Genetic and Phenotypic Associations between Peduncle Characteristics and Spike Productivity in Wheat Under Drought and Normal Conditions

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M. Ahmed, Saleh M. Ismail, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6983003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Peduncle stem plays an essential role in resource allocation and water transport to spike grains under normal conditions. Therefore, exploring peduncle traits and their relationships with spike production under drought stress may provide insights into the mechanisms that mitigate the effects of drought stress on grain yield in wheat. To address this challenge, a panel of 198 highly homozygous and diverse spring wheat varieties was evaluated under normal and drought conditions across two growing seasons. Peduncle traits, namely, length (PUL), diameter (PUD), and weight (PUW), as well as spike traits (spike length, number of spikelets/spike, grain number/spike, and grain yield per spike, and thousand kernel weight), were assessed. We revealed that PUW and PUD, unlike PUL, were significantly and strongly associated with spike traits and grain weight under all conditions. GWAS revealed that spike and peduncle traits were controlled by different genetic mechanisms, as no stable markers were shared between these two groups. The identification of distinct SNPs between genotypes with two contrasting peduncle traits revealed a key SNP marker located within a gene model that encodes a protein highly expressed in the peduncle and spike traits of wheat. Comparing cultivars with low peduncle trait values to cultivars with high peduncle trait values, particularly PUW and PUD, high peduncle trait cultivars had greater yield-related trait values under both drought and normal conditions. This study proposes to investigate the distinct SNPs between the selected genotypes, with contrasting target traits, to address the limitations of GWASs in detecting important marker-trait associations. Triticum aestivum L. peduncle weight peduncle diameter distinct SNPs genetic association Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Although great research efforts have been conducted to improve wheat production and productivity in dry environments, drought tolerance remains a complex trait, and many adaptive mechanisms remain to be understood. At the reproduction growth stage, drought tolerance was investigated by estimating the stress susceptibility index, reduction due to drought stress (%), or selecting high-performance genotypes under dry environments, etc. (Sallam et al., 2019 ). Drought escape, drought avoidance, and drought tolerance are the more well-defined mechanisms in plants. Exploring new adaptive traits/mechanisms is very important for expanding our knowledge of how plants alleviate the effects of drought stress, so that new cultivars with high drought tolerance can be bred (Ghazy et al., 2024 ). Improving new adaptive traits is very important for breeding programs that aim to select superior genotypes for production and crossing (Mourad et al., 2021 ). Plant height in wheat is one of the most important yield traits that are included in many breeding programs because they correlated with other important traits, such as lodging and grain yield (Ghazy et al., 2024 ). Plant height is routinely and frequently scored in any field experiment. Very few studies have reported the relationship between stem characteristics and grain yield in wheat (Ghazy et al., 2024 ). Sallam et al ., )2015( reported a significant correlation between stem characteristics (second internode diameter, main stem weight, and stem density) and grain yield per spike in 21 F1 and 28 F2 populations produced from a diallel cross without selfs and without reciprocal crosses of seven wheat genotypes under drought (D) and combined drought and heat (D + H) stresses. Additionally, the second internode diameter (from the soil) was significantly correlated with the thousand-kernel weight under D and D + H. Carbohydrate translocation to the grain is an important process that determines final grain weight, but this process is affected by drought stress (Kobata et al., 1992 ). Carbohydrates can be obtained from postanthesis photosynthesis but the carbohydrates are stored temporarily in the stem before being remobilized to the grains. Additionally, carbohydrates can also be obtained from preanthesis photosynthesis, where the carbohydrates are stored primarily in the stem and then remobilized to the grain during the grain-filling stage. When wheat plants are exposed to drought stress, a sharp decline in photosynthesis occurs, which leads to a reduction in grain assimilates, causing a significant decrease in grain weight (Kobata et al., 1992 ). Flag leaf photosynthesis alone cannot support grains with carbohydrates, and a considerable amount of carbohydrates for grain is needed and must come from reserves assimilated and stored in the stem. The peduncle is the stem from the internode below the spike of wheat to the spike, and it has been reported that the peduncle is an important trait for controlling photosynthetic efficiency (Wang et al., 2023 ), fungal disease resistance and lodging resistance (Madic et al., 2016 ). However, this part of the stem may play other important roles, especially under abiotic stresses (e.g., drought and heat stresses), as it is the part of the stem that is directly connected to the spike (Gebbing, 2003). The vascular system in the peduncle plays a critical role in transporting assimilates to grains (WARDLAW, 1990 ). Under drought stress, peduncle stems retain high amounts of potential water and nutrients compared with flag leaves.Wardlaw, 1965 reported that stems have many advantages over flag leaves because of their anatomical, ultrastructural and physiological structures for controlling photosynthesis. These authors reported that the high stomatal density found in the peduncle provided important adaptive mechanisms to the ecological environment during all the grain-filling stages (Gebbing, 2003). Hence, investigating the genetic variation in peduncle characteristics and their relationships with spike traits under abiotic stress may shed light on important mechanisms that alleviate the effects of drought stress in wheat. Genetic variation in peduncle length has been investigated only in dry environments and rain-fed environments by Mourad et al., ( 2021 ) and in well-watered environments (Liu et al., 2023 ). The correlations between PL and yield traits have been poorly studied. Looking at other important characteristics, such as stem diameter and weight, which have not been previously reported, may also be useful to expand the understanding of adaptive mechanisms under drought stress in wheat. Genome-wide association studies (GWASs) are still considered the most important type of association analysis for identifying alleles and genes associated with target traits. GWASs have been widely used to identify markers associated with spike traits in wheat. However, only one study reported markers associated with peduncle length in wheat under drought stress. Very few studies have performed GWASs for peduncle length under drought and normal conditions in wheat (Kobata et al., 1992 ). Therefore, a considerable gap in understanding the genetic control of peduncle traits in wheat remains. The objectives of this study were to investigate the genetic variation in peduncle traits, explore their associations with spike traits under normal and drought stress conditions, and elucidate the genetic control of peduncle traits by identifying promising SNP markers for further utilization in MAS to improve drought tolerance in wheat. 2. Materials and methods Plant material: A set of 198 highly diverse spring wheat genotypes representing a wide range of diverse agronomic and physiological traits were selected for the study (Supplementary Table S1 ). These genotypes were collected from 22 different countries obtained from the United States Department of Agriculture. The number of genotypes used from each country is shown in Supplementary Table 1. Experimental layout: All the genotypes were sown in two consecutive seasons, 2020 and 2021, under normal (N) and drought (D) conditions at the Experimental Field Station of the Department of Genetics, Assiut, Egypt (27°11′20.36′′N, 31°10′06.45′′E), where the soil is clay loam. Each condition was considered an environment: N2020, D2020, N2021, and N2021. A randomized complete block design (RCBD) with two replications was used. In both seasons and conditions, the seeds of each genotype were sown in one 1.5 m row, with 10 cm between seeds within a row and 10 cm between rows. The genotypes under N and D conditions were planted on the normal wheat sowing date. Under normal conditions, all the genotypes received planting irrigation and seven irrigations throughout the growing season, whereas under drought stress, the plants received two irrigations; at the sowing date and when the majority of lines were at the tillering stage t(Fig. 1). Soil moisture content: Soil moisture content was measured using the gravimetric method as described in(Holliday, 1990 ). The soil moisture content was measured in the upper soil layer with a 45 cm depth. The measurements were done two times during the growing season, before the earliest line headed and after the latest line headed. Six soil samples were randomly selected from each experimental soil area of the two studied scenarios (fully irrigated and stress condition). Once the soil samples were collected, they were put in weighted moisture canes, and the wet weight of the soil samples of each scenario was recorded. Then, the soil samples were transferred to the laboratory and dried in the oven at 110 degrees Celsius until constant weight and the dry weight of each soil sample was recorded. The gravimetric soil moisture content was calculated by dividing the loss in weight (sample wet weight – sample dry weight) by the sample dry weight. To convert the gravimetric moisture content to volumetric water content (Vol. %), the gravimetric moisture content was multiplied by soil bulk density, which was 1.2 g/cm 3 under the conditions of this experiment. Measured Traits: In both seasons, ten traits were scored for each genotype under both conditions: the chlorophyll content (CC) was measured via the chlorophyll content meter (Model CCM-200) of the flag leaves of each genotype. The heading date (HD; days) was scored as the number of days from sowing to the date when 50% of the plants started heading. At least five plants per replication, the following traits were measured. Plant height (PH; cm) was scored from the ground to the tip of the main culms´ spike at maturity. The following spike traits were recorded for each genotype: main spike length (SL; cm), number of grains per spike (GNPS), number of spikelets per spike (NSPS), and grain yield per spike (GYPS, gm). After harvest, the peduncle traits of the main culm were recorded as follows: peduncle length (PUL; cm) measured from the last internode of the main stem to the base of the spike, peduncle diameter (PUD; mm) measured at the center of peduncle via digital calipers (0–150 mm), and peduncle weight (PUW; gm) measured after harvest via a digital scale. Changes in a trait due to drought stress: The reduction due to drought stress (RDD) for each trait in the two seasons was calculated as follows: RDD = (Xn-Xd)/Xn ×100. where Xn is the main average over the trait under normal conditions and Xd is the main average of the same trait under drought stress. For the spike and peduncle traits, the reduction due to drought stress in each genotype was calculated. Statistical analyses of the phenotypic data: The statistical analysis of the phenotypic data was performed with PLABSTAT software via the following model: Y iknj = µ + y k + t n + g i + r j + yg + tg + ytrg (error). where Yij is the observation of genotype i in year k , treatment n , and replication j ; µ is the general mean; and y k , t n , g i and r j are the main effects of year, treatment, genotype, and replication, respectively. Years, genotypes, and replications were considered random effects, whereas treatment was considered a fixed effect. Heritability in the broad sense (H 2 ) was estimated as the ratio of genotypic (ơ2 g ) to genotypic (ơ2 p ) variance for each trait via PLABSTAT via the HERT command (Walker 1960). Selection index for drought tolerance Three selection indices (Wedel, 1962 ) were calculated to identify the best- and lowest-performing genotypes under drought stress. The spike index (SI) was used to better describe GYPS (X1) via two secondary traits, GNPS (X 2 ) and NSPS (X 3 ): SI = b 1 X 1 + b 2 X 2 + b 3 X 3 . The peduncle index (PI) was used to improve PUW (X 1 ) via the secondary trait PUD (X 2 ): PI = b 1 X 1 + b 2 X 2 . where b 1 , b 2 , and b 3 (for GYPS) are the index coefficients. The vector of the Smith–Horsel index coefficient b was calculated as shown in (Baker, 2021 )The drought index (DI) was calculated from the SI and PI as follows: DI = ½ [(SI/SD SI ) + (PI/SD PI )], where SD SI and SD PI are the phenotypic standard deviations of the SI and PI, respectively. High DI values identified the best genotypes were under drought stress. The 20 genotypes with the highest DI values were selected each year, and the Venny tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used to identify the common genotypes in both growing seasons under drought stress. Total-Soluble Carbohydrates (TSC) The TSCs were analyzed in the peduncle of the selected genotypes (highest and lowest DI). The dried stem peduncle (0.05 g) was boiled in glass tubes containing 5 mL distilled water at 100°C for two hours. The extract was then cooled and filtered, and the supernatant was kept in deep freeze until use. This extract was used for the estimation of TSC (mg/g DW ) according to Fales, ( 1951 ). Genetic analyses Genotyping and genome-wide association study The wheat genotypes were previously genotyped via two different types of genotyping methods, as described bySallam et al ., (2024) and as follows: I) 25K Infinium iSelect array (25K set): All 197 tested genotypes were genotyped via the GmbH TraitGenetics Section, Gatersleben, Germany. The results of genotyping via the 25K method revealed 21,093 SNPs after filtration. 2) Genotyping-by-sequencing (GBS set): Only 103 genotypes were genotyped via GBS methods by Mourad et al., ( 2020 ). A set of 11,362 SNP markers remained after marker and genotype filtration. In both methods, the markers were filtered on the basis of the criteria described by Alqudah et al., ( 2020 ) as follows: heterozygous loci were excluded, followed by the exclusion001 of markers with more than 20% missing data and a minor allele frequency < 5%. Finally, genotypes with more than 20% missing data were excluded. As a result, 197 (25K set) and 103 (GBS set) genotypes with 21,093 (25K) and 11,362 (GBS) markers, respectively, were used for GWAS. The 25K and GBS sets presented a clear population structure according to Sallam et al., ( 2024a ) and Mourad et al., ( 2020 ), respectively. In this study, genome-wide association analysis was performed for all traits scored in the two growing seasons and under both conditions as described in detail by Sallam et al., ( 2024a ). The GWAS was performed via nine different models via the memory-efficient, visualization-enhanced, and parallel-accelerated (rMVP) package: general linear model (GLM) + PCA, GLM + kinship, GLM + PCA + Kinship, mixed linear model (MLM) + PCA, MLM + kinship, MLM + PCA + Kinship, fixed and random model circulating probability unification (FarmCPU) + PCA, FarmCPU + kinship, and FarmCPU + PCA + Kinship (Yin et al., 2021 ). The best GWAS model for each trait was determined according to the distribution of the expected and observed p-values in the quantile‒quantile plot (Q‒Q plot). Two significant thresholds; a p-value ≤ 0.001 (− log10 > 3.00) and suggestive p-values (1/N (number of markers) (Li et al., 2012 ) were used to identify significant markers. Gene annotation The gene annotations for stable markers, which were significantly associated with the same traits under each condition in the two growing seasons, were investigated via the Ensembl Plants database for Triticum aestivum ( https://plants.ensembl.org/Triticum_aestivum/Info/Index ). The International Wheat Genome Sequencing Consortium (IWGSC) Reference Sequence v1.0 was used to determine the physical positions of the SNPs resulting from GBS. On the other hand, the flanking sequences of the SNP markers from the 25K set were obtained from the GrainGenes database ( https://wheat.pw.usda.gov/GG3/ ). The physical positions (GBS set) and flanking sequences (25K set) were then blasted against the Ensembl database ( https://plants.ensembl.org/Triticum_aestivum/Info/Index ) to identify candidate genes and their functional annotations. Candidate genes were selected if the significant SNPs were located within them. 3. Results 3.1 Soil moisture content A single-factor analysis of the soil moisture content under both conditions is presented in Supplementary Table 2. Highly significant differences in soil moisture were observed in the two growing seasons before and after the and heading growth stages. Before harvest, the differences in soil moisture were significant in 2020/2021 and highly significant in 2021/2022. Drought stress was more severe in the first season than in the second season. 3.2 Effects of drought stress on peduncle and spike traits On average, drought stress has a negative impact on all traits, in both seasons (supplementary Figure 1). All the traits were reduced due to drought stress except for TKW in 2020, which was slightly greater under drought conditions than under normal conditions (0.02%). Notably, for all traits except TKW, the reduction due to drought (RDD) was greater in the first season than in the second. Among the spike traits, the greatest RDD was found for GNPS in 2020 and SPL in 2021. The reduction in chlorophyll content due to drought stress was 7.15 and 3.61 in 2020 and 2021, respectively. NSPS was the spike trait least affected by drought stress. Among the peduncle traits, PUW presented the greatest reduction in both years, with 23.7% and 20.4% in 2020 and 2021, respectively, followed by PUD and PUL. In both seasons, all spike traits were greatly reduced due to drought stress, and NSPS presented the lowest reduction, with 2.56% and 1.64% in 2020 and 2021, respectively. The plant height decreased by 16.7% and 9.5% in 2020 and 2021, respectively. A percentage (5.79% of all genotypes) flowered earlier under drought than did the control in 2020, whereas in 2021, 2.36% of all genotypes flowered earlier under drought than did the control.3 3.3 Genetic variation in the peduncle and yield traits under drought stress The results of the combined ANOVAs of the studied traits are presented in Table (1). There were highly significant differences between the years in all traits except SPL and GYPS. Significant height differences were found among the genotypes between the treatments, G × Y, and G × T for all the traits. High H 2 estimates were found for all the traits, ranging from 0.87 (CC) to 0.97 (HD). The analysis of variance for each year is presented in supplementary Tables 3 and 4. A highly significant difference was found between the two treatments in each season for all measured traits. Moreover, highly significant variation among all the genotypes for all the studied traits was observed. The ANOVA revealed high genetic variation among the genotypes for all three selection indices in each year under drought stress (Table 1). Highly significant variation in the drought indices was observed among all the genotypes. All indices presented high heritability (H 2 ) in both years (supplementary Tables 5 and 6). The minimum, maximum, and mean values for all trait measurements in the two seasons for all the genotypes under normal and drought conditions are presented in Table 2. The three drought indices had lower values in 2021 than in 2020. The distributions of peduncle traits, as well as drought indices, for all the genotypes in the four environments are presented in Figure 2, while the density plot for the rest of the traits across the four environments is presented in supplementary Figure 2. 3.4 Phenotypic and genotypic correlations among measured traits The phenotypic correlations among all traits under normal and drought conditions in 2020 and 2021 are presented in Tables 3 and 4, respectively. In the four environments (N-2020, N-2021, D-2020, and D-2021), CC was significantly and negatively correlated with HD. Heading date was negatively correlated with PH, PUD, and PUW in the four environments and positively correlated with GYPS. PUD was positively and significantly associated with NSPS, GNPS, GYPS, and TKW in the four environments. Similarly, PUW was significantly and positively correlated with GNPS, GYPS, and TKW in the four environments. PUL did not show stable significant correlations with spike traits in each environments. Among the peduncle traits, PUW was highly significantly correlated with PUL and PUD across the four environments. PUL was significantly correlated with PUD only in 2021 under both conditions. Weak and nonstable correlations were found between SPL and peduncle traits. The genetic correlations among all traits in each environment are presented in Supplementary Tables 7 and 8. Notably, the genetic correlations among all the traits were greater than the phenotypic correlations under both conditions in the two seasons. Under drought stress in both growing seasons, highly significant correlations were found between the spike index (SI) and all peduncle traits in both growing seasons under drought stress. The peduncle index (PI) was also positively and significantly correlated with spike traits. The drought indices, including SI and PI, had highly significant and positive correlations with all spike traits except SPL and all peduncle traits. Moreover, highly significant correlations were found among the three indices in each year and between the two years (Figure 3a). The correlations between the reduction due to drought stress (RDD) in the peduncle and spike traits are presented in Table 5. In both growing seasons, RDD in PUD and PUW was positively and highly correlated with RDD in NSPS, GNPS, GYPS, and TKW. The RDD in PUL had positive and significant correlations with only GNPS and GYPS. 3.5 Identifying the best- and lowest-performing genotypes under drought As DI, including PI and SI, was highly significantly correlated with spike and peduncle traits under drought stress in the two growing seasons, all the genotypes were sorted on the basis of their DSI values (high values indicated high performance under drought stress). Then, the 20 highest and 20 lowest values were selected each year. In both years, a total of seven wheat genotypes were among the 20 genotypes with the highest DI values in both years (Figure 3b), whereas 10 genotypes presented the lowest DI values (Figure 3b, supplementary Table S9). Among the seven genotypes, Giza-36 was determined to be one of the highest-performing genotypes under drought stress and presented the highest yield and peduncle traits. OK91G158 was determined to be the genotype with the lowest peduncle trait and productivity. The differences in peduncle and grain traits between the five genotypes with the highest DI values and those with the lowest DI values are presented in Figure 3c. Compared with those with low peduncle traits, those with high peduncle traits presented greater spike traits. On the basis of the two groups of genotypes (high DI vs low DI), the differences in all traits between these two groups were tested (Figure 4). Interestingly, highly significant differences were found in all traits except SPL, CC, and PH under the four environments between the two contracting groups. Interestingly, the total-soluble carbohydrates (TSC) were analyzed in the stem peduncle in the two contracting groups based on DI. Highly significant variation was found between the two groups under drought and normal conditions (Figure 5a). Highly significant correlations were found between TSC and GYPS, TKW, and GNPS (except in D_2020) under the four environments (N_2020, D_2020, N_2021, and D_2021). The non-significant correlation was observed between TSC and NSPS (except D_2020). Genome-wide association study (GWAS): A GWAS was performed for all traits scored in this study under the four environments. The distributions of SNP markers generated from 25K and GBS on each chromosome and genome are presented in Figure 6a and supplementary Figure 3, respectively. The number of significant SNPs detected under drought stress was greater than that detected under normal conditions from the 25K genotyping method, whereas the opposite was true for the GBS genotyping methods. The number of significant markers detected for each trait in each environment is presented in Figure 6b. The detailed GWAS results for all traits scored under drought and normal conditions are presented in supplementary Tables 10 and 11, respectively. On the basis of the results of the QQ plot, the correct GWAS model was selected. FarmCPU+PCA+kin was the best-fitting model for most of the traits scored under the two conditions in each season (Supplementary Figure 4a-n). GWAS revealed a total of 26 (25K) and 39 (GBS) significant markers associated with the same trait under the same environment (drought or normal) and/or across all environments (supplementary Table 12). Most of these markers were located on chromosome 3B. A total of 32 and 34 stable markers were found to be associated with the same trait under normal and drought conditions, respectively. Interestingly, six markers were found to be associated with the same trait in the four environments: GYPS (two), PUW (one), NSPS (two), and TKW (one) (supplementary Table S13). The targ et al leles of these six markers had the same effect on the trait in all environments under both conditions. For example, allele A of AX-95233557 was associated with increased GYPS in all environments in both growing seasons, whereas the C allele of S1A_61088948 was associated with increased PUW in all environments in both years. To confirm the stability of the allele effect of the significant markers associated with the same trait under each condition and across all environments, the correlation of allele effects in the two years was calculated (Figure 7). The targ et al lele effects of the stable markers associated with the same trait in 2020 were highly and significantly correlated with the effects of the same allele in 2021 under normal (r=0.94**) and drought stress (0.95**) conditions. Notably, the common significant markers associated with the same traits were also associated with other yield traits. For example, the AX-111638065 marker was found to be associated with HD under drought stress in both growing seasons: HD under N-2021, PUW (D-2021), PUD (D-2020), and GYPS (D-2021). None of the significantly stable markers were shared between spike traits and peduncle traits. Stable markers for the selection of the three selection indices (PI, SI, and DSI), which were calculated under only drought stress in each year, were investigated (supplementary Table S12). One SNP marker (S1A_12369432) was associated with the SI in both years under drought stress, whereas two stable markers (S1A_61088948 and S3B_819948692) were associated with the PI under drought stress in both years. However, no shared stable markers were found between the PI and the SI. The gene annotations of the stable significant markers were investigated (supplementary Table S12). Among the 65 SNPs, 27 were located within 30 gene models, encoding 22 functional proteins and five hypothetical proteins. Among these 22 functional proteins, 11 strongly correlated with drought tolerance in wheat. Notably, some of the significant markers identified in this study were previously reported in earlier studies under drought stress conditions in wheat (supplementary Table S15). CAP8_rep_c4857_90, which was associated with the NSPS in this study, was found to be associated with awn length, the harvest index, and leaf area under drought stress according to (Qaseem et al ., 2018). The targ et al lele of each significant marker was determined in each selected genotype having high DI values (supplementary Table 16). WAS_026 (Omara-007) had the highest number of targ et al leles (368) detected all four environments, while WAS_141 (PI525241) had the lowest number of targ et al leles (281) SNP signals differentiating the tolerant and susceptible genotypes The seven drought-tolerant and ten drought-susceptible genotypes, on the basis of DSI, were used to determine whether there were distinct SNPs between these two groups. The 25K set was used for this purpose, as not all genotypes were genotyped via the GBS method. Among the 21,093 SNP markers (25K), only three distinct SNP markers clearly (0.01%) differentiated the tolerant and susceptible genotypes (Figure 8). Two of these markers were located on the 6D chromosome, whereas the other marker was located on the 2A chromosome. Single-marker analysis was performed between the three markers and the SI, PI, and DI using all the genotypes (198). The analysis revealed that the three markers were highly and significantly associated with all three indices in both years (supplementary Table 14). The markers were located within three different gene models that encode three different proteins. An investigation of the relationship between the functional protein of each gene and the corresponding trait revealed that Kukri_rep_c111032_99 (6D) was the most important marker that was significantly associated with the corresponding trait. This marker was found to be located within TraesCS6D02G401500, which encodes a neurolysin/thimet oligopeptidase with an N-terminus. The gene was highly expressed in the peduncle and spike traits of wheat during the development stages (https://bar.utoronto.ca/eplant_wheat/) (Figure 7). The biological process of this gene involves auxin transportation and auxin signals in wheat stems. Although the other two SNPs were also highly associated with the three indices, their gene and functional proteins did not provide evidence of an association with the spike or peduncle. Notably, the Excalibur_c7546_1286 and Kukri_rep_c111032_99 markers were also detected by GWAS, with significant associations with DI in 2020 (supplementary Table 10). Discussion Genetic variation in spikes and peduncles Drought stress significantly reduced all the studied traits in both seasons. The reduction due to drought stress in all the studied traits in the first season was greater than the reduction in all the traits in the second season. Peduncle weight was the trait most affected by drought stress, with reduced values of 23.7% and 20.4% in the D-2020 and D-2021 seasons, respectively. On the other hand, drought stress had little effect on NSPS compared with all the other traits scored in the two years under drought stress. All the traits followed a normal distribution across the four environments. The analysis of variance revealed high genetic variation among all the genotypes for all the traits. This highly significant genetic variation made the detection of novel allele variants possible via GWAS. Moreover, the population included highly diverse wheat genotypes originating from 36 different countries, and high genetic variation in all the traits was expected. The same population was successfully used earlier to identify genes and markers associated with fungal disease resistance (Esmail et al., 2023 ), heavy metal tolerance (Mourad et al., 2024 ; Mourad et al., 2025 ), salinity stress tolerance (Hasseb et al., 2022 ), and drought stress at the seedling stage (Sallam et al ., 2024). Significant differences in traits were found between the two treatments, indicating that drought stress was applied successfully. This can also be observed from differences in the soil moisture analyzed before anthesis and at or near maturity in both growing seasons. It was a successful treatment and that it accurately represented terminal drought. The broad-sense heritability was high for all the traits, indicating that selection for promising high-drought-tolerant wheat genotypes under drought stress is feasible. The significant differences in the genotype × treatment interaction indicated that the genotypes responded differently under drought and normal conditions, as would be expected. The phenotypic correlation between spike and peduncle traits provides very valuable and novel information on the critical role of the peduncle in supporting grain weight under drought stress. Among the peduncle traits scored in this study, PUW and PUD had highly significant and positive correlations with GNPS, GYPS, and TKW under normal and drought conditions in both growing seasons (four environments). In contrast, PUL exhibited weak or nonsignificant correlations with GNPS, GYPS, and TKW in the four environments. This clearly highlights the importance of peduncle weight and diameter, rather than length, in increasing grain weight and number under both conditions. The higher density of stomata in the peduncle may play a significant role in improving photosynthetic efficiency by increasing the surface area for gas exchange, regulating water loss through transpiration, and supporting grain filling in the late stages (Kong et al., 2010 ). Therefore, on the basis of our results of this study, a wider peduncle may indicate greater vascular capacity or greater stem reserves, which could support enhanced photosynthesis and nutrient accumulation and assimilate transportation or remobilization to the grain during the grain-filling stage. The advantages of having a wider peduncle can be extended by improving water transportation and minimizing water loss through stomatal density under drought stress. Kong et al., ( 2010 ) analyzed the anatomical traits of wheat peduncles during grain development in Jimai 22 (a winter wheat genotype). They reported that the peduncle has important anatomical, ultrastructural, and physiological characteristics compared with the flag leaf and that these important advantages play a critical role in grain filling. These characteristics regulate phosphoenolpyruvate carboxylase, which is important for controlling carbon assimilation activity and supplying substrates for carbohydrate synthesis during grain filling in the late growth stage. This can be noted the analysis of TSC in the selected genotypes with high and low DI. Genotypes with distinct peduncle characteristics showed higher levels of TSC compared to those with lower TSC. The strong, significant, and positive correlation between TSC and spike traits—especially GYPS, GNPS, and TKW (Fig. 5b)—further confirms the role of peduncle characteristics in supporting spike development under both normal and drought conditions. Therefore, a thicker peduncle can assimilate more TSC, which is subsequently transferred to the spike during the grain filling stage (Fig. 5c). Notably, in our study, only PUW and PUD were found to have stable and significant positive correlations with HD across the four environments, indicating that earlier flowering genotypes generally were higher in PUD and PUW. In addition, Kong et al., 2010 described the role of stomatal density in peduncles in increasing photosynthesis efficiency and water use efficiency. Under drought stress, photosynthesis can be impaired due to stomatal closure; therefore, genotypes with high PUD and PUW may overcome this problem because of their high stomatal density compared with those with low PUD and PUW. This conclusion can be observed from this study, as the correlation between HD and both PUW and PUD under drought stress was greater than that under normal conditions in both years. These results further support the importance of PUW and PUD in improving yield traits over PUL. PUL has been reported to play a role in and be associated with plant height, wheat pathogen resistance, and lodging resistance (Liu et al., 2023 ). In this study, highly positive and significant stable correlations were found between PUL and PH in all four environments. Wang et al., ( 2023 ) studied the variation in PUL under normal and drought conditions in a set of 282 wheat genotypes. However, the associations between peduncle length and yield traits were not reported or investigated in these studies. In a rain-fed environment, Rahimi et al., ( 2019 ) did not find any significant correlation between PUL and yield traits (grain number per spike, grain yield, TKW) in a set of 298 Iranian genotypes. Although genetic variation in PUL has also been studied by Liu et al., ( 2023 ) and Wang et al., ( 2023 ), its relationship with yield traits has not been investigated, and Liu et al., ( 2023 ) said that the relationship between PUL and final-grain wheat is still unclear. All these studies are in agreement with the results reported here with respect to the correlation between PUL and yield traits. In this study, PUL was found to be negatively and significantly correlated with CC under drought stress (in both years) and normal conditions (in the 2020–2021 growing season). A negative and significant correlation was found between these two traits under normal conditions by Yadav et al., ( 2023 ). However, different correlations between these two traits have been reported in earlier studies. The correlation between CC and PUL was found to be nonsignificant under normal conditions (Khalid et al., 2023 ), significant under normal conditions (Javed et al., 2022 ), and non-significant under drought conditions (Javed et al., 2022 ). The highly significant correlation between the reduction in peduncle traits and spike traits due to drought stress highlights the importance of the relationship between peduncle and spike traits, especially under drought stress, in both years. The greater the reduction in peduncle traits, especially PUD and PUW, was, the greater the reduction in NSPS, GNPS, and GYPS. The smaller the reduction in peduncle traits, especially PUD and PUW, was, the smaller the reduction in NSPS, GNPS, and GYPS. Notably, there was no stable significant correlation between either PUW or PUD and the NSPS. Moreover, no promising significant correlation was found between a reduction in peduncle traits (PUL, PUW, and PUD) due to drought stress and a reduction in TKW. This difference could be attributed mainly to the differences in the genotypes' responses. Notably, the genotypes with high PUW and PUD presented either a small reduction in TKW or a slight increase in TKW. Notably, the reduction in PUL due to drought stress was significantly and positively correlated with GNPS and GYPS. This result may shed light on the importance of how much the length of the peduncle is reduced rather than the absolute peduncle length under specific conditions. A greater reduction in PUL may lead to a significant decrease in starch, vascular bundles, and carbohydrates stored in the stem, hence reducing grain weight. Moreover, this may also explain the non-stable correlation found between PUL and other yield traits. Therefore, calculating the reduction in each genotype due to drought stress for the respective traits also provides valuable information on the relationships among these traits. Hence, the ideal genotype may exhibit a small reduction in PUW, PUD, and PUL under drought stress, as it is expected that the same genotype will show a small reduction in yield traits, thereby increasing the production of the final yield. These stable and consistent phenotypic correlations are important for providing new insights into the role of peduncle characteristics, for the first time, in enhancing key yield traits under normal and drought conditions in both growing seasons. Selection of promising wheat genotypes under drought stress To select the most promising high-yielding genotypes under drought stress, three selection indices were created: SI (including spike traits), PI (including peduncle traits), and DI (including SI and PI). The selection index, which includes many traits, is better than single-trait selection and integrates genetic and economic considerations into a single framework(Baker, 2021 ). Despite the complexity of the calculations and efforts required to estimate the three indices, such indices provide a potent tool for discriminating genotypes with high spike and peduncle traits under drought stress (Sallam et al., 2018 ). The selection index was also used to improve drought tolerance at the seedling stage in the same population byAhmed et al., ( 2022 ) .The correlations between PI and spike traits and between SI and peduncle traits, as well as the strong significant correlations between DI and both spike and peduncle traits, confirmed the strong relationships between peduncle and spike traits under drought stress. The highly significant correlation between DI in 2020 and 2021 (r = 0.92**) made the selection feasible. On the basis of the DI, the most promising high-performance (high spike and peduncle traits) genotypes were selected each year, resulting in seven genotypes that presented high spike and peduncle traits in both growing seasons under drought stress. Out of the seven genotypes, Sohag-5 was previously reported as a drought-tolerant genotype at the seedling stage (Ahmed et al., 2022 ). To further investigate this relationship (spike and peduncle), the lowest-performing genotypes were also selected (ten genotypes). The comparison of yield between the two groups of genotypes evaluated in this study under both conditions and across the two years revealed consistent and highly significant differences in all traits except PH, SPL, and CC. The high-performing genotypes (high DI values) presented very high PUL, PUW, PUD, GNSP, GYPS, and TKW values compared with the yield traits of the ten lowest-performing genotypes (low DI values). Moreover, the seven highly selected genotypes flowered earlier than those in the other groups did. These results confirmed the correlation between peduncle and spike traits found across all 198 genotypes. Genetic analyses for spike and peduncle traits Genome-wide association study In this study, SNP markers produced via two different genotyping methods were used for GWAS. The SNP array (25K) provides high accuracy, known marker positions, and reliable data for a fixed set of SNPs. More importantly, the majority of these SNPs fall within gene models, making the SNP array method ideal for gene identification, target analyses, or comparisons across studies (Geethanjali et al., 2024 ). The GBS method, on the other hand, offers high marker density and the ability to discover novel SNPs and/or genomic regions not covered by the SNP array, providing additional insights into genetic diversity and greater detection of important genes associated with target traits. Together, both methods can enhance the resolution and robustness of GWASs. According to marker and genotype filtration, two sets were produced, namely, the 25K and GBS sets, which included 198 and 103 genotypes, respectively. It has been reported that 100–500 individuals are needed for performing GWASs (Alqudah et al., 2020 ). The sets (25K and GBS) were individually used to identify alleles and genes associated with salinity stress tolerance, alkaline-saline tolerance, heavy metal tolerance, fungal disease resistance, and drought tolerance at the seedling stage via GWAS (Esmail et al., 2023 ). The GWAS in this study revealed a total of 2,243 significant SNPs associated with yield traits under normal and drought conditions in both growing seasons (four environments). The total number of SNPs varied by environment. The environment has a significant effect on SNP detection in GWASs, especially when analyzed traits are controlled by many genes related to drought tolerance, spikes, and peduncle traits (Eltaher et al., 2021 ). The marker‒trait associations were detected at a P value > 0.001, which is widely used in GWAS. Using stringent p-values, such as those derived from the Bonferroni correction or false discovery rate, may lead to the loss of important markers/genes with minor effects. A p-value of 0.001 in GWAS serves as a relaxed yet reasonable threshold for identifying potential candidate associations. However, functional validation remains critical to confirm the true association. For example, Sallam et al ., (2024) validated one important SNP marker associated with leaf wilting at a threshold of p > 0.001 in the spring and winter populations at the seedling stage (Eltaher et al., 2021 ). Moreover, a SNP marker associated with recovery after drought stress was found in the winter association set at a p value of < 0.001 and in the winter biparental population. As all the traits scored in this study are polygenic traits, identifying markers with major and minor effects is important for revealing the genetic control of peduncle and spike traits under both conditions. Interestingly, five significant markers that were detected at p < 0.001 were previously reported in different spring and winter wheat genetic backgrounds, with their associations with yield traits in wheat under drought stress (supplementary Table S14). In this study, stable significant markers that were found to be associated with the same trait under the same conditions in the two growing seasons as well as with the same trait under the four environments were prioritized for further analysis. The significant marker can be considered a validated marker if its effects remain significant across years, locations, or different populations (Sallam et al., 2023 ). Most of these stable markers were also found to be associated with other traits, indicating that these markers also have pleiotropic effects, indicating that these markers influence multiple traits simultaneously and would be very useful for marker-assisted selection after validation in different genetic backgrounds (Hashem et al., 2023 ). Notably, the effects of the stable markers on the trait, under normal or drought conditions or both conditions in the two years, were also consistent. This was observed from the high correlation found in the allele effects between the two years under normal (Fig. 6a) and drought stress (Fig. 6b). For example, the target C allele of the S1A_61088948 SNP marker was found to increase the PUW under both conditions in the two seasons. In both years, the C allele of S6A_538493174 had the same allele effect on TKW, as it increased the traits by ~ 3.0 g under normal conditions, whereas it increased TKW by an average of 2.6 g under drought conditions. Such markers with stable and consistent effects on traits could be valuable markers for marker-assisted selection to accelerate the genetic improvement of grain yield per se and resilience to drought in molecular breeding programs for wheat. The stable allele effects indicate that these alleles can contribute positively to yield components. More importantly, they present minimal interaction with environmental factors, confirming their reliable performance across years and environments. PUW and PUD were significantly associated with GYPS, TKW, and GNPS and stable shared markers were found between them. After the SI and PI were created, no markers were common between the two indices. Only one common stable marker was associated with DI, including PI and SI, in both growing seasons under drought stress. The use of the selection index in GWAS may help detect markers and genes for a group of traits when it is difficult to discover any marker for individual traits. This result indicated that although PUW and PUD enhanced yield traits under both conditions, it seems that spike and peduncle traits are controlled by different genetic mechanisms. Interestingly, the results of GWAS were utilized to identify the number of targ et al leles of each significant SNP in the seven selected genotypes. WAS_020 (Omara-007) possessed the highest number of targ et al leles, with 368 alleles. The same genotype was found to possess nine drought tolerant genes (BIN1, NIM1, RHD2, OAT, OBF5, PEPR1, EDSI, DREB1-D, and DREB1-D2) (Sallam et al., 2024b ). This result indicated that this genotype could be an important source of drought tolerance in wheat for future breeding programs to produce cultivars having high tolerance to drought stress. Gene annotation for promising and important markers under normal and drought conditions The gene annotation in this study was performed for the most important SNP markers, which were divided into two groups: 1. SNP markers that were previously reported in earlier studies under drought stress and 2. stable markers that were significantly associated with the same traits under the same conditions in the two growing seasons or the four environments. The BS00064935_51 marker was found to be located within the TraesCS4B02G194900 (FAD7) gene model, which encodes an N-terminal fatty acid desaturase. FAD 7, which is localized in chloroplasts, plays an important role in maintaining thylakoid membrane function, ensuring efficient photosynthesis under drought conditions. Mutation of FAD 7 reportedly results in a 15% reduction in chlorophyll content of the mutant Arabidopsis plants compared with wild-type plants (McCourt et al., 1987 ). These markers were found to be associated with CC under drought stress and with plant height under drought in earlier studies. There was a highly significant negative correlation between CC and PH in the four environments in this study. CAP8_rep_c4857_90 was associated with NSPS under D_2021 in this study and with three traits, namely, awn length, harvest index, and leaf area, in a spring wheat population (European genotypes) under drought stress in the study of (Qaseem et al., 2018 ). This marker was found to be located in TraesCS7D02G377300, which encodes the vesicle transport protein Got1/SFT2-like. The relationship between this protein and drought tolerance in plants was not identified in earlier studies. Distinct SNPs between the highest- and lowest-performing wheat genotypes Although GWAS is considered the most powerful tool for identifying linked SNPs with target traits, it has several limitations that affect the identification of important associated SNPs. GWASs do not account well for gene‒environment interactions, which affect many polygenic traits, such as drought tolerance (Eltaher et al., 2021 ). The same SNPs may have different effects under different environments, making confirming their true role difficult (Eltaher et al., 2021 ). Therefore, identifying genotypes with extreme phenotypes for a trait of interest, along with available SNP data, may help reveal important SNPs that GWASs might miss. In this context, the extremely contrasting genotypes for DI (Fig. 3b, supplementary Table S9) and their SNP data generated from 25K were used. Only three SNPs were clearly present in the seven genotypes with the highest DI values, and these SNPs were absent in the ten genotypes with the lowest DI values. Among these markers, the Kukri_rep_c111032_99 marker was found to be within the TraesCS6D02G401500 (TaOOP) gene model, which encodes Neurolysin/Thimet oligopeptidase, N-terminal. The ortholog of this protein in rice is encoded by the OsOOP gene, which is involved in the biological processes of auxin transport and auxin signaling (KnetMiner). The transcriptomic wheat datasets (Wheat eFP Browser) confirmed the high expression of this protein (Neurolysin/Thimet oligopeptidase, N-termina) in both peduncle and spike traits, confirming the role of this gene in enhancing peduncle and spike traits together. Transcriptomic and gene expression databases such as the Wheat eFP Browser, a tool that provides spatial and temporal gene expression profiles across different wheat tissues, developmental stages, and environmental conditions, were used to confirm the results of genetic association analyses(Borrill et al., 2016 ) (Borrill et al., 2015 ). Single-marker analysis between this marker and the three selection indices (SI, PI, and DI) confirmed the presence of highly significant differences between the two groups of genotypes carrying different alleles. This marker was detected via GWAS in the first growing season but not in the second season. This confirmed the notion that genotype‒environment interactions play a crucial role in identifying important and stable SNPs via GWAS. Additionally, phenotypic data quality and distribution can significantly affect the power of relatedness correction models in GWASs, potentially leading to the absence of SNP markers in some environments (Korte and Farlow, 2013 ). The other two distinct SNPs did not show any evidence associated with peduncle or spike traits under any conditions. However, they could provide important and novel information. Further expression analysis experiments should be conducted for these three genes to validate their associations with peduncle traits before their use for marker-assisted selection. Distinct SNPs between genotypes with two different and extreme phenotypes may provide new insights into important genes and help overcome some limitations of GWAS, such as gene‒environment interactions. Further genetic experiments should be conducted to confirm these findings. In conclusion, the results of this study shed light on novel important adaptive traits to drought stress. To the best of our knowledge, this is the first study reveals the association between the diameter and weight of the peduncle and spike traits under normal and drought conditions. The results revealed that PUD and PUW play a key role in mitigating the effects of drought stress and support grain and spike traits under normal and drought conditions. PUL was not associated with spike traits or grain weight. However, selecting for high peduncle traits could be highly beneficial for improving wheat production and productivity under these conditions. Seven genotypes with high peduncle and spike traits could be used in further breeding programs to produce drought-tolerant cultivars. GWAS revealed that peduncle and spike traits are controlled by different genetic mechanisms. Distinct SNPs between genotypes with extreme and contrasting phenotypes may help overcome these limitations, particularly genotype‒environment interactions, in GWASs for identifying important SNPs associated with target traits. Declarations Acknowledgement The author would like to express sincere gratitude to Dr. Mona A. Dawood, Faculty of Science, Assiut University, for her valuable and insightful tips on the analysis of total soluble carbohydrates in the selected genotypes . Authors’ contributions MH conducted all the field experiments in this study and analyzed the genetic data; AAMA helped in phenotyping the data and genetic data analyses; SMI analyzed the soil samples under normal and drought conditions; PSB provided the plant material for this study, led the GBS genotyping, discussed the results, and reviewed the manuscript; AB provided the plant material for this study, led the 25K SNP array genotyping, discussed the results, and reviewed the manuscript; and AS designed the study, supervised all the field experiments and genetic association analyses, and wrote the MS. Funding The SNP genotyping was funded by the University of Nebraska-Lincoln, USA, and Alexander Von Humbolt, Germany. The field and phenotyping experiments were funded by the Science and Technology Development Fund (STDF) under Project ID 39444, Egypt. Data availability All phenotypic analyses are presented in the supplementary files. The SNP datasets generated during and/or analyzed during the current study are not publicly available owing to their involvement in ongoing projects but are available from Prof. Dr. Ahmed Sallam upon reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The other authors declare that they have no competing interests. References Ahmed, A. A. M., Dawood, M. F. A., Elfarash, A., Mohamed, E. A., Hussein, M. Y., Börner, A., and Sallam, A. (2022). Genetic and morpho-physiological analyses of the tolerance and recovery mechanisms in seedling stage spring wheat under drought stress. Front Genet 13 . Alqudah, A. M., Sallam, A., Stephen Baenziger, P., and Börner, A. (2020). GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley – A review. J Adv Res 22 :119–135. Baker, R. J. . (2021). Selection indices in plant breeding R.J. Baker . CRC Press. 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Supplementary Files FinalTables.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 31 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 26 Jun, 2025 First submitted to journal 26 Jun, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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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-6983003","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492324771,"identity":"955ad4a6-62fb-4084-98f7-23a016dcbd48","order_by":0,"name":"Ahmed Sallam","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7811-728X","institution":"Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK): Leibniz-Institut fur Pflanzengenetik und Kulturpflanzenforschung (IPK)","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Sallam","suffix":""},{"id":492324772,"identity":"443b67c6-296e-4a63-9c76-2e9155d3ccf5","order_by":1,"name":"Mostafa Hashem","email":"","orcid":"","institution":"Assiut University Faculty of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Hashem","suffix":""},{"id":492324773,"identity":"72e07e11-de5b-48a5-9201-5186930d71c6","order_by":2,"name":"Asmaa A. M. Ahmed","email":"","orcid":"","institution":"Assiut University Faculty of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Asmaa","middleName":"A. M.","lastName":"Ahmed","suffix":""},{"id":492324774,"identity":"dc5ebb28-c61e-46bd-afc0-84036be23962","order_by":3,"name":"Saleh M. Ismail","email":"","orcid":"","institution":"Assiut University Faculty of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Saleh","middleName":"M.","lastName":"Ismail","suffix":""},{"id":492324775,"identity":"8364d590-2ea6-4b99-bb6f-86d3b44142f2","order_by":4,"name":"P. Stephen Baenziger","email":"","orcid":"","institution":"UNL: University of Nebraska-Lincoln","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"Stephen","lastName":"Baenziger","suffix":""},{"id":492324776,"identity":"854ea3b2-210d-438d-8a8e-135d6c60bbc5","order_by":5,"name":"Andreas Börner","email":"","orcid":"","institution":"Leibniz Institute of Plant Genetics and Crop Plant Research (IPK): Leibniz-Institut fur Pflanzengenetik und Kulturpflanzenforschung (IPK)","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Börner","suffix":""}],"badges":[],"createdAt":"2025-06-26 11:25:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6983003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6983003/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-05140-2","type":"published","date":"2026-01-13T16:31:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87953726,"identity":"86bd423a-a549-4bb5-b98c-8e6e13838344","added_by":"auto","created_at":"2025-07-30 18:22:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89382,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of irrigations in normal and drought conditions, and soil sampling time through the two seasons.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/09223ea76598ffc6c46f1e80.jpg"},{"id":87953727,"identity":"812dedbd-9190-4d94-b161-823515546269","added_by":"auto","created_at":"2025-07-30 18:22:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eDensity diagram for all genotypes in peduncle traits under normal and drought conditions in the two growing seasons (2020 and 2021),\u003cstrong\u003eb\u003c/strong\u003e) drought indices.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/f06840e80defdd2a849a02ac.jpg"},{"id":87953728,"identity":"8cd11b19-647d-49bd-8047-3fe3181a4938","added_by":"auto","created_at":"2025-07-30 18:22:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eThe phenotypic correlation between the drought indices in 2020 and 2021, b) The best and lowest 20 genotypes under drought stress and common genotypes between the two seasons, c) differences between top high (left) and low (right) genotypes for peduncle traits\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/214fa062d113881121ffbc60.jpg"},{"id":87954844,"identity":"f96a053b-b764-4380-9fd6-c1b466849b55","added_by":"auto","created_at":"2025-07-30 18:46:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185475,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot shows the performance of both the highest and lowest genotypes in each morphological traits under both conditions(\u003cstrong\u003enormal, drought stress\u003c/strong\u003e) and in the two growing seasons (\u003cstrong\u003e2020 and 2021\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/71a1e4de70b959947d96669b.jpg"},{"id":87953730,"identity":"fa57f61f-c59a-447e-88ad-333d4baf38ca","added_by":"auto","created_at":"2025-07-30 18:22:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Box plot showing the differences between genotypes with the highest and lowest DI under normal and drought conditions, (b) correlation between TSC and spike traits under normal and drought conditions, (c) the role of peduncle traits in supporting spike traits under drought stress.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/43a93bcf916051ebd8db967a.jpg"},{"id":87953733,"identity":"877ef372-74f2-411b-b94d-32c68db4eb3d","added_by":"auto","created_at":"2025-07-30 18:22:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":236174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Distribution of SNP markers resulted from 25K (\u003cstrong\u003e21,093\u003c/strong\u003e) and GBS (\u003cstrong\u003e11,363\u003c/strong\u003e) across the different wheat chromosomes, \u003cstrong\u003eb) \u003c/strong\u003eThe total number of significant SNPs (25K and GBS) detected under control and drought conditions in the two seasons (2020 and 2021), \u003cstrong\u003ec, d\u003c/strong\u003e) the total number of significant SNPs (25K, GBS) associated with each trait..\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/1acfdaebc5f778a12e22c324.jpg"},{"id":87953729,"identity":"46ad5ce0-e2a5-42ef-ab3b-477e8df44806","added_by":"auto","created_at":"2025-07-30 18:22:56","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":92809,"visible":true,"origin":"","legend":"\u003cp\u003eThe allele effect of all common markers in both growing seasons (2020-2021) under both conditions (\u003cstrong\u003ea\u003c/strong\u003e) control and (\u003cstrong\u003eb\u003c/strong\u003e) drought.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/6060a26dbff4e3448606dbcb.jpg"},{"id":87953736,"identity":"5b98f11d-cac3-4855-9679-ee1e784188e9","added_by":"auto","created_at":"2025-07-30 18:22:56","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":176207,"visible":true,"origin":"","legend":"\u003cp\u003eThree distinct SNPs between genotypes with the highest and lowest DI (including PI and SI).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/4eb9e470ca2edfbc518ea415.jpg"},{"id":100616216,"identity":"c4a08a51-9df8-4de3-896d-212a23e830a6","added_by":"auto","created_at":"2026-01-19 17:41:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2862517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/0b1e76fe-387d-4856-99dd-b7dc1863c7cf.pdf"},{"id":87954338,"identity":"1c4877e5-ea9a-4dae-8d83-07f0abee9ed9","added_by":"auto","created_at":"2025-07-30 18:30:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43764,"visible":true,"origin":"","legend":"","description":"","filename":"FinalTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6983003/v1/5bd2f0f475a56f637768ee4f.docx"}],"financialInterests":"","formattedTitle":"Genetic and Phenotypic Associations between Peduncle Characteristics and Spike Productivity in Wheat Under Drought and Normal Conditions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlthough great research efforts have been conducted to improve wheat production and productivity in dry environments, drought tolerance remains a complex trait, and many adaptive mechanisms remain to be understood. At the reproduction growth stage, drought tolerance was investigated by estimating the stress susceptibility index, reduction due to drought stress (%), or selecting high-performance genotypes under dry environments, etc. (Sallam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Drought escape, drought avoidance, and drought tolerance are the more well-defined mechanisms in plants. Exploring new adaptive traits/mechanisms is very important for expanding our knowledge of how plants alleviate the effects of drought stress, so that new cultivars with high drought tolerance can be bred (Ghazy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Improving new adaptive traits is very important for breeding programs that aim to select superior genotypes for production and crossing (Mourad et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePlant height in wheat is one of the most important yield traits that are included in many breeding programs because they correlated with other important traits, such as lodging and grain yield (Ghazy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plant height is routinely and frequently scored in any field experiment. Very few studies have reported the relationship between stem characteristics and grain yield in wheat (Ghazy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sallam \u003cb\u003eet al\u003c/b\u003e., )2015( reported a significant correlation between stem characteristics (second internode diameter, main stem weight, and stem density) and grain yield per spike in 21 F1 and 28 F2 populations produced from a diallel cross without selfs and without reciprocal crosses of seven wheat genotypes under drought (D) and combined drought and heat (D\u0026thinsp;+\u0026thinsp;H) stresses. Additionally, the second internode diameter (from the soil) was significantly correlated with the thousand-kernel weight under D and D\u0026thinsp;+\u0026thinsp;H.\u003c/p\u003e\u003cp\u003eCarbohydrate translocation to the grain is an important process that determines final grain weight, but this process is affected by drought stress (Kobata et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Carbohydrates can be obtained from postanthesis photosynthesis but the carbohydrates are stored temporarily in the stem before being remobilized to the grains. Additionally, carbohydrates can also be obtained from preanthesis photosynthesis, where the carbohydrates are stored primarily in the stem and then remobilized to the grain during the grain-filling stage. When wheat plants are exposed to drought stress, a sharp decline in photosynthesis occurs, which leads to a reduction in grain assimilates, causing a significant decrease in grain weight (Kobata et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Flag leaf photosynthesis alone cannot support grains with carbohydrates, and a considerable amount of carbohydrates for grain is needed and must come from reserves assimilated and stored in the stem. The peduncle is the stem from the internode below the spike of wheat to the spike, and it has been reported that the peduncle is an important trait for controlling photosynthetic efficiency (Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), fungal disease resistance and lodging resistance (Madic et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, this part of the stem may play other important roles, especially under abiotic stresses (e.g., drought and heat stresses), as it is the part of the stem that is directly connected to the spike (Gebbing, 2003). The vascular system in the peduncle plays a critical role in transporting assimilates to grains (WARDLAW, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Under drought stress, peduncle stems retain high amounts of potential water and nutrients compared with flag leaves.Wardlaw, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1965\u003c/span\u003e reported that stems have many advantages over flag leaves because of their anatomical, ultrastructural and physiological structures for controlling photosynthesis. These authors reported that the high stomatal density found in the peduncle provided important adaptive mechanisms to the ecological environment during all the grain-filling stages (Gebbing, 2003). Hence, investigating the genetic variation in peduncle characteristics and their relationships with spike traits under abiotic stress may shed light on important mechanisms that alleviate the effects of drought stress in wheat.\u003c/p\u003e\u003cp\u003eGenetic variation in peduncle length has been investigated only in dry environments and rain-fed environments by Mourad et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in well-watered environments (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The correlations between PL and yield traits have been poorly studied. Looking at other important characteristics, such as stem diameter and weight, which have not been previously reported, may also be useful to expand the understanding of adaptive mechanisms under drought stress in wheat.\u003c/p\u003e\u003cp\u003eGenome-wide association studies (GWASs) are still considered the most important type of association analysis for identifying alleles and genes associated with target traits. GWASs have been widely used to identify markers associated with spike traits in wheat. However, only one study reported markers associated with peduncle length in wheat under drought stress. Very few studies have performed GWASs for peduncle length under drought and normal conditions in wheat (Kobata et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Therefore, a considerable gap in understanding the genetic control of peduncle traits in wheat remains.\u003c/p\u003e\u003cp\u003eThe objectives of this study were to investigate the genetic variation in peduncle traits, explore their associations with spike traits under normal and drought stress conditions, and elucidate the genetic control of peduncle traits by identifying promising SNP markers for further utilization in MAS to improve drought tolerance in wheat.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003ePlant material:\u003c/p\u003e\u003cp\u003eA set of 198 highly diverse spring wheat genotypes representing a wide range of diverse agronomic and physiological traits were selected for the study (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These genotypes were collected from 22 different countries obtained from the United States Department of Agriculture. The number of genotypes used from each country is shown in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eExperimental layout:\u003c/p\u003e\u003cp\u003eAll the genotypes were sown in two consecutive seasons, 2020 and 2021, under normal (N) and drought (D) conditions at the Experimental Field Station of the Department of Genetics, Assiut, Egypt (27\u0026deg;11\u0026prime;20.36\u0026prime;\u0026prime;N, 31\u0026deg;10\u0026prime;06.45\u0026prime;\u0026prime;E), where the soil is clay loam. Each condition was considered an environment: N2020, D2020, N2021, and N2021. A randomized complete block design (RCBD) with two replications was used. In both seasons and conditions, the seeds of each genotype were sown in one 1.5 m row, with 10 cm between seeds within a row and 10 cm between rows. The genotypes under N and D conditions were planted on the normal wheat sowing date. Under normal conditions, all the genotypes received planting irrigation and seven irrigations throughout the growing season, whereas under drought stress, the plants received two irrigations; at the sowing date and when the majority of lines were at the tillering stage t(Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eSoil moisture content:\u003c/p\u003e\u003cp\u003eSoil moisture content was measured using the gravimetric method as described in(Holliday, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The soil moisture content was measured in the upper soil layer with a 45 cm depth. The measurements were done two times during the growing season, before the earliest line headed and after the latest line headed. Six soil samples were randomly selected from each experimental soil area of the two studied scenarios (fully irrigated and stress condition). Once the soil samples were collected, they were put in weighted moisture canes, and the wet weight of the soil samples of each scenario was recorded. Then, the soil samples were transferred to the laboratory and dried in the oven at 110 degrees Celsius until constant weight and the dry weight of each soil sample was recorded. The gravimetric soil moisture content was calculated by dividing the loss in weight (sample wet weight \u0026ndash; sample dry weight) by the sample dry weight. To convert the gravimetric moisture content to volumetric water content (Vol. %), the gravimetric moisture content was multiplied by soil bulk density, which was 1.2 g/cm\u003csup\u003e3\u003c/sup\u003e under the conditions of this experiment.\u003c/p\u003e\u003cp\u003eMeasured Traits:\u003c/p\u003e\u003cp\u003eIn both seasons, ten traits were scored for each genotype under both conditions: the chlorophyll content (CC) was measured via the chlorophyll content meter (Model CCM-200) of the flag leaves of each genotype. The heading date (HD; days) was scored as the number of days from sowing to the date when 50% of the plants started heading. At least five plants per replication, the following traits were measured. Plant height (PH; cm) was scored from the ground to the tip of the main culms\u0026acute; spike at maturity. The following spike traits were recorded for each genotype: main spike length (SL; cm), number of grains per spike (GNPS), number of spikelets per spike (NSPS), and grain yield per spike (GYPS, gm). After harvest, the peduncle traits of the main culm were recorded as follows: peduncle length (PUL; cm) measured from the last internode of the main stem to the base of the spike, peduncle diameter (PUD; mm) measured at the center of peduncle via digital calipers (0\u0026ndash;150 mm), and peduncle weight (PUW; gm) measured after harvest via a digital scale.\u003c/p\u003e\u003cp\u003eChanges in a trait due to drought stress:\u003c/p\u003e\u003cp\u003eThe reduction due to drought stress (RDD) for each trait in the two seasons was calculated as follows: RDD = (Xn-Xd)/Xn \u0026times;100. where Xn is the main average over the trait under normal conditions and Xd is the main average of the same trait under drought stress. For the spike and peduncle traits, the reduction due to drought stress in each genotype was calculated.\u003c/p\u003e\u003cp\u003eStatistical analyses of the phenotypic data:\u003c/p\u003e\u003cp\u003eThe statistical analysis of the phenotypic data was performed with PLABSTAT software via the following model: \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eiknj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e + \u003cem\u003et\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e + \u003cem\u003eg\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e + \u003cem\u003er\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e + \u003cem\u003eyg\u0026thinsp;+\u0026thinsp;tg\u0026thinsp;+\u0026thinsp;ytrg\u003c/em\u003e (error). where Yij is the observation of genotype \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003ek\u003c/em\u003e, treatment \u003cem\u003en\u003c/em\u003e, and replication \u003cem\u003ej\u003c/em\u003e; \u0026micro; is the general mean; and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e, \u003cem\u003eg\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e and \u003cem\u003er\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e are the main effects of year, treatment, genotype, and replication, respectively. Years, genotypes, and replications were considered random effects, whereas treatment was considered a fixed effect. Heritability in the broad sense (H\u003csup\u003e2\u003c/sup\u003e) was estimated as the ratio of genotypic (ơ2\u003csub\u003eg\u003c/sub\u003e) to genotypic (ơ2\u003csub\u003ep\u003c/sub\u003e) variance for each trait via PLABSTAT via the HERT command (Walker 1960).\u003c/p\u003e\u003cp\u003eSelection index for drought tolerance\u003c/p\u003e\u003cp\u003eThree selection indices (Wedel, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) were calculated to identify the best- and lowest-performing genotypes under drought stress. The spike index (SI) was used to better describe GYPS (X1) via two secondary traits, GNPS (X\u003csub\u003e2\u003c/sub\u003e) and NSPS (X\u003csub\u003e3\u003c/sub\u003e): SI\u0026thinsp;=\u0026thinsp;b\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;b\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e+ b\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e. The peduncle index (PI) was used to improve PUW (X\u003csub\u003e1\u003c/sub\u003e) via the secondary trait PUD (X\u003csub\u003e2\u003c/sub\u003e): PI\u0026thinsp;=\u0026thinsp;b\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;b\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e. where b\u003csub\u003e1\u003c/sub\u003e, b\u003csub\u003e2\u003c/sub\u003e, and b\u003csub\u003e3\u003c/sub\u003e (for GYPS) are the index coefficients. The vector of the Smith\u0026ndash;Horsel index coefficient b was calculated as shown in (Baker, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)The drought index (DI) was calculated from the SI and PI as follows: DI = \u0026frac12; [(SI/SD\u003csub\u003eSI\u003c/sub\u003e) + (PI/SD\u003csub\u003ePI\u003c/sub\u003e)], where SD\u003csub\u003eSI\u003c/sub\u003e and SD\u003csub\u003ePI\u003c/sub\u003e are the phenotypic standard deviations of the SI and PI, respectively. High DI values identified the best genotypes were under drought stress. The 20 genotypes with the highest DI values were selected each year, and the Venny tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify the common genotypes in both growing seasons under drought stress.\u003c/p\u003e\u003cp\u003eTotal-Soluble Carbohydrates (TSC)\u003c/p\u003e\u003cp\u003eThe TSCs were analyzed in the peduncle of the selected genotypes (highest and lowest DI). The dried stem peduncle (0.05 g) was boiled in glass tubes containing 5 mL distilled water at 100\u0026deg;C for two hours. The extract was then cooled and filtered, and the supernatant was kept in deep freeze until use. This extract was used for the estimation of TSC (mg/g DW ) according to Fales, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1951\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenetic analyses\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGenotyping and genome-wide association study\u003c/h2\u003e\u003cp\u003eThe wheat genotypes were previously genotyped via two different types of genotyping methods, as described bySallam \u003cb\u003eet al\u003c/b\u003e., (2024) and as follows:\u003c/p\u003e\u003cp\u003eI) 25K Infinium iSelect array (25K set): All 197 tested genotypes were genotyped via the GmbH TraitGenetics Section, Gatersleben, Germany. The results of genotyping via the 25K method revealed 21,093 SNPs after filtration.\u003c/p\u003e\u003cp\u003e2) Genotyping-by-sequencing (GBS set): Only 103 genotypes were genotyped via GBS methods by Mourad et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A set of 11,362 SNP markers remained after marker and genotype filtration.\u003c/p\u003e\u003cp\u003eIn both methods, the markers were filtered on the basis of the criteria described by Alqudah et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as follows: heterozygous loci were excluded, followed by the exclusion001 of markers with more than 20% missing data and a minor allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;5%. Finally, genotypes with more than 20% missing data were excluded. As a result, 197 (25K set) and 103 (GBS set) genotypes with 21,093 (25K) and 11,362 (GBS) markers, respectively, were used for GWAS.\u003c/p\u003e\u003cp\u003eThe 25K and GBS sets presented a clear population structure according to Sallam et al., (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) and Mourad et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), respectively. In this study, genome-wide association analysis was performed for all traits scored in the two growing seasons and under both conditions as described in detail by Sallam et al., (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). The GWAS was performed via nine different models via the memory-efficient, visualization-enhanced, and parallel-accelerated (rMVP) package: general linear model (GLM)\u0026thinsp;+\u0026thinsp;PCA, GLM\u0026thinsp;+\u0026thinsp;kinship, GLM\u0026thinsp;+\u0026thinsp;PCA\u0026thinsp;+\u0026thinsp;Kinship, mixed linear model (MLM)\u0026thinsp;+\u0026thinsp;PCA, MLM\u0026thinsp;+\u0026thinsp;kinship, MLM\u0026thinsp;+\u0026thinsp;PCA\u0026thinsp;+\u0026thinsp;Kinship, fixed and random model circulating probability unification (FarmCPU)\u0026thinsp;+\u0026thinsp;PCA, FarmCPU\u0026thinsp;+\u0026thinsp;kinship, and FarmCPU\u0026thinsp;+\u0026thinsp;PCA\u0026thinsp;+\u0026thinsp;Kinship (Yin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The best GWAS model for each trait was determined according to the distribution of the expected and observed p-values in the quantile‒quantile plot (Q‒Q plot). Two significant thresholds; a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.001 (\u0026minus;\u0026thinsp;log10\u0026thinsp;\u0026gt;\u0026thinsp;3.00) and suggestive p-values (1/N (number of markers) (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) were used to identify significant markers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene annotation\u003c/h3\u003e\n\u003cp\u003eThe gene annotations for stable markers, which were significantly associated with the same traits under each condition in the two growing seasons, were investigated via the Ensembl Plants database for \u003cem\u003eTriticum aestivum\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plants.ensembl.org/Triticum_aestivum/Info/Index\u003c/span\u003e\u003cspan address=\"https://plants.ensembl.org/Triticum_aestivum/Info/Index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The International Wheat Genome Sequencing Consortium (IWGSC) Reference Sequence v1.0 was used to determine the physical positions of the SNPs resulting from GBS. On the other hand, the flanking sequences of the SNP markers from the 25K set were obtained from the GrainGenes database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat.pw.usda.gov/GG3/\u003c/span\u003e\u003cspan address=\"https://wheat.pw.usda.gov/GG3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The physical positions (GBS set) and flanking sequences (25K set) were then blasted against the Ensembl database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plants.ensembl.org/Triticum_aestivum/Info/Index\u003c/span\u003e\u003cspan address=\"https://plants.ensembl.org/Triticum_aestivum/Info/Index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify candidate genes and their functional annotations. Candidate genes were selected if the significant SNPs were located within them.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Soil moisture content\u003c/p\u003e\n\u003cp\u003eA single-factor analysis of the soil moisture content under both conditions is presented in Supplementary Table 2. Highly significant differences in soil moisture were observed in the two growing seasons before and after the and heading growth stages. Before harvest, the differences in soil moisture were significant in 2020/2021 and highly significant in 2021/2022. Drought stress was more severe in the first season than in the second season.\u003c/p\u003e\n\u003cp\u003e3.2 Effects of drought stress on peduncle and spike traits\u003c/p\u003e\n\u003cp\u003eOn average, drought stress has a negative impact on all traits, in both seasons (supplementary Figure 1). All the traits were reduced due to drought stress except for TKW in 2020, which was slightly greater under drought conditions than under normal conditions (0.02%). Notably, for all traits except TKW, the reduction due to drought (RDD) was greater in the first season than in the second. Among the spike traits, the greatest RDD was found for GNPS in 2020 and SPL in 2021. The reduction in chlorophyll content due to drought stress was 7.15 and 3.61 in 2020 and 2021, respectively. NSPS was the spike trait least affected by drought stress. Among the peduncle traits, PUW presented the greatest reduction in both years, with 23.7% and 20.4% in 2020 and 2021, respectively, followed by PUD and PUL. In both seasons, all spike traits were greatly reduced due to drought stress, and NSPS presented the lowest reduction, with 2.56% and 1.64% in 2020 and 2021, respectively. The plant height decreased by 16.7% and 9.5% in 2020 and 2021, respectively. A percentage (5.79% of all genotypes) flowered earlier under drought than did the control in 2020, whereas in 2021, 2.36% of all genotypes flowered earlier under drought than did the control.3\u003c/p\u003e\n\u003cp\u003e3.3 Genetic variation in the peduncle and yield traits under drought stress\u003c/p\u003e\n\u003cp\u003eThe results of the combined ANOVAs of the studied traits are presented in Table (1). There were highly significant differences between the years in all traits except SPL and GYPS. Significant height differences were found among the genotypes between the treatments, G × Y, and G × T for all the traits. High H\u003csup\u003e2 \u003c/sup\u003eestimates were found for all the traits, ranging from 0.87 (CC) to 0.97 (HD).\u003c/p\u003e\n\u003cp\u003eThe analysis of variance for each year is presented in supplementary Tables 3 and 4. A highly significant difference was found between the two treatments in each season for all measured traits. Moreover, highly significant variation among all the genotypes for all the studied traits was observed. The ANOVA revealed high genetic variation among the genotypes for all three selection indices in each year under drought stress (Table 1). Highly significant variation in the drought indices was observed among all the genotypes. All indices presented high heritability (H\u003csup\u003e2\u003c/sup\u003e) in both years (supplementary Tables 5 and 6).\u003c/p\u003e\n\u003cp\u003eThe minimum, maximum, and mean values for all trait measurements in the two seasons for all the genotypes under normal and drought conditions are presented in Table 2. The three drought indices had lower values in 2021 than in 2020.\u003c/p\u003e\n\u003cp\u003eThe distributions of peduncle traits, as well as drought indices, for all the genotypes in the four environments are presented in Figure 2, while the density plot for the rest of the traits across the four environments is presented in supplementary Figure 2.\u003c/p\u003e\n\u003cp\u003e3.4 Phenotypic and genotypic correlations among measured traits\u003c/p\u003e\n\u003cp\u003eThe phenotypic correlations among all traits under normal and drought conditions in 2020 and 2021 are presented in Tables 3 and 4, respectively. \u003c/p\u003e\n\u003cp\u003eIn the four environments (N-2020, N-2021, D-2020, and D-2021), CC was significantly and negatively correlated with HD. Heading date was negatively correlated with PH, PUD, and PUW in the four environments and positively correlated with GYPS. PUD was positively and significantly associated with NSPS, GNPS, GYPS, and TKW in the four environments. Similarly, PUW was significantly and positively correlated with GNPS, GYPS, and TKW in the four environments. PUL did not show stable significant correlations with spike traits in each environments. Among the peduncle traits, PUW was highly significantly correlated with PUL and PUD across the four environments. PUL was significantly correlated with PUD only in 2021 under both conditions. Weak and nonstable correlations were found between SPL and peduncle traits. The genetic correlations among all traits in each environment are presented in Supplementary Tables 7 and 8. Notably, the genetic correlations among all the traits were greater than the phenotypic correlations under both conditions in the two seasons.\u003c/p\u003e\n\u003cp\u003eUnder drought stress in both growing seasons, highly significant correlations were found between the spike index (SI) and all peduncle traits in both growing seasons under drought stress. The peduncle index (PI) was also positively and significantly correlated with spike traits. The drought indices, including SI and PI, had highly significant and positive correlations with all spike traits except SPL and all peduncle traits. Moreover, highly significant correlations were found among the three indices in each year and between the two years (Figure 3a).\u003c/p\u003e\n\u003cp\u003eThe correlations between the reduction due to drought stress (RDD) in the peduncle and spike traits are presented in Table 5. In both growing seasons, RDD in PUD and PUW was positively and highly correlated with RDD in NSPS, GNPS, GYPS, and TKW. The RDD in PUL had positive and significant correlations with only GNPS and GYPS.\u003c/p\u003e\n\u003cp\u003e3.5 Identifying the best- and lowest-performing genotypes under drought\u003c/p\u003e\n\u003cp\u003eAs DI, including PI and SI, was highly significantly correlated with spike and peduncle traits under drought stress in the two growing seasons, all the genotypes were sorted on the basis of their DSI values (high values indicated high performance under drought stress). Then, the 20 highest and 20 lowest values were selected each year. In both years, a total of seven wheat genotypes were among the 20 genotypes with the highest DI values in both years (Figure 3b), whereas 10 genotypes presented the lowest DI values (Figure 3b, supplementary Table S9). Among the seven genotypes, Giza-36 was determined to be one of the highest-performing genotypes under drought stress and presented the highest yield and peduncle traits. OK91G158 was determined to be the genotype with the lowest peduncle trait and productivity. The differences in peduncle and grain traits between the five genotypes with the highest DI values and those with the lowest DI values are presented in Figure 3c. Compared with those with low peduncle traits, those with high peduncle traits presented greater spike traits.\u003c/p\u003e\n\u003cp\u003eOn the basis of the two groups of genotypes (high DI vs low DI), the differences in all traits between these two groups were tested (Figure 4). Interestingly, highly significant differences were found in all traits except SPL, CC, and PH under the four environments between the two contracting groups. Interestingly, the total-soluble carbohydrates (TSC) were analyzed in the stem peduncle in the two contracting groups based on DI. Highly significant variation was found between the two groups under drought and normal conditions (Figure 5a). Highly significant correlations were found between TSC and GYPS, TKW, and GNPS (except in D_2020) under the four environments (N_2020, D_2020, N_2021, and D_2021). The non-significant correlation was observed between TSC and NSPS (except D_2020).\u003c/p\u003e\n\u003cp\u003eGenome-wide association study (GWAS):\u003c/p\u003e\n\u003cp\u003eA GWAS was performed for all traits scored in this study under the four environments. The distributions of SNP markers generated from 25K and GBS on each chromosome and genome are presented in Figure 6a and supplementary Figure 3, respectively. The number of significant SNPs detected under drought stress was greater than that detected under normal conditions from the 25K genotyping method, whereas the opposite was true for the GBS genotyping methods. The number of significant markers detected for each trait in each environment is presented in Figure 6b. The detailed GWAS results for all traits scored under drought and normal conditions are presented in supplementary Tables 10 and 11, respectively. On the basis of the results of the QQ plot, the correct GWAS model was selected. FarmCPU+PCA+kin was the best-fitting model for most of the traits scored under the two conditions in each season (Supplementary Figure 4a-n).\u003c/p\u003e\n\u003cp\u003eGWAS revealed a total of 26 (25K) and 39 (GBS) significant markers associated with the same trait under the same environment (drought or normal) and/or across all environments (supplementary Table 12). Most of these markers were located on chromosome 3B. A total of 32 and 34 stable markers were found to be associated with the same trait under normal and drought conditions, respectively. Interestingly, six markers were found to be associated with the same trait in the four environments: GYPS (two), PUW (one), NSPS (two), and TKW (one) (supplementary Table S13). The targ\u003cstrong\u003eet al\u003c/strong\u003eleles of these six markers had the same effect on the trait in all environments under both conditions. For example, allele A of AX-95233557 was associated with increased GYPS in all environments in both growing seasons, whereas the C allele of S1A_61088948 was associated with increased PUW in all environments in both years. To confirm the stability of the allele effect of the significant markers associated with the same trait under each condition and across all environments, the correlation of allele effects in the two years was calculated (Figure 7). The targ\u003cstrong\u003eet al\u003c/strong\u003elele effects of the stable markers associated with the same trait in 2020 were highly and significantly correlated with the effects of the same allele in 2021 under normal (r=0.94**) and drought stress (0.95**) conditions.\u003c/p\u003e\n\u003cp\u003eNotably, the common significant markers associated with the same traits were also associated with other yield traits. For example, the AX-111638065 marker was found to be associated with HD under drought stress in both growing seasons: HD under N-2021, PUW (D-2021), PUD (D-2020), and GYPS (D-2021).\u003c/p\u003e\n\u003cp\u003eNone of the significantly stable markers were shared between spike traits and peduncle traits. Stable markers for the selection of the three selection indices (PI, SI, and DSI), which were calculated under only drought stress in each year, were investigated (supplementary Table S12). One SNP marker (S1A_12369432) was associated with the SI in both years under drought stress, whereas two stable markers (S1A_61088948 and S3B_819948692) were associated with the PI under drought stress in both years. However, no shared stable markers were found between the PI and the SI.\u003c/p\u003e\n\u003cp\u003eThe gene annotations of the stable significant markers were investigated (supplementary Table S12). Among the 65 SNPs, 27 were located within 30 gene models, encoding 22 functional proteins and five hypothetical proteins. Among these 22 functional proteins, 11 strongly correlated with drought tolerance in wheat.\u003c/p\u003e\n\u003cp\u003eNotably, some of the significant markers identified in this study were previously reported in earlier studies under drought stress conditions in wheat (supplementary Table S15). CAP8_rep_c4857_90, which was associated with the NSPS in this study, was found to be associated with awn length, the harvest index, and leaf area under drought stress according to (Qaseem \u003cstrong\u003eet al\u003c/strong\u003e., 2018).\u003c/p\u003e\n\u003cp\u003eThe targ\u003cstrong\u003eet al\u003c/strong\u003elele of each significant marker was determined in each selected genotype having high DI values (supplementary Table 16). WAS_026 (Omara-007) had the highest number of targ\u003cstrong\u003eet al\u003c/strong\u003eleles (368) detected all four environments, while WAS_141 (PI525241) had the lowest number of targ\u003cstrong\u003eet al\u003c/strong\u003eleles (281)\u003c/p\u003e\n\u003cp\u003eSNP signals differentiating the tolerant and susceptible genotypes\u003c/p\u003e\n\u003cp\u003eThe seven drought-tolerant and ten drought-susceptible genotypes, on the basis of DSI, were used to determine whether there were distinct SNPs between these two groups. The 25K set was used for this purpose, as not all genotypes were genotyped via the GBS method. Among the 21,093 SNP markers (25K), only three distinct SNP markers clearly (0.01%) differentiated the tolerant and susceptible genotypes (Figure 8). Two of these markers were located on the 6D chromosome, whereas the other marker was located on the 2A chromosome. Single-marker analysis was performed between the three markers and the SI, PI, and DI using all the genotypes (198). The analysis revealed that the three markers were highly and significantly associated with all three indices in both years (supplementary Table 14). The markers were located within three different gene models that encode three different proteins. An investigation of the relationship between the functional protein of each gene and the corresponding trait revealed that Kukri_rep_c111032_99 (6D) was the most important marker that was significantly associated with the corresponding trait. This marker was found to be located within TraesCS6D02G401500, which encodes a neurolysin/thimet oligopeptidase with an N-terminus. The gene was highly expressed in the peduncle and spike traits of wheat during the development stages (https://bar.utoronto.ca/eplant_wheat/) (Figure 7). The biological process of this gene involves auxin transportation and auxin signals in wheat stems. Although the other two SNPs were also highly associated with the three indices, their gene and functional proteins did not provide evidence of an association with the spike or peduncle. Notably, the Excalibur_c7546_1286 and Kukri_rep_c111032_99 markers were also detected by GWAS, with significant associations with DI in 2020 (supplementary Table 10).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eGenetic variation in spikes and peduncles\u003c/h2\u003e\u003cp\u003eDrought stress significantly reduced all the studied traits in both seasons. The reduction due to drought stress in all the studied traits in the first season was greater than the reduction in all the traits in the second season. Peduncle weight was the trait most affected by drought stress, with reduced values of 23.7% and 20.4% in the D-2020 and D-2021 seasons, respectively. On the other hand, drought stress had little effect on NSPS compared with all the other traits scored in the two years under drought stress. All the traits followed a normal distribution across the four environments. The analysis of variance revealed high genetic variation among all the genotypes for all the traits. This highly significant genetic variation made the detection of novel allele variants possible via GWAS. Moreover, the population included highly diverse wheat genotypes originating from 36 different countries, and high genetic variation in all the traits was expected. The same population was successfully used earlier to identify genes and markers associated with fungal disease resistance (Esmail et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), heavy metal tolerance (Mourad et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mourad et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), salinity stress tolerance (Hasseb et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and drought stress at the seedling stage (Sallam \u003cb\u003eet al\u003c/b\u003e., 2024). Significant differences in traits were found between the two treatments, indicating that drought stress was applied successfully. This can also be observed from differences in the soil moisture analyzed before anthesis and at or near maturity in both growing seasons. It was a successful treatment and that it accurately represented terminal drought. The broad-sense heritability was high for all the traits, indicating that selection for promising high-drought-tolerant wheat genotypes under drought stress is feasible. The significant differences in the genotype \u0026times; treatment interaction indicated that the genotypes responded differently under drought and normal conditions, as would be expected.\u003c/p\u003e\u003cp\u003eThe phenotypic correlation between spike and peduncle traits provides very valuable and novel information on the critical role of the peduncle in supporting grain weight under drought stress. Among the peduncle traits scored in this study, PUW and PUD had highly significant and positive correlations with GNPS, GYPS, and TKW under normal and drought conditions in both growing seasons (four environments). In contrast, PUL exhibited weak or nonsignificant correlations with GNPS, GYPS, and TKW in the four environments. This clearly highlights the importance of peduncle weight and diameter, rather than length, in increasing grain weight and number under both conditions. The higher density of stomata in the peduncle may play a significant role in improving photosynthetic efficiency by increasing the surface area for gas exchange, regulating water loss through transpiration, and supporting grain filling in the late stages (Kong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, on the basis of our results of this study, a wider peduncle may indicate greater vascular capacity or greater stem reserves, which could support enhanced photosynthesis and nutrient accumulation and assimilate transportation or remobilization to the grain during the grain-filling stage. The advantages of having a wider peduncle can be extended by improving water transportation and minimizing water loss through stomatal density under drought stress. Kong et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) analyzed the anatomical traits of wheat peduncles during grain development in Jimai 22 (a winter wheat genotype). They reported that the peduncle has important anatomical, ultrastructural, and physiological characteristics compared with the flag leaf and that these important advantages play a critical role in grain filling. These characteristics regulate phosphoenolpyruvate carboxylase, which is important for controlling carbon assimilation activity and supplying substrates for carbohydrate synthesis during grain filling in the late growth stage. This can be noted the analysis of TSC in the selected genotypes with high and low DI. Genotypes with distinct peduncle characteristics showed higher levels of TSC compared to those with lower TSC. The strong, significant, and positive correlation between TSC and spike traits\u0026mdash;especially GYPS, GNPS, and TKW (Fig.\u0026nbsp;5b)\u0026mdash;further confirms the role of peduncle characteristics in supporting spike development under both normal and drought conditions. Therefore, a thicker peduncle can assimilate more TSC, which is subsequently transferred to the spike during the grain filling stage (Fig.\u0026nbsp;5c).\u003c/p\u003e\u003cp\u003eNotably, in our study, only PUW and PUD were found to have stable and significant positive correlations with HD across the four environments, indicating that earlier flowering genotypes generally were higher in PUD and PUW. In addition, Kong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e described the role of stomatal density in peduncles in increasing photosynthesis efficiency and water use efficiency. Under drought stress, photosynthesis can be impaired due to stomatal closure; therefore, genotypes with high PUD and PUW may overcome this problem because of their high stomatal density compared with those with low PUD and PUW. This conclusion can be observed from this study, as the correlation between HD and both PUW and PUD under drought stress was greater than that under normal conditions in both years. These results further support the importance of PUW and PUD in improving yield traits over PUL.\u003c/p\u003e\u003cp\u003ePUL has been reported to play a role in and be associated with plant height, wheat pathogen resistance, and lodging resistance (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, highly positive and significant stable correlations were found between PUL and PH in all four environments. Wang et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied the variation in PUL under normal and drought conditions in a set of 282 wheat genotypes. However, the associations between peduncle length and yield traits were not reported or investigated in these studies. In a rain-fed environment, Rahimi et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) did not find any significant correlation between PUL and yield traits (grain number per spike, grain yield, TKW) in a set of 298 Iranian genotypes. Although genetic variation in PUL has also been studied by Liu et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Wang et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), its relationship with yield traits has not been investigated, and Liu et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) said that the relationship between PUL and final-grain wheat is still unclear. All these studies are in agreement with the results reported here with respect to the correlation between PUL and yield traits. In this study, PUL was found to be negatively and significantly correlated with CC under drought stress (in both years) and normal conditions (in the 2020\u0026ndash;2021 growing season). A negative and significant correlation was found between these two traits under normal conditions by Yadav et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, different correlations between these two traits have been reported in earlier studies. The correlation between CC and PUL was found to be nonsignificant under normal conditions (Khalid et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), significant under normal conditions (Javed et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and non-significant under drought conditions (Javed et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe highly significant correlation between the reduction in peduncle traits and spike traits due to drought stress highlights the importance of the relationship between peduncle and spike traits, especially under drought stress, in both years. The greater the reduction in peduncle traits, especially PUD and PUW, was, the greater the reduction in NSPS, GNPS, and GYPS. The smaller the reduction in peduncle traits, especially PUD and PUW, was, the smaller the reduction in NSPS, GNPS, and GYPS. Notably, there was no stable significant correlation between either PUW or PUD and the NSPS. Moreover, no promising significant correlation was found between a reduction in peduncle traits (PUL, PUW, and PUD) due to drought stress and a reduction in TKW. This difference could be attributed mainly to the differences in the genotypes' responses. Notably, the genotypes with high PUW and PUD presented either a small reduction in TKW or a slight increase in TKW. Notably, the reduction in PUL due to drought stress was significantly and positively correlated with GNPS and GYPS. This result may shed light on the importance of how much the length of the peduncle is reduced rather than the absolute peduncle length under specific conditions. A greater reduction in PUL may lead to a significant decrease in starch, vascular bundles, and carbohydrates stored in the stem, hence reducing grain weight. Moreover, this may also explain the non-stable correlation found between PUL and other yield traits. Therefore, calculating the reduction in each genotype due to drought stress for the respective traits also provides valuable information on the relationships among these traits. Hence, the ideal genotype may exhibit a small reduction in PUW, PUD, and PUL under drought stress, as it is expected that the same genotype will show a small reduction in yield traits, thereby increasing the production of the final yield. These stable and consistent phenotypic correlations are important for providing new insights into the role of peduncle characteristics, for the first time, in enhancing key yield traits under normal and drought conditions in both growing seasons.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSelection of promising wheat genotypes under drought stress\u003c/h2\u003e\u003cp\u003eTo select the most promising high-yielding genotypes under drought stress, three selection indices were created: SI (including spike traits), PI (including peduncle traits), and DI (including SI and PI). The selection index, which includes many traits, is better than single-trait selection and integrates genetic and economic considerations into a single framework(Baker, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the complexity of the calculations and efforts required to estimate the three indices, such indices provide a potent tool for discriminating genotypes with high spike and peduncle traits under drought stress (Sallam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The selection index was also used to improve drought tolerance at the seedling stage in the same population byAhmed et al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) .The correlations between PI and spike traits and between SI and peduncle traits, as well as the strong significant correlations between DI and both spike and peduncle traits, confirmed the strong relationships between peduncle and spike traits under drought stress. The highly significant correlation between DI in 2020 and 2021 (r\u0026thinsp;=\u0026thinsp;0.92**) made the selection feasible. On the basis of the DI, the most promising high-performance (high spike and peduncle traits) genotypes were selected each year, resulting in seven genotypes that presented high spike and peduncle traits in both growing seasons under drought stress. Out of the seven genotypes, Sohag-5 was previously reported as a drought-tolerant genotype at the seedling stage (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To further investigate this relationship (spike and peduncle), the lowest-performing genotypes were also selected (ten genotypes). The comparison of yield between the two groups of genotypes evaluated in this study under both conditions and across the two years revealed consistent and highly significant differences in all traits except PH, SPL, and CC. The high-performing genotypes (high DI values) presented very high PUL, PUW, PUD, GNSP, GYPS, and TKW values compared with the yield traits of the ten lowest-performing genotypes (low DI values). Moreover, the seven highly selected genotypes flowered earlier than those in the other groups did. These results confirmed the correlation between peduncle and spike traits found across all 198 genotypes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenetic analyses for spike and peduncle traits\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide association study\u003c/h2\u003e\u003cp\u003eIn this study, SNP markers produced via two different genotyping methods were used for GWAS. The SNP array (25K) provides high accuracy, known marker positions, and reliable data for a fixed set of SNPs. More importantly, the majority of these SNPs fall within gene models, making the SNP array method ideal for gene identification, target analyses, or comparisons across studies (Geethanjali et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The GBS method, on the other hand, offers high marker density and the ability to discover novel SNPs and/or genomic regions not covered by the SNP array, providing additional insights into genetic diversity and greater detection of important genes associated with target traits. Together, both methods can enhance the resolution and robustness of GWASs. According to marker and genotype filtration, two sets were produced, namely, the 25K and GBS sets, which included 198 and 103 genotypes, respectively. It has been reported that 100\u0026ndash;500 individuals are needed for performing GWASs (Alqudah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The sets (25K and GBS) were individually used to identify alleles and genes associated with salinity stress tolerance, alkaline-saline tolerance, heavy metal tolerance, fungal disease resistance, and drought tolerance at the seedling stage via GWAS (Esmail et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe GWAS in this study revealed a total of 2,243 significant SNPs associated with yield traits under normal and drought conditions in both growing seasons (four environments). The total number of SNPs varied by environment. The environment has a significant effect on SNP detection in GWASs, especially when analyzed traits are controlled by many genes related to drought tolerance, spikes, and peduncle traits (Eltaher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The marker‒trait associations were detected at a P value\u0026thinsp;\u0026gt;\u0026thinsp;0.001, which is widely used in GWAS. Using stringent p-values, such as those derived from the Bonferroni correction or false discovery rate, may lead to the loss of important markers/genes with minor effects. A p-value of 0.001 in GWAS serves as a relaxed yet reasonable threshold for identifying potential candidate associations. However, functional validation remains critical to confirm the true association. For example, Sallam \u003cb\u003eet al\u003c/b\u003e., (2024) validated one important SNP marker associated with leaf wilting at a threshold of p\u0026thinsp;\u0026gt;\u0026thinsp;0.001 in the spring and winter populations at the seedling stage (Eltaher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, a SNP marker associated with recovery after drought stress was found in the winter association set at a p value of \u0026lt;\u0026thinsp;0.001 and in the winter biparental population. As all the traits scored in this study are polygenic traits, identifying markers with major and minor effects is important for revealing the genetic control of peduncle and spike traits under both conditions. Interestingly, five significant markers that were detected at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 were previously reported in different spring and winter wheat genetic backgrounds, with their associations with yield traits in wheat under drought stress (supplementary Table S14).\u003c/p\u003e\u003cp\u003eIn this study, stable significant markers that were found to be associated with the same trait under the same conditions in the two growing seasons as well as with the same trait under the four environments were prioritized for further analysis. The significant marker can be considered a validated marker if its effects remain significant across years, locations, or different populations (Sallam et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Most of these stable markers were also found to be associated with other traits, indicating that these markers also have pleiotropic effects, indicating that these markers influence multiple traits simultaneously and would be very useful for marker-assisted selection after validation in different genetic backgrounds (Hashem et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the effects of the stable markers on the trait, under normal or drought conditions or both conditions in the two years, were also consistent. This was observed from the high correlation found in the allele effects between the two years under normal (Fig.\u0026nbsp;6a) and drought stress (Fig.\u0026nbsp;6b). For example, the target C allele of the S1A_61088948 SNP marker was found to increase the PUW under both conditions in the two seasons. In both years, the C allele of S6A_538493174 had the same allele effect on TKW, as it increased the traits by ~\u0026thinsp;3.0 g under normal conditions, whereas it increased TKW by an average of 2.6 g under drought conditions. Such markers with stable and consistent effects on traits could be valuable markers for marker-assisted selection to accelerate the genetic improvement of grain yield per se and resilience to drought in molecular breeding programs for wheat. The stable allele effects indicate that these alleles can contribute positively to yield components. More importantly, they present minimal interaction with environmental factors, confirming their reliable performance across years and environments.\u003c/p\u003e\u003cp\u003ePUW and PUD were significantly associated with GYPS, TKW, and GNPS and stable shared markers were found between them. After the SI and PI were created, no markers were common between the two indices. Only one common stable marker was associated with DI, including PI and SI, in both growing seasons under drought stress. The use of the selection index in GWAS may help detect markers and genes for a group of traits when it is difficult to discover any marker for individual traits. This result indicated that although PUW and PUD enhanced yield traits under both conditions, it seems that spike and peduncle traits are controlled by different genetic mechanisms.\u003c/p\u003e\u003cp\u003eInterestingly, the results of GWAS were utilized to identify the number of targ\u003cb\u003eet al\u003c/b\u003eleles of each significant SNP in the seven selected genotypes. WAS_020 (Omara-007) possessed the highest number of targ\u003cb\u003eet al\u003c/b\u003eleles, with 368 alleles. The same genotype was found to possess nine drought tolerant genes (BIN1, NIM1, RHD2, OAT, OBF5, PEPR1, EDSI, DREB1-D, and DREB1-D2) (Sallam et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). This result indicated that this genotype could be an important source of drought tolerance in wheat for future breeding programs to produce cultivars having high tolerance to drought stress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGene annotation for promising and important markers under normal and drought conditions\u003c/h2\u003e\u003cp\u003eThe gene annotation in this study was performed for the most important SNP markers, which were divided into two groups: 1. SNP markers that were previously reported in earlier studies under drought stress and 2. stable markers that were significantly associated with the same traits under the same conditions in the two growing seasons or the four environments.\u003c/p\u003e\u003cp\u003eThe BS00064935_51 marker was found to be located within the TraesCS4B02G194900 (FAD7) gene model, which encodes an N-terminal fatty acid desaturase. FAD 7, which is localized in chloroplasts, plays an important role in maintaining thylakoid membrane function, ensuring efficient photosynthesis under drought conditions. Mutation of FAD 7 reportedly results in a 15% reduction in chlorophyll content of the mutant Arabidopsis plants compared with wild-type plants (McCourt et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). These markers were found to be associated with CC under drought stress and with plant height under drought in earlier studies. There was a highly significant negative correlation between CC and PH in the four environments in this study. CAP8_rep_c4857_90 was associated with NSPS under D_2021 in this study and with three traits, namely, awn length, harvest index, and leaf area, in a spring wheat population (European genotypes) under drought stress in the study of (Qaseem et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This marker was found to be located in TraesCS7D02G377300, which encodes the vesicle transport protein Got1/SFT2-like. The relationship between this protein and drought tolerance in plants was not identified in earlier studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDistinct SNPs between the highest- and lowest-performing wheat genotypes\u003c/h2\u003e\u003cp\u003eAlthough GWAS is considered the most powerful tool for identifying linked SNPs with target traits, it has several limitations that affect the identification of important associated SNPs. GWASs do not account well for gene‒environment interactions, which affect many polygenic traits, such as drought tolerance (Eltaher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The same SNPs may have different effects under different environments, making confirming their true role difficult (Eltaher et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, identifying genotypes with extreme phenotypes for a trait of interest, along with available SNP data, may help reveal important SNPs that GWASs might miss. In this context, the extremely contrasting genotypes for DI (Fig.\u0026nbsp;3b, supplementary Table S9) and their SNP data generated from 25K were used. Only three SNPs were clearly present in the seven genotypes with the highest DI values, and these SNPs were absent in the ten genotypes with the lowest DI values. Among these markers, the Kukri_rep_c111032_99 marker was found to be within the TraesCS6D02G401500 (TaOOP) gene model, which encodes Neurolysin/Thimet oligopeptidase, N-terminal. The ortholog of this protein in rice is encoded by the OsOOP gene, which is involved in the biological processes of auxin transport and auxin signaling (KnetMiner). The transcriptomic wheat datasets (Wheat eFP Browser) confirmed the high expression of this protein (Neurolysin/Thimet oligopeptidase, N-termina) in both peduncle and spike traits, confirming the role of this gene in enhancing peduncle and spike traits together. Transcriptomic and gene expression databases such as the Wheat eFP Browser, a tool that provides spatial and temporal gene expression profiles across different wheat tissues, developmental stages, and environmental conditions, were used to confirm the results of genetic association analyses(Borrill et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Borrill et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Single-marker analysis between this marker and the three selection indices (SI, PI, and DI) confirmed the presence of highly significant differences between the two groups of genotypes carrying different alleles. This marker was detected via GWAS in the first growing season but not in the second season. This confirmed the notion that genotype‒environment interactions play a crucial role in identifying important and stable SNPs via GWAS. Additionally, phenotypic data quality and distribution can significantly affect the power of relatedness correction models in GWASs, potentially leading to the absence of SNP markers in some environments (Korte and Farlow, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The other two distinct SNPs did not show any evidence associated with peduncle or spike traits under any conditions. However, they could provide important and novel information. Further expression analysis experiments should be conducted for these three genes to validate their associations with peduncle traits before their use for marker-assisted selection. Distinct SNPs between genotypes with two different and extreme phenotypes may provide new insights into important genes and help overcome some limitations of GWAS, such as gene‒environment interactions. Further genetic experiments should be conducted to confirm these findings.\u003c/p\u003e\u003cp\u003eIn conclusion, the results of this study shed light on novel important adaptive traits to drought stress. To the best of our knowledge, this is the first study reveals the association between the diameter and weight of the peduncle and spike traits under normal and drought conditions. The results revealed that PUD and PUW play a key role in mitigating the effects of drought stress and support grain and spike traits under normal and drought conditions. PUL was not associated with spike traits or grain weight. However, selecting for high peduncle traits could be highly beneficial for improving wheat production and productivity under these conditions. Seven genotypes with high peduncle and spike traits could be used in further breeding programs to produce drought-tolerant cultivars. GWAS revealed that peduncle and spike traits are controlled by different genetic mechanisms. Distinct SNPs between genotypes with extreme and contrasting phenotypes may help overcome these limitations, particularly genotype‒environment interactions, in GWASs for identifying important SNPs associated with target traits.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to express sincere gratitude to Dr. Mona A. Dawood, Faculty of Science, Assiut University, for her valuable and insightful tips on the analysis of total soluble carbohydrates in the selected genotypes\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH conducted all the field experiments in this study and analyzed the genetic data; AAMA helped in phenotyping the data and genetic data analyses; SMI analyzed the soil samples under normal and drought conditions; PSB provided the plant material for this study, led the GBS genotyping, discussed the results, and reviewed the manuscript; AB provided the plant material for this study, led the 25K SNP array genotyping, discussed the results, and reviewed the manuscript; and AS designed the study, supervised all the field experiments and genetic association analyses, and wrote the MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SNP genotyping was funded by the University of Nebraska-Lincoln, USA, and Alexander Von Humbolt, Germany. The field and phenotyping experiments were funded by the Science and Technology Development Fund (STDF) under Project ID 39444, Egypt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll phenotypic analyses are presented in the supplementary files. The SNP datasets generated during and/or analyzed during the current study are not publicly available owing to their involvement in ongoing projects but are available from Prof. Dr. Ahmed Sallam upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe other authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eAhmed, A. A. M., Dawood, M. F. A., Elfarash, A., Mohamed, E. A., Hussein, M. Y., B\u0026ouml;rner, A., and Sallam, A.\u003c/strong\u003e (2022). 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Advance Access published August 16, 2023, doi:10.20944/PREPRINTS202308.1120.V1.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., et al.\u003c/strong\u003e (2021). rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study. \u003cem\u003eGenomics Proteomics Bioinformatics\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e:619\u0026ndash;628.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Triticum aestivum L., peduncle weight, peduncle diameter, distinct SNPs, genetic association","lastPublishedDoi":"10.21203/rs.3.rs-6983003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6983003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeduncle stem plays an essential role in resource allocation and water transport to spike grains under normal conditions. Therefore, exploring peduncle traits and their relationships with spike production under drought stress may provide insights into the mechanisms that mitigate the effects of drought stress on grain yield in wheat. To address this challenge, a panel of 198 highly homozygous and diverse spring wheat varieties was evaluated under normal and drought conditions across two growing seasons. Peduncle traits, namely, length (PUL), diameter (PUD), and weight (PUW), as well as spike traits (spike length, number of spikelets/spike, grain number/spike, and grain yield per spike, and thousand kernel weight), were assessed. We revealed that PUW and PUD, unlike PUL, were significantly and strongly associated with spike traits and grain weight under all conditions. GWAS revealed that spike and peduncle traits were controlled by different genetic mechanisms, as no stable markers were shared between these two groups. The identification of distinct SNPs between genotypes with two contrasting peduncle traits revealed a key SNP marker located within a gene model that encodes a protein highly expressed in the peduncle and spike traits of wheat. Comparing cultivars with low peduncle trait values to cultivars with high peduncle trait values, particularly PUW and PUD, high peduncle trait cultivars had greater yield-related trait values under both drought and normal conditions. This study proposes to investigate the distinct SNPs between the selected genotypes, with contrasting target traits, to address the limitations of GWASs in detecting important marker-trait associations.\u003c/p\u003e","manuscriptTitle":"Genetic and Phenotypic Associations between Peduncle Characteristics and Spike Productivity in Wheat Under Drought and Normal Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 18:22:51","doi":"10.21203/rs.3.rs-6983003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-08-31T06:36:52+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-31T06:16:50+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T04:40:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-27T03:15:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-06-26T07:25:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b5cf621b-226d-4d70-a0d6-aadcac07c36e","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:07:56+00:00","versionOfRecord":{"articleIdentity":"rs-6983003","link":"https://doi.org/10.1007/s00122-025-05140-2","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-01-13 16:31:03","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-07-30 18:22:51","video":"","vorDoi":"10.1007/s00122-025-05140-2","vorDoiUrl":"https://doi.org/10.1007/s00122-025-05140-2","workflowStages":[]},"version":"v1","identity":"rs-6983003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6983003","identity":"rs-6983003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}