and phenological traits. This study tested 19 quantitative wheat traits; the plant biomass (PB) of all genotypes was between 0.07 and 0.20 g with a mean value of 0.12 g. For shoot phenotypic parameters, the shoot length (SL) of the W16 genotype was the longest (18.26 cm) while the shortest (7.90 cm) was noted in the W2 genotype with an average value of 14.73 cm. Length of first internodes (LFI) values were between 1.12 and 5.16 cm with a mean value of 3.15 cm. Diameter of first internode (DFI) values varied from 0.15 to 0.23 cm with an average value of 0.18 cm as recorded (Table 3). In accordance with Lukovi et al. [17], the KG-1/6 genotype (78.5 cm) had the lowest average stem height of 14 winter wheat (T. aestivum ssp. vulgare L.) genotypes at three locations across Serbia at the yield stage, while the KG-162/7 genotype had the highest (102.3 cm). Pobeda genotype had the largest internode diameter (4.0 mm), while (KG-191/5-13) genotype had the smallest diameter (3.5 mm). The average length of the first internode was 3.5 cm (KG-1/6) to 5.1 cm (KG-191/5-13).  In this investigation, the leaf area (LA) and width (LW) were essential traits and these traits were in the ranges of 1.13-4.48 cm 2 and 0.24-0.42 cm, respectively. The widest leaf area was from the W8 genotype and the smallest was from the W2 genotype, respectively (Table 3). Collectively, the W2 genotype had the lowest SL, LFI and LA values, while the W8 genotype had the highest DFI and LA values, and W7 genotype had the maximum PB and LW values.
Wheat selection and breeding programs have largely ignored the impact of root traits on aboveground traits and yield [48]. Recognizing the morphology of the root system in bread wheat is essential for identifying root traits in order to produce genotypes with enhanced resource uptake and tolerance to harsh conditions [49]. Some studies have examined the phenology and morphology of large collections of wheat germplasm [50]. Root phenes are root system morphological traits that comprise root numbers, length, angles, and different root diameter categories [51]. Table 3. Descriptive statistics of 16 days seedling, shoot leaf and root morphometric traits and seed geometric traits of the 16 T. aestivum genotypes studied.  In the current study, root maximum length (RL), root width (RW), and tip angle of root (TA) indicated significant variations across all wheat genotypes, as shown ( Figure 1 and Table 3). W2 genotype had the highest RN (6.00), while W1, W10, W14, and W16 genotypes were recorded as (3.00). The average RL of all genotypes was 8.60 cm, whereas the highest was in W10 (11.09 cm) and the lowest was found in W9 (6.83 cm); similar to Chen et al. [49], who discovered that the total RL at the Z2.1 stage, 35 days after wheat plant transplanting varied from 670 to 3538 cm, with an average of 1937 cm. Among the 184 genotypes, 79 genotypes had RL values between 1000-2000 cm per plant, 6 genotypes had RL values less than 1000 cm per plant, and 5 genotypes had RL greater than 3000 cm. Other studies of Waines and Ehdaie [52] and Fang et al. [53] have confirmed modern wheat varieties with smaller root systems than older ones as an unintended result of breeding for improved grain yield.

Morphometric and
Root length and number, emergence and tip angles, rooting depth and width, convex hull area, and root mass center are all architectural characteristics [54]. The architectural characteristics of seedling roots also significantly affect the phasic development. Remarkably, a larger root system is linked with delayed maturation and, consequently, extended grain filling, most likely due to improved nutrient and water uptake for photosynthesis [55]. In this investigation, the average RW of all genotypes was 7.03 cm, whereas the highest was in W3 genotype (10.23 cm) and the lowest was found in W15 genotype (5.07 cm). TA average of all genotypes was 35.89 • , the largest angle was in W11 genotype (48.63 • ), and the smallest acute angle (27.34 • ) was recorded in W3 genotype ( Figure 1 and Table 3). Furthermore, in the W3 genotype, a smaller tip angle of seminal roots was associated with a wider root system (RW). Our findings were consistent with those of Xie et al. [56], who studied 226 recombinant inbred lines (RIL) of 13-day-old seedlings obtained from a cross between T. aestivum 'Forno' (wide and small root system) and Triticum spelta 'Oberkulmer' (narrow and large root system).
Tetra-and hexaploid species have larger grains. Morphological variations of wheat kernels from various genotypes were recorded [57]. In this study, modern shape analysis methods depart from the coordinate of the points in the outlines of a figure based on descriptive parameters for seed phenotypic and geometric traits (i.e., single seed weight (SW); seed area (SA); seed perimeter (SP); length of seed major axis (L); aspect ratio (AR); length of seed minor axis (W); circulatory (Circ.); roundness (Round); Feret diameter (Feret). On average, SW of all genotypes was 0.05g and W2, W3, W4, W6, W7, W9, W10, W11, W12 and W16 genotypes shared the same seed weight (0.05 g). The average of SA and SP values were 0.34 cm 2 and 2.45, respectively ( Table 3). The genotypes W7 and W13 had maximum SA and SP values whereas the minimum values were obtained in W11 and W15 genotypes, as represented ( Figure 1 and Table 3). In addition, the average Feret diameter of all wheat genotypes was 0.90 cm, whereas the highest was in W7 genotype (1.04 cm) and the lowest was found in W8 and W15 genotypes (0.84 cm) as scored (Table 3).
Recent developments of comparing the shapes of two-dimensional figures rely on artificial visions and incorporate the point coordinate in a figure's profile. Using these virtues, the application of algorithms series allows for the calculation of several traits for each figure, including: its shorter and longer diameters, centroid, perimeter, area and circularity index [3,58,59]. Kernels of W13 genotype had a lower AR (1.55) than the other genotypes, while kernels of W12 genotype had a higher AR (2.03). However, the kernel of W13 genotype had higher Circ. and Round values and in the case of W12 genotype had lower Circ. and Round values (kernels are more elongated). Different models for quantifying seed shape were proposed based on variations in roundness and circularity, as well as a visual examination of kernel shape. According to the outcomes of the two models, wheat kernels are divided into two groups based on their shape: "rounded" kernels and "nearly elongated" kernels, as shown ( Figure 2 and Table 3).
In the present study, an AR = 1.8 ellipse was designed to fit the shape of the W2, W10, W15, and W16 genotypes (more rounded), and an AR = 2.03 ellipse was designed to fit the shape of the W12 and W14 genotypes (more near elongated). Our findings agreed with those of Martín-Gómez et al. [3], who concluded that kernels of modern varieties (hexaploid common wheat) with an ellipse of AR = 1.8 were made to integrate the shape of T. aestivum ssp. aestivum Zebra (TaaZ).
Round values and in the case of W12 genotype had lower Circ. and Round values (kernels are more elongated). Different models for quantifying seed shape were proposed based on variations in roundness and circularity, as well as a visual examination of kernel shape. According to the outcomes of the two models, wheat kernels are divided into two groups based on their shape: "rounded" kernels and "nearly elongated" kernels, as shown ( Figure 2 and Table 3). In the present study, an AR = 1.8 ellipse was designed to fit the shape of the W2, W10, W15, and W16 genotypes (more rounded), and an AR = 2.03 ellipse was designed to fit the shape of the W12 and W14 genotypes (more near elongated). Our findings agreed with those of Martín-Gómez et al. [3], who concluded that kernels of modern varieties (hexaploid common wheat) with an ellipse of AR = 1.8 were made to integrate the shape of T. aestivum ssp. aestivum Zebra (TaaZ).

Availability and Hereditability of the Wheat Genotypes
Heritability, a measure of phenotypic variation, plays a key role in crop breeding [60]. A crop population's phenotypic and genotypic variances (PCV and GCV) are critical for successful plant breeding. They are critical parameters for

Availability and Hereditability of the Wheat Genotypes
Heritability, a measure of phenotypic variation, plays a key role in crop breeding [60]. A crop population's phenotypic and genotypic variances (PCV and GCV) are critical for successful plant breeding. They are critical parameters for determining the impact of the environment on the genotype's performance [61]. The examination of mean sum of squares values revealed that genotype variance was significant for all 19 morphometric and phenological traits, indicating that genetic variation existed among the 16 T. aestivum genotypes, as shown in Table 4. Mean squares of TA along with other determinants were the highest among different genotypes. The character (TA) had the highest genotypic and replication coefficient of variation (GCV and RCV) (20.017 and 8.2029%), respectively. Our findings were in agreement with that of Lukina et al. [62]. Furthermore, Hossain et al. [63] discovered that the PCV of all traits was greater than the GCV and that their values were closer to each other among 25 genotypes of spring wheat cultivar. Analogous findings were recorded by Varsha et al. It was found by [16] that the biological yield per plot had the highest GCV value (18.09%), while the GCV and PCV showed the desired values for the remaining traits, such as the plant height of 98 wheat genotypes, which were comparatively lower than (10).
High heritability demonstrated that classification for these characters would be efficient, as it would be less influenced by environmental effects [64]. The simpler are the selection procedures, the higher are the heritability estimates [65]. In this investigation, heritability (H 2 ) ranged from 4.93% to 100%. The highest H 2 values were recorded for RN (100%), then came SL (88.72%), LFI (88.30%), LA (87.76%), and Feret (86.68%), while the lowest was recorded in DFI (4.93%) ( Table 4). The traits TA, SL, RN, and LA showed high GCV and heritability. Sharma and Garg [66] reported similar results in wheat (T. aestivum L.) crosses grown in variable natural and salinity conditions. Hossain et al. [63] also discovered that the estimated heritability for various physiological and yield contributing traits of 25 spring wheat cultivar genotypes varied from 29.41% to 97.44%.  Genotypic correlation values are used for further analysis because correlations must be made in light of their genetic behavior. Trait genetic relationships can result from the pleotropic effects of a gene, chromogema, the linkage of two genes and regimental affiliation, or environmental influences [67]. In this investigation, the genotypic (r g ) and phenotypic (r p ) correlations were estimated to determine the relationships between various quantitative characters at the phenotypic level ( Table 5). The results showed that the (r p ) was smaller than (r g ) correlation among all phenotypic traits. These correlation results confirmed the cell plot phenological data of different studied genotypes. Among the shoot phenotypic scores, LFI had the maximum correlation with SL, LA, LW, RN, SA, SP, L, W, Circ., Round and Feret, in which LFI was significantly positive correlated with SL (phenotypic correlation (r p ) = 0.73 **, p < 0.01, (genotypic correlation (r g ) = 0.78+) as shown in W2 genotype. Among the leaf phenotypic scores, LA was significantly positive correlated with SL and LFI (r p ) = 0.80 ** and 0.66 **, p < 0.01 and (r g ) = 0.84+ and 0.75++, respectively, as shown in W2 genotype. In addition, LA was significantly positive correlated with LW (r p ) = 0.75 **, p < 0.01 and (r g ) = 1.51+. Among root phenotypic scores, RN was significantly negative correlated with SL and LA (r p ) = −0.67 **, p < 0.01 and −0.45 and (r g ) = −0.72++ and −0.48 ++, respectively. Along with, RL was negatively correlated with RN (r p ) = −0.61 *, p < 0.05 and (r g ) = −0.74+ as recorded in W10 genotype (Table 5). Green box: *, ** significant at the 0.05 and 0.01 level of the probability, respectively. Blue box: +, ++ coefficient of correlation is larger than one and two times the standard error, respectively.
The co-efficient of variation (CV%) was low for all parameters, indicating that the difference between each individual and another was minimal, indicating that the results for these traits were more acceptable [68]. In this investigation, the r p was generally lower than the r g for all 19 phenotypic traits of 16 wheat genotypes, and PB was positively correlated with all traits except DFI and AR. According to Ahmed et al. [69], seedling length (SL) had a negative correlation with DTW and highly significant and positive correlations with the five leaf wilting scores. SL can be used as a predictor of drought tolerance.
Mukherjee et al. [70] investigated genotypic and phenotypic coefficients of variation in bread wheat and revealed high coefficients of variations of spikes per plant. Likewise, Sarkar et al. [71] concluded that spikelets per spike, plant height, and yield per plant of 37 wheat lines had higher heritability, genetic advance, and phenotypic coefficient of variation.

Hierarchical Co-Clustering Analysis Based on Morphological and Phenological Markers
Selection of genotype groups is primarily influenced by the breeding program's objectives [72]. Multivariate analysis based on hierarchical co-cluster analysis and constellation plot using Ward's method was performed to identify the phenotypic diversity of the 16 wheat genotypes due to differences in morphological and phenological traits and grouped the genotypes into 5 distinct clusters as represented in Figure 3A,B. A cocluster matrix consisted of 5 rows (genotypes) and 4 column clusters (traits). Genotypes of cluster I were W1, W4, and W6 which had similar seed geometric traits. Additionally, cluster II had the largest number of genotypes (W3, W11, W12 and W16), which had similar RL and RW values. Cluster III consisted of W7, W8 and W13 genotypes, which had similar leaf traits (LA and LW), while genotypes of cluster IV were W2, W10 and W5, which had similar TA of root architecture and cluster V had W9, W15 and W14 genotypes which shared the same seed geometric traits ( Figure 3A,B). In the same trend, Mohi-Ud-Din et al. [72] concluded that, using Ward's method, the genotypes of different hierarchical row clusters exhibited variable alterations in the effectiveness of analyzed wheat seedling characteristics.

Molecular Genetic Diversity Analyses: SCoT and ISSR Markers
Molecular characterization is now a valuable tool for assessing variation in plant genetic resources. Polymorphism is also considered a useful selection tool in monitoring alien genome introgression in wheat breeding programs. In the present study, two marker systems (ISSR and SCoT) were used to investigate the genetic variability of wheat genotypes. The polymorphism level and the discriminating capacity of these markers are summarized in Table 6.

Molecular Genetic Diversity Analyses: SCoT and ISSR Markers
Molecular characterization is now a valuable tool for assessing variation in plant genetic resources. Polymorphism is also considered a useful selection tool in monitoring alien genome introgression in wheat breeding programs. In the present study, two marker systems (ISSR and SCoT) were used to investigate the genetic variability of wheat genotypes. The polymorphism level and the discriminating capacity of these markers are summarized in Table 6.   (9) were recorded in ISSR-857 primer, which achieved the highest %PPB as 100% ( Table 6). The %PPB value based on ISSR markers was variable in previous reports. El-Aref et al. [73] found that the polymorphism percentage has an average of 64.95% among wheat varieties. In addition, a high P% was reported in wheat genotypes using ISSR markers [74,75].
Polymorphism information content (PIC) plays a key role in genetic variation analysis [76,77]. Furthermore, several parameters were calculated in this study to categorize the informativeness of the primers under consideration as PIC, heterozygosity index (H), average expected heterozygosity (H. av), marker index (MI), discriminating power (D), effective multiplex ratio (E) and resolving power (Rp). In this investigation, the patterns of ISSR-807, ISSR-825 and ISSR-827 primers recorded the highest values of 0.50 and 0.37 for H and PIC, respectively, whereas ISSR-826 primer noted the lowest values for H (0.35) and PIC (0.29). According to Botstein et al. [39] and Ramadugu et al. [78], markers with a PIC value of 0.25 to 0.50 provide valuable information for genetic variation studies. In the present study, the mean PIC value for ISSR markers was 0.35, with a range of 0.29 to 0.37. As a result, all of the primers tested in this study were found to be effective.
The PIC and Rp prepared a degree of marker system capacity, assisting in the study of primer efficiency in the evaluation of genetic diversity [79]. In this investigation, E values ranged from 0.  (Table 6).

SCoT Markers
Using 6 SCoT primers, however, revealed 64 amplified fragments with molecular sizes ranging from 177 to 3658 bp and an average of 10.70 bands for each SCoT primer, with 61 fragments being polymorphic with 54 UB and the remaining 3 fragments being monomorphic ( Table 6). The %PPB ranged from 82% (SCoT 2) to 100% (SCoT 1, SCoT 4, SCoT 5, and SCoT 6) with a mean of 95.46%. Furthermore, the %PPB of the studied SCoT marker in this study was greater than that of Khodaee et al. [80], who discovered that the SCoT primers produced 162 amplified fragments. The majority of them (90.74%) were polymorphic among Iranian Aegilops triuncialis accessions. Similarly, Shaban et al. [31] confirmed that the primers SCoT3, SCoT5, SCoT10, and SCoT12 revealed 100% polymorphism and identified a significant degree of variation among wheat germplasm.
In this investigation, as for the frequency of the bands, MBF was scored as 0.49. SCoT 1, SCoT 4 and SCoT 6 primers recorded the lowest values of H (0.40) and PIC (0.30), while primers SCoT 2, SCoT 3 and SCoT 5 showed the highest values of H (0.50) and PIC (0.40), respectively. Our results in accordance with Nosair [30] found that the %PPB revealed by the different primers varied from 100% for SCoT (7, 14, 24, 28, 35 & 46) primer to 90% for SCoT11 primer. PIC values ranged from 0.40 (SCoT 24) to 0.62 (SCoT 35), with an average value of 0.52 per primer. In this study, SCoT 1 primer had the lowest E value (2.6), while SCoT 2 primer had the highest E and MI values (6.3 and 0.02). In contrast, SCoT 2 primer had the lowest D value (0.7), whereas the highest D value (0.9) was recorded in SCoT 1, SCoT 3, SCoT 4 and SCoT 6 primers. In addition, Rp values ranged from 3.3 to 7.1 in SCoT 1 and SCoT 5 primers, respectively, as recorded in (Table 6). In the same trend, Shaban et al. [31] reported that the ISSR and SCoT markers have PIC values of 0.61 and 0.62, respectively. SCoT markers had the highest levels of Rp, MI, and polymorphism percentage.

ISSR and SCoT Analysis
ISSR and SCoT markers have been shown to be useful in genetic variation studies due to their excellent reproducibility and strength for polymorphism detection [26,81]. In this study, a total of 143 amplified bands or fragments (125 polymorphic and 18 monomorphic) resulted from combined ISSR and SCoT primers of wheat genotypes with an average of 9.53 bands per primer. Table 6 shows that the %PPB was as high (88.94%), with 15 unique polymorphic bands with an average of approximately 1 for each primer, 110 non-unique polymorphic bands (NUB) with an average of 7.33, and 18 monomorphic bands (MB) with an average of 1.2. The efficiency of both ISSR and SCoT markers is estimated in this study using parameters, such as PIC, MI, and Rp. In fact, SCoT markers were more effective than ISSR markers at detecting polymorphisms, detecting 95.46% versus 82.41% polymorphisms. The average values of H, PIC, E, MI, D, and RP of ISSR primers were 0.45, 0.35, 4.04, 0.01, 0.74, and 2.92, respectively, while the average values of H, PIC, E, MI, D, and RP of SCoT primers were 0.40, 0.30, 3.9, 0.01, 0.9, and 5.4, respectively. According to our findings, the mean values of %PPB and Rp for SCoT primers were higher than those for ISSR primers, demonstrating the great potential of SCoT markers in assessing genetic diversity and determining relationships among wheat genotypes. These findings support the findings of Nosair [30] and Shaban et al. [31], who concluded that SCoT markers were more informative in studying genetic diversity among wheat cultivars. According to Khodaee et al. [80], the most relevant indices for measuring marker efficiency were Rp and MI, though the ISSR had a higher resolution than the SCoT markers among Iranian A. triuncialis accessions, contradicting our findings.
The present study showed that the ISSR-857 and SCoT 4 primers were the most informative and had the greatest potential of the primers tested. ISSR primers resulted in 56 amplified UB, while SCoT primers resulted in 54 amplified UB. Interestingly, the ISSR-857 primer revealed the most UB (9 bands), while the SCoT 4 primer revealed the least UB (11 bands). The HB-10 and SCoT 1 had the highest values for all parameters studied. Gowayed and Abd El-Moneim [27] found that the HB-10 primer revealed the highest value of P% of 96% and the SCoT 1 primer revealed the highest value of P% of 92.3% of some Egyptian wheat genotypes.
These studies indicate that these molecular markers are crucial criteria for evaluating wheat genotypes genetic diversity. Similarly, Carvalho et al. [74] used ISSR markers to analyze 48 wheat cultivars and discovered that most cultivars from the same botanical variety were grouped together. El-Aref et al. [73] discovered that the ISSR markers were successful in distinguishing wheat varieties based on their ploidy level and location, with tetraploid varieties and hexaploid varieties grouped together in one group. diversity. Similarly, Carvalho et al. [74] used ISSR markers to analyze 48 wheat cultivars and discovered that most cultivars from the same botanical variety were grouped together. El-Aref et al. [73] discovered that the ISSR markers were successful in distinguishing wheat varieties based on their ploidy level and location, with tetraploid varieties and hexaploid varieties grouped together in one group. Correlation levels are colored dark blue for high intensities and white for low intensities. Row clusters were obtained at genotype level, whereas the column cluster were recorded at trait or marker level.

Hierarchical Co-clustering Analysis
The dendrogram reflected the differences in genetic diversity within the 19 phenological and molecular markers (ISSR and SCoT) levels and discriminated among the wheat genotypes as represented in Figure 4B. The 16 wheat genotypes were divided into 4 main clusters. A co-cluster matrix consisted of four row

Principal Component Analysis (PCA)
The principal component analysis (PCA) technique was used to analyze object diversity in terms of quality traits and group them according to the similarity hierarchy [83]. As a result, correlation and principal component analyses assist breeders in genetically improving traits with low heritability, such as yield, particularly in early generations, through indirect selection for traits effective on this [84].
PCA was also performed to support the results of the hierarchical clustering analysis ( Figure 5A), which was based on their genomic constitution and 19 phenological traits. PCA was used to analyze the average data. PCA reflects the significance of the largest contributor to total variation along each differentiation axis. PCA revealed similar groups, correlating the results of neighbor-joining clustering. In a PCA scatterplot, variation in the 16 wheat genotypes described by the first 2 PCs per cluster, ranged from 15.1 to22.3% of the total variation as shown in Figure 5A. For these dendrogram co-clusters, PCA produced mostly congruent results. The studied genotypes were also grouped into four clusters: cluster I (blue group) included six genotypes, cluster II (orange group) had 7 genotypes, cluster III (red group) had one only genotype which were distinct and well supported and cluster IV (green group) had two genotypes. Furthermore, objects with similar ordinates were more similar than those with dissimilar ordinates.
In the system of two first components, length of vector and cosine of angle were used for discrimination of wheat genotypes were shown in Figure 5B. Circ. and round are the longest vectors, and the small angle between these vectors proves a significant strong positive correlation between these traits. On the other side, circ. and round traits had a strong positive correlation with each other and a negative correlation with AR. Moreover, vectors for PB and some seed geometric parameters (W, SA, SP, Feret, L) showed the strong discrimination power of these parameters and might be important for the evaluation of wheat genotypes. PB created 90 degrees with AR, the same pattern was shown also between W and AR in which they were not likely to be correlated. These results confirmed the cell plot, phenotypic and genotypic correlation coefficients, and heat map correlation of phenological traits.
Concerning molecular markers, the most frequent vectors were SCoT 2 and SCoT 5, which had a positive direction of discriminating traits that showed their better performance. It also results from the graph that ISSR-835, ISSR-857 and ISSR-826 vectors were strongly correlated variables. Thus, PCA analysis enabled the decline of 25 key traits to a few variables, while retaining a significant portion of primary data variance. In a similar manner, Beheshtizadeh et al. [85] reported that PCA revealed that 4 components accounted for approximately 76% of the total variation of wheat genotypes traits. Furthermore, Adilova et al. [86] discovered that the PCA revealed four principal components (PC) with eigenvalues greater than one, indicating approximately 90.8% of the total variability. plot, phenotypic and genotypic correlation coefficients, and heat map correlation of phenological traits. Concerning molecular markers, the most frequent vectors were SCoT 2 and SCoT 5, which had a positive direction of discriminating traits that showed their better performance. It also results from the graph that ISSR-835, ISSR-857 and ISSR-826 vectors were strongly correlated variables. Thus, PCA analysis enabled the decline of 25 key traits to a few variables, while retaining a significant portion of primary data variance. In a similar manner, Beheshtizadeh et al. [85] reported

DNA Barcoding: Plastid rbcL and matK loci Sequencing
The interpretation of plastid DNA sequences in many plants revealed that the plastid genome changed only possibly a bit during evolutionary events [87,88]. In many studies, the rbcL gene has been successfully utilized in phylogenetic studies at above-species taxonomic levels [89,90]. Most studies have emphasized the importance of using rbcL in conjunction with other plant DNA barcodes [91]. Furthermore, the matK gene is a useful region because it is the fastest evolving plastid gene, providing enough information to identify phylogenies [92]. The Consortium for the Barcode of Life (CBOL) has established the rbcL and matK as standard plant barcodes, with high species identification and ideal sequential efficiency for plants [87,93].
In the present study, the recorded amplicons were shorter (approximately 600 and 900 bp for rbcL and matK, respectively), allowing for effective sequencing even though many authors deny the region as a barcode if it is longer than 1000 bp due to difficulties in bi-directional sequencing [87]. Our findings agreed with those of Mohamed et al. [94], who concluded that the matK gene is more flexible than the rbcL gene among Egyptian barley (Hordeum vulgare L.) cultivars, and the documented size of the matK region's PCR product was 900 bp, whereas the rbcL region was 600 bp.
After successfully amplifying a portion of the rbcL gene, the amplicons were sequenced and deposited in GenBank under accession numbers in six T. MW864568 and MW864569, respectively. A BLAST function was used to confirm the correct amplification of the matK and rbcL sequences, which revealed that all of the sequences were strongly coordinated with the matK and rbcL sequences from T. aestivum. The conserved matK and rbcL gene sequences were used to calculate and evaluate pairwise distances. More information on the matK and rbcL DNA barcoding regions in six Egyptian T. aestivum genotypes is provided in (Figures 6 and 7, respectively). The phylogenetic tree was built using the neighbor-joining method, and evolutionary distances were calculated using the maximum composite likelihood method. In the rbcL gene region ( Figure 8A), the six genotypes and 10 rbcL sequences, obtained from NCBI and used as outgroups, were distributed into two clusters: the first cluster included two sub-clusters: sub-cluster I had T. aestivum candenza (LT576864) NCBI outgroup, and the five wheat genotypes except W6 (SAKHA 95) genotype, which grouped alone in sub-cluster II. Cluster 2 also had two sub-clusters, one contained T. aestivum Jinnong6 (MK348601) and the other sub-cluster had the rest of the eight NCBI outgroups. Figure 8B depicts a phylogenetic tree of matK sequence variation using the UPGMA algorithm to distinguish between the 6 genotypes studied and 10 matK outgroups. The tree has two major clusters: the first included two sub clusters containing the six genotypes and one of the NCBI outgroups with the accession number T. aestivum 07-JMS-1109 (MF597643), which is close to both W6 (SAKHA 95) and W7 (GIZA 171) genotypes. Cluster II also had two sub-clusters, one had only one of the NCBI outgroups T. aestivum Jinnong6 (MK348601). The second sub-cluster comprises all the rest of the eight NCBI outgroups. In the same trend, Burgess et al. [96] found that the rbcL+ matK barcode system could identify 93.1% of taxonomic groups sampled from native plants. On the other hand, Awad et al. [97] demonstrated that the core-barcode genes, 6 matK and 15 rbcL, have limited discrimination The genetic variation estimates of DNA barcoding regions of matK and rbcL loci in 6 Egyptian wheat genotypes were given in Table 8. The length of the amplified rbcL gene was approximately 728 bp and 757 bp for matK (partial gene) in all studied genotypes. The number of variable sites was 13 and 24 with proportions of 0.018 and 0.032 for rbcL and matK, respectively. Similarly, the matK region was characterized by the greatest number of parsimony informative sites (PICs), 8 with its proportion 0.011 were observed. The average GC content was 0.433 for rbcL and 0.316 for matK and AT content average was 0.567 and 0.684 for rbcL and matK, respectively. In the same context, Osman and Ramadan [95] discovered that the length of the amplified matK gene in all studied samples was approximately 454 bp (partial gene). The average GC% content in all Triticum species was found to be approximately 35.3%.
The phylogenetic tree was built using the neighbor-joining method, and evolutionary distances were calculated using the maximum composite likelihood method. In the rbcL gene region (Figure 8A), the six genotypes and 10 rbcL sequences, obtained from NCBI and used as outgroups, were distributed into two clusters: the first cluster included two sub-clusters: sub-cluster I had T. aestivum candenza (LT576864) NCBI outgroup, and the five wheat genotypes except W6 (SAKHA 95) genotype, which grouped alone in sub-cluster II. Cluster 2 also had two sub-clusters, one contained T. aestivum Jinnong6 (MK348601) and the other sub-cluster had the rest of the eight NCBI outgroups. Figure 8B depicts a phylogenetic tree of matK sequence variation using the UPGMA algorithm to distinguish between the 6 genotypes studied and 10 matK outgroups. The tree has two major clusters: the first included two sub clusters containing the six genotypes and one of the NCBI outgroups with the accession number T. aestivum 07-JMS-1109 (MF597643), which is close to both W6 (SAKHA 95) and W7 (GIZA 171) genotypes. Cluster II also had two sub-clusters, one had only one of the NCBI outgroups T. aestivum Jinnong6 (MK348601). The second sub-cluster comprises all the rest of the eight NCBI outgroups. In the same trend, Burgess et al. [96] found that the rbcL+ matK barcode system could identify 93.1% of taxonomic groups sampled from native plants. On the other hand, Awad et al. [97] demonstrated that the core-barcode genes, 6 matK and 15 rbcL, have limited discrimination capacity of the 18 different Triticum plants in Egypt. Taken together, the use of the two barcodes was ideal for differentiating between different Triticum species in terms of overall barcode system performance and all examined parameters. The best single candidate barcode was matK. Because hybridization and mutation are unavoidable, the discovery of efficient barcodes necessitates well-coordinated efforts. This is consistent with the findings of Osman and Ramadan [95], who discovered that the matK gene sequence plays an important role in distinguishing between closely related Triticum species. As a result, these sequences could be used as a DNA barcode to track the evolution of Triticum species. capacity of the 18 different Triticum plants in Egypt. Taken together, the us the two barcodes was ideal for differentiating between different Triticum spe in terms of overall barcode system performance and all examined parameters. best single candidate barcode was matK. Because hybridization and mutation unavoidable, the discovery of efficient barcodes necessitates well-coordinated forts. This is consistent with the findings of Osman and Ramadan [95], who covered that the matK gene sequence plays an important role in distinguish between closely related Triticum species. As a result, these sequences could used as a DNA barcode to track the evolution of Triticum species.

Conclusions
Significant differences in morphological and phenological traits indicated high level of genetic variability in wheat genotypes, which could be used in futu breeding programs for wheat genotype improvement and development. Furth more, the high polymorphism percentage obtained by ISSR and SC

Conclusions
Significant differences in morphological and phenological traits indicated a high level of genetic variability in wheat genotypes, which could be used in future breeding programs for wheat genotype improvement and development. Furthermore, the high polymorphism percentage obtained by ISSR and SCoT demonstrates the power of SCoT as the best in fingerprinting and diversity analyses. The matK gene barcode plays an important role in distinguishing among closely related Egyptian T. aestivum genotypes. Overall, the combination of desirable phenological parameters with molecular markers and barcodes would be convenient for monitoring Egyptian T. aestivum genotypes. As a result, these genotypes and traits are deserving of more attention in future breeding programs aimed at improving wheat.  Data Availability Statement: All the data supporting this study are included in this manuscript.