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Article

Genome-Wide Linkage Mapping of Root System Architecture-Related Traits Under Drought Stress in Common Wheat (Triticum aestivum L.)

by
Yirong Jin
1,
Guiju Chen
2,
Xiaodong Qiu
3,
Fuyan Wang
1,
Hui Jin
4,
Liang Zhang
1,
Cheng Liu
5,
Jianjun Liu
5,
Wenjing Li
6,* and
Peng Liu
1,*
1
Dezhou Academy of Agricultural Sciences, Dezhou 253015, China
2
Wheat Research Institute, Jining Academy of Agricultural Sciences, Jining 272000, China
3
Department of Science and Technology of Shandong Province, Jinan 250101, China
4
Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
5
Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China
6
College of Life Science, Langfang Normal University, Langfang 065000, China
*
Authors to whom correspondence should be addressed.
Plants 2025, 14(19), 3023; https://doi.org/10.3390/plants14193023
Submission received: 16 June 2025 / Revised: 11 September 2025 / Accepted: 19 September 2025 / Published: 30 September 2025
(This article belongs to the Special Issue Recent Advances in Plant Genetics and Genomics)
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Abstract

Drought severely threatens wheat production. Under drought conditions, root system architecture (DRSA)-related traits in common wheat significantly affect wheat production. In China, Zhoumai16 is a high-yield winter wheat variety in the Huang-Huai wheat region. It is suitable for high-fertilizer and high-water cultivation and has moderate drought tolerance. DK171 is a newly developed high-yield and stress-tolerant variety, with higher drought tolerance. Thus, identifying genetic loci associated with DRSA-related traits from DK171 and developing available molecular markers are of great importance for enhancing wheat stress tolerance breeding. In this study, DRSA-related traits, including the total root dry weight (DDRW), total root length (DTRL), total root area (DTRA), and the number of root tips (DNRT) under drought stress, were assessed using the hydroponic system in Zhoumai16/DK171 recombinant inbred lines (RIL) population. A total of five quantitative trait loci (QTL) for DRSA-related traits were identified, e.g., QDDRW.daas-1BL, QDTRS.daas-4AL, QDNRT.daas-4DS, QDTRL.daas-3AL, and QDDRW.daas-5D, and explained 6.1% to 18.9% of the phenotypic variances, respectively. Among these, QDTRS.daas-4AL and QDTRL.daas-3AL were consistent with previous reports, whereas the QDDRW.daas-1BL, QDNRT.daas-4DS, and QDDRW.daas-5D are novel. The favorable alleles of QDTRS.daas-4AL and QDNRT.daas-4DS were inherited from Zhoumai16, whereas the favorable alleles for QDDRW.daas-1BL, QDTRL.daas-3AL, and QDDRW.daas-5D were contributed by DK171. Furthermore, five kompetitive allele-specific PCR (KASP) markers, Kasp_1BL_DTRS (QDDRW.daas-1BL), Kasp_3AL_DTRS (QDTRL.daas-3AL), Kasp_4A_DTRS (QDTRA.daas-4A), Kasp_5D_DDRW (QDDRW.daas-5D), and Kasp_4D_DNRT (QDNRT.daas-4D), were developed and validated in a diverse panel with 108 wheat varieties mainly from China. Additionally, eight candidate genes related to plant hormone regulation, ABC transporters, and calcium-dependent lipid-binding domain proteins were identified. This study offers new loci, candidate genes, and available KASP markers for wheat drought tolerance breeding and facilitating progress in developing drought-tolerant wheat cultivars.

1. Introduction

Common wheat is one of the most crucial staple crops for human consumption and serves as the primary food crop in arid and semi-arid regions worldwide. Drought is the most significant abiotic stress affecting wheat production, severely constraining its sustainable cultivation [1]. Agricultural water inputs are required, resulting in considerable waste of labor and water resources. Therefore, breeding drought-tolerant and water-efficient varieties is the most effective, economical, and environmentally safe approach to reduce the losses caused by drought [2,3,4].
Wheat root system morphology and physiological regulation determine its drought tolerance. Drought tolerance of wheat seedlings is crucial for plant development and production. Root length, surface area, and number of root tips are key root system architecture under drought conditions (DRSA) traits [5,6,7,8]. These characteristics determine the root spatial distribution and primary composition, significantly affecting water and nutrient uptake. Maximum root length forms the basis for water uptake, while the root tip number, root diameter, root length, and root density are closely related to root dry weight, volume, and surface area. Together, these traits determine the plant anchoring strength in the soil profile and its capacity to absorb soil solutions [9,10]. However, the evaluation of these traits is limited by environmental conditions and measurement methods, making it time-consuming and labor-intensive [7,9,10]. Therefore, identifying stable and effective loci for wheat drought tolerance, developing molecular markers for breeding, and elucidating the genetic mechanisms of DRSA-related traits will accelerate the efficient improvement of drought tolerance.
Recently, advancements in high-throughput genotyping technologies, such as re-sequencing and SNP chips [11], effective analysis tools, such as linkage mapping [12,13,14,15,16], and association mapping [17,18] have become common methods for identifying loci of complex traits [6,8,12,13,14]. Over the past two decades, over 50 loci associated with DRSA-related traits have been reported and mainly distributed on chromosomes 1A, 2B, 3A, 3B, 5B, and 6D [6,8,19,20,21]. Although some genetic loci related to root traits in wheat seedlings under drought stress have been reported, many of these loci are linked to SSR markers, which are not efficient for practical use. Additionally, some loci are influenced by complex genetic backgrounds or are linked to non-desirable agronomic traits, making them unsuitable for breeding applications. Therefore, discovering new genetic loci and developing molecular markers that are applicable in breeding holds significant importance for optimizing wheat root systems and achieving high and stable yields. Zhoumai16 (Zhou8425B/Zhou 9) is a high-yield, disease-resistant wheat variety with a significant cultivation area in the Huang-Huai wheat region of China. It is suitable for high-fertilizer and high-water cultivation and has moderate drought tolerance. DK171 (Liangxing66/Shixin828) is a newly developed high-yield and stress-tolerant variety in recent years, with strong drought resistance inherited by Shixin828. In this study, five loci for seedling-stage DRSA-related traits were identified in the Zhoumai16/DK171 recombinant inbred line (RIL) population using the wheat 90K SNP array. The main goal of this study is to uncover the genetic basis of DRSA traits and develop breeding-friendly kompetitive allele-specific PCR (KASP) markers to enhance wheat DRSA-related trait improvement.

2. Results

2.1. Phenotypic Evaluation

Zhoumai16 is a high-yielding variety suitable for high-fertility, water-rich regions, while DK171 is a wheat variety that combines high yield with water conservation. For Zhoumai16, the means of total root length (DTRL), total root surface (DTRS), number of root tips (DNRT), and dry root weight (DDRW) were 35.9 cm, 8.0 cm2, 123.6, and 0.0312 g, respectively. For DK171, these values were 47.2 cm, 8.9 cm2, 168.9, and 0.0339 g. The DTRL, DTRS, DNRT, and DDRW of DK171 were significantly higher than Zhoumai16 (p < 0.05). All four DRSA-related traits exhibited continuous and significantly wide variation across the 262 RILs (Table A1 and Figure 1 and Figure A1). The means of DTRL, DTRS, DNRT, and DDRW were 40.2 cm (range: 24.1–63.8 cm), 7.8 cm2 (range: 5.4–10.7 cm2), 106.9 (range: 24.7–213.7), and 0.0282 g (range: 0.0162–0.0413 g). The standard deviation and coefficient of variation for DTRL, DTRS, DNRT, and DDRW were 6.58 cm (16.4%), 1.06 cm2 (13.6%), 35.7 (33.4%), and 0.0041 g (14.5%). Significant correlation was observed between DDRW, DTRL, DTRA, and DNRT, with a correlation coefficient of 0.603 (p < 0.05) between DTRL and DTRS (R2 = 0.56), DTRL and DNRT (R2 = 0.31), DTRL and DRW (R2 = 0.32), DTRS and DNRT (R2 = 0.33), and DNRT and DRW (R2 = 0.30) (p < 0.05).

2.2. QTL Identification

This genetic map includes all 21 chromosomes, with red representing QTL and black lines representing backbone SNP markers.
Two QTL for DDRW were identified on chromosomes 1BL and 5DL, referred to as QDDRW.daas-1BL (wsnp_Ex_rep_c67299_65845319-Excalibur_rep_c107035_354) and QDDRW.daas-5D (BobWhite_c5176_1164-RAC875_rep_c78046_324), respectively. These QTLs explained 18.9% (additive effect: −0.002 g) and 7.5% (additive effect: −0.0007 g) of the total phenotypic variances (PVEs) (Table 1; Figure 1). QDTRS.daas-4AL for DTRS was identified on 4AL chromosome (613.6–615.2 Mb, IAAV7132-wsnp_JD_c38619_27992279) and explained 9.6% of the PVEs with additive effect −0.275 cm2. QDNRT.daas-4DS for DNRT was identified on chromosome 4DS (1.2–3.6 Mb, RAC875_rep_c76650_164-Kukri_c15720_884) and explained 8.1% of the PVEs with additive effect -7.6. QDTRL.daas-3AL for DNRT was identified on chromosome 3AL (650.4–659.4 Mb, Kukri_rep_c69970_717-Kukri_rep_c103783_1380) and explained 6.1% (additive effect: −1.272 g) of the PVEs. The favorable allele of QDTRL.daas-3AL, QDDRW.daas-1BL and QDDRW.daas-5D were contributed by DK171, whereas the favorable allele of QDTRS.daas-4AL and QDNRT.daas-4DS were contributed by Zhoumai16 (Table 1; Figure 2).

2.3. Candidate Genes Identification

Eight candidate genes were identified and involved in the biological metabolism, including the plant hormones, ABC transporter and calcium-dependent lipid-binding domain protein. Two candidate genes for QDDRW.daas-1BL were identified, e.g., TraesCS1B01G356600 and TraesCS1B01G373900, encoded the auxin-responsive protein and the ABC transporter family protein, respectively. Both TraesCS3A01G406200 and TraesCS3A01G416300 were candidate genes for QDTRL.daas-3AL and encoded the gibberellin 20 oxidase and the auxin transport protein, respectively. For QDTRS.daas-4AL, TraesCS4A01G326400 were selected as the candidate gene and encoded the ethylene-responsive transcription factor. TraesCS4D01G001900 (QDNRT.daas-4DS) encoded the calcium-dependent lipid-binding domain protein. TraesCS4D01G002400 of QDDRW.daas-5D encoded the ethylene-responsive transcription factor. TraesCS5D01G285900 identified at the genetic interval of QDDRW.daas-5D and encoded the auxin-induced in root cultures protein (Table 2 and Table A2, Figure 3). The expressions of the seven candidate genes in Zhoumai16 and DK171 were detected using the qRT-PCR. Of these, TraesCS1B01G356600, TraesCS4D01G002400, and TraesCS5D01G285900 showed no significant differences between the parents, whereas TraesCS4D01G002400 and TraesCS5D01G285900 showed more than 2.3–3.5 folds higher expression in Zhoumai16 compared to DK171; TraesCS3A01G416300, TraesCS4A01G326400, and TraesCS4D01G001900 showed more than 3.1–4.3 folds higher expression in DK171 compared to Zhoumai16 (Figure 4).

2.4. QTL Validation

All five QTLs were employed in the development of KASP markers. A total of 5 KASP markers, Kasp-1BL-DDRW (QDDRW.daas-1BL, wsnp_Ex_rep_c67299_65845319, 586.3 Mb) and Kasp-3AL-DTRL (QDTRL.daas-3AL, Kukri_rep_c69970_717, 650.4 Mb), Kasp_DTRS_4A (QDTRL.daas-4A, wsnp_Ex_c7280_12498193, 725.6 Mb), Kasp_DDRW_5D (QDDRW.daas-5D, IACX2960, 347.5 Mb) and Kasp_DNRT_4D (QDNRT.daas-4D, Kukri_rep_c68594_530, 12.7 Mb), were successfully developed. To validate the efficacy of the 5 KASP markers, a diverse panel of 108 cultivars was employed. For Kasp-1BL-DDRW, the allele (AA) account for 60.2% (mean DDRW: 0.0262 g) exhibited lower DDRW compared to the allele (GG), which account for 38.9% with mean DDRW 0.0230 g (p < 0.05). For Kasp-3AL-DTRL, the allele (AA) account for 66.7% (mean DTRL: 63.52 cm) exhibited higher DTRL compared to the allele (GG), which account for 32.4% with mean DTRL of 56.60 cm2 (p < 0.05). For Kasp_DTRS_4A, the allele (AA) account for 38.9% (mean DTRL: 5.968 cm2) exhibited lower DTRS compared to the allele (GG), which account for 55.6% with mean DTRS 6.993 cm2 (p < 0.05). For Kasp_DDRW_5D, the favorable allele (CC 10.2%, a mean DRW of 0.0297 g) showed higher DDRW than unfavorable allele (TT, 88.9%, mean DDRW of 0.0244 g) at p = 0.05 level. For Kasp_DNRT_4D, the favorable allele (CC 65.7%, mean DNRT of 97.8) showed higher DNRT than the unfavorable allele (TT, 30.6%, mean DNRT of 83.0) (p = 0.05) (Table 3, Table 4 and Table A3).
To assess the accuracy of markers in detecting root phenotypes in natural populations, we selected the median value as the threshold for each trait. Phenotype values below the median were categorized as non-superior phenotypes, while those above were classified as superior phenotypes. We calculated the consistency rate between genotypes and phenotypes to provide a reference for the detection and evaluation of wheat root systems under drought conditions. The accuracy rates of Kasp_1BL_DTRS, Kasp_3AL_DTRS, Kasp_DTRS_4A, Kasp_DDRW_5D, and Kasp_DNRT_4D in detecting superior and non-superior phenotypes for DTRS, DDRW, and DNRT were 66.0% and 75.5%; 90.6% and 92.5%; 60.4% and 73.6%; 58.5% and 79.2%; 52.8% and 66.0%, respectively.

3. Discussion

Roots are the main parts of plants that take up water and nutrients. They also provide support and stability. Wheat has a fibrous root system. As it grows after germination, it develops adventitious roots. These roots are vital for anchoring the plant and absorbing water and nutrients [2,13]. They form the genetic foundation for desirable traits like drought tolerance, salt tolerance, and resistance to falling over (lodging). However, studying the root system of mature plants is difficult. Soil conditions and farming practices easily affect it. Traditional methods for taking root samples are often destructive and complicated, making it hard to measure root traits efficiently [5,14]. Research shows that the root systems of young seedlings are different. They are strongly inherited and less affected by the environment. These young roots can indicate the shape and spread of the mature root system and are closely linked to the plant’s ability to handle stress. A good root structure is the basis for having enough root volume and surface area to function well.
To adapt to diverse environments, wheat varieties have accumulated numerous genetic variations. Genetic enhancement of crop roots has seldom been analyzed [14,15]. The identification of QTL provides an effective strategy to develop molecular markers and identify candidate genes [16]. A thorough understanding of the genetic foundation of drought root system architecture traits would aid in optimizing root systems under conditions of nutrient deficiency [2,3,5,6,7,8,12,13,14]. Several QTLs associated with root system architecture-related traits have been uncovered in wheat [17,18]. Root system architecture is a highly adaptable trait across various environments [22]; QTLs governing root system architecture under drought conditions are crucial for enhancing drought tolerance. Over 10 loci influencing the number of root tips under drought stress have been mapped on chromosomes 4A, 4B, 4D, 5B, 1D, 6D, and 7D [23]. The locus on chromosome 4D (360.3–396.8 Mb) is different from the loci identified in this study (QDNRT.daas-4DS 1.2–3.6 Mb). Thus, QDNRT.daas-4DS is a new loci. Over 10 loci for root surface area under drought stress were identified on chromosomes 1D, 2A, 2B, 2D, 3B, 4A, 5B, 5D, 7A, and 7D in common wheat [23]. Of these, the locus on 4A (565.6–598.2 Mb) is nearly adjacent to the QDTRS.daas-4AL (613.6–615.2 Mb).
In total, 12 loci for total root length under drought stress were identified on chromosomes 1A, 1D, 2A, 2D, 3A, 3B, 3D, 4B, 5B, 5D, 7A, and 7D [23,24,25]. In this study, we have identified a locus for total root length under drought stress, QDTRL.daas-3AL (650.4–659.4 Mb), which is near the loci on 3AL (632.8–646.3 Mb). Until now, eight loci for DDRW were identified on chromosomes 1B, 2A, 2D, 4A, 4B, 5A, 7A, and 7D [1,26,27,28,29]. We identified two loci for DDRW, QDDRW.daas-1BL (586.3–609.0 Mb) and QDDRW.daas-5D (378.9–393.5 Mb), which differ from the loci located on chromosome 1B (120.2–159.6 Mb) and 5D (489.6–540.3 Mb) mentioned above. Thus, both QDDRW.daas-1BL and QDDRW.daas-5D are novel.
The genes associated with plant height and vernalization may also have significant effects on root system architecture-related traits [30]. Over the past 80 years, several genetic loci associated with plant height and vernalization have been identified in common wheat, and a number of functional genes have been cloned on chromosome 1B (Rht2/Rht10 at 19.18 Mb), 4D (SVP3-4D/BM1-4D at 469.46 Mb, Vrn2-4D/ZCCT1-4D at 509.43 Mb), and 5D (TaDEP1-5D at 329.11 Mb, Rht23 at 524.96 Mb, and Vrn1-5D at 470.00 Mb) [31], and 6A (Rht24, 411.93–414.88 Mb) [32]. Based on physical positions, the loci QDTRS.daas-4AL (613.6–615.2 Mb) and QDNRT.daas-4DS (1.2–3.6 Mb), QDTRL.daas-3AL (650.4–659.4 Mb), QDDRW.daas-1BL (586.3–609.0 Mb), and QDDRW.daas-5D (378.9–393.5 Mb) are different from the reported plant height and vernalization genes.
Notably, compared with previous results and meta-analyses, QDDRW.daas-1BL, QDNRT.daas-4DS, and QDDRW.daas-5D were novel. We have presented the linkage mapping results for agronomic traits in the Zhoumai16/DK171 RIL population [15] and pinpointed several genomic regions linked to both drought root system architecture-related traits and agronomic traits. Specifically, QDDRW.daas-1BL (586.3–609.0 Mb) overlaps with a QTL cluster (556–654 Mb) influencing kernel number per spike (KNS), PH, and flag leaf width (FLW), and QDDRW.daas-5D (378.9–393.5 Mb) co-locates with a QTL cluster (277.0–491.2 Mb) related to KNS, FLW, TKW, and heading date [15]. These findings suggest that the loci associated with drought response and survival mechanisms may also serve as targets for enhancing yield potential and stability.
The limitation raised regarding the absence of control experiments in this study, which currently prevents definitive verification of whether the identified QTLs are specific to drought conditions. Although these QTLs demonstrated significant effects under drought stress, their potential functionality under non-stress conditions remains unexamined. Unfortunately, insufficient seed availability precluded the inclusion of control treatments in the present experiment. To address this gap, subsequent phenotyping of root-related traits will be conducted under optimal growing conditions. The acquired data will support two analytical approaches: direct QTL mapping under control conditions to compare with drought-induced QTLs, and mapping based on trait ratios between stress and control conditions. These analyses will help distinguish QTLs unique to drought response from those constitutive to plant growth—a distinction critical for breeding applications. While the current study provides directly relevant insights for drought tolerance breeding and genetic dissection under water-limited environments, further validation under controlled conditions will significantly enhance the biological interpretation and practical utility of these loci.
A total of eight candidate genes were identified, primarily implicated in the biological metabolism of plant hormones and calcium-dependent lipid-binding domain proteins. Among these, TraesCS4D01G001900 (QDNRT.daas-4DS) encodes a CDPK-related kinase, playing a crucial role in diverse signaling pathways for root growth and development [33,34], such as root hair growth and cell length [35]. Additionally, TraesCS1B01G373900, associated with QDDRW.daas-1BL, encodes an ABC transporter family protein essential for primary root growth and shoot development. Root development is governed by various plant hormones [36]. TraesCS4A01G326400 (QDTRS.daas-4AL) and TraesCS4D01G002400 (QDNRT.daas-4DS) encode ethylene-responsive transcription factors [37]. Ethylene plays diverse roles in growth, development, signal transduction, and cell differentiation, including root growth [8,9], and influences drought root system architecture-related traits like root hair and cluster root formation [25]. TraesCS1B01G356600 (QDDRW.daas-1BL), TraesCS4D01G002400 (QDNRT.daas-4DS), and TraesCS5D01G285900 (QDDRW.daas-5D) encode the auxin-responsive proteins, auxin transport protein, and auxin-induced protein 12 in root cultures. Auxin, a core regulator, integrates with other plant hormones to regulate root development. The biosynthesis, transport, and signaling pathways of auxin, particularly indole-3-acetic acid, are crucial for plant root development [20]. TraesCS3A01G406200 (QDTRL.daas-3AL) encodes the gibberellin 20 oxidase 2 (GA20ox), a key enzyme in gibberellin synthesis [36,37], which regulate various stages of plant growth and development, promoting seed germination, plant growth, flowering induction, and other biological functions.
In this study, the candidate genes were preliminarily screened through bioinformatic annotation and expression profiling analyses. These candidate genes currently serve only as reference targets as their biological functions remain to be experimentally validated. To systematically characterize these candidate genes, the following research pipeline were applied: (1) construction of a secondary mapping population coupled with KASP marker development for high-resolution genetic mapping; (2) comprehensive identification of target genes through integrated transcriptomic and genomic variation analyses; (3) functional validation employing both gene editing (e.g., CRISPR/Cas9) and transgenic complementation approaches. It should be emphasized that the KASP markers utilized in this study were specifically designed as genetic linkage markers rather than functional markers.
Traditional wheat breeding primarily focuses on yield and disease-related traits, with root system architecture closely linked to yield traits. Although traditional breeding has improved root system characteristics, the selection process remains lengthy and less efficient due to the challenges in field measurement of drought root system architecture-related traits [19]. Moreover, seedling root development is crucial for early wheat growth. KASP markers have been widely adopted for detecting genetic variations in wheat, enabling high-throughput genotyping. By utilizing genotype data from wheat SNP arrays for QTL mapping and genome-wide association studies, linked SNPs can be converted into KASP markers, which can then be directly applied in marker-assisted selection breeding programs. This approach facilitates the efficient identification and selection of desirable traits in wheat breeding efforts [19]. KASP markers are extensively applied in the improvement of yield, disease resistance, and quality traits in wheat. In this study, we successfully developed based on tightly linked SNP markers. The accuracy rates of Kasp_1BL_DTRS, Kasp_3AL_DTRS, Kasp_DTRS_4A, Kasp_DDRW_5D, and Kasp_DNRT_4D in detecting superior and non-superior phenotypes for DTRS, DDRW, and DNRT were 66.0% and 75.5%; 90.6% and 92.5%; 60.4% and 73.6%; 58.5% and 79.2%; 52.8% and 66.0%, respectively. Thus, these KASP markers could be used as valuable tools in MAS breeding programs. Additionally, accessions carrying more favorable alleles and exhibiting superior DRSA traits along with desirable agronomic characteristics, such as Jinmai 61, Liangxing 99, Yumai 35, Yumai 47, Liangxing 66, Bainong 64, Lumai 8, Yanzhan 4110, Zhengmai 366, Jimai 22, and Aikang 58, are recommended as parental lines for the improvement of drought root system architecture traits.

4. Materials and Methods

4.1. Plant Materials and Phenotypic Traits

Zhoumai16 is a high-yield winter wheat variety in the Huang-Huai wheat region. It is suitable for high-fertilizer and high-water cultivation and has moderate drought tolerance. DK171 is a newly developed high-yield and stress-tolerant winter wheat variety, with higher drought tolerance. This study utilized a Zhoumai16/DK171 F2:6~ RIL population to conduct hydroponic experiments under greenhouse drought stress conditions, measuring DRSA-related traits with three replicates.
The standard Hoagland nutrient solution includes: Macronutrients (mg/L): Calcium Nitrate (Ca(NO3)2·4H2O) at 945 mg/L, Potassium Nitrate (KNO3) at 607 mg/L, Ammonium Dihydrogen Phosphate (NH4H2PO4) at 115 mg/L, Magnesium Sulfate (MgSO4·7H2O) at 493 mg/L. Micronutrients (mg/L): EDTA-Iron (Fe-EDTA) at 20 mg/L, Boric Acid (H3BO3) at 2.86 mg/L (or Borax), Manganese Sulfate (MnSO4·H2O) at 2.13 mg/L, Zinc Sulfate (ZnSO4·7H2O) at 0.22 mg/L, Copper Sulfate (CuSO4·5H2O) at 0.08 mg/L, and Ammonium Molybdate ((NH4)6Mo7O24·4H2O) at 0.02 mg/L. The solution pH should be adjusted to 5.5–6.5 using acid/base before use. Prepare 1× full-strength Hoagland solution in advance and adjust its pH to 5.8–6.0. Weigh 5.0 g, 7.5 g, 10.0 g, 12.5 g, 15.0 g, 17.5 g, and 20.0 g of PEG 6000 into seven clean beakers. Add about 60 mL of the pre-warmed (50–60 °C) 1× Hoagland solution to each beaker and stir gently on a magnetic stirrer until the PEG 6000 is completely dissolved. Transfer each solution to its corresponding 100 mL volumetric flask, bring the volume to the mark with additional 1× Hoagland solution, and mix thoroughly by inverting the flasks.
The root dry weights of Zhoumai16 plants subjected to 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, and 20% PEG6000 were 0.0128 g, 0.0122 g, 0.0098 g, 0.0090 g, 0.0065 g, 0.0060 g, 0.0052 g, and 0.0026 g, respectively. A 12.5% concentration of PEG 6000 was added to the culture medium to simulate drought conditions. The methodology is as follows: 20 wheat seeds from each line were randomly selected and surface-sterilized with 10% H2O2 for 20 min, then placed in Petri dishes containing moist filter paper. When the coleoptile length reached approximately 2 cm, the seedlings were transferred to plastic trays (53 × 27 cm) with Hoagland nutrient solution supplemented with 12.5% PEG 6000 to induce osmotic stress.
These trays were then placed in a constant temperature culture room maintained at 25 °C, with 16 h light and 8 h darkness. After three weeks of growth in the greenhouse, seedling DRSA-related traits, including DDRW, DTRL, DTRA, and DNRT, were measured using the WinRHIZO software V1.0 (https://www.quantitative-plant.org/software/winrhizo, accessed on 24 June 2025) (Regent, Vancouver, BC, Canada). The specific method was as follows: thoroughly washed wheat roots were neatly arranged in a scanning tray, and scanned images were obtained using an Expression 11000XL scanner (Seiko Epson Corporation, Nagano Prefecture, Japan). The scanned images were analyzed using WinRHIZO software. Five plants were measured for each line, with three biological replicates.
This study used 108 varieties to validate the effects of KASP markers. The phenotypic values of DDRW, DTRL, DTRA, and DNRT for this validation population were also uniformly identified using the aforementioned method.

4.2. Genome Wide Linkage Mapping

The Zhoumai16/DK171 RIL population was genotyped using the wheat 90K SNP chip (CapitalBio Corporation, Beijing, China). The quality control followed: SNPs were set at missing data exceeding 20% or minor allele frequency (MAF) below 0.5. Filtered SNPs were classified into bin markers by IciMapping v4.2 [17]. Subsequently, the regression mapping algorithm in JoinMap v4.0 was used to calculate linkage distances for the obtained BIN markers and construct a higher-density linkage map. The successfully constructed linkage map has been previously reported by Wen et al. [18] and Li et al. [15]. Based on the constructed high-density SNP genetic map and the obtained root DRSA-related traits under drought, genome-wide linkage mapping was conducted by the inclusive composite interval mapping (ICIM-add) using IciMapping v4.1 [17]. The logarithm of odds (LOD) threshold for significant QTLs was determined to be 2.60 based on 1000 permutations. The physical positions were determined by IWGSC v1.0.

4.3. Identification of Candidate Genes for Drought-Related Traits

To identify the candidate genes associated with drought-related trait QTLs detected in the Zhoumai16/DK171 RIL population, high-confidence annotation genes within the LD block region surrounding each QTL peak SNP were extracted from the wheat IWGSC v1.0 [21]. Combining annotation, high-confidence genes with relevant annotated functions and differences in the coding regions between the two parents were screened as candidate genes. To investigate the expression patterns and identify candidate genes with notable transcription levels in seedlings or root tissues, we utilized the publicly accessible Triticum aestivum gene expression database [12] (http://wheat-expression.com/, accessed on 24 June 2025). After phenotyping, roots were sampled, and RNA was extracted from the root samples using the TRIzol method. cDNA was synthesized using the HiScript II cDNA Synthesis Kit. Primers for qRT-PCR were designed with Primer Premier 5.0. The reaction mixture consisted of 20 µL, including 2 µL of cDNA, 10 µL of ChamQ Universal SYBR qPCR Master Mix, and 0.4 µL of each primer. TaActin1 was used as an internal control to normalize the expression levels of different samples. The gene expression levels were analyzed using the 2−ΔΔCT method. All assays were performed with two biological replicates and three technical replicates.

4.4. KASP Marker Development and Validation

For all loci, flanking SNPs were converted to KASP markers [19], designed using PolyMarker (http://www.polymarker.info/, accessed on 24 June 2025). The 384-well plates were analyzed on a PHERAstarplus SNP, and genotyping was conducted by KlusterCaller (LGC) (https://www.lgcstandards.com/, accessed on 24 June 2025) (London, UK). All developed KASP markers required genetic effect validation using 108 varieties mainly from the Yellow and Huai Wheat Region [20]. These 108 materials primarily include the main popularized varieties, key backbone parents, and representative lines from the Yellow and Huai River Valleys Wheat Zone.

5. Conclusions

In conclusion, this study highlights the critical role of drought-related root system architecture in enhancing wheat resilience to drought. By analyzing the Zhoumai16/DK171 RIL population, five QTLs associated with DRSA-related traits were identified, including novel loci QDDRW.daas-1BL, QDNRT.daas-4DS, and QDDRW.daas-5D. These QTLs explained 6.1% to 18.9% of the phenotypic variances, with favorable alleles contributed by both Zhoumai16 and DK171. Additionally, five KASP markers were developed and validated, providing valuable tools for MAS breeding. The identification of eight candidate genes further enriches the genetic resources for wheat drought tolerance. This research advances our understanding of the genetic basis of drought resistance and offers practical tools for breeding drought-tolerant wheat cultivars.

Author Contributions

Y.J. carried out the experimental and wrote the paper. G.C., X.Q., F.W., and H.J. participated in field trials. L.Z., C.L., J.L., and W.L. contributed to data analysis. P.L. designed the experiment and assisted in writing the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the Shandong Province Wheat Industry Technology System (SDAIT-01-23); Shandong Agricultural Seeds Engineering Project (2023LZGC009, 2021LZGC013); Shandong Province Key R&D Plan (2022LZG002-4); Taishan Scholarship (tspd20221108); Jinan 20 New Universities Project (202228067).

Institutional Review Board Statement

We declare that these experiments complied with the ethical standards in China.

Data Availability Statement

All datasets generated for this study are included in the article; further inquiries can be directed to the first author.

Conflicts of Interest

The authors declare that they have no competing interests.

Appendix A

Table A1. The drought root system architecture-related traits in Zhoumai16/DK171 RIL population.
Table A1. The drought root system architecture-related traits in Zhoumai16/DK171 RIL population.
AccessionsTotal Root
Length-Drought (mm)		Total Root
Surface-Drought (cm2)		Total Root
Volume-Drought (mm3)		Dry Root
Weight-Drought (g)
LINE126.55.8201.00.0203
LINE225.86.024.70.0169
LINE324.15.435.70.0181
LINE427.66.548.30.0277
LINE535.07.645.30.0276
LINE642.58.673.30.0254
LINE735.58.794.30.0295
LINE838.16.181.00.0271
LINE935.56.9105.70.0213
LINE1050.010.379.30.0268
LINE1126.85.7108.00.0235
LINE1224.66.557.30.0176
LINE1335.98.148.70.0267
LINE1437.96.463.00.0209
LINE1560.68.570.70.0244
LINE1640.59.2136.70.0237
LINE1742.56.799.00.0279
LINE1836.27.2137.30.0248
LINE1931.96.286.00.0246
LINE20NNNNNNNN
LINE2149.18.179.30.0232
LINE2242.87.9119.70.0284
LINE2341.87.4104.70.0305
LINE2440.86.1147.00.0233
LINE2544.17.899.70.0286
LINE2640.68.184.70.0237
LINE2745.38.390.00.0226
LINE2843.610.785.70.0271
LINE2939.48.4103.30.0278
LINE3046.78.9140.00.0255
LINE31NNNNNNNN
LINE3241.98.3128.30.0273
LINE3335.27.6198.70.0310
LINE3434.57.096.70.0315
LINE3541.68.2116.70.0231
LINE3638.07.2156.70.0206
LINE3737.46.984.00.0342
LINE3840.510.499.00.0235
LINE3935.96.0129.30.0298
LINE4039.38.282.30.0346
LINE4135.08.687.00.0272
LINE4240.78.391.00.0259
LINE4337.07.4163.00.0246
LINE4447.59.0105.00.0285
LINE45 6.8103.70.0297
LINE4640.47.7124.70.0292
LINE4747.37.9101.70.0313
LINE4835.99.9134.30.0331
LINE4935.06.088.00.0238
LINE5048.59.8114.00.0284
LINE5136.18.4117.00.0273
LINE5252.87.679.00.0296
LINE5336.27.8114.00.0304
LINE5456.610.372.00.0298
LINE5542.58.1176.30.0285
LINE5641.98.2128.00.0261
LINE5735.77.1113.30.0275
LINE5836.96.996.00.0303
LINE5948.07.5126.00.0332
LINE6032.27.8116.30.0278
LINE6147.58.677.30.0266
LINE6240.38.1129.00.0245
LINE6337.17.199.00.0267
LINE6444.57.595.70.0299
LINE6547.86.9139.00.0310
LINE6640.17.0117.70.0364
LINE6741.29.2122.30.0295
LINE6842.87.7141.00.0288
LINE6939.76.2151.70.0304
LINE70NNNNNNNN
LINE7137.19.2114.70.0278
LINE7241.97.8109.00.0315
LINE7338.67.3112.70.0289
LINE7447.39.095.00.0370
LINE7542.110.6122.00.0221
LINE7637.16.8144.30.0374
LINE77NNNNNNNN
LINE7839.07.889.70.0274
LINE7939.66.9109.70.0301
LINE8043.88.5135.00.0255
LINE8143.18.4102.70.0276
LINE8245.07.1100.00.0283
LINE8340.37.8123.30.0256
LINE8430.86.882.00.0317
LINE8534.27.862.30.0278
LINE86NNNNNNNN
LINE8741.16.293.00.0282
LINE8837.39.5136.30.0236
LINE8950.69.1143.30.0286
LINE9029.86.498.30.0271
LINE9130.48.796.70.0274
LINE9245.59.3100.70.0308
LINE9338.17.0114.00.0342
LINE9444.47.9141.70.0282
LINE9546.98.7139.00.0363
LINE9638.17.4153.30.0301
LINE9727.76.899.70.0247
LINE9832.86.453.30.0245
LINE9934.98.2156.30.0309
LINE10046.18.099.70.0298
LINE10142.47.4117.30.0323
LINE10248.48.168.30.0345
LINE10350.49.2117.00.0272
LINE10447.38.6121.30.0431
LINE10540.37.7164.70.0297
LINE10645.86.080.00.0305
LINE10735.77.0145.00.0329
LINE10850.48.187.70.0296
LINE10943.28.2136.30.0287
LINE110NNNNNN0.0286
LINE11141.06.471.70.0307
LINE112NNNNNN0.0274
LINE113NNNNNN0.0284
LINE114NNNNNN0.0413
LINE115NNNNNN0.0302
LINE11654.510.397.00.0320
LINE11744.67.7109.70.0328
LINE118NNNNNN0.0256
LINE119NNNNNN0.0312
LINE12041.37.0104.30.0272
LINE12138.36.091.00.0278
LINE12250.37.677.70.0325
LINE12345.18.3165.30.0317
LINE12444.78.277.00.0306
LINE12537.87.2101.70.0328
LINE12638.26.2117.00.0227
LINE12730.75.7164.70.0206
LINE12843.87.955.00.0288
LINE12957.47.7102.70.0327
LINE13049.57.8147.70.0345
LINE13144.47.6125.30.0354
LINE13238.67.3145.30.0292
LINE13345.88.697.70.0278
LINE13444.39.067.30.0286
LINE13536.16.7117.00.0296
LINE13634.87.3126.30.0322
LINE13742.38.2143.30.0294
LINE13827.36.3151.30.0264
LINE13936.27.568.00.0308
LINE14042.17.9137.00.0299
LINE141NNNNNNNN
LINE14250.28.3204.00.0304
LINE14328.06.798.70.0162
LINE14436.17.166.30.0284
LINE14532.96.470.00.0258
LINE14638.69.383.30.0221
LINE14745.28.688.70.0280
LINE14844.68.9196.70.0278
LINE14940.58.2187.30.0326
LINE15046.69.4126.70.0308
LINE15145.47.7150.70.0287
LINE15241.38.8115.30.0255
LINE15337.46.8113.30.0251
LINE15441.07.489.30.0330
LINE15539.56.2108.00.0243
LINE15656.67.9127.30.0207
LINE15730.67.0142.00.0236
LINE15841.09.048.00.0318
LINE15949.99.9165.30.0323
LINE16046.77.4213.70.0372
LINE16146.68.787.70.0268
LINE16233.07.0115.00.0293
LINE16339.98.586.00.0242
LINE16437.17.167.70.0267
LINE16543.78.660.30.0350
LINE16644.48.6119.30.0293
LINE16763.89.2145.00.0306
LINE16841.07.6136.00.0279
LINE16939.09.0122.70.0287
LINE17035.77.7180.00.0241
LINE17152.49.059.70.0230
LINE17249.410.1172.70.0343
LINE17341.97.2142.30.0279
LINE17444.79.2101.70.0271
LINE17536.88.6121.00.0268
LINE17649.08.681.00.0346
LINE17748.78.8131.30.0304
LINE17845.59.1202.00.0301
LINE17944.18.285.70.0301
LINE18032.66.786.70.0253
LINE18133.77.853.00.0259
LINE18233.86.472.00.0230
LINE18350.89.794.00.0212
LINE18440.67.671.30.0324
LINE18540.58.9107.00.0257
LINE18641.98.2109.30.0277
LINE18745.89.396.30.0314
LINE18834.67.0138.00.0316
LINE18935.08.0114.00.0317
LINE19032.07.3120.00.0203
LINE19135.37.151.30.0314
LINE19241.67.2116.70.0311
LINE19343.97.758.00.0369
LINE19444.78.5124.00.0280
LINE19542.29.3117.30.0323
LINE19639.17.3189.30.0292
LINE197NNNNNNNN
LINE19843.57.985.70.0321
LINE19929.37.1130.00.0195
LINE20051.98.377.30.0233
LINE20139.58.565.70.0246
LINE20240.17.144.70.0212
LINE20340.38.287.70.0284
LINE20438.68.396.70.0217
LINE20539.28.565.00.0263
LINE20639.28.282.70.0229
LINE20749.07.972.70.0232
LINE20835.87.4102.00.0221
LINE209NNNNNNNN
LINE21039.17.356.70.0234
LINE21144.07.968.30.0306
LINE212NNNNNNNN
LINE21334.07.8133.70.0309
LINE21451.47.287.00.0331
LINE21546.39.497.70.0306
LINE21646.87.861.70.0338
LINE21740.29.2107.00.0297
LINE21840.58.276.30.0265
LINE21936.59.173.70.0258
LINE22041.47.467.00.0327
LINE22147.19.486.00.0304
LINE22238.99.6126.00.0276
LINE22338.56.7103.00.0285
LINE22439.27.367.30.0306
LINE22535.07.180.30.0281
LINE22637.47.882.70.0276
LINE22730.57.084.70.0182
LINE22833.57.227.70.0280
LINE22943.47.181.70.0302
LINE23035.57.774.70.0294
LINE231NN0.0NNNN
LINE23242.39.1114.70.0268
LINE23339.79.2102.00.0287
LINE234NNNNNNNN
LINE23533.27.895.70.0335
LINE23633.67.179.30.0251
LINE23741.87.368.30.0303
LINE23842.48.195.00.0284
LINE23947.08.5173.70.0278
LINE24038.18.179.30.0322
LINE24133.26.080.00.0304
LINE24227.66.0113.30.0344
LINE24333.06.1105.30.0262
LINE24436.88.3157.00.0285
LINE24540.27.3102.00.0418
LINE24632.27.4213.70.0227
LINE24733.16.1112.70.0311
LINE24829.78.285.30.0264
LINE24926.25.685.70.0235
LINE25037.77.857.00.0247
LINE25141.76.877.00.0312
LINE25228.76.9159.00.0230
LINE25335.17.579.70.0315
LINE25438.57.1150.00.0302
LINE25528.67.2126.70.0294
LINE25638.97.5108.70.0273
LINE25737.07.0155.00.0250
LINE25841.89.088.30.0348
LINE25944.18.0118.70.0262
LINE26044.19.587.30.0281
LINE26134.67.0165.70.0234
LINE26239.76.478.70.0380
Table A2. The qRT-PCR primers for the candidate genes.
Table A2. The qRT-PCR primers for the candidate genes.
Candidate Gene (IWGSC1.0)Candidate Gene (IWGSC2.1)Forward Primers (5′-3′)Reverse Primers (5′-3′)
TraesCS1B01G356600TraesCS1B03G0973300AGGCTTTCTTGAAGCCCAGAGACCAGCATCCTGTCTCCTT
TraesCS1B01G373900TraesCS1B03G1018500GCCCTCAAGGTGAAAGGTCTCTTCCAGAGCTGCAAGTCG
TraesCS3A01G406200TraesCS3A03G0950800CATGCGGTGCAACTACTACCGTGTCGCCGATGTTGATGAC
TraesCS3A01G416300TraesCS3A03G0969200CATGAGCCTCAACGAGAAGCCTCTCATGCTGGTACGACGA
TraesCS4A01G326400TraesCS4A03G0812600AGCAGAACCACCACCTACCCTGGTGTCGAACGGAAGAAC
TraesCS4D01G001900TraesCS4D03G0002800AGGAATTTGAGGCTGAAGCGGGGACGTTGGAGTCGTAGTA
TraesCS4D01G002400TraesCS4D03G0003700GACACTGGAGTGGAACTGGACTAACAGCGGTCATCAAGGC
TraesCS5D01G285900TraesCS5D03G0654200CTGAGTCTGGTAGCGTGGAGCCGTCGTTGCCCACGCCGTC
Table A3. The DRSA-related traits in the 108 wheat varieties and the genotype data for the developed KASP markers.
Table A3. The DRSA-related traits in the 108 wheat varieties and the genotype data for the developed KASP markers.
NameKasp_1BL_DDRWDDRW (g)Kasp_5D_DDRWKasp_3AL_DTRLDDRT
(mm)		Kasp_4DS_DNRTDNRTKasp_4AL_DTRSDTRS
(cm2)
Aikang 58GG0.0304CCAA74.0CC100.2GG7.7
Bainong 3217AA0.0316TTAA54.1CC18.6AA3.1
Bainong 64AA0.0358TTAA88.3TT115.2GG9.2
Bima 4GG0.0198TTGG48.9TT11.7GG2.4
Gaoyou 503AA0.0291TTAA58.6CC57.6GG5.2
Gaocheng 8901AA0.0316TTAA90.5CC123.5GG10.4
Huapei 5GG0.0235TTAA61.5CC22.7GG3.6
Huaimai 18AA0.0251TTGG64.5TC69.5GG5.9
Huaimai 20GG0.0287TTAA80.2CC48.8AA6.1
Huaimai 21GG0.0345CCGG64.0TT16.7AA3.2
Jimai 19GG0.0313CCGG86.1CC170.8AA11.4
Jimai 20AA0.0324TTGG62.9CC69.8GG6
Jimai 21GG0.0222TTAA46.8CC10.8AA2.2
Jimai 22GG0.0307CCGG59.6CC103.9AA6.3
Jinan 13AA0.0299TTAA77.9TT23AA4.1
Jinan 17GG0.0233TTAA36.8CC14.4AA2.1
Jining 16AA0.0196TTAA44.8CC10.3AA2.1
Jishi 02-1AA0.0259TTGG36.0CC8GG1.7
Jinmai 61AA0.0384TTAA66.5CC44.9GG4.8
Lankao 24GG0.0279TTGG61.6TT30.9AA4.2
Lankao 2GG0.0252TTAA69.5TT162.5AA9.4
Lankao 906AA0.0326TTAA54.9TT58.3AA5.3
Liangxing 66AA0.0361CCAA92.7CC234AA12.8
Liangxing 99GG0.0364CCGG61.1CC18.6GG3.3
Linhan 2AA0.0247TTAA79.5TT49.6GG5.8
Linkang 12AA0.0265TTAA63.9TT62AA5.6
Linmai 2GG0.0184TTGG58.6CC55.8GG5
Linmai 4AA0.0245TTAA45.1CC47.8AA4
Lumai 15AA0.0271TTGG57.9CC23.7GG3.6
Lumai 21GG0.0203TTAA54.3CC79.4GG5.8
Lumai 23AA0.0283TTAA76.1CC70.1AA6.8
Lumai 5GG0.0118CCGG60.7TT23.9AG3.6
Lumai 6AA0.024TTAA63.1CC61.7AA5.6
Lumai 7AA0.0297TTAA70.3CC68GG6.6
Lumai 8AA0.0357TTGG62.0CC24.8GG3.9
Lumai 9GG0.0184TTAA81.6CC150.8GG10.9
Luyuan 502AA0.0243TTAA51.8CC129.4GG7.1
Luohan 2AA0.023TTGG61.9TT79AA6.8
Luomai 21GG0.0271TTAA66.7CC122.5GG8.9
Neixiang 188AA0.028TTGG58.1TT85GG5.9
Neixiang 5AA0.019TTAA63.2CC77.8AA6.8
Shannong 20AA0.0232TTGG63.7TT166.8GG9.5
Shan 150AA0.028TTAA67.1TT72.2GG6.8
Shan 229AA0.0294TTAA89.5CC140GG10.5
Shan 253AA0.0202TTAA69.5CC295.4GG12.2
Shan 354AA0.022TTGG55.0CC66.9AA5.9
Shan 512AA0.0226TTAA47.3CC71AA5
Shan 715GG0.0316CCGG72.0CC80AA7
Shanmai 94AA0.0158TTAA59.7TT224.4AA11.3
Shannong 78-59AA0.0279TTGG45.2TT64.4AA5
Shanyou 225AA0.0219TTGG68.0TT90.7AA7.1
DK171GG0.0214TTAA62.3CC54.5GG5.4
Shijiazhuang 15AA0.0252TTAA65.4CC31.4AA4.3
Shixin 733GG0.0199TTGG63.0TT60.5AA5.9
Shiyou 17AA0.0246TTAA68.2CC83.2GG7.1
Sunong 6AA0.0237TTAA69.8CC199.5GG11.6
Taishan 5GG0.0288TTAA64.5TT177.3GG8.8
Wanmai 19AA0.0183TTAA62.5CC228.1GG11.3
Wanmai 29AA0.026TTAA47.6CC235.6GG8.4
Wanmai 38AA0.0208TTAA77.1CC190.9GG11.8
Wanmai 50AA0.017TTAA70.0CC293.3GG13.8
Wanmai 52GG0.0212TTAA58.6CC99GG7.1
Wanmai 53AA0.0213TTAA71.6CC140.1GG9.5
Wennong 14AA0.0211TTAA47.8CC156.2AA7.7
Wennong 5AA0.025TTAA71.3CC159.4GG10.2
Wunong 148AA0.023TTAA50.1TT23.7AA3.2
Xinnong 291AA0.0372TTAA71.3TT39GG5
Xiaoyan 22AA0.0224TTGG63.6CC128.8AA8.1
Xiaoyan 54GG0.023TTAA60.2CC102.1AA5.6
Xiaoyan 6AA0.0287TTAA46.3CC119.9AA8.6
Xiaoyan 81AA0.026TTAA75.6CC91.2AA6.9
Xinmai 19AA0.0231TTAA68.7CC108GG9
Xinmai 9GG0.0268CCGG81.0TT12.7AG2.8
Yannong 18AA0.0223TTAA61.3TT68.1GG6
Yannong 19GG0.0244TTAA64.9CC99.2GG6
Yanzhan 4110GG0.0356CCGG52.6TC54.6GG5.8
Yumai 18AA0.0268TTAA67.6CC155.8AA8.6
Yumai 21AA0.0293TTAA58.9CC42.7AA5.3
Yumai 2AA0.0224TTAA75.3CC62.4AA5.5
Yumai 34AA0.0254TTAA60.6TT168.9GG9.2
Yumai 35AA0.0363TTAA61.2TT44.9GG5.4
Yumai 47GG0.0363CCGG68.8CC76.1AG5.6
Yumai 49AA0.0194TTAA48.2TT169.7GG7.1
Yumai 50AA0.0151TTAA49.3CC9.7AA1.9
Yumai 63AA0.02TTGG37.9CC97.3AA7.2
Yumai 7AA0.0218TTAA59.1CC80.5GG6.2
Zheng 9023AA0.0227TTAA53.5CC58.9AA4.5
Zhengmai 366GG0.0318TTGG41.8CC74.1GG5.6
Zhengyin 1AA0.0237TTAA56.8TT181.6GG8.6
Zhengzhou 3AA0.0302TTAA63.4TC140.2AA9.4
Zhongmai 871AA0.0235TTGG77.7CC306.2GG10.8
Zhongmai 875AA0.0266TTAA52.3CC93.6GG7
Zhongmai 895GG0.026TTGG61.3CC148.6GG9.2
Zhongyu 5AA0.0259TTAA65.3TT94.4GG7.1
Zhou 8425BAA0.0262TTAA58.1TT68.7GG6
Zhoumai16GG0.0182TTAA57.9CC58GG5.5
Zhoumai 18AA0.0236TTAA59.1CC13.3GG2.6
Zhoumai 19AA0.0252TTGG52.4CC40.5GG3.9
Zhoumai 22AA0.0197TTGG47.5TT60.9GG4.7
Zhoumai 23GG0.0181TTGG46.7CC88.5GG5.3
Zhoumai 25AA0.0155TTGG47.8TT74.1GG4.5
Zhoumai 26GG0.0166CCGG40.6CC133.9GG8.4
Zhoumai 28AA0.0195TTAA56.7CC94.6GG5.6
Zhoumai 30GG0.015TTGG46.2TT107.8AA5.5
Zhoumai 31AA0.0162TTGG44.4CC36.8GG3.8
Zhoumai 32GG0.0178TTAA48.1CC127AA9
Zimai 12AA0.0263TTAA73.7TT50AA5.6
Zixuan 2AA0.0224TTAA67.8CC141.5GG7.9
Plants 14 03023 g0a1
Figure A1. Frequency distribution of the DRSA-related traits in the Zhoumai16/DK171 RIL population.
Figure A1. Frequency distribution of the DRSA-related traits in the Zhoumai16/DK171 RIL population.
Plants 14 03023 g0a1
Plants 14 03023 g0a2
Figure A2. The genotype of the KASP markers.
Figure A2. The genotype of the KASP markers.
Plants 14 03023 g0a2

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Plants 14 03023 g001
Figure 1. The DRSA-related traits in the Zhoumai16/DK171 RIL population. (a) Root growth of wheat seedlings under different concentrations. Root development status under different concentrations of PEG6000, with 12.5% ultimately selected as the drought condition for root phenotypic characterization (5-day). PEG-6000 was used to simulate drought stress. At concentrations of 5–10%, there was no significant reduction in germination potential or germination rate, while concentrations of 12.5–20% significantly inhibited germination. Based on the comparative analysis of relative germination rates and germination potentials at different concentrations, a concentration of 12.5% was selected for the assessment of seedling-related phenotypes. (b) Bulk phenotyping of wheat seedling root traits. Bulk evaluation of root phenotypes under 12.5% PEG6000 conditions (8-day growth status).
Figure 1. The DRSA-related traits in the Zhoumai16/DK171 RIL population. (a) Root growth of wheat seedlings under different concentrations. Root development status under different concentrations of PEG6000, with 12.5% ultimately selected as the drought condition for root phenotypic characterization (5-day). PEG-6000 was used to simulate drought stress. At concentrations of 5–10%, there was no significant reduction in germination potential or germination rate, while concentrations of 12.5–20% significantly inhibited germination. Based on the comparative analysis of relative germination rates and germination potentials at different concentrations, a concentration of 12.5% was selected for the assessment of seedling-related phenotypes. (b) Bulk phenotyping of wheat seedling root traits. Bulk evaluation of root phenotypes under 12.5% PEG6000 conditions (8-day growth status).
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Figure 2. QTL for the DRSA-related traits in the Zhoumai16/DK171 RIL population.
Figure 2. QTL for the DRSA-related traits in the Zhoumai16/DK171 RIL population.
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Figure 3. The expression patterns for the eight candidate genes associated with DRSA-related traits. The left side of the figure represents the expression patterns of candidate genes in different tissues and at various developmental stages. The data originates from wheat expression data. The specific transcriptome data can be downloaded from this website (http://wheat-expression.com/, accessed on 24 June 2025).
Figure 3. The expression patterns for the eight candidate genes associated with DRSA-related traits. The left side of the figure represents the expression patterns of candidate genes in different tissues and at various developmental stages. The data originates from wheat expression data. The specific transcriptome data can be downloaded from this website (http://wheat-expression.com/, accessed on 24 June 2025).
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Figure 4. The qRT-PCR results for the candidate genes identified in this study. Transcriptional analysis was conducted in the RIL population of Zhoumai16 and DK171, where different letters indicate significant differences at the p < 0.05 level.
Figure 4. The qRT-PCR results for the candidate genes identified in this study. Transcriptional analysis was conducted in the RIL population of Zhoumai16 and DK171, where different letters indicate significant differences at the p < 0.05 level.
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Table 1. QTL for DRSA-related traits in Zhoumai16/DK171 RIL population.
Table 1. QTL for DRSA-related traits in Zhoumai16/DK171 RIL population.
QTLChromosomeGenetic IntervalPhysical
Position
(Mb)		LODR2Add
QDDRW.daas-1BL1Bwsnp_Ex_rep_c67299_65845319~
Excalibur_rep_c107035_354		586.3–609.011.118.9−0.002
QDTRL.daas-3AL3AKukri_rep_c69970_717~Kukri_rep_c103783_1380650.4–659.43.06.1−1.27
QDTRS.daas-4AL4AIAAV7132~wsnp_JD_c38619_27992279613.6–615.23.59.60.28
QDNRT.daas-4DS4DRAC875_rep_c76650_164~Kukri_c15720_8841.2–3.63.28.17.61
QDDRW.daas-5D5DBobWhite_c5176_1164~RAC875_rep_c78046_324378.9–393.53.17.5−0.001
Table 2. The candidate genes for DRSA-related traits identified in the Zhoumai16/DK171 RIL population.
Table 2. The candidate genes for DRSA-related traits identified in the Zhoumai16/DK171 RIL population.
QTLCandidate GeneChromosomeStart (bp)Annotation
QDDRW.daas-1BLTraesCS1B01G3566001B585539303Auxin-responsive protein
QDDRW.daas-1BLTraesCS1B01G3739001B604319205ABC transporter A family protein
QDTRL.daas-3ALTraesCS3A01G4062003A650429434Gibberellin 20 oxidase 2
QDTRL.daas-3ALTraesCS3A01G4163003A658983005Auxin transport protein (BIG)
QDTRS.daas-4ALTraesCS4A01G3264004A613543523Ethylene-responsive transcription factor
QDNRT.daas-4DSTraesCS4D01G0019004D1239483Calcium-dependent lipid-binding domain protein
QDNRT.daas-4DSTraesCS4D01G0024004D1288444Ethylene-responsive transcription factor
QDDRW.daas-5DTraesCS5D01G2859005D386209788Auxin-induced in root cultures protein 12
Table 3. Effects of Kasp_4AL_DTRS, Kasp_4DS_DNRT, and Kasp_5D_DDRW on DRSA-related traits in the natural population.
Table 3. Effects of Kasp_4AL_DTRS, Kasp_4DS_DNRT, and Kasp_5D_DDRW on DRSA-related traits in the natural population.
Marker NameQTLGenotype aNumber of LinesPhenotypep-Value
Kasp_1BL_DDRWQDDRW.daas-1BLAA746.80 (DTRS)0.023 *
GG335.85 (DTRS)
Kasp_3AL_DTRLQDTRL.daas-3ALAA7263.52 (DTRL)0.010 *
GG3556.60 (DTRL)
Kasp_4AL_DTRSQDTRS.daas-4ALGG606.99 (DTRS)0.021 *
AA425.97 (DTRS)
Kasp_4DS_DNRTQDNRT.daas-4DSTT3383.0 (DNRT)0.009 *
CC7197.7 (DNRT)
Kasp_5D_DDRWQDDRW.daas-5DCC110.0297 (DDRW)0.039 *
TT960.0245 (DDRW)
a The italic is the favorable allele. * Significant at p < 0.05.
Table 4. The primers of the KASP markers identified in this study.
Table 4. The primers of the KASP markers identified in this study.
Kasp NamePrimerSequence
Kasp-1BL-DDRWFAMGAAGGTGACCAAGTTCATGCTTTGAGGCGACCACCCTGA
HEXGAAGGTCGGAGTCAACGGATTTTGAGGCGACCACCCTGG
COMMONGCAGCCGTTATTCAACTTCTAA
Kasp-3AL-DTRLFAMGAAGGTGACCAAGTTCATGCTCCTTCTGGATTGATGGTTCTCA
HEXGAAGGTCGGAGTCAACGGATTCCTTCTGGATTGATGGTTCTCG
COMMONTCTGCCTCGAAGTCTTCATTT
Kasp_4AL_DTRSFAMGAAGGTGACCAAGTTCATGCTCATTGCCAAATGTTTGCTGTATT
HEXGAAGGTCGGAGTCAACGGATTCATTGCCAAATGTTTGCTGTATC
COMMONCATTATCAGATGATACCACGTCG
Kasp_4DS_DNRTFAMGAAGGTGACCAAGTTCATGCTTGAACTCGGCTGATACCAGA
HEXGAAGGTCGGAGTCAACGGATTGAACTCGGCTGATACCAGG
COMMONGGTGATGGCGAACCTAGAAAC
Kasp_5D_DDRWFAMGAAGGTGACCAAGTTCATGCTCATTGCCAAATGTTTGCTGTATT
HEXGAAGGTCGGAGTCAACGGATTCATTGCCAAATGTTTGCTGTATC
COMMONCATTATCAGATGATACCACGTCG
The underlined nucleotides are the FAM and HEX primers.
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MDPI and ACS Style

Jin, Y.; Chen, G.; Qiu, X.; Wang, F.; Jin, H.; Zhang, L.; Liu, C.; Liu, J.; Li, W.; Liu, P. Genome-Wide Linkage Mapping of Root System Architecture-Related Traits Under Drought Stress in Common Wheat (Triticum aestivum L.). Plants 2025, 14, 3023. https://doi.org/10.3390/plants14193023

AMA Style

Jin Y, Chen G, Qiu X, Wang F, Jin H, Zhang L, Liu C, Liu J, Li W, Liu P. Genome-Wide Linkage Mapping of Root System Architecture-Related Traits Under Drought Stress in Common Wheat (Triticum aestivum L.). Plants. 2025; 14(19):3023. https://doi.org/10.3390/plants14193023

Chicago/Turabian Style

Jin, Yirong, Guiju Chen, Xiaodong Qiu, Fuyan Wang, Hui Jin, Liang Zhang, Cheng Liu, Jianjun Liu, Wenjing Li, and Peng Liu. 2025. "Genome-Wide Linkage Mapping of Root System Architecture-Related Traits Under Drought Stress in Common Wheat (Triticum aestivum L.)" Plants 14, no. 19: 3023. https://doi.org/10.3390/plants14193023

APA Style

Jin, Y., Chen, G., Qiu, X., Wang, F., Jin, H., Zhang, L., Liu, C., Liu, J., Li, W., & Liu, P. (2025). Genome-Wide Linkage Mapping of Root System Architecture-Related Traits Under Drought Stress in Common Wheat (Triticum aestivum L.). Plants, 14(19), 3023. https://doi.org/10.3390/plants14193023

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