Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Genomic DNA Extraction and SSR Markers
2.2. Plant Materials and Experimental Design
2.3. Measurements of Traits
2.3.1. Agro-Physio-Biochemical Traits
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- The estimation of Chl in leaves was performed using a colorimetric method by measuring absorbances at 663 nm and 646 nm with 80% acetone as the solvent. Approximately 0.5 g of leaf tissue was crushed in liquid nitrogen, and about 100 mg of the crushed tissue was taken. Then, 2 mL of acetone was added to the sample, which was then left in a dark place in the refrigerator for 48 h. Afterward, the sample was centrifuged, and the extract obtained was used for spectrophotometer readings to estimate Chl. The calculations for estimating Chl were based on the equations provided by Lichtenthaler and Wellburn [44].
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- To measure the activity of antioxidant enzymes, including SOD, CAT, POD, and PPO, fresh leaf samples weighing 0.5 g were utilized. The extraction of these enzymes involved crushing the leaves in liquid nitrogen and suspending them in a buffer containing 50 mM potassium phosphate buffer (pH 7.8) and 1% (w/v) polyvinyl polypyrrolidone. Afterward, the samples were subjected to centrifugation at 14,000× g for 10 min at 4 °C. The resulting supernatant, as described in references [45,46,47], was used as an enzyme extract for the subsequent tests assessing the activity of CAT, POD, PPO, and SOD.
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- The determination of DPPH radical scavenging ability involved assessing the decrease in absorbance at 517 nm [48]. The analyses were conducted using a UV–vis spectrophotometer in 3 mL cuvettes. To facilitate the analysis, a freshly prepared stock solution of DPPH (3.94 mg/100 mL methanol) radicals in methanol was utilized. Subsequently, 3 mL of the DPPH working solution was mixed with 0.5 mL of the extract and left in darkness for 30 min. The presence of an antioxidant agent in the reaction medium led to the disappearance of the purple color associated with DPPH radicals. In parallel, a reference sample consisting of 0.5 mL of the solvent was prepared. The maximal absorption of the newly prepared DPPH radical solution was observed at 517 nm. All analyses were performed in 3 replicates, and the absorbance was recorded at 517 nm. The blank sample referred to the reaction mixture that lacked any test compounds [49,50]. DPPH scavenging effect (%) = [A0 − A1)/A0] × 100.
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- The quantification of TPC was conducted using the Folin–Ciocalteau method, as previously described by Sarker and Oba [51]. In this procedure, extracts (100 μL) or a series of standards (12.5, 25, 50, 100, 150, and 200 μg mL−1 gallic acid) were added. Following reagent mixing and the ensuing reaction, 300 μL of the solution was transferred to a 96-well plate, and the absorbance was measured at 740 nm. The obtained results were expressed as the equivalent amount of gallic acid standard (mg GAE/g FW).
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- To quantify the contents of GB, leaf samples were ground using liquid nitrogen to ensure proper homogenization, as described by Grieve and Grattan [52]. Subsequently, 1 mg of the sample was transferred to a glass tube, and 1.5 mL of 2 N H2SO4 was added to it. The mixture was then placed in a water bath at 60 °C for ten minutes to extract Glycine betaine. After centrifugation at 3500× rpm for 10 min, the supernatants were collected for further analysis. To analyze the GB concentration, 125 μL of the supernatant sample was combined with 50 μL of cold Potassium tri-iodide KI-I2, which was prepared by dissolving 15.7 g of iodine and 20 g of potassium iodide in 100 mL of distilled water. This mixture was left at 0–4 °C for 16 h and then centrifuged at 10,000× rpm for 15 min. The upper liquid was discarded, leaving behind small crystals in the chamber of the tube. These crystals were dissolved by adding 1.4 mL of 1,2-dichloroethane and incubating the solution for 2–2.5 h. The samples were then examined using a spectrophotometer at 365 nm (U-2000, Hitachi Instruments, Tokyo, Japan). To determine the GB concentration, a standard curve was prepared using stock solutions of betaine with concentrations of 1, 2, 4, 6, and 8 μL. These stock solutions were used to calculate the GB concentration in the samples.
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- For proline content, the estimation of proline was performed using the protocol described by Boctor [53] with certain modifications. Initially, the sample was ground using liquid nitrogen. Then, 100 mg of the sample was taken and mixed with 500 μL of 3% Sulpho salicylic acid. The mixture was vortexed and placed on ice for five minutes, followed by centrifugation at the highest speed for five minutes at room temperature. Next, 200 μL of the supernatant was combined with 200 μL of 3% Sulphosalicylic acid, 400 μL of glacial acetic acid, and 400 μL of ninhydrin acid. The reaction components were vortexed thoroughly and placed in a water bath at 100 °C for one hour. To halt the reaction, the tubes were then placed on ice. In the final step, 1 mL of toluene was added to the reaction mixture. The solution was vigorously shaken by hand and left undisturbed for five minutes to allow the components to separate into two layers. The top layer was extracted, and the absorbance was measured using a spectrometer at 520 nm, with toluene serving as the blank.
2.3.2. Quality Traits
2.4. Statistical Analysis
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- Genotyping Analysis: SSR bands were scored (present (1) or absent (0)) to create a binary matrix. The genetic dissimilarity (matrix of pairwise) between genotypes was calculated using the coefficient of Jaccard dissimilarity. Agglomerative HC analysis was implemented using the unweighted pair group average method (UPGAM).
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- Phenotypic analysis: ANOVA (split-plot design) and genetic parameters for 30 traits were implemented using SAS v9.2 software (SAS Institute, Inc., Cary, NC, USA). The variance (mean squares) of data for 30 traits was used to compute variance components that are used to compute genetic parameters (genetic variance (σ2G), residual variance (σ2e), phenotypic variance (σ2Ph), heritability (h2 %), genotypic coefficient of variability (G.C.V. %), phenotypic coefficient of variability (Ph.C.V. %), genetic advance (GA), and genetic gain (GG)), as described by Al-Ashkar et al. [14]. Principal component analysis (PCA) was carried out based on data provided by the correlation matrix to find out the variables contributing the most to the variance and the components loading the most on the variables. PCA is useful for trait reduction, dealing with the problem of multicollinearity, and identifying important traits that are located in the first two components, and its outcomes were used to detect the drought tolerance index, which was used in SMLR (Stepwise multiple linear regression), PC (path coefficient), HC (hierarchical cluster), and LD (liner discriminant) analyses. PCA eliminated five traits that exhibited high multicollinearity. Twenty-five out of thirty traits (index) were used in SMLR to determine the key traits that contribute to enhancing and developing the variable of interest (GY), after which PC analysis was used to divide variation into direct and indirect effects. The effective indices (nine out of twenty-five traits) were used in the HC analysis to evaluate the genetic dissimilarity matrix between thirteen genotypes, characterized into three tolerance groups using Euclidean distance and Ward’s method of agglomeration. LD Analysis was employed to validate the genotype tolerance categories (the nine indices used as quantitative variables) with the three categories (as qualitative variables). Statistical analyses (PCA, SMLR, PC, HC, and DFA) were implemented through XLSTAT statistical package software (vers. 2019.1, Excel Add-ins soft SARL, New York, NY, USA).
3. Results
3.1. Screening Genetic Diversity of Drought Tolerance Genotypes
3.2. Phenotypic Analysis of Genotypes and Traits
3.2.1. ANOVA, Genetic Parameters, and Genotype Performance
3.2.2. Multidimensional Analyses in the Classification of Drought-Tolerant Genotypes
Principal Component Analysis (PCA)
SMLR and PC Analyses for the Performance of Yield Trait
Hierarchical Clustering and Linear Discriminant Analyses
3.3. Genotypic Analysis Based on SSR Markers
3.3.1. Hierarchical Clustering of Genotypes Based on SSR Markers
3.3.2. Association of SSR Markers with Agro-Physio-Biochemical Traits
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Source | DF | Pn | Gs | Ci | E | Chl | PC | GI | WGC | DGC | PH | DH | DM | SL | NS | NSS |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Rep | 2 | 0.365 | 0.0002 | 25.906 | 0.277 | 0.004 | 0.631 | 12.747 | 0.616 | 0.003 | 3.128 | 0.051 | 13.128 | 0.474 | 762.859 | 0.154 |
I | 1 | 1365.716 ** | 0.292 ** | 166,828.1 ** | 149.66 ** | 30.688 * | 17.033 * | 211.207 ns | 139.361 ** | 5.600 ** | 4063.705 ** | 714.051 ** | 5008.013 ** | 11.538 ** | 213,938.782 ** | 149.53 ** |
Error a | 2 | 0.382 | 0.00001 | 103.586 | 0.188 | 0.395 | 0.905 | 30.756 | 1.044 | 0.003 | 0.359 | 0.051 | 13.128 | 0.115 | 158.244 | 1.077 |
G | 12 | 7.055 ** | 0.003 ** | 2131.897 ** | 0.96 ** | 0.964 ** | 6.609 ** | 555.003 ** | 43.507 ** | 4.933 ** | 167.513 ** | 32.218 ** | 150.427 ** | 14.421 ** | 3908.987 ** | 8.872 ** |
I * G | 12 | 3.004 ** | 0.001 ** | 601.333 ** | 0.431 ** | 0.215 ** | 2.573 ** | 54.762 ** | 24.021 ** | 2.043 ** | 87.205 ** | 12.218 ** | 50.568 ** | 0.344 ** | 2395.226 ** | 2.65 ** |
Errorb | 48 | 0.632 | 0.0003 | 74.685 | 0.138 | 0.055 | 0.721 | 12.296 | 1.077 | 0.021 | 10.244 | 0.051 | 0.517 | 0.517 | 425.44 | 1.004 |
Genetic Parameters | ||||||||||||||||
σ2G | 0.675 | 0.000 | 255.094 | 0.088 | 0.125 | 0.673 | 83.374 | 3.248 | 0.482 | 13.385 | 3.333 | 16.643 | 2.346 | 252.294 | 1.037 | |
σ2e | 0.105 | 0.000 | 12.448 | 0.023 | 0.009 | 0.120 | 2.049 | 0.180 | 0.004 | 1.707 | 0.009 | 0.086 | 0.086 | 70.907 | 0.167 | |
σ2Ph | 1.176 | 0.001 | 355.316 | 0.160 | 0.161 | 1.102 | 92.501 | 7.251 | 0.822 | 27.919 | 5.370 | 25.071 | 2.432 | 651.498 | 1.479 | |
h2 % | 57.420 | 66.667 | 71.794 | 55.104 | 77.697 | 61.068 | 90.133 | 44.788 | 58.585 | 47.941 | 62.077 | 66.384 | 96.457 | 38.725 | 70.131 | |
G.C.V. % | 9.191 | 7.347 | 7.662 | 6.948 | 13.036 | 7.579 | 10.916 | 9.083 | 10.185 | 4.249 | 2.593 | 3.698 | 15.601 | 3.269 | 6.507 | |
Ph.C.V. % | 12.130 | 8.998 | 9.043 | 9.360 | 14.789 | 9.698 | 11.498 | 13.573 | 13.306 | 6.137 | 3.291 | 4.539 | 15.885 | 5.253 | 7.770 | |
GA | 1.283 | 0.031 | 27.878 | 0.454 | 0.642 | 1.320 | 17.858 | 2.484 | 1.094 | 5.218 | 2.963 | 6.847 | 3.099 | 20.362 | 1.757 | |
GG % | 14.348 | 12.358 | 13.374 | 10.625 | 23.671 | 12.200 | 21.348 | 12.523 | 16.059 | 6.060 | 4.209 | 6.206 | 31.563 | 4.190 | 11.226 | |
Source | DF | GFD | GFR | TKW | GY | RT | FLA | CAT | POD | PPO | SOD | DPPH | TPC | Pro | RWC | GB |
Rep | 2 | 0.051 | 0.304 | 3.962 | 0.012 | 0.279 | 2.665 | 0.005 | 0.94 | 0.007 | 0.034 | 0.096 | 0.004 | 0.006 | 0.279 | 0.079 |
I | 1 | 1354.167 ** | 44.192 * | 1117.30 ** | 79.50 ** | 13,479.04 ** | 4185.28 ** | 216.430 ** | 230.043 ** | 2.619 ** | 14.878 ** | 34,491.283 ** | 0.180 ns | 5.865 ** | 13,479.05 ** | 46.066 * |
Error a | 2 | 0.051 | 0.574 | 3.882 | 0.067 | 62.231 | 4.203 | 0.2 | 0.06 | 0.009 | 0.024 | 0.096 | 0.168 | 0.002 | 62.231 | 0.735 |
G | 12 | 57.013 ** | 2.76 ** | 38.715 ** | 1.088 ** | 73.831 ** | 194.66 ** | 6.78 ** | 5.312 ** | 0.038 ** | 0.403 ** | 460.565 ** | 0.14 * | 0.163 ** | 73.831 ** | 3.371 ** |
I * G | 12 | 25.333 ** | 1.045 ** | 13.241 ** | 0.416 ** | 34.313 ** | 54.192 ** | 3.208 ** | 4.641 ** | 0.02 ** | 0.222 ** | 190.791 ** | 0.08 ** | 0.062 ** | 34.313 ** | 1.688 ** |
Error b | 48 | 0.051 | 0.531 | 1.645 | 0.078 | 3.248 | 14.302 | 0.067 | 0.139 | 0.001 | 0.006 | 8.877 | 0.045 | 0.009 | 3.248 | 0.185 |
Genetic Parameters | ||||||||||||||||
σ2G | 5.280 | 0.286 | 4.246 | 0.112 | 6.586 | 23.411 | 0.595 | 0.112 | 0.003 | 0.030 | 44.962 | 0.010 | 0.017 | 6.586 | 0.281 | |
σ2e | 0.009 | 0.089 | 0.274 | 0.013 | 0.541 | 2.384 | 0.011 | 0.023 | 0.000 | 0.001 | 1.480 | 0.008 | 0.002 | 0.541 | 0.031 | |
σ2Ph | 9.502 | 0.460 | 6.453 | 0.181 | 12.305 | 32.443 | 1.130 | 0.885 | 0.006 | 0.067 | 76.761 | 0.023 | 0.027 | 12.305 | 0.562 | |
h2 % | 55.566 | 62.138 | 65.799 | 61.765 | 53.525 | 72.161 | 52.684 | 12.632 | 47.368 | 44.913 | 58.575 | 42.857 | 61.963 | 53.525 | 49.926 | |
G.C.V. % | 5.638 | 3.259 | 4.890 | 4.983 | 14.227 | 12.019 | 35.450 | 10.005 | 19.542 | 32.210 | 14.267 | 7.109 | 37.975 | 3.131 | 17.304 | |
Ph.C.V. % | 7.564 | 4.135 | 6.028 | 6.340 | 19.446 | 14.149 | 48.840 | 28.150 | 28.394 | 48.062 | 18.641 | 10.860 | 48.243 | 4.280 | 24.490 | |
GA | 3.528 | 0.868 | 3.443 | 0.542 | 3.868 | 8.467 | 1.154 | 0.245 | 0.078 | 0.240 | 10.572 | 0.135 | 0.210 | 3.868 | 0.771 | |
GG % | 8.658 | 5.293 | 8.171 | 8.067 | 21.441 | 21.033 | 53.006 | 7.325 | 27.707 | 44.467 | 22.493 | 9.588 | 61.580 | 4.719 | 25.188 |
PCA1 | PCA2 | PCA3 | PCA4 | |
---|---|---|---|---|
Eigenvalue | 18.769 | 5.648 | 1.504 | 1.016 |
Variability (%) | 62.564 | 18.827 | 5.013 | 3.386 |
Cumulative % | 62.564 | 81.391 | 86.404 | 89.790 |
Eigenvectors: | ||||
RWC | 0.974 | 0.004 | 0.005 | 0.0002 |
RT | 0.974 | 0.004 | 0.0005 | 0.0002 |
Chl | 0.931 | 0.020 | 0.003 | 0.0001 |
CAT | 0.496 | 0.372 | 0.057 | 0.016 |
POD | 0.549 | 0.299 | 0.069 | 0.000 |
PPO | 0.485 | 0.383 | 0.085 | 0.001 |
SOD | 0.647 | 0.275 | 0.034 | 0.005 |
Pro | 0.534 | 0.301 | 0.013 | 0.0003 |
TPC | 0.069 | 0.026 | 0.293 | 0.381 |
DPPH | 0.646 | 0.273 | 0.036 | 0.008 |
GB | 0.417 | 0.334 | 0.0001 | 0.002 |
Pn | 0.97 | 0.003 | 0.003 | 0.001 |
Gs | 0.959 | 0.010 | 0.012 | 0.003 |
Ci | 0.958 | 0.021 | 0.002 | 0.000 |
E | 0.978 | 0.007 | 0.000 | 0.001 |
FAL | 0.776 | 0.100 | 0.0003 | 0.001 |
DH | 0.776 | 0.146 | 0.008 | 0.005 |
DM | 0.846 | 0.121 | 0.010 | 0.003 |
GFD | 0.822 | 0.095 | 0.011 | 0.002 |
GFR | 0.641 | 0.040 | 0.002 | 0.019 |
PH | 0.645 | 0.001 | 0.029 | 0.033 |
NS | 0.859 | 0.074 | 0.001 | 0.010 |
SL | 0.187 | 0.440 | 0.058 | 0.108 |
NSS | 0.717 | 0.133 | 0.040 | 0.014 |
TWK | 0.817 | 0.108 | 0.003 | 0.006 |
GY | 0.876 | 0.079 | 0.005 | 0.007 |
WGC | 0.098 | 0.464 | 0.262 | 0.135 |
DGC | 0.029 | 0.515 | 0.285 | 0.131 |
GI | 0.001 | 0.589 | 0.001 | 0.109 |
PC | 0.091 | 0.410 | 0.173 | 0.173 |
Stepwise Regression | Path Coefficient | ||||||||
---|---|---|---|---|---|---|---|---|---|
Dependent Variable | Source | Partitioning the Correlation | R2 | ||||||
Regression Coefficient | p-Value | R2 Par. | R2 Com. | Direct Effect | Indirect Effect | Correlation Value | Direct Effect | ||
GY | Intercept | 18.876 | |||||||
FLA | 14.247 | <0.0001 | 0.870 | 0.870 | 0.681 | 0.230 | 0.911 | 0.464 | |
Gs | 0.790 | <0.0001 | 0.049 | 0.919 | 0.340 | −0.371 | −0.031 | 0.115 | |
PH | 21.076 | 0.001 | 0.056 | 0.975 | 0.294 | −0.282 | 0.011 | 0.086 | |
RT | −1.151 | 0.017 | 0.014 | 0.988 | −0.128 | 0.278 | 0.150 | 0.016 | |
Total direct effect | 0.681 | ||||||||
Total indirect effect | 0.307 | ||||||||
Total R2 | 0.988 | 0.988 | |||||||
Residual | 0.109 | 0.109 | |||||||
FAL | Intercept | 0.309 | |||||||
GB | −0.319 | <0.0001 | 0.827 | 0.827 | −0.661 | −0.240 | −0.901 | 0.437 | |
PPO | −0.171 | 0.002 | 0.059 | 0.886 | −0.257 | 0.070 | −0.186 | 0.066 | |
Chl | −0.380 | 0.005 | 0.048 | 0.935 | −0.214 | 0.114 | −0.099 | 0.046 | |
GFD | −0.150 | 0.007 | 0.040 | 0.975 | −0.292 | 0.792 | 0.500 | 0.085 | |
Total direct effect | 0.634 | ||||||||
Total indirect effect | 0.341 | ||||||||
Total R2 | 0.975 | 0.975 | |||||||
Residual | 0.158 | 0.158 |
Genotypes | Classification | Cross-Validation | |||||||
---|---|---|---|---|---|---|---|---|---|
Prior | Posterior | Membership Probabilities | Posterior | Membership Probabilities | |||||
Pr (MT) | Pr (S) | Pr (T) | MT | S | T | ||||
16HTWYT30 | S | S | 0.000 | 1.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
DHL2 | T | T | 0.000 | 0.000 | 1.000 | T | 0.000 | 0.000 | 1.000 |
16HTWYT20 | S | S | 0.000 | 1.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
16HTWYT38 | MT | MT | 1.000 | 0.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
16HTWYT9 | S | S | 0.000 | 1.000 | 0.000 | S | 0.000 | 1.000 | 0.000 |
KSU105 | MT | MT | 1.000 | 0.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
16HTWYT12 | S | S | 0.000 | 1.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
Yecora Rojo | MT | MT | 1.000 | 0.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
16HTWYT22 | T | T | 0.000 | 0.000 | 1.000 | S | 0.000 | 1.000 | 0.000 |
KSU115 | T | T | 0.000 | 0.000 | 1.000 | T | 0.000 | 0.000 | 1.000 |
Klassic | MT | MT | 1.000 | 0.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
Line47 | S | S | 0.000 | 1.000 | 0.000 | S | 0.000 | 1.000 | 0.000 |
ksu106 | MT | MT | 1.000 | 0.000 | 0.000 | MT | 1.000 | 0.000 | 0.000 |
Traits | Treatments | Markres | R2 Par. | R2 Com. | p-Value * |
---|---|---|---|---|---|
GY | Control | Gwm337 | 0.336 | 0.336 | 0.038 |
Drought | Wmc326 | 0.385 | 0.385 | 0.024 | |
index | wmc326 | 0.425 | 0.425 | 0.016 | |
FLA | Drought | Wmc326 | 0.460 | 0.460 | 0.11 |
index | Wmc65 | 0.359 | 0.359 | 0.030 | |
GB | Control | Wmc154 | 0.579 | 0.579 | 0.000 |
Cfd1 | 0.204 | 0.783 | 0.012 | ||
Drought | Wmc326 | 0.623 | 0.623 | 0.000 | |
Wmc503 | 0.195 | 0.819 | 0.001 | ||
Wmc65 | 0.114 | 0.933 | 0.001 | ||
Cfd9 | 0.034 | 0.967 | 0.22 | ||
index | Wmc326 | 0.456 | 0.456 | 0.000 | |
Wmc65 | 0.219 | 0.675 | 0.000 | ||
Wmc170 | 0.211 | 0.886 | 0.001 | ||
Wmc249 | 0.049 | 0.935 | 0.012 | ||
Wmc405 | 0.030 | 0.965 | 0.043 | ||
Gs | Control | Wmc405 | 0.313 | 0.313 | 0.047 |
PPO | Control | Cfd9 | 0.389 | 0.389 | 0.015 |
Gwm369 | 0.222 | 0.611 | 0.038 | ||
Drought | Wmc326 | 0.458 | 0.458 | 0.11 | |
index | Cfd1 | 0.434 | 0.434 | 0.002 | |
Gwm369 | 0.210 | 0.644 | 0.036 | ||
Chl | Control | Wmc503 | 0.392 | 0.392 | 0.022 |
Drought | Wmc326 | 0.418 | 0.418 | 0.017 | |
index | Wmc326 | 0.401 | 0.401 | 0.005 | |
Cfd9 | 0.228 | 0.629 | 0.033 | ||
RT | Control | Gwm369 | 0.699 | 0.699 | 0.000 |
Wmc326 | 0.159 | 0.857 | 0.006 | ||
Wmc154 | 0.054 | 0.911 | 0.044 | ||
Drought | Wmc326 | 0.321 | 0.321 | 0.000 | |
Cfd18 | 0.306 | 0.628 | 0.000 | ||
Cfd9 | 0.150 | 0.778 | 0.011 | ||
Wmc18 | 0.108 | 0.886 | 0.008 | ||
Wmc154 | 0.058 | 0.944 | 0.031 | ||
index | Gwm369 | 0.546 | 0.546 | 0.017 | |
Cfd1 | 0.256 | 0.801 | 0.000 | ||
Wmc177 | 0.121 | 0.922 | 0.005 | ||
GFD | Drought | Wmc326 | 0.356 | 0.356 | 0.031 |
index | Wmc170 | 0.350 | 0.350 | 0.033 | |
PH | Control | Wmc65 | 0.396 | 0.396 | 0.001 |
Wmc154 | 0.334 | 0.730 | 0.000 | ||
Wmc74 | 0.169 | 0.899 | 0.002 | ||
Wmc18 | 0.048 | 0.920 | 0.028 | ||
index | Wmc65 | 0.464 | 0.464 | 0.010 |
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Sallam, M.; Ghazy, A.; Al-Doss, A.; Al-Ashkar, I. Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques. Life 2024, 14, 183. https://doi.org/10.3390/life14020183
Sallam M, Ghazy A, Al-Doss A, Al-Ashkar I. Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques. Life. 2024; 14(2):183. https://doi.org/10.3390/life14020183
Chicago/Turabian StyleSallam, Mohammed, Abdelhalim Ghazy, Abdullah Al-Doss, and Ibrahim Al-Ashkar. 2024. "Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques" Life 14, no. 2: 183. https://doi.org/10.3390/life14020183
APA StyleSallam, M., Ghazy, A., Al-Doss, A., & Al-Ashkar, I. (2024). Combining Genetic and Phenotypic Analyses for Detecting Bread Wheat Genotypes of Drought Tolerance through Multivariate Analysis Techniques. Life, 14(2), 183. https://doi.org/10.3390/life14020183