Selection of High-Yielding and Fungal Disease-Resistant Bread Wheat Mutants under Marginal Soil Conditions1Ayman Anter and 2Ahemd Sahab

Selection of High-Yielding and Fungal Disease-Resistant Bread Wheat Mutants under Marginal Soil Conditions1Ayman Anter and 2Ahemd Sahab | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Selection of High-Yielding and Fungal Disease-Resistant Bread Wheat Mutants under Marginal Soil Conditions1Ayman Anter and 2Ahemd Sahab Ayman Anter Saber Abdalla, Farahat Sahab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9125268/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Development of high-yielding, fungal disease-resistant varieties and adapted to marginal soils represents great strategy for enhancing bread wheat productivity in Egypt. In this context, newly developed mutant lines (M4 and M5 generations) were evaluated based on agronomic traits and susceptibility to fungal diseases over two consecutive seasons (2023–2024) to detect the most productive and fungal disease-resistant lines. The genotypes were arranged in a randomized complete block design with three replicates, while in vitro pathology tests were performed for the M5 generation. Highly significant differences (p ≤ 0.05) were observed among mutant lines for all studied traits, indicating sufficient genetic variability induced through mutagenesis. Mutant lines outperformed their mother varieties in studied traits. High heritability coupled with high to moderate expected genetic advance most studied traits suggested the predominance of additive gene action in trait expression, indicating that phenotypic selection would be effective for improving grain yield. Mutant lines G2 and G9 were classified as novel sources of high–yielding and fungal disease resistance. Phenotypic selection for both 1000-grain weight and grains per spike significantly enhanced final grain yield, as indicated by phenotypic and genotypic correlations and supported by regression analysis. Principal components and cluster analysis were confirmed that genetic variation was generated and separated the genotypes into three main groups based on the studied traits, highlighting spikes m -2 and 1000-grain weight as key selection criteria. These mutant lines represent available genetic sources for enhancing bread wheat productivity in marginal soil environments and enhancing tolerance to fungal diseases. Wheat Beard Mutant Line Mutagenesis Marginal Soil Heritability Fungal Diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Bread wheat (Triticum aestivum L.), an annual plant belonging to the grass Poaceae family, is a staple crop in Egypt, providing essential macronutrients including carbohydrates, protein, fats, and fiber, alongside important micronutrients including B vitamins, iron, and zinc [ 1 , 2 ]. Despite its benefits, domestic wheat production meets merely ~ 45% of domestic demand. This production deficit was attributed to rapid population growth (1.94% annually) [ 3 , 4 ]. Furthermore, climate change is expected to increase both biotic and abiotic stresses, posing an additional challenge to sustaining wheat production [ 5 , 6 ]. Bread Wheat is significantly affected by numerous fungal diseases, including rusts, powdery mildew and septoria leaf blotch, which can reduce both grain yield and quality [ 7 ]. Under Egyptian conditions, rusts diseases are commonly distributed, particularly leaf rust generated by Puccinia triticina [ 8 ], stem rust generated by Puccinia graminis f. sp. Tritici ,[ 9 ] and stripe rust generated by Puccinia striiformis f. sp. Tritici [ 10 ]. Among these rusts, leaf rust is the common distributed in Egypt [ 11 ]. These diseases cause yellow pustules on the leaves, decreasing the leaf area index, impairing grain filling, and ultimately causing loss of final grain yield [ 12 , 13 ]. Unfortunately, the majority of current commercial wheat varieties remain susceptible to fungal diseases, attributable to replacement of genetically uniform varieties. Furthermore, wheat improvement has been limited by a narrow genetic base of current varieties, the extraordinarily large and complex allohexaploid wheat genome (~ 17 Gb), and significant genotype x environment interaction effects that complicated the selection process [ 14 , 15 ]. To address Egypt's wheat import dependency and increase production, the development of high-yielding, disease-resistant varieties is urgent. In particular, the currently available area for bread wheat cultivation is limited; therefore, developing new varieties with better adaptation to sandy soils represents a strategically vital approach for increasing wheat domestic production [ 16 , 17 ]. Generating a wide genetic diversity is a key driver for successful crop improvement programs, as it essential for selection and evaluation of genotypes, helping breeders to detect desirable alleles for improved grain yield and stress tolerance [ 18 , 19 ]. Mutation breeding is a validated approach for generating a new genetic variation through application of physical (e.g., gamma rays and fast neutrons) or chemical mutagens (e.g., sodium azide and ethyl methanesulfonate) [ 20 – 22 ]. Mutagenesis offers the advantage of modifying desirable traits without changing the genetic makeup of superior commercial varieties, thereby circumventing the backcrossing as in traditional hybridization programs. Mutation breeding represents a great opportunity for expressing hidden or recessive alleles that are extremely useful for improving crop productivity under biotic and abiotic stresses [ 23 – 26 ]. Even modifications or improvements in key grain yield components such as spikes m − 2 or 1000-grain weight, can translate into increases in final grain yield. Additionally, mutation breeding contributed to the conservation of genetic diversity by introducing new alleles into the breeding pool, reducing genetic erosion that resulted from the replacement of local varieties with modern varieties, and providing diverse resistance mechanisms against pests and diseases [ 27 , 28 ]. Furthermore, crop breeders strive to improve resistance to major diseases by variation introduction, evaluating segregating generations, and selecting promising lines within a short timeframe [ 29 , 30 ]. Additionally, the development of new wheat varieties can help to decrease infection levels of certain disease-causing pathogen races, thereby durability of host resistance [ 31 , 32 ]. Phenotypic selection is an effective breeding strategy targeting secondary traits that showed strong genetic correlations with grain yield, thereby accelerating genetic improvement within shortened timeframes [ 33 ]. Such traits are often controlled predominantly by additive gene action, which increases selection efficiency. In contrast, grain yield per se is a complex quantitative trait subjected to genotype X environment interaction effects. Consequently, direct selection for secondary traits (highly heritable) can improve selection efficiency and accelerate genetic advance [ 34 ]. Estimation of genetic parameters, including phenotypic (PCV) and genotypic (GCV) coefficients of variation, broad-sense heritability (hb%), and genetic advance as a percentage of the mean (GA%) are necessary for optimization selection efficiency. Traits governed primarily by additive gene action, particularly those with high heritability and high expected genetic advance, represent ideal targets for selection, ensuring better transmission of favorable alleles across generations [ 35 – 38 ]. Phenotypic and genotypic correlations have been used to reveal traits that are more positively and significantly associated with grain yield, thereby raising selection efficiency [ 39 ]. It helps to detect traits that are closely associated with yield. Moreover, genotypic correlation reflected the genetic relationships between traits, whereas phenotypic correlations represent the mixed effects of genetic and environmental conditions. The determination these associations enable breeders to apply phenotypic selection for yield improvement in early segregating generations. In addition, regression analysis is used to determine the relative contribution of individual traits (predictor variables) to final grain yield (response variables), helping crop breeders to determine the most influential grain yield components [ 40 ]. Therefore, breeders seek to increase selection efficiency, which is easier to implement [ 41 ]. Principal component analysis (PCA), as a multivariate analytic tool, and hierarchical cluster analysis serve as powerful tools for visualizing genetic diversity among genotypes and detecting promising genotypes that can serve as a novel source in breeding programs [ 42 , 43 ]. Therefore, the present study aimed to detect promising bread wheat mutant lines combining high yield and resistance to fungal disease under marginal soil conditions using genetic parameters and multivariate statistical analyses. 2. Materials and Methods 2.1. Experimental site Field experiments were conducted over two consecutive winter seasons (2023–2024) at the Agricultural Production and Research Station of the National Research Center, located in the Nubaria District, Beheira Governorate, Egypt (30°40′00″N 30°04′00″E). The Nubaria area was classified as marginal soil conditions and highly vulnerable to climate change impacts, including recurrent heat waves and drought [ 44 , 45 ]. Physical and chemical properties of the experiment site were characterized prior to experimentation (Table 1 ). Table 1 Physical and chemical properties of the experimental site. Soil Layer Depth (cm) Physical properties Chemical properties Texture Coarse Sand (%) Fine Sand (%) Silt + Clay (%) Bulk Density (t/m³) Field Capacity (θv%) Wilting Point (θv%) EC₁:₅ (dS/m) pH (1:2.5) Total CaCO₃ (%) Organic Matter (%) 0–15 Sandy 48.6 48.8 2.5 1.7 8.1 2.0 0.45 8.7 7.0 0.87 15–30 Sandy 55.71 40.58 3.71 1.68 8.2 3.0 0.57 8.8 2.3 0.65 Physical properties analyzed according to the procedure of Carter and Gregorich [ 46 ] and chemical properties analyzed according to the procedure Page et al. [ 47 ]. Soil texture was sandy, with low field capacity, minimal water retention and low organic matter content. 2.2. Plant Materials and genetic background This study forms part of an ongoing bread wheat mutation breeding program [ 25 ]. The genetic material consisted of five commercial verities of bread wheat ( Triticum aestivum L.,2n = 6x = 42, AABBCC genome, Linnaeus 1753), a widely cultivated across Egypt. These materials were obtained from the Wheat Research Department, Field Crops Institute, Agricultural Research Center (ARC), Egyptian Ministry of Agriculture and land reclamation. All commercial varieties were officially released with no wild collection requirements. Bread wheat is not regulated by CITES conventions and not listed as threatened under the IUCN Red List. Therefore, no specific permit was needed for utilization. Table 2 showed the pedigree and characteristics of the mother varieties used in the current study. Table 2 Pedigree and characterizes of mother varieties (commercial varieties) Variety Pedigree Characterizes Sakha 93 (P 1 ) Sakha 92/TR 810328 S 8871-1S-2S-1S-0S Resistant to rusts, tolerates salinity and heat, short plant height, more tillers Sids 13 (P 2 ) KAUZ"S"//TSI//TSI/SNB"S"ICW94-0375-4AP-2AP-030AP-0APS-3AP-0APS-050AP-0AP-0SD Resistant to rusts and water deficit, early maturing (152 d). Tolerates water salinity Giza 168 (P 3 ) MIL/BUC//Seri CM93046-8M-0Y-0M-2Y-0B Tolerates water deficit, heat tolerant, resistant to rusts, late maturing (165–170 days), medium plant height, white grain color, thin spikes Gemmeiza-9 (P 4 ) Ald”S”/Huac”S”//CMH74A.630/5x CGM4583-5GM-1GM-0GM Sensitive to salinity and water deficit, tall plant height, long spikes, late maturing (160 days),resistant to rusts Maryout 5 (P 5 ) Giza 162 // Bchʼs /4/ PI-ICW 79Su511Mr-38Mr-1Mr-0Mr High yielding and salt tolerant 2.3. Irradiation treatments At the initiation of the breeding program, a total of 300 dry, healthy seeds per treatment per variety were used. The seed moisture content was adjusted to 12% before irradiation to minimize physical damage. The grains of each variety were irradiated in three doses of 100, 200, and 300 Gy of gamma rays (Cobalt-60) at the Egyptian Atomic Energy Authority, at a dose rate of 0.9 Gy s⁻¹. The exposure times were 111.0, 222.0, and 333.0 S -1 . The previous generations (M1-M3) were published earlier [ 25 ]. The current study used advanced mutant generations (M4 and M5). In the M₄ generation, thirty-one mutant lines along with their mother varieties were evaluated for plant height and grain yield components. Based on their performance for studied traits, sixteen mutant lines were selected and advanced to obtain the M5 generation. Ten out of sixteen mutant lines were selected based on their superior performance and used for disease studies. Figure 1 showed mutation breeding program and selection procedures across generations (M1-M6). Regarding plant reproducibility, at the time of manuscript submission, the studied mutant lines are still under experimental evaluation, and therefore, voucher specimens have not yet been deposited in a public herbarium of the National Research Centre. After completion of the breeding program, voucher specimens will be deposited at the Herbarium of the NRC. 2.4. Agricultural applications Sowing was done manually in November at a seeding rate of 350 grain m -2 . Each line planted in the plot consisted of a row measuring 3 meters in length and 60 cm in width. Drip irrigation was applied using emitters with a discharge rate of 4 L h⁻¹, spaced 25 cm apart along the lateral lines. Irrigation was scheduled twice-weekly to keep soil moisture near field capacity, and each irrigation lasted two hours before flowering and three hours after flowering to meet the increased water demand through growth seasons. Nitrogen fertilization (urea, 46% N) was applied at a rate of 175 kg ha⁻¹, divided into 12 split doses started from the first week via fertigation. Phosphorus (15% P 2 O 5 ) was applied at a rate of 40 kg ha⁻¹ during soil preparation. Potassium (48% K 2 SO 4 ) was applied at a total rate of 72 kg ha⁻¹, divided into 8 split doses starting from the first week after sowing via fertigation. In this study, a split application of nutrients was used to reduce nutrient losses under the sandy soil with low water-holding capacity. Agricultural applications were applied uniformly to reduce environmental variation among and between plots. 2.5. Data collection Data were recorded on the following eight quantitative traits: Plant height (PH, cm): the distance from the soil surface to the tip of the guarded spikes. Spike length (SL, cm): lengths of the guarded spikes from base to apex. Spike weight (SW, g): weight of the guarded spike. Spike yield (SY, g): grain weight of the individual spike. Grains per spike (GS): number of grains per spike. 1000-grain weight (GWT, g): weight of 1000 grains. Spikes m -2 (NS): number of spikes m -2 . Grain yield (t ha -1 ): Were estimated using grain yield components using the formula; Grain yield = (Spikes m -2 x grains per spike x (1000-grain weight/10) /10000 [ 48 ]. The grain yield values were approximate. 2.6. Statistical and genetic Analyses Genotypes were arranged in a randomized complete block design (RCBD) with three replications. Analysis of variance (ANOVA) to test significance levels of genotypes. Genotypic variance (V₉) and phenotypic variance (Vₚ) were estimated following the method of Singh and Chaudhary [ 49 ]. 2.7. Genetic parameters The genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%) were calculated according to Burton [ 50 ]. Broad- sense heritability (hb%) and genetic advance (GA) as a percentage of the mean were computed based on the methods of Burton and DeVane [ 51 ]. 2.8. Multivariate Analyses : Phenotypic and genotypic correlation coefficients, the regression analysis and principal component analysis (PCA) and biplot PCA were estimated. These parameters were used to evaluate the potential for genetic improvement of the studied traits. Data were statistically analyzed using GENSTAT (18th Edition.VSN International,Hempstead, UK). 2.9. Diseases studies A screening study was made to evaluate the infection and identification of different fungal genera and species associated with storage mother varieties and the top ten mutant lines in the M5 generation. 100 grains of genotypes were used to make the isolation, which was surface sterilized in 2% Hg Cl2 for 5 minutes before being rinsed for 2 minutes in three different batches of sterile distilled water in preparation for plating. Five grains were plated in each petri dish containing 15 ml of PDA medium and incubated at 27 ± 2°C under alternating cycles of 12 hours of light and 12 hours of darkness. Over the next 10 days, incubated grains were observed for germination, fungal growth, and identification under a microscope. The obtained fungal colonies were cultured on PDA plates and incubated at 27 ± 2°C for 5 days for complete sporulation. The texture and color of the fungal colonies were observed. The fungal isolates were identified based on the shape of the conidia and the arrangement of spores on the mycelia (conidial ontogeny), according to Samson et al. [ 52 ]. The number of grains infected with each kind of fungus was counted. The percentage contamination (PC) of fungi in the grains of genotypes was calculated according to the following equation: The viability of grains, estimated as the percentage of germination, was also recorded. 3. Results 3.1.Variance and mean performance Significant differences (p ≤ 0.05) were observed among the genotypes for all studied traits, accompanied by a wide range of variation and relatively high coefficient of variation (CV%). The CV % ranged from 6.4% for plant height to 20% for spike weight (Table 3 ). Table 3 Mean square observed from ANOVA for the studied traits in the M4 generation Source of variation PH(cm) SL(cm) SW(g) SY(g) GS GWT (g) NS GY (t ha − 1 ) Genotypes 332.1** 16.1 4.4** 2.2** 1476.5** 128.5** 5668.0** 8.1** Error 28.0 1.1 0.3 0.4 45.0 11.2 645.0 2.0 CV% 6.4 11.0 20.0 18.0 16.0 12.7 19.0 16.3 PH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m − 2 (NS), grain yield t ha − 1 (GY),**:significant level at 0.05, CV%: coefficient of variation. Table 4 provided a comprehensive overview of the phenotypic performance of 31 mutant lines in the M₄ generation along with their mother varieties (P1-P5) under marginal soil conditions. Table 4 Showed the mean performance, range, of wheat genotypes for the studied traits in the M4 generation under marginal soil conditions Genotype PH(cm) SL(cm) SW(g) SY(g) GS GWT (g) NS GY (t ha − 1 ) G1 84.8 12.7 5.5 4.2 79.5 54.6 159.0 6.9 G2 81.6 10.6 6.6 4.1 84.8 48.8 153.7 6.4 G3 82.7 10.6 4.2 2.8 53.2 55.1 148.4 4.4 G4 93.3 12.7 7.1 5.4 84.8 61.5 159.0 8.3 G5 84.8 12.7 4.3 2.9 49.6 60.4 152.6 4.6 G6 88.0 12.7 4.5 3.7 66.4 59.4 132.5 5.2 G7 95.4 12.7 6.8 5.0 79.5 63.6 137.8 7.0 G8 95.4 12.7 6.9 4.7 74.2 58.3 152.6 6.6 G9 90.6 11.7 3.9 3.0 52.5 59.1 176.0 5.5 G10 84.8 11.7 4.7 2.9 56.1 54.4 159.0 4.8 G11 93.3 10.6 5.2 4.0 65.2 65.2 141.0 6.0 G12 87.5 11.7 5.5 4.7 84.8 57.8 159.0 7.8 G13 84.8 6.4 4.9 3.6 85.8 44.5 169.6 6.5 G14 83.7 11.7 4.9 3.7 71.3 54.9 142.0 5.6 G15 95.4 8.5 5.1 3.6 68.7 55.7 159.0 6.1 G16 95.4 12.7 2.7 2.5 44.0 61.0 164.3 4.4 G17 95.4 10.6 4.9 3.3 58.5 58.8 196.1 6.8 G18 84.8 10.6 4.9 2.7 57.7 48.2 242.7 6.8 G19 93.3 10.6 5.1 3.6 80.8 47.2 174.9 6.7 G20 74.2 12.7 6.7 5.2 86.0 63.6 115.5 6.3 G21 89.0 8.5 4.8 3.1 71.0 45.9 186.6 6.1 G22 89.0 11.7 4.6 4.0 78.1 54.6 139.9 6.0 G23 93.3 10.6 3.4 2.1 37.8 58.8 254.4 5.7 G24 95.4 6.4 5.3 2.9 57.3 52.5 187.6 5.6 G25 93.3 12.7 4.3 3.2 58.3 58.3 159.0 5.4 G26 84.8 12.7 4.9 3.8 72.7 55.1 131.4 5.3 G27 106.0 10.6 5.0 3.3 63.6 55.1 148.4 5.2 G28 94.3 10.6 3.9 2.9 49.8 60.7 170.7 5.2 G29 72.1 11.7 5.2 3.5 62.8 59.9 124.0 4.7 G30 106.0 12.7 6.7 5.2 86.0 63.6 79.5 4.3 G31 74.2 10.6 5.6 2.5 51.9 51.9 132.5 3.6 X 89.2 11.1 5.1 3.6 66.9 56.4 158.4 5.8 Range 72.1–106.0 6.4–12.7 2.7–7.1 2.1–5.4 37.8–86.0 44.5–65.2 79.5-254.4 3.6–8.3 33.9 6.3 4.4 3.3 48.2 20.7 174.9 4.7 P1 98.0 13.0 5.2 3.0 80.0 49.0 105.0 4.1 P2 100.0 17.0 7.1 5.9 90.0 44.5 110.0 4.4 P3 90.0 17.0 5.2 4.2 100.0 44.0 103.0 4.5 P4 94.5 15.2 5.0 4.0 98.0 42.3 112.0 4.6 P5 96.6 16.3 5.8 4.7 102.0 49.1 100.0 5.0 X 95.8 15.7 5.7 4.4 113.2 45.8 119.4 4.5 LSD 0.05 8.8 2.4 1.9 1.4 27.0 9.4 32.0 0.5 PH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m − 2 (NS), grain yield t ha − 1 (GY),X: grand mean. Mother varieties: (P1: Giza 168, P2: Maryout 5,P3: Sids 13, P4: Sakha 93,.P5: Gemmeiza 9 ). LSD 0.05 :least significant difference. The observed ranges were as followed: plant height (33.9 cm), spike length (6.3 cm), spike weight (4.4 g), spike yield (3.3 g), grains per spike (48.2), 1000-grain weight (20.7 g), spikes m-2 (174.9), and grain yield (4.7 t/ha). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (Fig. 2 ). In the M5 generation, sixteen promising mutant lines were detected and selected in the M4 generation and re-evaluated for the studied traits. Highly significant differences (p ≤ 0.05) were observed among genotypes for all studied traits (Table 5 ). Table 5 Mean square observed from ANOVA for the studied traits in the M4 generation Source of variation PH(cm) SL(cm) SW(g) SY(g) GS GWT (g) NS GY (t ha-1) Genotypes 43.0** 2.0** 1.7** 1.4** 350.0** 100.0** 2792.0** 5.1** Error 11.0 0.8 0.2 0.3 45.0 11.2 380.0 1.2 CV% 7.6 8.0 11.4 12.1 8.4 4.4 15.8 15.5 PH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m − 2 (NS), grain yield t ha − 1 (GY), **: significant level at 0.05, CV%: coefficient of variation. LSD 0.05 : least significant difference. Table 6 showed a comprehensive overview of the phenotypic performance of 61 mutant lines in the M5 generation along with their mother varieties (P1-P5) under marginal soil conditions. The observed ranges were as followed: plant height (15.0 cm), spike length (4.8 cm), spike weight (1.8 g), spike yield (1.6 g), grains per spike (25.0), 1000-grain weight (14.0g), spikes m − 2 (60.0), and grain yield (3.0 t ha − 1 ). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (PH). Table 6 Showed the mean performance, range and variance of the best mutant lines and their mother varieties for the studied traits in the M5 generation under marginal soil conditions Genotype PH (cm) SL (cm) SW (g) SY (g) GS GWT(g) NS GY (ha -1) G1 80.0 8.9 6.0 4.6 80.0 50.0 175.0 7.0 G2 74.3 9.6 6.3 4.8 77.0 54.7 165.0 6.9 G3 80.5 9.0 5.3 4.2 75.0 52.0 160.0 6.2 G4 74.0 10.8 6.1 4.9 73.0 49.5 155.0 5.6 G5 71.0 9.0 4.5 3.6 70.0 56.0 154.0 6.0 G6 72.0 10.4 5.6 4.4 68.0 53.8 151.0 5.5 G7 70.0 8.4 4.8 3.9 77.0 55.0 140.0 5.9 G8 71.2 11.5 5.7 4.6 88.3 49.2 133.0 5.8 G9 70.0 11.6 5.3 4.3 90.0 49.5 135.0 5.6 G10 70.5 7.8 4.7 3.8 85.0 54.0 137.0 6.0 G11 71.2 11.5 5.7 4.6 88.3 49.2 125.0 5.8 G12 70.0 11.6 5.3 4.3 84.3 48.2 130.0 5.3 G13 70.5 7.8 4.7 3.8 76.0 54.0 125.0 5.1 G14 66.0 9.2 5.0 4.0 79.0 53.4 121.0 5.1 G15 80.0 9.6 5.2 4.0 88.0 44.0 118.0 4.6 G16 75.0 12.0 4.5 3.3 80.0 44.0 115.0 4.0 X 72.9 9.9 5.3 4.2 79.8 51.0 139.5 5.7 Range 66.0-80.5 (14.5 cm) 7.2–12.0 (4.8 cm) 4.5–6.3 (1.8 g) 3.3–4.9 (1.6 g) 68.0–90.0 (22) 44.0–56.0 (12.0 g) 115.0-175.0 ( 60) 4.0–7.0 (4 t ha − 1 ) P1 88.2 10.0 4.6 3.9 80.0 53.0 122.0 5.1 P2 90.0 10.0 5.7 4.2 85.0 51.0 116.0 5.0 P3 81.0 8.9 4.5 3.8 85.0 43.0 133.0 4.9 P4 85.8 9.4 5.2 4.0 77.0 45.0 131.0 4.5 P5 86.9 10.2 4.7 4.1 75.0 44.6 133.0 4.4 X 86.4 9.7 4.9 4.0 80.4 47.3 127.0 4.8 LSD 0.05 6.7 0.9 0.8 0.7 9.1 5.4 25.0 0.4 plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m − 2 (NS), grain yield t ha − 1 (GY),X: grand mean, Mother varieties (P1: Giza 168, P2: Maryout 5,P3: Sids 13, P4: Sakha 93,.P5: Gemmeiza 9 ). The observed ranges were as followed: plant height (15.0 cm), spike length (4.8 cm), spike weight (1.8 g), spike yield (1.6 g), grains per spike (25.0), 1000-grain weight (14.0g), spikes m-2 (60.0), and grain yield (3.0 t ha-1). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (Fig. 3 ). Notable, through this generation, based on the studied traits promising mutant lines (G1-G6, G9, G11 and G12) were detected and recommended for yield trials in many locations for many years. 3.2.Genetic parameters Genetic parameters, including phenotypic and genotypic variances, heritability, phenotypic and genotype coefficients of variation and genetic advance from selection for studied traits across two generations (M4 and M5) were shown in Table 7 . Table 7 phenotypic (Vp) and genotypic (Vg) variance, heritability (hb%), coefficients of phenotypic (PCV%) and genotypic (GCV%) variation and genetic advance (GA% as mean trait) of wheat mutants in the M4 and M5 generations Parameter PH (cm) SL (cm) SW (g) SY (g) GS GWT(g) NS GY(t/ha − 1 ) M4 generation Vp 110.7 5.4 1.5 0.7 492.2 42.8 1889.3 2.7 Vg 101.4 5.0 1.4 0.6 477.2 39.1 1674.3 2.0 hb% 91.6 93.2 93.2 81.8 97.0 91.3 88.6 75.3 PCV% 11.8 20.9 23.7 23.8 33.2 11.6 27.4 28.3 GCV% 11.3 20.1 22.9 21.5 32.7 11.1 25.8 24.6 GA% 22.2 40.1 45.6 40.1 66.2 21.8 50.1 44.0 M5 generation Vp 14.33 0.67 0.57 0.47 116.67 33.33 930.67 1.70 Vg 10.67 0.40 0.50 0.37 101.67 29.60 804.00 1.30 hb% 74.4 60.0 88.2 78.6 87.1 88.8 86.4 76.5 PCV% 5.2 8.2 14.2 16.3 13.5 11.3 21.9 22.9 GCV% 4.5 6.4 13.3 14.4 12.6 10.7 20.3 20.0 GA% 8.0 10.2 25.8 26.3 24.3 20.7 38.9 36.0 PH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m − 2 (NS), grain yield t ha − 1 (GY). In the M4 generation, high phenotypic (PCV %) and genotypic (GCV %) coefficients of variation (≥ 20%) observed for spike length, spike weight, spike yield, grains per spike, spikes m − 2 and grain yield. Broad-sense heritability (hb%) was high (≥ 60%) for all traits. Genetic advance expressed as % of the trait mean (GA%) were high (≥ 20%) for all studied traits. In the M5 generation, spikes m-2 and grain yield showed high PCV % and GCV % and high heritability and genetic advance values. Across generation, the small differences noted between PCV % and GCV % values. 3.3. Diseases studies In the subsequent phase of the study, promising mutant lines selected from the M5 generation, along with their mother varieties, were evaluated for germination percentage, infection percentage, and colony-forming (CFU/ per 100 grains) (Fig. 4 ). Significant variation was observed among wheat genotypes for all studied parameters. Mutant lines G2, G8, and G9, P4, along with parent P1 exhibited outstanding performance, recording higher germination percentage (> 90%) and lower CFU (< 150 per 100 grains) compared with the remaining genotypes. 3.3.1.Frequency occurrence of fungi As shown in Fig. 5 , the frequency and diversity of fungal contamination among the mother varieties and mutant lines, where each fungal species has a certain color. Mutant line G3, G5, G7, along with the mother variety Gemmeiza 9, displayed contamination by a wide spectrum of fungal species (an undesirable trend). In contrast, mutant lines, G1,G2, G4, and G9, along with the mother variety Maryout 5, exhibited limited lower levels of contamination with fungal species (desirable trend). 4. Correlation coefficients The heatmap of genotypic correlations showed strong positive associations between grain yield and spikes per plant (0.81) and 1000-grain weight (0.76), followed by spike weight (0.67) and spike yield (0.64). In contrast, spike length showed a moderate positive correlation with grain yield (0.38). Plant height showed a weak negative association (–0.09). Grains per spike showed a low positive correlation with grain yield (0.29). Genotypic correlations were higher than phenotypic correlation (Fig. 6 ). 5. Regression analysis The regression analysis further supported these correlations (Table 8 ). Both 1000-grain weight and grains per spike had positive effects on final grain yield, explaining approximately 81.0% of the sum variation. Spikes m − 2 showed the highest regression coefficient and a highly significant effect, followed by 1000-grain weight, whereas grains per spike showed a minor and non-significant contribution. Table 8 Linear regression coefficients of grains per spike (GS), 1000-grain weight (GWT), and spikes m − 2 ( NS) on grain yield (GY) of wheat mutant lines in the M5 generation. Predictor Regression Coefficient (b) Standard Error (SE) t-value p-value Partial R² Intercept 0.85 0.27 3.15 0.004** – GS 0.07 0.05 1.34 0.190 ns 0.03 GWT 0.32 0.08 3.96 0.001** 0.22 NS 0.51 0.09 5.67 < 0.001** 0.45 Model summary R² = 0.81 Adj. R² = 0.78 F = 23.9 p < 0.001 – **: significant level, ns: non-significant, R 2 : Coefficient of Determination 6. Principal component analysis The first three principal components (PC1, PC2 and PC3) explained 80.7% of the total variation, while the rest components (PC₃–PC₈) explained smaller proportions of total variability (Fig. 7 ). PC₁ was associated with 1000-weight grain, spikes per m 2 , and spike yield. High positive PC1 scores were recorded for four mutant lines G2,G1,G4, G3 and G6, while high negative PC₁ scores were recorded for G16,P5,P3 and G15. PC2 was associated with spike length. High positive PC2 scores were recorded for P2,G11,G4 and G15, whereas high negative PC₂ Scores recorded for G5,G13,G9 and G14. Other PCs (PC₆–PC₈) reflected minor trait variation (Table 9 ). Table 9 PCA scores (PC₁–PC₈) of mutant lines in the M5 generation with their mother varieties based the studied traits Genotype PC₁ PC₂ PC₃ PC₄ PC₅ PC₆ PC₇ PC₈ G1 3.44 0.43 -1.17 -1.56 -0.16 -0.53 0.15 -0.06 G2 3.76 0.72 0.38 0.13 0.22 0.21 0.20 -0.03 G3 1.52 -0.28 -0.76 -0.47 0.64 0.35 0.01 0.00 G4 2.13 1.47 0.34 1.40 -0.15 -0.20 -0.13 0.04 G5 0.77 -3.26 -0.60 -0.60 0.46 -0.08 0.15 0.02 G6 1.51 -0.31 0.26 1.55 0.22 0.28 0.16 0.08 G7 -0.86 -0.08 -1.21 1.26 -0.22 -0.75 0.04 -0.02 G8 -1.13 -0.55 -0.98 1.81 0.12 -0.45 -0.01 -0.03 G9 0.63 -2.00 0.72 -0.56 0.16 0.12 -0.11 -0.02 G10 -0.24 -0.14 -1.26 0.91 -1.46 0.31 0.01 -0.02 G11 0.86 1.55 2.33 -0.08 0.24 -0.06 -0.29 -0.01 G12 -0.20 0.86 2.08 0.32 0.42 -0.23 -0.18 -0.01 G13 -0.45 -2.26 0.72 -0.50 -0.80 0.27 -0.10 0.01 G14 -0.26 -1.45 1.86 0.07 -0.65 0.05 -0.05 0.01 G15 -1.67 1.01 0.92 -1.18 -0.79 0.07 0.07 0.00 G16 -3.58 -0.05 1.31 0.37 0.89 0.04 0.65 -0.05 P1 -0.86 0.10 -1.83 -0.01 1.14 0.48 -0.28 0.02 P2 -0.60 2.32 -0.72 -1.01 -0.20 0.37 0.39 0.04 P3 -1.74 0.20 -0.43 -1.57 0.26 -0.81 -0.32 0.06 P4 -1.29 0.98 -1.06 -0.38 -0.37 0.02 0.23 0.03 P5 -1.75 0.76 -0.88 0.09 0.04 0.55 -0.60 -0.60 7. Hierarchical cluster analysis Based on the studied traits, the hierarchical cluster analysis clustered the wheat genotypes into three clusters (I, II and III).Maximum genetic distance was found between cluster I vs cluster III. A moderate divergence was found between cluster I and cluster II, whereas the lowest divergence was found between cluster II and cluster III (Fig. 8 ). Cluster I contained the high-performing mutant lines (G1,G2,G3 and G6), while cluster II contained the intermediate -performing mutant lines (G4,G7,G8,G9,G10, G11,G12 and G16), along with the mother variety P2. Cluster III contained the low-performing mutant lines (G5,G9,G13,G14 and G15) along with the mother varieties (P1,P3,P4 and P5).The heatmap combined with hierarchical clustering illustrated that high-yielding mutant lines (G1, G2, and G3) which showed higher values for 1000-grain weight, spikes m⁻², and 1000-grain weight and grain yield (Fig. 9 ). 8. Discussion Significant differences (p ≤ 0.05) were detected among genotypes for the traits studied across generations, which resulted from the accumulation of mutational effects over successive generations [ 53 , 54 ]. This indicated that mutagenesis successfully generated a wide genetic variability, which constituted the primary objective of the current study. Regarding plant height, the more homozygous M5 mutant lines showed shorter lengths compared with their mother varieties. This reduction conferred greater lodging resistance [ 55 , 56 ], an agronomically desirable trait, particularly under sandy soil conditions where plants were frequently exposed to wind stress. These findings highlighted one of the important benefits of mutation breeding in crop improvement: mutagens can manipulate the entire genome, helping the discovery of novel allelic variants or the modifications in key genes controlling grain yield components, as observed in the current study [ 57 , 58 ]. Moreover, mutagenesis played a pivotal role in increasing the spikes m − 2 relative to the mother varieties. Several mutant lines exceeded their mother varieties for the studied traits across generations, indicated by LSD values. These results were consistent with previous studies [ 25 , 26 , 59 , 60 ]. These modifications exerted a positive effect on mutant lines (longer spikes) compared to their mother varieties, which enhanced spike weight and higher grains per spike [ 61 – 64 ]. Seed-borne fungal pathogens posed a major danger affecting wheat production [ 12 , 13 ]. Consequently, increasing the availability of resistant varieties is one of the most important strategies to address this issue [ 7 ]. Several investigations have illustrated that fungal diseases were associated with reduced seed germination and seeding vigor [ 7 , 65 ]. The current study used an extensive survey to quantify variations in grain germination capacity, infection percentage, and the colony-forming units (CFU per 100 grains), with determination of fungal species connected with bread wheat grains. This survey focused on ten mutant lines that had outperformed their mother varieties across all studied traits at the M5 generation. As shown in Fig. 4 , significant variations were observed among wheat genotypes regarding fungal load and seed health. Significantly, mutant lines, G2 and G9, along with the mother variety P4, illustrated that high germination rate connected with CFU values, indicating that these genotypes possessed a high level of resistance to seed-borne fungal diseases [ 66 , 67 ]. Additionally, these differences among genotypes reflected genetic variability of these promising lines may indicated the discovery of novel resistance alleles through mutational processes [ 64 , 68 ]. Furthermore, mutant lines G2 and G9 exhibited significantly reduced contamination across different fungal taxa (Fig. 5 ), suggesting untapped sources of resistance to fungal diseases. These findings confirmed the importance of selecting new genotypes combining high germination capacity with low seed-borne pathogens load for developing fungal-tolerant wheat varieties. These findings aligned with previous pervious results [ 11 , 69 ]. Phenotypic and genotypic correlation coefficients, together with regression analyses, emphasized that improvements in grain yield among the mutant lines were primarily associated with increases in spikes m⁻² and 1000-grain weight [ 25 , 35 , 36 ]. The stronger genotypic correlations reflected real genetic linkage or pleiotropic effects governing these traits. The bar chart comparison (Fig. 6 ) showed that genotypic correlations (rg) were higher in magnitude than phenotypic correlations (rp) for most studied traits, indicating the presence of true genetic relationships between traits and low environmental influence on the expression traits. As a result, phenotypic selection for traits such as spikes m − 2 , 1000-grain weight, spike weight and spike yield would be effective in detecting promising high-yielding mutant lines. These findings suggested that mutagenesis effectively modified these traits. Moreover, the regression analysis revealed that both spikes m − 2 and 1000-grain weight were the most influencing traits affecting final grain yield (Fig. 10 ). Consequently, these traits can be used as selection criteria to detect high-yielding mutant lines. These findings agree with previous studies reported by Jaenisch et al. [ 70 ]. Overall, the results highlighted that spikes m − 2 and 1000-grain weight as the most reliable indirect selection criteria for enhancing grain yield, particularly in early generations where direct selection for yield may be less efficient due to environmental influence. The findings regarding genetic parameters further indicated that the environmental effects on the studied traits were low (Table 5 ), as evidenced by the small differences between the phenotypic and genetic coefficients of variation. The high heritability values recorded for the studied traits indicated that phenotypic variation was largely attributed to genetic factors, making these traits suitable for phenotypic selection. This was further supported by the correlation between high heritability estimates (60% ≤) and high genetic advance (20%≤) as illustrated in Fig. 11 . This pattern suggested the predominance of additive gene action in the expression of the studied traits. Accordingly, these traits can serve as effective selection criteria for improving grain yield, validating the effectiveness of phenotypic selection and its potential to achieve genetic advance [ 30 , 31 ]. The reduction in genetic variation from the M4 to M5 generations may be attributed to progressive self-pollination, which reduced heterozygosity; elimination of unstable or heterozygous genotypes; and enhanced fixation of mutant alleles across generations, leading to a decline in phenotypic and genotypic variance in the next generations (20,21). Principal component analysis (PCA) was determined the traits contributing most to the total variability among mutant lines. The first principal component (PC1) was associated with 1000-weight grain, spikes per m 2 , and spike yield, results highlighted these traits as important selection criteria for detecting promising mutant lines. The PCA biplot confirmed the effectiveness mutagen in generating novel genetic variation under marginal soil conditions and that clearly separated high-and low-yielding genotypes, indicating that novel genetic variation was generated. Mutant lines G1, G2, G3, G4 and G6 were distinguished as top-performing. These findings demonstrated that PCA and biplot analyses were powerful multivariate tools for detecting promising wheat mutant lines adapted to marginal soil conditions. Similar results were recorded by Singh et al. [ 42 ] and Al-Ghumaiz et al. [ 43 ]. Cluster analysis of the wheat genotypes clearly separated them into three main clusters based on overall agronomic performance (Fig. 12 ). The consistency among phenotypic and genotypic correlation coefficients and principle components analysis illustrated the importance of spikes m − 2 and 1000-grain weight as reliable selection criteria for improving bread productivity under marginal soils. Specifically, cluster I consisted of four mutant lines G1, G2, G3 and G6, which can be used as parents in crossing programs to improve spikes m⁻² and high 1000-grain weight, with G2 as a source of fungal disease resistance. Cluster II consisted of mutant lines G5—G10, 13, and G14, which can use as parents in crossing programs to enhance spike length and grains per spike, with G8 and G9 as a source of fungal disease resistance. Cluster III consisted the mother varieties and other mutant lines. Crossing between clusters I (high–yield mutant lines) and cluster III could integrate high grain yield with specific traits and disease resistance, while intra-cluster crosses within cluster I could exploit heterosis effects for yield improvement. Similar results were reported by Poudel et al. [ 71 ] and Zewdu et al. [ 72 ]. Therefore, the promising mutant lines detected in this study will be advanced to initial yield trials, particularly because they have reached a high level of homozygosity. These lines represent valuable genetic resources for wheat programs aimed at enhancing grain yield and fungal diseases resistance under marginal soil conditions. 9. CONCLUSIONS This study demonstrated that the mutation process in bread wheat played a pivotal role in generating substantial genetic variation for grain yield components under marginal soil conditions. Mutant lines exhibited substantial variation for most studied traits, coupled by high heritability and moderate to high genetic advanced, indicating their validation of phenotypic selection in the next generation. Genetic correlations and regression coefficients identified spikes per m² and 1000-grain weight as the most influential contributors to final grain yield. Principal component analysis (PCA) confirmed these traits as key selection criteria under marginal soil conditions and clearly separated high- and low-yielding genotypes. Additionally, disease screening determined that both G2 and G9 were promising new sources of resistance to fungal pathogens, showing low infection levels by specific fungal pathogens. Cluster analysis revealed maximum divergence between clusters I and cluster III, indicating the possibility for crossing genotypes of these clusters to improve grain yield. Overall, the detected promising mutant lines represented valuable novel genetic sources and recommended for yield trials and as parents in hybridization programs targeting marginal environments. Declarations Competing Interest The authors have declared that no competing interest exists Consent to participate - declaration: not applicable Consent to publish - declaration: not applicable Funding This work was funded by the Academy of Scientific Research and Technology (ASRT), Egypt. Through research project No. 4662 during the period from 2019 to 2023. Author Contribution Anter was responsible for field experiments and Sahab was responsible for disease experiments. Acknowledgement To Prof. Ragab Abdel Mohsen, Department of Botany, Institute of Agricultural and Biological Research, for his great assistance in creating mutations. Data Availability The data that support the findings of the current study, including experiment results, studied traits, and in vitro fungal resistance evaluations of the two generations (M4 and M5) of wheat mutant lines, are only available from the corresponding author. The datasets are publicly available for research purposes and scientific cooperation. References Garg M, Sharma N, Sharma S, Kapoor P, Kumar A, Chunduri V, Arora P. Vitamins in cereals: a critical review of content, health effects, processing losses, bioaccessibility, fortification, and biofortification strategies for their improvement. Front Nutr. 2021;8:586815. https://doi.org/10.3389/fnut.2021.586815 . Khalid A, Hameed A, Tahir M. 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Procrop wheat growth and development. NSW DPI; 2008. https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0008/516185/Procrop-wheat-growth-and-development.pdf . Accessed [date if needed]. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani; 1977. Burton GW. (1952) Quantitative inheritance in grasses. Proceedings of the 6th International Grassland Congress, pp 277–283. Burton GW, DeVane EH. Estimating heritability in tall fescue (Festuca arundinacea L.) from replicated clonal material. Agron J. 1953;45:478–81. https://doi.org/10.2134/agronj1953.00021962004500090005x . Samson RA, Houbraken J, Thrane U, Frisvad JC, Andersen B. Food and indoor fungi. Utrecht: CBS-KNAW Fungal Biodiversity Centre; 2010. Shorinola O, Kaye N, Gao L, Balcke G, Bollina V, Kagale S, Higgins J, Obermeier C, Sevanthi V, Ray RV, Lage J, Olohan L, Ashling B, Schoonbeek H, Beynon J, Dixon R, Hedden P, Phillips A, Uauy C. Screening for mutants with altered seminal root numbers in hexaploid wheat using a high-throughput root phenotyping platform. G3 (Bethesda). 2019;9:2799–809. https://doi.org/10.1534/g3.119.400537 . Olaolorun BM, Shimelis H, Laing M, Mathew I. Development of wheat ( Triticum aestivum L.) populations for drought tolerance and improved biomass allocation through ethyl methanesulphonate mutagenesis. Front Agron. 2021;3:655820. https://doi.org/10.3389/fagro.2021.655820 . Todaka D, Shinozaki K, Yamaguchi-Shinozaki K. Recent advances in the dissection of drought-stress regulatory networks and strategies for development of drought-tolerant transgenic rice plants. Front Plant Sci. 2015;6:84. https://doi.org/10.3389/fpls.2015.00084 . Li Y, Cui J, Yang X, Li Y, Chen S, Li H. Genetic improvement of lodging resistance in wheat: a review. Euphytica. 2017;213:163. https://doi.org/10.1007/s10681-017-1941-3 . Ahloowalia BS, Maluszynski M, Nichterlein K. Global impact of mutation-derived varieties. Euphytica. 2021;217:76. https://doi.org/10.1007/s10681-021-02774-w . Li Y, Li C, Bradbury PJ, Wu X, Shi Y, Song Y, Zhang D, Zhang Z, Buckler ES, Wang T, Zhang Z. Mutation breeding and genome editing: roles and integration for crop improvement. Front Plant Sci. 2022;13:852778. https://doi.org/10.3389/fpls.2022.852778 . Louali Y. Effect of gamma irradiation on morphological, biochemical, physiological character and cytological studies of durum wheat mutants. Int J Adv Res. 2015;3(10):246–56. Hong MJ, Kim DY, Ahn JW, Kim JB. Biological effect of gamma rays according to exposure time on germination and plant growth in wheat. Appl Sci. 2022;12:3208. https://doi.org/10.3390/app12063208 . Joe Y, Kim J. Frequency and spectrum of radiation-induced mutations revealed by whole-genome sequencing analyses of plants. Quantum Beam Sci. 2019;3:7. https://doi.org/10.3390/qubs3010007 . Anne S, Lim J. Mutation breeding using gamma irradiation in the development of ornamental plants: a review. Flower Res J. 2020;28:102–15. https://doi.org/10.25257/FRJ.2020.28.2.11 . Wang M, Shimelis H, Horn L, Sarsu F. The effect of single and combined use of gamma radiation and ethylmethane sulfonate on early growth parameters in sorghum. Plants. 2020;9:827. https://doi.org/10.3390/plants9070827 . Sobieh S, Al-Azab F. Induced mutations for improved yield and its components in bread wheat using gamma radiation. Egypt J Plant Breed. 2020;24(4):915–26. Hassani F, Zare L, KhalediN. (2019) Evaluation of germination and vigor indices associated with Fusarium-infected seeds in pre-basic seeds wheat fields. Journal of Plant Protection Research.59(1): 69–85, 201910.24425/jppr.2019.126037 Najeebullah S, Rajput N, Khanzada K, Lodhi A, Rajput A, Mubeen F. Grains borne mycoflora of some commercial wheat ( Triticum aestivum L.) cultivars in Sindh and Balochistan. Int J Acad Appl Res. 2019;3(8):14–22. Lisiecki K, Nczykm G, Piesik D, Mayhew C. Screening winter wheat genotypes for resistance traits against Rhizoctonia cereals and Rhizoctonia solani infection. Agriculture. 2022;12:1–12. https://doi.org/10.3390/agriculture12020203 . Oladosu Y, Rafii MY, Abdullah N, Ghazi P, Magaji U. Principles and application of plant mutagenesis in crop improvement: a review. Biotechnol Biotechnol Equip. 2020;34(1):1–16. https://doi.org/10.1080/13102818.2020.1730313 . El-Orabey WM, Elbasyoni IS, El-Moghazy SM, Ashmawy MA. Effective and ineffective of some resistance genes to wheat leaf, stem and yellow rust diseases in Egypt. J Plant Prod Mansoura Univ. 2019;10(4):361–71. Jaenisch B, Munaro L, Jagadish S, Lollato R. Modulation of wheat yield components in response to management intensification to reduce yield gaps.Front. Plant Sci. 2022;13:772232. 10.3389/fpls.2022.772232 . Poudel A, Thapa D, Sapkota M. Assessment of genetic diversity of bread wheat ( Triticum aestivum L.) genotypes through cluster and principal component analysis. Int J Exp Res Rev. 2017;11(1):1–9. Zewdu D, Mekonnen F, Geleta N. Cluster and principal component analysis for yield and yield related traits of bread wheat ( Triticum aestivum L.) genotypes. AGBIR. 2024;40(2):962–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 29 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9125268","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638233493,"identity":"fa15ab4c-5693-4772-ae00-cec13d5d2450","order_by":0,"name":"Ayman Anter Saber Abdalla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFACNgjFDyISCojQwAPTItkA0mJAihaDA2CSCC327G2JHz7m2OUZn1+d+OGBAYM8v9gBArbwHDssOXNbcrHZjbebJYAOM5w5O4GAFon0BmnebcyJ226c3QDSkmBwm7CW5t9/t9Unbp5xdvMPIrWkHZNm3HY4cQN/7zYibTlzLM2yd9vxxBk3eLdZJBhIEPYLe3ub8Y2f26oT+/vPbr75o8JGnl+agBYEkACrlCBWOQjwHyBF9SgYBaNgFIwkAACz5UUadptH0wAAAABJRU5ErkJggg==","orcid":"","institution":"National Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Ayman","middleName":"Anter Saber","lastName":"Abdalla","suffix":""},{"id":638233494,"identity":"81f90bd8-8e4c-4d98-ab7d-1b34f0fe97fb","order_by":1,"name":"Farahat Sahab","email":"","orcid":"","institution":"National Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Farahat","middleName":"","lastName":"Sahab","suffix":""}],"badges":[],"createdAt":"2026-03-14 22:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9125268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9125268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109186686,"identity":"89d7a343-9fd5-4409-ab36-3d23a1bc41cf","added_by":"auto","created_at":"2026-05-13 11:14:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166171,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the mutation breeding program and selection procedures across generations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/82860584a63c8533f01115cb.png"},{"id":109186703,"identity":"dde3c7eb-2843-4307-a005-0a5e09f0e4a0","added_by":"auto","created_at":"2026-05-13 11:15:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51703,"visible":true,"origin":"","legend":"\u003cp\u003eComparative performance of \u0026nbsp;the best mutant lines \u0026nbsp;in the M4 generation and their mother varieties \u0026nbsp;for the studied traits\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/1b1b2bf1d69da11ec3ce13f1.png"},{"id":109186695,"identity":"7cfc9430-0c33-4f6d-be1a-33b8941def1c","added_by":"auto","created_at":"2026-05-13 11:14:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71219,"visible":true,"origin":"","legend":"\u003cp\u003eComparative performance of the best mutant lines in the M5 generation and their mother varieties (P1-P5) for the studied traits\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/b681455dffd83218d75cb6da.png"},{"id":109186694,"identity":"aab06b35-0a4a-4239-91cb-44048dffee84","added_by":"auto","created_at":"2026-05-13 11:14:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21641,"visible":true,"origin":"","legend":"\u003cp\u003eComparison performance of ten M5 mutant lines (G1-G10) in terms of germination percentage, infection rate, and colony-forming units (CFU per 100 grains), to mother varieties (P1-P5).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/fb2087bdb6a2a7519d90f3d2.png"},{"id":109186705,"identity":"9794ee4a-8398-4bf1-b78e-fbd06f6e0428","added_by":"auto","created_at":"2026-05-13 11:15:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":153147,"visible":true,"origin":"","legend":"\u003cp\u003eshowed the frequency and diversity of fungal contamination across mutant lines and mother varieties\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/60ab6e074545cc05a2e7b845.png"},{"id":109186679,"identity":"13fd3256-a52b-464f-a707-9b4ad5decc8c","added_by":"auto","created_at":"2026-05-13 11:14:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":107003,"visible":true,"origin":"","legend":"\u003cp\u003ePresented both the genotypic correlation matrix and comparison between phenotypes (rp) and genotypic (rg) correlations of studied traits in the M5 generation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/a27e2680ec94ce4d4f6b3c2b.png"},{"id":109186681,"identity":"7199711a-4a93-499d-be89-13e0833fd37b","added_by":"auto","created_at":"2026-05-13 11:14:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33581,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component analysis for studied traits of the mutant lines and their mother varieties\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/78d808ebea4635dea1e966d7.png"},{"id":109186704,"identity":"40845665-1b95-4dc8-8acd-9a018fadcd89","added_by":"auto","created_at":"2026-05-13 11:15:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27172,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical cluster analysis of wheat genotype based on the studied traits using Ward’s method.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/e094ffd2900913e2aef60253.png"},{"id":109186678,"identity":"cf1d34f8-c6f0-4390-8482-d323393d48ae","added_by":"auto","created_at":"2026-05-13 11:14:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":112210,"visible":true,"origin":"","legend":"\u003cp\u003eShowed heatmap with hierarchical clustering of wheat genotypes based on standardized agronomic traits\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/1cd836b7c67e394fec58a882.png"},{"id":109186706,"identity":"d0a404da-4aca-4d76-82cf-ab34c5c25b11","added_by":"auto","created_at":"2026-05-13 11:15:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109235,"visible":true,"origin":"","legend":"\u003cp\u003eShowed the relationship strength between grain yield (GY) and key grain yield components grans per spike (GS), 1000-grain weight (GWT) and \u0026nbsp;spikes m\u003csup\u003e-2\u003c/sup\u003e (NS).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/1807bc5a15a74b98dba39d0f.png"},{"id":109186696,"identity":"33f4e2cc-9053-4220-b99a-7f6a73a42d0b","added_by":"auto","created_at":"2026-05-13 11:14:48","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":39995,"visible":true,"origin":"","legend":"\u003cp\u003ehigh heritability associated with high genetic advanced form selection for the studied traits in the M5 generation.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/0fdcedcd4bcaca09f637b40d.png"},{"id":109186702,"identity":"eb0e56be-b112-4010-a878-b5ec743c6a2f","added_by":"auto","created_at":"2026-05-13 11:14:58","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":78064,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot ( PC1 vs PC2 ) showed three clusters grouping of wheat mutant lines in the M5 generation and their mother varieties \u0026nbsp;based on the studied traits\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/cd29a0bb8cad1aa3cf2a9b98.png"},{"id":109205107,"identity":"4d43ccd4-1a66-4cac-8ecf-87ccaf3c0a55","added_by":"auto","created_at":"2026-05-13 15:03:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1656308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9125268/v1/cc4973bf-4528-403e-b509-cbbe09f2918e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Selection of High-Yielding and Fungal Disease-Resistant Bread Wheat Mutants under Marginal Soil Conditions1Ayman Anter and 2Ahemd Sahab","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eBread wheat (Triticum aestivum L.), an annual plant belonging to the grass \u003cem\u003ePoaceae\u003c/em\u003e family, is a staple crop in Egypt, providing essential macronutrients including carbohydrates, protein, fats, and fiber, alongside important micronutrients including B vitamins, iron, and zinc [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its benefits, domestic wheat production meets merely\u0026thinsp;~\u0026thinsp;45% of domestic demand. This production deficit was attributed to rapid population growth (1.94% annually) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, climate change is expected to increase both biotic and abiotic stresses, posing an additional challenge to sustaining wheat production [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Bread Wheat is significantly affected by numerous fungal diseases, including rusts, powdery mildew and septoria leaf blotch, which can reduce both grain yield and quality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Under Egyptian conditions, rusts diseases are commonly distributed, particularly leaf rust generated by \u003cem\u003ePuccinia triticina\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], stem rust generated by \u003cem\u003ePuccinia graminis f. sp. Tritici\u003c/em\u003e,[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and stripe rust generated by \u003cem\u003ePuccinia striiformis f. sp. Tritici\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among these rusts, leaf rust is the common distributed in Egypt [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These diseases cause yellow pustules on the leaves, decreasing the leaf area index, impairing grain filling, and ultimately causing loss of final grain yield [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Unfortunately, the majority of current commercial wheat varieties remain susceptible to fungal diseases, attributable to replacement of genetically uniform varieties. Furthermore, wheat improvement has been limited by a narrow genetic base of current varieties, the extraordinarily large and complex allohexaploid wheat genome (~\u0026thinsp;17 Gb), and significant genotype x environment interaction effects that complicated the selection process [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To address Egypt's wheat import dependency and increase production, the development of high-yielding, disease-resistant varieties is urgent. In particular, the currently available area for bread wheat cultivation is limited; therefore, developing new varieties with better adaptation to sandy soils represents a strategically vital approach for increasing wheat domestic production [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerating a wide genetic diversity is a key driver for successful crop improvement programs, as it essential for selection and evaluation of genotypes, helping breeders to detect desirable alleles for improved grain yield and stress tolerance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Mutation breeding is a validated approach for generating a new genetic variation through application of physical (e.g., gamma rays and fast neutrons) or chemical mutagens (e.g., sodium azide and ethyl methanesulfonate) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Mutagenesis offers the advantage of modifying desirable traits without changing the genetic makeup of superior commercial varieties, thereby circumventing the backcrossing as in traditional hybridization programs. Mutation breeding represents a great opportunity for expressing hidden or recessive alleles that are extremely useful for improving crop productivity under biotic and abiotic stresses [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Even modifications or improvements in key grain yield components such as spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e or 1000-grain weight, can translate into increases in final grain yield. Additionally, mutation breeding contributed to the conservation of genetic diversity by introducing new alleles into the breeding pool, reducing genetic erosion that resulted from the replacement of local varieties with modern varieties, and providing diverse resistance mechanisms against pests and diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, crop breeders strive to improve resistance to major diseases by variation introduction, evaluating segregating generations, and selecting promising lines within a short timeframe [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, the development of new wheat varieties can help to decrease infection levels of certain disease-causing pathogen races, thereby durability of host resistance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhenotypic selection is an effective breeding strategy targeting secondary traits that showed strong genetic correlations with grain yield, thereby accelerating genetic improvement within shortened timeframes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such traits are often controlled predominantly by additive gene action, which increases selection efficiency. In contrast, grain yield per se is a complex quantitative trait subjected to genotype X environment interaction effects. Consequently, direct selection for secondary traits (highly heritable) can improve selection efficiency and accelerate genetic advance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEstimation of genetic parameters, including phenotypic (PCV) and genotypic (GCV) coefficients of variation, broad-sense heritability (hb%), and genetic advance as a percentage of the mean (GA%) are necessary for optimization selection efficiency. Traits governed primarily by additive gene action, particularly those with high heritability and high expected genetic advance, represent ideal targets for selection, ensuring better transmission of favorable alleles across generations [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Phenotypic and genotypic correlations have been used to reveal traits that are more positively and significantly associated with grain yield, thereby raising selection efficiency [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It helps to detect traits that are closely associated with yield. Moreover, genotypic correlation reflected the genetic relationships between traits, whereas phenotypic correlations represent the mixed effects of genetic and environmental conditions. The determination these associations enable breeders to apply phenotypic selection for yield improvement in early segregating generations. In addition, regression analysis is used to determine the relative contribution of individual traits (predictor variables) to final grain yield (response variables), helping crop breeders to determine the most influential grain yield components [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, breeders seek to increase selection efficiency, which is easier to implement [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Principal component analysis (PCA), as a multivariate analytic tool, and hierarchical cluster analysis serve as powerful tools for visualizing genetic diversity among genotypes and detecting promising genotypes that can serve as a novel source in breeding programs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, the present study aimed to detect promising bread wheat mutant lines combining high yield and resistance to fungal disease under marginal soil conditions using genetic parameters and multivariate statistical analyses.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental site\u003c/h2\u003e \u003cp\u003eField experiments were conducted over two consecutive winter seasons (2023\u0026ndash;2024) at the Agricultural Production and Research Station of the National Research Center, located in the Nubaria District, Beheira Governorate, Egypt (30\u0026deg;40\u0026prime;00\u0026Prime;N 30\u0026deg;04\u0026prime;00\u0026Prime;E). The Nubaria area was classified as marginal soil conditions and highly vulnerable to climate change impacts, including recurrent heat waves and drought [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Physical and chemical properties of the experiment site were characterized prior to experimentation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical properties of the experimental site.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil Layer Depth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003ePhysical properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eChemical properties\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoarse Sand (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFine Sand (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSilt\u0026thinsp;+\u0026thinsp;Clay (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBulk Density (t/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eField Capacity (θv%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWilting Point (θv%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEC₁:₅ (dS/m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003epH (1:2.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal CaCO₃ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOrganic Matter (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePhysical properties analyzed according to the procedure of Carter and Gregorich [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and chemical properties analyzed according to the procedure Page et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Soil texture was sandy, with low field capacity, minimal water retention and low organic matter content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Plant Materials and genetic background\u003c/h2\u003e \u003cp\u003eThis study forms part of an ongoing bread wheat mutation breeding program [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The genetic material consisted of five commercial verities of bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.,2n\u0026thinsp;=\u0026thinsp;6x\u0026thinsp;=\u0026thinsp;42, AABBCC genome, Linnaeus 1753), a widely cultivated across Egypt. These materials were obtained from the Wheat Research Department, Field Crops Institute, Agricultural Research Center (ARC), Egyptian Ministry of Agriculture and land reclamation. All commercial varieties were officially released with no wild collection requirements. Bread wheat is not regulated by CITES conventions and not listed as threatened under the IUCN Red List. Therefore, no specific permit was needed for utilization. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the pedigree and characteristics of the mother varieties used in the current study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePedigree and characterizes of mother varieties (commercial varieties)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacterizes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakha 93 (P\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSakha 92/TR 810328 S 8871-1S-2S-1S-0S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResistant to rusts, tolerates salinity and heat, short plant height, more tillers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSids 13 (P\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKAUZ\"S\"//TSI//TSI/SNB\"S\"ICW94-0375-4AP-2AP-030AP-0APS-3AP-0APS-050AP-0AP-0SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResistant to rusts and water deficit, early maturing (152 d). Tolerates water salinity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiza 168 (P\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIL/BUC//Seri CM93046-8M-0Y-0M-2Y-0B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTolerates water deficit, heat tolerant, resistant to rusts, late maturing (165\u0026ndash;170 days), medium plant height, white grain color, thin spikes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGemmeiza-9 (P\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAld\u0026rdquo;S\u0026rdquo;/Huac\u0026rdquo;S\u0026rdquo;//CMH74A.630/5x CGM4583-5GM-1GM-0GM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitive to salinity and water deficit, tall plant height, long spikes, late maturing (160 days),resistant to rusts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaryout 5 (P\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiza 162 // Bchʼs /4/ PI-ICW 79Su511Mr-38Mr-1Mr-0Mr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh yielding and salt tolerant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Irradiation treatments\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt the initiation of the breeding program, a total of 300 dry, healthy seeds per treatment per variety were used. The seed moisture content was adjusted to 12% before irradiation to minimize physical damage. The grains of each variety were irradiated in three doses of 100, 200, and 300 Gy of gamma rays (Cobalt-60) at the Egyptian Atomic Energy Authority, at a dose rate of 0.9 Gy s⁻\u0026sup1;. The exposure times were 111.0, 222.0, and 333.0 S\u003csup\u003e-1\u003c/sup\u003e. The previous generations (M1-M3) were published earlier [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The current study used advanced mutant generations (M4 and M5). In the M₄ generation, thirty-one mutant lines along with their mother varieties were evaluated for plant height and grain yield components. Based on their performance for studied traits, sixteen mutant lines were selected and advanced to obtain the M5 generation. Ten out of sixteen mutant lines were selected based on their superior performance and used for disease studies. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed mutation breeding program and selection procedures across generations (M1-M6).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegarding plant reproducibility, at the time of manuscript submission, the studied mutant lines are still under experimental evaluation, and therefore, voucher specimens have not yet been deposited in a public herbarium of the National Research Centre. After completion of the breeding program, voucher specimens will be deposited at the Herbarium of the NRC.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Agricultural applications\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSowing was done manually in November at a seeding rate of 350 grain m\u003csup\u003e-2\u003c/sup\u003e. Each line planted in the plot consisted of a row measuring 3 meters in length and 60 cm in width. Drip irrigation was applied using emitters with a discharge rate of 4 L h⁻\u0026sup1;, spaced 25 cm apart along the lateral lines. Irrigation was scheduled twice-weekly to keep soil moisture near field capacity, and each irrigation lasted two hours before flowering and three hours after flowering to meet the increased water demand through growth seasons. Nitrogen fertilization (urea, 46% N) was applied at a rate of 175 kg ha⁻\u0026sup1;, divided into 12 split doses started from the first week via fertigation. Phosphorus (15% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) was applied at a rate of 40 kg ha⁻\u0026sup1; during soil preparation. Potassium (48% K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) was applied at a total rate of 72 kg ha⁻\u0026sup1;, divided into 8 split doses starting from the first week after sowing via fertigation. In this study, a split application of nutrients was used to reduce nutrient losses under the sandy soil with low water-holding capacity. Agricultural applications were applied uniformly to reduce environmental variation among and between plots.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData were recorded on the following eight quantitative traits:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePlant height (PH, cm): the distance from the soil surface to the tip of the guarded spikes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpike length (SL, cm): lengths of the guarded spikes from base to apex.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpike weight (SW, g): weight of the guarded spike.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpike yield (SY, g): grain weight of the individual spike.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGrains per spike (GS): number of grains per spike.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1000-grain weight (GWT, g): weight of 1000 grains.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpikes m\u003csup\u003e-2\u003c/sup\u003e (NS): number of spikes m\u003csup\u003e-2\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGrain yield (t ha\u003csup\u003e-1\u003c/sup\u003e): Were estimated using grain yield components using the formula;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGrain yield = (Spikes m\u003csup\u003e-2\u003c/sup\u003e x grains per spike x (1000-grain weight/10) /10000 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The grain yield values were approximate.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical and genetic Analyses\u003c/h2\u003e \u003cp\u003eGenotypes were arranged in a randomized complete block design (RCBD) with three replications. Analysis of variance (ANOVA) to test significance levels of genotypes. Genotypic variance (V₉) and phenotypic variance (Vₚ) were estimated following the method of Singh and Chaudhary [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Genetic parameters\u003c/h2\u003e \u003cp\u003eThe genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%) were calculated according to Burton [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Broad- sense heritability (hb%) and genetic advance (GA) as a percentage of the mean were computed based on the methods of Burton and DeVane [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.8. Multivariate Analyses\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003ePhenotypic and genotypic correlation coefficients, the regression analysis and principal component analysis (PCA) and biplot PCA were estimated. These parameters were used to evaluate the potential for genetic improvement of the studied traits. Data were statistically analyzed using GENSTAT (18th Edition.VSN International,Hempstead, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Diseases studies\u003c/h2\u003e \u003cp\u003eA screening study was made to evaluate the infection and identification of different fungal genera and species associated with storage mother varieties and the top ten mutant lines in the M5 generation. 100 grains of genotypes were used to make the isolation, which was surface sterilized in 2% Hg Cl2 for 5 minutes before being rinsed for 2 minutes in three different batches of sterile distilled water in preparation for plating. Five grains were plated in each petri dish containing 15 ml of PDA medium and incubated at 27\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C under alternating cycles of 12 hours of light and 12 hours of darkness. Over the next 10 days, incubated grains were observed for germination, fungal growth, and identification under a microscope. The obtained fungal colonies were cultured on PDA plates and incubated at 27\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 5 days for complete sporulation. The texture and color of the fungal colonies were observed. The fungal isolates were identified based on the shape of the conidia and the arrangement of spores on the mycelia (conidial ontogeny), according to Samson et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The number of grains infected with each kind of fungus was counted. The percentage contamination (PC) of fungi in the grains of genotypes was calculated according to the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"438\" height=\"99\"\u003e\u003c/p\u003e\n\u003cp\u003eThe viability of grains, estimated as the percentage of germination, was also recorded.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Variance and mean performance\u003c/h2\u003e \u003cp\u003eSignificant differences (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were observed among the genotypes for all studied traits, accompanied by a wide range of variation and relatively high coefficient of variation (CV%). The CV % ranged from 6.4% for plant height to 20% for spike weight (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean square observed from ANOVA for the studied traits in the M4 generation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWT (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGY (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332.1**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1476.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e128.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5668.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.1**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e645.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (NS), grain yield t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GY),**:significant level at 0.05, CV%: coefficient of variation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provided a comprehensive overview of the phenotypic performance of 31 mutant lines in the M₄ generation along with their mother varieties (P1-P5) under marginal soil conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShowed the mean performance, range, of wheat genotypes for the studied traits in the M4 generation under marginal soil conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWT (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGY (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e153.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e148.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e152.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e132.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e137.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e152.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e176.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e141.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e169.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e142.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e164.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e196.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e242.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e174.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e115.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e186.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e139.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e254.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e187.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e131.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e148.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e170.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e124.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e132.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e158.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.1\u0026ndash;106.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u0026ndash;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7\u0026ndash;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026ndash;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.8\u0026ndash;86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.5\u0026ndash;65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.5-254.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.6\u0026ndash;8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e174.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e105.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e103.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e112.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e113.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e119.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD \u003csub\u003e0.05\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (NS), grain yield t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GY),X: grand mean. Mother varieties: (P1: Giza 168, P2: Maryout 5,P3: Sids 13, P4: Sakha 93,.P5: Gemmeiza 9 ). LSD 0.05 :least significant difference.\u003c/p\u003e \u003cp\u003eThe observed ranges were as followed: plant height (33.9 cm), spike length (6.3 cm), spike weight (4.4 g), spike yield (3.3 g), grains per spike (48.2), 1000-grain weight (20.7 g), spikes m-2 (174.9), and grain yield (4.7 t/ha). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the M5 generation, sixteen promising mutant lines were detected and selected in the M4 generation and re-evaluated for the studied traits. Highly significant differences (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were observed among genotypes for all studied traits (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean square observed from ANOVA for the studied traits in the M4 generation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWT (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGY (t ha-1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2792.0**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e380.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (NS), grain yield t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GY), **: significant level at 0.05, CV%: coefficient of variation. LSD \u003csub\u003e0.05\u003c/sub\u003e: least significant difference.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e showed a comprehensive overview of the phenotypic performance of 61 mutant lines in the M5 generation along with their mother varieties (P1-P5) under marginal soil conditions. The observed ranges were as followed: plant height (15.0 cm), spike length (4.8 cm), spike weight (1.8 g), spike yield (1.6 g), grains per spike (25.0), 1000-grain weight (14.0g), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (60.0), and grain yield (3.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (PH).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShowed the mean performance, range and variance of the best mutant lines and their mother varieties for the studied traits in the M5 generation under marginal soil conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWT(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGY (ha -1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e175.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e165.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e160.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e155.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e154.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e151.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e140.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e135.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e137.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e125.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e130.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e125.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e121.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e118.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e115.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e139.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0-80.5\u003c/p\u003e \u003cp\u003e(14.5 cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026ndash;12.0\u003c/p\u003e \u003cp\u003e(4.8 cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026ndash;6.3\u003c/p\u003e \u003cp\u003e(1.8 g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u0026ndash;4.9\u003c/p\u003e \u003cp\u003e(1.6 g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.0\u0026ndash;90.0\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.0\u0026ndash;56.0\u003c/p\u003e \u003cp\u003e(12.0 g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e115.0-175.0\u003c/p\u003e \u003cp\u003e( 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.0\u0026ndash;7.0\u003c/p\u003e \u003cp\u003e(4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e122.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e116.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e131.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e127.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eplant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (NS), grain yield t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GY),X: grand mean, Mother varieties (P1: Giza 168, P2: Maryout 5,P3: Sids 13, P4: Sakha 93,.P5: Gemmeiza 9 ).\u003c/p\u003e \u003cp\u003eThe observed ranges were as followed: plant height (15.0 cm), spike length (4.8 cm), spike weight (1.8 g), spike yield (1.6 g), grains per spike (25.0), 1000-grain weight (14.0g), spikes m-2 (60.0), and grain yield (3.0 t ha-1). The mutant lines outperformed their mother varieties in all studied traits, except for plant height (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotable, through this generation, based on the studied traits promising mutant lines (G1-G6, G9, G11 and G12) were detected and recommended for yield trials in many locations for many years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Genetic parameters\u003c/h2\u003e \u003cp\u003eGenetic parameters, including phenotypic and genotypic variances, heritability, phenotypic and genotype coefficients of variation and genetic advance from selection for studied traits across two generations (M4 and M5) were shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ephenotypic (Vp) and genotypic (Vg) variance, heritability (hb%), coefficients of phenotypic (PCV%) and genotypic (GCV%) variation and genetic advance (GA% as mean trait) of wheat mutants in the M4 and M5 generations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSL (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGWT(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGY(t/ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eM4 generation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e492.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1889.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e477.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1674.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehb%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eM5 generation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e930.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e804.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehb%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePH: plant height (PH), Spike length (SL), spike weight (SW), spike yield (SY), grains per spike (GS), 1000-grain weight (GWT), spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (NS), grain yield t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GY).\u003c/p\u003e \u003cp\u003eIn the M4 generation, high phenotypic (PCV %) and genotypic (GCV %) coefficients of variation (\u0026ge;\u0026thinsp;20%) observed for spike length, spike weight, spike yield, grains per spike, spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and grain yield. Broad-sense heritability (hb%) was high (\u0026ge;\u0026thinsp;60%) for all traits. Genetic advance expressed as % of the trait mean (GA%) were high (\u0026ge;\u0026thinsp;20%) for all studied traits. In the M5 generation, spikes m-2 and grain yield showed high PCV % and GCV % and high heritability and genetic advance values. Across generation, the small differences noted between PCV % and GCV % values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Diseases studies\u003c/h2\u003e \u003cp\u003eIn the subsequent phase of the study, promising mutant lines selected from the M5 generation, along with their mother varieties, were evaluated for germination percentage, infection percentage, and colony-forming (CFU/ per 100 grains) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignificant variation was observed among wheat genotypes for all studied parameters. Mutant lines G2, G8, and G9, P4, along with parent P1 exhibited outstanding performance, recording higher germination percentage (\u0026gt;\u0026thinsp;90%) and lower CFU (\u0026lt;\u0026thinsp;150 per 100 grains) compared with the remaining genotypes.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1.Frequency occurrence of fungi\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the frequency and diversity of fungal contamination among the mother varieties and mutant lines, where each fungal species has a certain color. Mutant line G3, G5, G7, along with the mother variety Gemmeiza 9, displayed contamination by a wide spectrum of fungal species (an undesirable trend). In contrast, mutant lines, G1,G2, G4, and G9, along with the mother variety Maryout 5, exhibited limited lower levels of contamination with fungal species (desirable trend).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Correlation coefficients","content":"\u003cp\u003eThe heatmap of genotypic correlations showed strong positive associations between grain yield and spikes per plant (0.81) and 1000-grain weight (0.76), followed by spike weight (0.67) and spike yield (0.64). In contrast, spike length showed a moderate positive correlation with grain yield (0.38). Plant height showed a weak negative association (\u0026ndash;0.09). Grains per spike showed a low positive correlation with grain yield (0.29). Genotypic correlations were higher than phenotypic correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Regression analysis","content":"\u003cp\u003eThe regression analysis further supported these correlations (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Both 1000-grain weight and grains per spike had positive effects on final grain yield, explaining approximately 81.0% of the sum variation. Spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e showed the highest regression coefficient and a highly significant effect, followed by 1000-grain weight, whereas grains per spike showed a minor and non-significant contribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression coefficients of grains per spike (GS), 1000-grain weight (GWT), and spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e ( NS) on grain yield (GY) of wheat mutant lines in the M5 generation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression Coefficient (b)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePartial R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel summary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2; = 0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdj. R\u0026sup2; = 0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**: significant level, ns: non-significant, R\u003csup\u003e2\u003c/sup\u003e: Coefficient of Determination\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Principal component analysis","content":"\u003cp\u003eThe first three principal components (PC1, PC2 and PC3) explained 80.7% of the total variation, while the rest components (PC₃\u0026ndash;PC₈) explained smaller proportions of total variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePC₁ was associated with 1000-weight grain, spikes per m\u003csup\u003e2\u003c/sup\u003e, and spike yield. High positive PC1 scores were recorded for four mutant lines G2,G1,G4, G3 and G6, while high negative PC₁ scores were recorded for G16,P5,P3 and G15. PC2 was associated with spike length. High positive PC2 scores were recorded for P2,G11,G4 and G15, whereas high negative PC₂ Scores recorded for G5,G13,G9 and G14. Other PCs (PC₆\u0026ndash;PC₈) reflected minor trait variation (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCA scores (PC₁\u0026ndash;PC₈) of mutant lines in the M5 generation with their mother varieties based the studied traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC₁\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC₃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC₄\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC₅\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC₆\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePC₇\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePC₈\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.75\u003c/p\u003e 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char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"7. Hierarchical cluster analysis","content":"\u003cp\u003eBased on the studied traits, the hierarchical cluster analysis clustered the wheat genotypes into three clusters (I, II and III).Maximum genetic distance was found between cluster I vs cluster III. A moderate divergence was found between cluster I and cluster II, whereas the lowest divergence was found between cluster II and cluster III (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster I contained the high-performing mutant lines (G1,G2,G3 and G6), while cluster II contained the intermediate -performing mutant lines (G4,G7,G8,G9,G10, G11,G12 and G16), along with the mother variety P2. Cluster III contained the low-performing mutant lines (G5,G9,G13,G14 and G15) along with the mother varieties (P1,P3,P4 and P5).The heatmap combined with hierarchical clustering illustrated that high-yielding mutant lines (G1, G2, and G3) which showed higher values for 1000-grain weight, spikes m⁻\u0026sup2;, and 1000-grain weight and grain yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"8. Discussion","content":"\u003cp\u003eSignificant differences (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were detected among genotypes for the traits studied across generations, which resulted from the accumulation of mutational effects over successive generations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This indicated that mutagenesis successfully generated a wide genetic variability, which constituted the primary objective of the current study. Regarding plant height, the more homozygous M5 mutant lines showed shorter lengths compared with their mother varieties. This reduction conferred greater lodging resistance [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], an agronomically desirable trait, particularly under sandy soil conditions where plants were frequently exposed to wind stress. These findings highlighted one of the important benefits of mutation breeding in crop improvement: mutagens can manipulate the entire genome, helping the discovery of novel allelic variants or the modifications in key genes controlling grain yield components, as observed in the current study [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Moreover, mutagenesis played a pivotal role in increasing the spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e relative to the mother varieties. Several mutant lines exceeded their mother varieties for the studied traits across generations, indicated by LSD values. These results were consistent with previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These modifications exerted a positive effect on mutant lines (longer spikes) compared to their mother varieties, which enhanced spike weight and higher grains per spike [\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeed-borne fungal pathogens posed a major danger affecting wheat production [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, increasing the availability of resistant varieties is one of the most important strategies to address this issue [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several investigations have illustrated that fungal diseases were associated with reduced seed germination and seeding vigor [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The current study used an extensive survey to quantify variations in grain germination capacity, infection percentage, and the colony-forming units (CFU per 100 grains), with determination of fungal species connected with bread wheat grains. This survey focused on ten mutant lines that had outperformed their mother varieties across all studied traits at the M5 generation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, significant variations were observed among wheat genotypes regarding fungal load and seed health. Significantly, mutant lines, G2 and G9, along with the mother variety P4, illustrated that high germination rate connected with CFU values, indicating that these genotypes possessed a high level of resistance to seed-borne fungal diseases [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Additionally, these differences among genotypes reflected genetic variability of these promising lines may indicated the discovery of novel resistance alleles through mutational processes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Furthermore, mutant lines G2 and G9 exhibited significantly reduced contamination across different fungal taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting untapped sources of resistance to fungal diseases. These findings confirmed the importance of selecting new genotypes combining high germination capacity with low seed-borne pathogens load for developing fungal-tolerant wheat varieties. These findings aligned with previous pervious results [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhenotypic and genotypic correlation coefficients, together with regression analyses, emphasized that improvements in grain yield among the mutant lines were primarily associated with increases in spikes m⁻\u0026sup2; and 1000-grain weight [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The stronger genotypic correlations reflected real genetic linkage or pleiotropic effects governing these traits. The bar chart comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that genotypic correlations (rg) were higher in magnitude than phenotypic correlations (rp) for most studied traits, indicating the presence of true genetic relationships between traits and low environmental influence on the expression traits. As a result, phenotypic selection for traits such as spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 1000-grain weight, spike weight and spike yield would be effective in detecting promising high-yielding mutant lines. These findings suggested that mutagenesis effectively modified these traits.\u003c/p\u003e \u003cp\u003eMoreover, the regression analysis revealed that both spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 1000-grain weight were the most influencing traits affecting final grain yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsequently, these traits can be used as selection criteria to detect high-yielding mutant lines. These findings agree with previous studies reported by Jaenisch et al. [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Overall, the results highlighted that spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 1000-grain weight as the most reliable indirect selection criteria for enhancing grain yield, particularly in early generations where direct selection for yield may be less efficient due to environmental influence.\u003c/p\u003e \u003cp\u003eThe findings regarding genetic parameters further indicated that the environmental effects on the studied traits were low (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), as evidenced by the small differences between the phenotypic and genetic coefficients of variation. The high heritability values recorded for the studied traits indicated that phenotypic variation was largely attributed to genetic factors, making these traits suitable for phenotypic selection. This was further supported by the correlation between high heritability estimates (60% \u0026le;) and high genetic advance (20%\u0026le;) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis pattern suggested the predominance of additive gene action in the expression of the studied traits. Accordingly, these traits can serve as effective selection criteria for improving grain yield, validating the effectiveness of phenotypic selection and its potential to achieve genetic advance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The reduction in genetic variation from the M4 to M5 generations may be attributed to progressive self-pollination, which reduced heterozygosity; elimination of unstable or heterozygous genotypes; and enhanced fixation of mutant alleles across generations, leading to a decline in phenotypic and genotypic variance in the next generations (20,21).\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was determined the traits contributing most to the total variability among mutant lines. The first principal component (PC1) was associated with 1000-weight grain, spikes per m\u003csup\u003e2\u003c/sup\u003e, and spike yield, results highlighted these traits as important selection criteria for detecting promising mutant lines. The PCA biplot confirmed the effectiveness mutagen in generating novel genetic variation under marginal soil conditions and that clearly separated high-and low-yielding genotypes, indicating that novel genetic variation was generated. Mutant lines G1, G2, G3, G4 and G6 were distinguished as top-performing. These findings demonstrated that PCA and biplot analyses were powerful multivariate tools for detecting promising wheat mutant lines adapted to marginal soil conditions. Similar results were recorded by Singh et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and Al-Ghumaiz et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCluster analysis of the wheat genotypes clearly separated them into three main clusters based on overall agronomic performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe consistency among phenotypic and genotypic correlation coefficients and principle components analysis illustrated the importance of spikes m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 1000-grain weight as reliable selection criteria for improving bread productivity under marginal soils. Specifically, cluster I consisted of four mutant lines G1, G2, G3 and G6, which can be used as parents in crossing programs to improve spikes m⁻\u0026sup2; and high 1000-grain weight, with G2 as a source of fungal disease resistance. Cluster II consisted of mutant lines G5\u0026mdash;G10, 13, and G14, which can use as parents in crossing programs to enhance spike length and grains per spike, with G8 and G9 as a source of fungal disease resistance. Cluster III consisted the mother varieties and other mutant lines. Crossing between clusters I (high\u0026ndash;yield mutant lines) and cluster III could integrate high grain yield with specific traits and disease resistance, while intra-cluster crosses within cluster I could exploit heterosis effects for yield improvement. Similar results were reported by Poudel et al. [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and Zewdu et al. [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Therefore, the promising mutant lines detected in this study will be advanced to initial yield trials, particularly because they have reached a high level of homozygosity. These lines represent valuable genetic resources for wheat programs aimed at enhancing grain yield and fungal diseases resistance under marginal soil conditions.\u003c/p\u003e"},{"header":"9. CONCLUSIONS","content":"\u003cp\u003eThis study demonstrated that the mutation process in bread wheat played a pivotal role in generating substantial genetic variation for grain yield components under marginal soil conditions. Mutant lines exhibited substantial variation for most studied traits, coupled by high heritability and moderate to high genetic advanced, indicating their validation of phenotypic selection in the next generation. Genetic correlations and regression coefficients identified spikes per m\u0026sup2; and 1000-grain weight as the most influential contributors to final grain yield. Principal component analysis (PCA) confirmed these traits as key selection criteria under marginal soil conditions and clearly separated high- and low-yielding genotypes. Additionally, disease screening determined that both G2 and G9 were promising new sources of resistance to fungal pathogens, showing low infection levels by specific fungal pathogens. Cluster analysis revealed maximum divergence between clusters I and cluster III, indicating the possibility for crossing genotypes of these clusters to improve grain yield. Overall, the detected promising mutant lines represented valuable novel genetic sources and recommended for yield trials and as parents in hybridization programs targeting marginal environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors have declared that no competing interest exists\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e- declaration: not applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003e- declaration: not applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by the Academy of Scientific Research and Technology (ASRT), Egypt. Through research project No. 4662 during the period from 2019 to 2023.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAnter was responsible for field experiments and Sahab was responsible for disease experiments.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eTo Prof. Ragab Abdel Mohsen, Department of Botany, Institute of Agricultural and Biological Research, for his great assistance in creating mutations.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of the current study, including experiment results, studied traits, and in vitro fungal resistance evaluations of the two generations (M4 and M5) of wheat mutant lines, are only available from the corresponding author. The datasets are publicly available for research purposes and scientific cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGarg M, Sharma N, Sharma S, Kapoor P, Kumar A, Chunduri V, Arora P. Vitamins in cereals: a critical review of content, health effects, processing losses, bioaccessibility, fortification, and biofortification strategies for their improvement. Front Nutr. 2021;8:586815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2021.586815\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.586815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalid A, Hameed A, Tahir M. Wheat quality: A review on chemical composition, nutritional attributes, grain anatomy, types, classification, and function of seed storage proteins in bread making quality. 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AGBIR. 2024;40(2):962\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wheat Beard, Mutant Line, Mutagenesis, Marginal Soil, Heritability, Fungal Diseases","lastPublishedDoi":"10.21203/rs.3.rs-9125268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9125268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDevelopment of high-yielding, fungal disease-resistant varieties and adapted to marginal soils represents great strategy for enhancing bread wheat productivity in Egypt. In this context, newly developed mutant lines (M4 and M5 generations) were evaluated based on agronomic traits and susceptibility to fungal diseases over two consecutive seasons (2023\u0026ndash;2024) to detect the most productive and fungal disease-resistant lines. The genotypes were arranged in a randomized complete block design with three replicates, while in vitro pathology tests were performed for the M5 generation. Highly significant differences (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were observed among mutant lines for all studied traits, indicating sufficient genetic variability induced through mutagenesis. Mutant lines outperformed their mother varieties in studied traits. High heritability coupled with high to moderate expected genetic advance most studied traits suggested the predominance of additive gene action in trait expression, indicating that phenotypic selection would be effective for improving grain yield. Mutant lines G2 and G9 were classified as novel sources of high\u0026ndash;yielding and fungal disease resistance. Phenotypic selection for both 1000-grain weight and grains per spike significantly enhanced final grain yield, as indicated by phenotypic and genotypic correlations and supported by regression analysis. Principal components and cluster analysis were confirmed that genetic variation was generated and separated the genotypes into three main groups based on the studied traits, highlighting spikes m\u003csup\u003e-2\u003c/sup\u003e and 1000-grain weight as key selection criteria. These mutant lines represent available genetic sources for enhancing bread wheat productivity in marginal soil environments and enhancing tolerance to fungal diseases.\u003c/p\u003e","manuscriptTitle":"Selection of High-Yielding and Fungal Disease-Resistant Bread Wheat Mutants under Marginal Soil Conditions1Ayman Anter and 2Ahemd Sahab","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 11:13:14","doi":"10.21203/rs.3.rs-9125268/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"334500488788802619027187597722917186336","date":"2026-05-11T11:45:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T05:30:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T13:52:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T04:27:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-29T15:00:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Agriculture","date":"2026-03-29T14:55:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4b8aa39-4121-4238-bfcf-f2947d89bb5a","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"334500488788802619027187597722917186336","date":"2026-05-11T11:45:11+00:00","index":61,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-05T05:30:52+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:13:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 11:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9125268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9125268","identity":"rs-9125268","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}