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Article

Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping

by
Xiaole Ma
1,2,
Juncheng Wang
1,2,
Hong Zhang
1,2,
Lirong Yao
1,2,
Erjing Si
1,2,
Baochun Li
2,
Yaxiong Meng
1,2 and
Huajun Wang
1,2,*
1
College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
2
State Key Laboratory of Aridland Crop Science/Gansu Key Laboratory of Crop Improvement and Germplasm Enhancement, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Plants 2025, 14(3), 490; https://doi.org/10.3390/plants14030490
Submission received: 20 January 2025 / Revised: 31 January 2025 / Accepted: 4 February 2025 / Published: 6 February 2025
(This article belongs to the Special Issue Recent Advances in the Genetics, Genomics and Breeding of Cereal Crops)
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Review Reports Versions Notes

Abstract

Common wheat production is significantly influenced by abiotic stresses. Identifying the genetic loci for seedling root traits and developing the available molecular markers are crucial for breeding high yielding and stable varieties. In this study, five wheat seedling root traits, including root length (RL), root surface area (RA), root volume (RV), number of root tips (RT), and root dry weight (RW), were measured in the Wp-072/Wp-119 recombinant inbred line (RIL) population. Genotyping was conducted for the RIL population and their parents using the wheat 90K single-nucleotide polymorphism (SNP) chip. In total, three quantitative trait loci (QTLs) for RL (QRL.gau-1DS, QRL.gau-1DL and QRL.gau-4AL), two QTLs for RA (QRA.gau-1D and QRA.gau-2DL), one locus for RV (QRV.gau-6AS), two loci for RW (QRW.gau-2DL and QRW.gau-2AS), and two loci for RT (QRT.gau-3AS and QRT.gau-6DL) were identified, with each explaining 4.5–8.4% of the phenotypic variances, respectively. Among these, QRT.gau-3AS, QRL.gau-4AL, and QRV.gau-6AS overlapped with the previous reports, whereas the other seven QTLs were novel. The favorable alleles of QRL.gau-1DS, QRL.gau-1DL, QRL.gau-4AL, QRA.gau-1D, QRW.gau-2AS, QRV.gau-6AS, QRT.gau-3AS, and QRT.gau-6DL were contributed by Wp-072, whereas the other two loci originated from Wp-119. Additionally, five kompetitive allele-specific PCR (KASP) markers, KASP-RL-1DL for RL, KASP-RA-1D and KASP-RA-2DL for RA, KASP-RW-2AS and KASP-RW-2DL for RW, were developed and validated successfully in 149 wheat accessions. Furthermore, seven candidate genes mainly for plant hormones were selected and validated by quantitative real-time PCR (qRT-PCR). This study provides new loci, new candidate genes, available KASP markers, and varieties for optimizing wheat root system architecture.

1. Introduction

Common wheat production is significantly influenced by abiotic stresses [1,2]. In recent years, climate change has intensified the effects of stress factors on wheat production, leading to yield losses of 35–50% (https://www.natesc.org.cn/, accessed on 19 January 2025). To actively address the significant losses caused by abiotic stresses such as drought, salinity, and alkalinity, it is imperative to harness modern biotechnologies to enhance crop tolerance and ensure the security of wheat production [3,4,5,6]. The root system plays a pivotal role in anchoring a plant, absorbing and transporting water and nutrients while also storing essential substances [3,5]. A well-developed root architecture serves as the genetic basis for the water-saving, salt-tolerance, and lodging-tolerance traits in wheat [7]. In the process of improving wheat landraces into cultivars, optimizing the root system has played a pivotal role. This optimization has not only significantly boosted yield and stress tolerance but has also underscored the importance of favorable root architecture [6,7,8].
Wheat is a typical fibrous-rooted crop. Adventitious roots, which form during post-embryonic development, are the core tissues responsible for plant anchorage and the absorption of water and nutrients [3,4]. The composition of adventitious roots during the seedling stage is a crucial factor in root system architecture. The development of the root system at the adult stage is easily influenced by soil conditions and cultivation practices, and conventional sampling methods are highly destructive and complex, making phenotypic investigations challenging and inefficient [5,6]. Previous studies have shown that root system heritability is high during the seedling stage, with minimal environmental influence, and can reflect the root architecture and distribution at the adult stage. This characteristic is closely related to abiotic tolerance and yield [7]. The root length, root surface area, root volume, and the number of root tips influence the spatial arrangement of the root system, significantly affecting water and nutrient uptake [9]. The formation of adventitious roots in wheat is influenced by both internal and external factors. External factors primarily include light, carbon sources, nitrogen sources, inorganic salts, metal ions, and trace elements. Among these, light (photoperiod, light quality, light intensity, and light cycle) is the most significant external factor affecting the production of adventitious roots. Plant hormones are the key internal factors influencing root development. The primary regulatory hormones are auxins (IAAs), followed by cytokinins (CTKs) and ethylene (ETH) [10,11].
Currently, major wheat-producing countries, including the United States, Canada, Australia, and the International Maize and Wheat Improvement Center (CIMMYT) have identified root system optimization as a key breeding objective [9]. However, the measurement of adventitious roots in wheat is time-consuming, labor-intensive, and often damaging to the plants, making it impractical for breeders to select for them directly in the field. Marker-assisted selection (MAS) has thus become an effective method for optimizing the root system [12,13]. Identifying the loci responsible for regulating wheat seedling root traits, cloning target genes, validating gene functions, and developing gene-specific molecular markers are the foundations of MAS breeding [6,9,14]. To improve wheat seedling root traits, it is essential to identify the significant genomic regions and develop practical molecular markers [8. Previous studies have reported that wheat seedling root traits are governed by multiple minor genes [8,15,16]. Linkage and association mapping have been widely employed to elucidate the genetic basis of complex traits in wheat, leveraging advancements in high-throughput genotyping technologies [17]. To date, numerous quantitative trait loci (QTLs) have been identified for wheat seedling root traits, primarily distributed on chromosomes 1A, 1D, 2A, 3A, 3B, 4B, 5A, 5B, 5D, 6A, and 7B [5,8,16,18,19,20,21,22,23].
Although several studies on seedling root traits at the seedling stage have been conducted, the identified genetic regions are broad, and the markers are not tightly linked, rendering them ineffective for practical breeding applications. Therefore, it is crucial to elucidate the genetic mechanisms of seedling root traits, develop tightly linked markers, and conduct gene pyramiding during breeding. In this study, we conducted linkage mapping for seedling root traits using the wheat 90K SNP array in a bi-parental recombinant inbred line (RIL) population derived from a Wp-072/Wp-119 cross. The objective of this study is to uncover the genetic basis of wheat seedling root traits, leveraging the available kompetitive allele-specific PCR (KASP) markers and the outstanding wheat accessions to improve seedling root traits.

2. Results

2.1. Phenotypic Evaluation of Wheat Seedling Root Traits

A total of five seedling root traits showed continuous and significant variations across 243 RILs (Figure 1). Wp-072 is an advanced breeding line with a higher tolerance to abiotic stress and with well-developed root systems. In contrast, Wp-119 has poor abiotic stress tolerance and weaker root development. The mean values for RL, RA, RV, RW, and RT of Wp-072 and Wp-119 were 79.5 cm and 65.2 cm, 10.6 mm2 and 7.9 mm2, 442.3 mm3 and 289.6 mm3, 0.033 g and 0.026g, 236.1 and 203.2, respectively (Figure 1). The mean values for RL, RA, RV, RW, and RT of the RIL population were 73.5 cm (range: 24.8–146.3 cm), 10.3 mm2 (4.0–17.5 mm2), 370.1 mm3 (22.0–1027.0 mm3), 0.030 g (0.015–0.046 g), and 214.6 (57.8–389.3), respectively (Table S1). The standard deviations (stds) and coefficients of variation for root length (RL), root surface area (RA), root volume (RV), root dry weight (RW), and number of root tips (RT) were 28.1 cm (38.0%), 2.7 mm2 (26.0%), 208.2 mm3 (56.0%), 0.006 g (19.0%), and 64.7 (30.0%), respectively. Significant correlations were observed between RL, RA, and RT, with correlation coefficients of 0.713 (p < 0.01) between RL and RA, 0.256 (p < 0.05) between RL and RT, and 0.273 (p < 0.05) between RL and RV. Similarly, significant correlations were observed among RA, RW, and RT, with correlation coefficients of 0.235 (p < 0.05) between RA and RW, as well as 0.252 (p < 0.05) between RA and RT. Additionally, RV was significantly associated with RT (R2 = 0.678, p < 0.01), and RW was significantly associated with RT (R2 = 0.251, p < 0.05) (Table S2).

2.2. Construction of the Linkage Map

Using the wheat 90K SNP chip, we genotyped the Wp-072/Wp-119 RIL population and constructed a high-density genetic linkage map. This linkage map contains 2243 backbone markers representing 6525 SNPs. The map length is 2924.4 cM, with an average length of 138.9 cM per chromosome and an average marker spacing of 1.30 cM. Among the 21 chromosomes, 1B, 2B, and 6B have the most markers, while 4D and 6D have the fewest markers. The B genome contains the most markers (41.0%), followed by the A genome (44.8%), while the D genome contains the fewest markers (14.2%).

2.3. QTL Detection for Seedling Root Traits

Three loci for RL were detected on chromosomes 1D and 4A. These loci are referred to as QRL.gau-1DS (7.1–7.9 Mb, flanked by Excalibur_c17152_454 and RAC875_c1471_566), QRL.gau-1DL (424.7–431.8 Mb, flanked by RAC875_c103613_441 and Excalibur_c1236_840), and QRL.gau-4AL (731.4–732.5 Mb, flanked by Kukri_c18350_151 and wsnp_Ex_rep_c70574_69491038). These loci explained 5.2% (additive effect (add): −6.1), 8.4% (add: −9.2), and 5.4% (add: −6.1) of the total PVEs, respectively (Table 1). All favorable alleles for QRL.gau-1DS, QRL.gau-1DL, and QRL.gau-4AL were contributed by Wp-072.
For RA, the following two QTLs were identified: QRA.gau-1D (294.5–302.8 Mb, flanked by BobWhite_c6770_617 and BS00026262_51) and QRA.gau-2DL (439.6–446.0 Mb, flanked by RAC875_c55313_89 and Kukri_c64788_552). These QTLs explained 8.2% (add: -0.7) and 8.4% (add: 0.7) of the total PVEs, respectively. The favorable allele of QRA.gau-1D was contributed by Wp-072, while the favorable allele of QRA.gau-2DL was contributed by Wp-119. QRV.gau-6AS was located on the genetic interval of 31.0–45.3 Mb on chromosome 6AS and flanked by BS00059454_51 and Kukri_c19883_816, explaining 5.1% (add: −58.2) of the PVEs. The favorable allele of QRV.gau-6AS was contributed by Wp-072.
The following two QTLs for RW were located on chromosomes 2A and 2D: QRW.gau-2AS (203.0–208.5 Mb, flanked by Ex_c31468_763 and tplb0055d02_624) and QRW.gau-2DL (623.3–629.2 Mb, flanked by CAP11_c4727_205 and Tdurum_contig11539_81). These QTLs explained 8.2% (add: 0.0186 g) and 8.1% (add: 0.0182 g) of the total PVEs, respectively. The favorable alleles of QRW.gau-2AS and QRW.gau-2DL were contributed by Wp-072 and Wp-119, respectively. For RT, the following two QTLs were identified: QRT.gau-3AS (50.0–70.2 Mb, flanked by BS00039489_51 and Kukri_c15325_1360) and QRT.gau-6DL (462.6–468.3 Mb, flanked by RAC875_c66820_684 and Kukri_c22718_1072). These QTLs explained 4.5% (add: −6.0) and 7.2% (add: −7.5) of the PVEs, respectively. All favorable alleles for QRT.gau-3AS and QRT.gau-6DL were contributed by Wp-072 (Table 1, Figure 2).

2.4. KASP Markers Development and Validation

Five QTLs with PVE > 8.0% were used to develop the KASP markers. Over 20 SNPs were tested for conversion into KASP markers in the Wp-072/Wp-199 RIL population. In addition, a total of 149 wheat varieties were used to validate the effectiveness of the KASP markers. Consequently, five KASP markers were successfully developed and validated in the natural population, including KASP-RL-1DL (QRL.gau-1DL, converted by RAC875_c103613_441, 431.8 Mb), KASP-RA-1D (QRA.gau-1D, corresponding to wsnp_Ex_rep_c70574_69491038, 302.8 Mb), KASP-RA-2DL (QRA.gau-2DL, originating from BobWhite_c6770_617, 439.6 Mb), KASP-RW-2AS (QRW.gau-2AS, converted by BS00039489_51, 203.0 Mb), and KASP-RW-2DL (QRW.gau-2DL, converted by Kukri_c22718_1072, 629.2 Mb) (Table 2, Figure 3).
For KASP-RL-1DL, the favorable allele (TT, 37.6%) showed a longer RL (88.6 cm) compared to the unfavorable allele (CC, 51.7%), which had a mean root length of 81.7 cm (p < 0.05) in the natural population. In the case of KASP-RA-1D, the favorable allele AA (37.6%) exhibited a higher mean RA of 12.5 mm2, as opposed to the unfavorable allele CC (56.4%) with a mean root area of 11.2 mm2 (p < 0.05) in the natural population. With KASP-RA-2DL, the favorable allele GG (35.6%, mean root area: 12.1 mm2) demonstrated a greater RA than the unfavorable allele AA (59.7%, mean root area: 10.7 mm2) (p < 0.05) in the natural population. For KASP-RW-2AS, the favorable allele AA (44.3%, mean root weight: 0.037 g) resulted in higher RW compared to the unfavorable allele GG (53.0%, mean root weight: 0.032 g), with the difference being statistically significant at the p < 0.05 level in the natural population. Similarly, for KASP-RW-2DL, the favorable allele AA (27.5%, mean root weight: 0.037 g) led to higher RW than the unfavorable allele GG (68.5%, mean root weight: 0.033 g) (p < 0.05) (Table 3 and Table S3) in the natural population.

2.5. Candidate Gene Identification

Identifying the candidate genes for important target traits is crucial for conducting further fine mapping and gene cloning. A total of nine candidate genes, selected through QTL mapping and annotation, were found to be involved in the biological metabolism of plant hormones, leucine-rich repeat receptor-like protein kinase (LRR-RLK), and zinc finger family proteins (Table 4 and Table S4). Specifically, the gene TraesCS1D01G018200 from QRL.gau-1DS encodes a zinc finger family protein. Meanwhile, TraesCS3A01G101400 from QRT.gau-3AS encodes an E3 ubiquitin protein ligase. The gene TraesCS6A01G070300 from QRV.gau-6AS codes for an F-box family protein. Both TraesCS2A01G220500 from QRW.gau-2AS and TraesCS2D01G344600 from QRA.gau-2DL are involved in encoding LRR-RLKs. Four candidate genes related to plant hormones were identified. Of these, TraesCS1D01G216500 from QRA.gau-1D corresponds to an auxin canalization protein. TraesCS1D01G336900 from QRL.gau-1DL encodes gibberellin 2-oxidase. TraesCS2D01G548900 from QRW.gau-2DL is an auxin response factor (ARF) gene. Lastly, TraesCS6D01G383600 from QRT.gau-6DL codes for an ethylene (ETH) receptor. The expression levels of the nine candidate genes in the seedling roots of Wp-072 and Wp-119 were detected using quantitative real-time PCR (qRT-PCR). Among these, TraesCS2A01G220500 and TraesCS3A01G101400 showed no significant differences between Wp-072 and Wp-119, while TraesCS1D01G018200, TraesCS1D01G216500, TraesCS1D01G336900, TraesCS2D01G344600, TraesCS2D01G548900, TraesCS6A01G070300, and TraesCS6D01G383600 exhibited a 2.3–8.0-fold higher expression in Wp-072 compared to Wp-119 (Figure 4).

3. Discussion

For a long time, wheat breeding efforts have primarily focused on above-ground traits. However, the research and improvement in seedling root traits have been significantly constrained due to the complexity of phenotypic evaluation [7]. A deeper understanding of the genetic basis underlying root system traits, coupled with the identification of novel genes and the development of KASP markers, will contribute to the enhancement of wheat root systems and lead to the high and stable yield of these crops. In this study, we identified three QTLs for root length (QRL.gau-1DS, QRL.gau-1DL, and QRL.gau-4AL), two loci for total root surface area (QRA.gau-1D and QRA.gau-2DL), a QTL for total root volume (QRV.gau-6AS), two QTLs for root dry weight (QRW.gau-2DL and QRW.gau-2AS), and two loci for number of root tips (QRT.gau-3AS and QRT.gau-6DL).

3.1. Seven Novel Loci for Wheat Root System Traits Were Identified

Although the research uncovering the genetic basis of wheat root system traits is limited, some studies have reported relevant findings. Jin et al. [14] identified a QTL on chromosome 4A for root length (QTRL.caas-4A.2, 732.6 Mb) in the Doumai/Shi4185 RIL population, which overlapped with QRL.gau-4AL (731.4–732.5 Mb) also identified in this study. Saini et al. [24] conducted a meta-analysis for wheat seedling root traits and identified six meta-QTLs on chromosome 4A. Among these, the MQTL4A.1 (651.78–705.73 Mb) for root length, root volume, and number of root tips was nearly overlapping with QRL.gau-4AL (731.4–732.5 Mb) identified in this study. Sallam et al. [25] reported 11 loci for wheat seedling root traits on chromosomes 1A, 2A, 2B, 3A, 5B, 7A, and 7B. The locus tightly linked with CAP8_c359_95 (74.4 Mb on chromosome 3A) was close to QRT.gau-3AS (60.1–70.2 Mb) identified in this study. Siddiqui et al. [26] reported 25 marker–trait associations (MTAs) for wheat seedling root traits in 200 diverse cultivars which were located on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 4B, 5A, 5D, 7A, and 7B. The locus on chromosome 3A (20.0–55.0 Mb) was similar to QRT.gau-3AS (60.1–70.2 Mb) but still different. Furthermore, Zaman et al. [27] reported 323 SNPs distributed across 20 loci after using GWAS for root system traits in 161 accessions, and they were mainly distributed on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D. The loci on chromosomes 4A (639.6 Mb), 6A (542.5 Mb), and 6D (151.2 Mb) differed from QRL.gau-4AL (731.4–732.5 Mb), QRV.gau-6AS (31.0–45.3 Mb), and QRT.gau-6DL (462.6–468.3 Mb) identified in this study, respectively.
Using the Yangmai 16/Zhongmai895 DH population, Yang et al. [28] identified 13 QTLs for seedling traits on chromosomes 2B, 3B, 4A, 4D, and 7D. Among these, the QTL on chromosome 4A (18.0–40.5 Mb) differed from QRL.gau-4AL (4A: 731.4–732.5 Mb) identified in this study, whereas the loci on 3A (45.8–75.2 Mb) partially overlapped with QRT.gau-3AS (60.0–70.0 Mb). Liu et al. [29] reported 19 QTLs for seedling root traits, primarily distributed on chromosomes 1A, 2B, 2D, 3A, 3B, 3D, 5A, and 5D. The loci on chromosomes 2D (425.6 Mb) and 3A (556.9 Mb) differed from QRA.gau-2DL (439.6–446.0 Mb), QRW.gau-2DL (623.3–629.2 Mb), and QRT.gau-3AS (60.1–70.2 Mb). Alemu et al. [22] identified 38 QTLs for seedling root traits from 192 wheat varieties using association mapping. The loci on chromosome 4A (17.0 Mb) differed from QRL.gau-4AL (731.4–732.5 Mb) identified in this study. Although several studies on wheat seedling root traits have used traditional SSR and DArT markers [16,23], meaningful comparisons cannot be made based on the existing consensus map.
Genes affecting plant height (PH) and vernalization can also influence the wheat root system establishment. Several PH genes have been reported on chromosomes 2A (Rht7 and cqTN-2D.2) [30,31], 2D (Rht8, QPht/Sl.cau-2D.1, QPht/Sl.cau-2D.2, and qRht.2D) [32,33,34], 3A (Rht27 and qRht.3A) [35], and 6A (QPh.cas-6A, Rht14, and Rht16) [36,37,38]. Except for QPh.cas-6A (29.8 Mb), which is close to QRV.gau-6AS (31.0–45.3 Mb), no overlap was identified between the loci identified in this study and the reported PH genes. Additionally, no overlap was found among the vernalization genes (VRN1, VRN2, VRN3, and VRN4), the photoperiod gene Ppd-D1, and the root system loci identified in this study. Therefore, QRL.gau-1DS, QRL.gau-1DL, QRA.gau-1D, QRA.gau-2DL, QRW.gau-2DL, QRW.gau-2AS, and QRT.gau-6DL may be novel.

3.2. Candidate Genes for Wheat Seedling Root Traits

Plant hormones are the key internal factors that influence root system development. The primary regulatory hormones are IAA [39,40], CTK, and ETH [41]. IAA affects the entire root system development. At certain concentrations, IAA can inhibit root growth, promote cell differentiation, enhance cell division in meristematic tissues, and facilitate the formation of lateral and adventitious roots [41,42]. CTK can inhibit the transition of root primordium cells from the G2 phase to the M phase, affecting the development of adventitious roots.
The over-expression of CTK oxidase genes leads to a decrease in CTK levels and an increase in the number of adventitious roots [41,43]. In rice, the regulation of root system development by CTK and IAA is antagonistic; IAA promotes the formation of lateral and adventitious roots while CTK inhibits it. The synthesis and accumulation of ETH induce the growth of adventitious root primordia and the death of outer epidermal cells, thereby facilitating the formation of adventitious roots. The coordinated regulatory model involving CTK, IAA, and ETH suggests that IAA serves as the primary signaling molecule for adventitious root production [40,44]. IAA is transported polarly to the root tip through the pericycle, and ETH diffuses into adjacent tissues, regulating IAA and CTK transport in the target area to control the division of pericycle meristematic cells. Additionally, ETH influences lateral root formation by regulating the activity of the AUX1 protein and IAA transport. In addition, abscisic acid (ABA), gibberellin (GA), jasmonic acid (JA), and salicylic acid (SA) also participate in the regulation of adventitious root formation [40,45].
Seven genes were selected as high-confidence candidate genes and initially validated by qRT-PCR. The development of lateral roots in cereals involves the following three stages: organ initiation, cortex growth, and epidermis emergence [5], all of which are regulated by various plant hormones [15,46]. TraesCS1D01G216500 of QRA.gau-1D encodes an IAA canalization protein, and TraesCS2D01G548900 of QRW.gau-2DL encodes an IAA response factor (ARF). TraesCS6D01G383600 of QRT.gau-6DL encodes an ethylene-regulated nuclear protein. TraesCS1D01G336900 of QRL.gau-1DL encodes gibberellin 2-oxidase, which is essential in the catabolic pathway of gibberellins through 2β-hydroxylation [43] and controls semi-dwarfism, tillering, and root development [39]. Plant hormones, including IAA, ET, CKs, GA, ABA, BRs, and JA, directly influence root system development through their interactions with each other [47,48]. Of these, IAA serves as a basic signaling molecule, interacting with ETH, GA, and ABA and influencing seedling root growth [41,43,48].
The candidate gene TraesCS1D01G018200 of QRL.gau-1DS encodes a zinc finger family (C2H2) protein. C2H2 family proteins are involved in primary root growth and shoot development. Chen et al. [49] reported that the over-expression of TaZAT8-5B (C2H2) enhances drought tolerance and root growth in Arabidopsis thaliana. TraesCS3A01G101400 of QRT.gau-3AS encodes an E3 ubiquitin protein ligase, which is responsible for recognizing specific substrates and transferring ubiquitin to them, playing a crucial role in the entire process of plant growth [3]. TraesCS2A01G220500 of QRW.gau-2AS and TraesCS2D01G344600 of QRA.gau-2DL both encode leucine-rich repeat receptor protein kinases (LRR-RPKs). LRR-RPKs regulate seed germination by activating ABA-responsive genes in rice [50]. TraesCS6A01G070300 of QRV.gau-6AS encodes an F-box family protein, which plays a significant role in signal transduction, cell differentiation, and stress tolerance in plants. However, no significant differences were observed between the parents of TraesCS2A01G220500 and TraesCS3A01G101400.

3.3. Application of the KASP Markers for Wheat MAS Breeding

Modern wheat breeding primarily focuses on above-ground traits, including agronomic traits such as plant height and growth duration, yield such as thousand kernel weight and grain size, and resistance to diseases such as stripe rust and leaf rust. However, underground traits such as root system architecture are also closely linked to high and stable wheat yields. Although traditional breeding has made some progress in improving wheat seedling root system traits, the selection process remains lengthy and less efficient due to the challenges and labor-intensive nature of field measurements for underground traits. The previous studies have shown that seedling root development is critical for early wheat growth and significantly associated with high yields and stability. KASP markers have been widely adopted for detecting genetic variations in wheat, enabling high-throughput genotyping, which facilitates the efficient identification and selection of desirable traits in wheat breeding efforts [10]. KASP markers have been extensively applied to enhance the yield, disease resistance, and quality traits in wheat. In this study, we successfully developed five KASP markers; KASP-RL-1DL for RL, KASP-RA-1D and KASP-RA-2DL for RA, and KASP-RW-2AS and KASP-RW-2DL for RW were developed and validated successfully in 149 varieties, demonstrating their effectiveness as valuable tools in MAS breeding programs. In addition, based on the KASP markers we have developed and the root-related phenotypic data, we selected five varieties with excellent root phenotypes and favorable alleles as references for use in the breeding combination formulation for wheat stress tolerance (Table S5).

4. Materials and Methods

4.1. Plant Materials

We evaluated seedling root traits using 243 F2:6 RILs derived from the cross between Wp-072 and Wp-119. W-072 is an advanced breeding line developed by Gansu Agricultural University, known for its tolerance to abiotic stress, high yield, and well-developed root systems. In contrast, Wp-119 has poor stress tolerance and weaker root development. A total of 40 mature seeds for each accession were sown in a plastic tray with nutrient soil (Pindstrup, Arhus, Denmark) at a depth of 13 cm, then the plastic tray was placed in a pallet with nutrient soil at a depth of 10 cm. The whole devices were then kept in a greenhouse (25 °C/65% RH and 14 h light/10 h dark) for about 21 days (tillering stage). Approximately 30 plants growing consistently for each accession were selected and surface-sterilized with 15% H2O2 for 15 min. Another 10 plants were collected for RNA extraction. Additionally, a diverse panel of 149 varieties, mainly from China, was used to validate the effectiveness of the developed KASP markers.

4.2. Phenotype Evaluation

First, the wheat roots were carefully washed with flowing deionized water, then arranged in an orderly manner in glass Petri dishes. Next, images were captured using an Expression 11000XL high-resolution scanner (Epson, Nagano-ken, Japan) and imported into the WinRHIZO root analysis system (LA6400XL). Default parameters were selected for both steps. Subsequently, a standard analytical balance was used for phenotypic assessment. The RL, RA, RV, and RT were recorded and statistically analyzed. After phenotyping with the WinRHIZO root analysis system, fresh root samples were collected and placed into kraft paper bags labeled with the corresponding identifiers, dried at 105 °C for 30 min, and then further dried at 80 °C for 24 h. The RW was recorded and statistically analyzed. In total, data were collected from 30 uniformly selected plants, and the mean values for all seedling root traits were taken as the final phenotypic values. Basic statistical analyses and frequency distributions were performed using SAS v9.3 (http://www.sas.com, accessed on 19 January 2025) and Excel 2021.

4.3. Linkage Map Construction

Using the wheat 90K SNP chip, the RIL population and parental lines were genotyped by CapitalBio, Beijing, China. After obtaining accurate genotype data, the markers were subjected to quality control based on the following criteria: (1) markers with no differences between the parents were filtered out; (2) heterozygous genotypes were treated as missing; (3) markers with a missing rate greater than 20% were filtered out; (4) markers with significant segregation distortion were excluded; and (5) low-quality SNP markers (missing rate < 0.2) were removed.
The high-quality SNP markers remaining after quality control were used to construct the genetic linkage map, following these steps: (1) Redundant marker filtering: Using the BIN function of IciMapping v4.1 [50] (http://www.isbreeding.net, accessed on 19 January 2025), the SNP markers were optimized by placing markers with identical genotypic information within the RIL population into a bin, treating them as a single genetic locus, deleting redundant markers, and selecting the marker with the lowest missing rate as the skeleton marker for mapping. (2) Marker grouping: The genotypes of the skeleton markers were imported into Joinmap v4.0 (Stam, 1993; http://www.kyazma.com, accessed on 19 January 2025) for grouping, with the LOD value ranging from 3 to 20. (3) Linkage map construction: Genotypes of markers within the same linkage group were imported into Joinmap v4.0, with the genetic distances calculated based on the Kosambi mapping function for linkage map construction. (4) Chromosome assignment: Based on the BLAST alignment results of the flanking sequences of the SNP markers with the latest released IWGSC v1.0 reference genome (http://www.wheatgenome.org/, accessed on 19 January 2025), the chromosomal positions of the markers were determined, and the linkage groups were anchored to the corresponding chromosomes.

4.4. Linkage Mapping and KASP Marker Development

Based on the genetic map of the Wp-072/Wp-119 RIL population, QTL mapping for five seedling root traits was performed using the Composite Interval Mapping (CIM) method in ICIMapping V4.1 [50]. The scanning step length was set to 1.0 cM, and after 2000 permutation runs at p ≤ 0.01, the LOD thresholds ranged from 1.8 to 2.7. To ensure the accuracy of the results, the LOD threshold was set to 2.7. The physical positions of the QTLs were derived from the IWGSC v1.0 reference genome. QTLs with higher PVEs values were used to develop KASP markers for subsequent application in wheat MAS breeding.
KASP primers consist of two competitive primers and one common primer [10]. Primer design was performed using PolyMarker (http://polymarker.tgac.ac.uk/, accessed on 19 January 2025). The PCR mix was prepared as follows: 40 μL of common primer (100 μM), 16 μL of each competitive primer (100 μM), and 60 μL of ddH2O. The reaction system included 2.5 μL of 2 × KASP master mix (LGC, Biosearch Technologies, Hoddesdon, UK), 0.07 μL of KASP primer premix, and 2.5 μL of template DNA (50 ng/μL). The PCR program consisted of pre-denaturation at 95 °C for 5 min, followed by 10 cycles of touchdown program (denaturation at 94 °C for 20 s, annealing at 65 °C for 25 s, decreasing by 0.8 °C per cycle), then 30 cycles (denaturation at 95 °C for 20 s, annealing at 57 °C for 60 s) and, finally, the PCR products were stored at 4 °C. After PCR amplification, fluorescence signals were detected using the PHERAstar Plus automatic focusing fluorescence multi-function microplate reader (BMG Labtech GmbH, Ortenberg, Germany). Genotyping was performed using the KlusterCaller software v4.1.2 (BMG Labtech GmbH, Ortenberg, Germany).

4.5. Identification of Candidate Genes for Wheat Seedling Root Traits

To identify the candidate genes for wheat seedling root traits, genes located within the linkage disequilibrium (LD) block region around the peak SNP (±5.0 Mb) of each QTL according to the IWGSC v1.0 were selected. To further validate the candidate genes identified based on QTL mapping and annotation, qRT-PCR was used to test the expression differences of candidate genes.
Twenty days after seeding (tillering stage), the root samples of Wp-072 and Wp-119 were collected for RNA extraction using the Trizol method. Subsequently, cDNA synthesis was performed with the HiScript II 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China). Primers were designed using Primer Premier V5.0. The PCR reaction system was 20 μL, including 2 μL of cDNA (50 ng/μL), 10 μL of ChamQ Universal SYBR qPCR Master Mix, and 0.4 μL of each primer (10 μM). qRT-PCR was conducted on the ABI StepOne Plus Real-Time PCR System, and the gene expression levels were analyzed using the 2−ΔΔCT method. All qRT-PCR assays for the candidate genes were designed with two biological replicates and three technical replicates. TaActin1 was used as the reference gene.

5. Conclusions

In this study, a total of 10 loci for wheat seedling root system traits were identified in the Wp-072/Wp-119 RIL population. Among these, QRL.gau-1DS, QRL.gau-1DL, QRA.gau-1D, QRA.gau-2DL, QRW.gau-2DL, QRW.gau-2AS, and QRT.gau-6DL were novel. Additionally, Kasp_4A_RL for root length, KASP-RA-1D and KASP-RA-2DL for root angle, and KASP-RW-2AS and KASP-RW-2DL for root width were validated and can be applied in wheat MAS breeding. Finally, seven candidate genes were selected and validated by qRT-PCR. This study provides new loci and candidate genes, the available KASP markers, and novel varieties for optimizing wheat root system architecture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14030490/s1. Table S1. The wheat seedling root traits of the Wp-072/Wp-119 RIL population. Table S2. The correlation (r2) for the wheat seedling root traits in the Wp-072/Wp-119 RIL population. Table S3. The wheat seedling root traits of the Wp-072/Wp-119 RIL population. Table S4. The primers used for the qRT-PCR of the candidate gene identified in Wp-072/Wp-119 RIL population. Table S5. The five best varieties with favorable allele and corresponding seedling root traits identified by the five KASP markers.

Author Contributions

Conceptualization, X.M. and H.W.; methodology, J.W.; software, H.Z.; validation, L.Y. and E.S.; formal analysis, X.M.; investigation, X.M.; resources, X.M.; data curation, X.M.; writing—original draft preparation, H.W.; writing—review and editing, B.L.; visualization, Y.M.; supervision, H.W.; project administration, X.M.; funding acquisition, X.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Gansu Provincial Science and Technology Program (24CXNA038), Gansu Provincial Wheat Industrial System (GARS-01-05), Agricultural Science and Technology Support Project of Gansu Province (KJZC-2023-2), Industrial Support Project of Colleges and Universities in Gansu Province (2021CYZC-12), Gansu Provincial Science and Technology Program Project Natural Science Foundation Key Project (24JRRA637), Gansu Provincial Science and Technology Program Project Joint Research Fund (24JRRA840), National Natural Science Foundation of China (31760386).

Data Availability Statement

All the data generated or analyzed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

RLRoot length
RARoot surface area
RVRoot volume
RTNumber of root tips
RWRoot dry weight
RILRecombinant inbred lines
SNPSingle-nucleotide polymorphism
QTLQuantitative trait loci
KASPKompetitive allele-specific PCR
MASMarker-assisted selection
PVEPhenotypic variance explained

References

  1. Rogers, E.D.; Benfey, P.N. Regulation of plant root system architecture: Implications for crop advancement. Curr. Opin. Biotech. 2015, 32, 93–98. [Google Scholar] [CrossRef] [PubMed]
  2. Li, C.; Li, L.; Reynolds, M.P.; Wang, J.; Chang, X.; Mao, X.; Jing, R. Recognizing the hidden half in wheat: Root system attributes associated with drought tolerance. J. Exp. Bot. 2021, 72, 5117–5133. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, X.; Ding, Q.; Błaszkiewicz, Z.; Sun, J.; Sun, Q.; He, R.; Li, Y. Phenotyping for the dynamics of field wheat root system architecture. Sci. Rep. 2017, 7, 37649. [Google Scholar] [CrossRef] [PubMed]
  4. Colombo, M.; Roumet, P.; Salon, C.; Jeudy, C.; Lamboeuf, M.; Lafarge, S.; Dubois, F.; Habash, D.; Liu, Y.; Nadot, S. Genetic analysis of platform-phenotyped root system architecture of bread and durum wheat in relation to agronomic traits. Front. Plant Sci. 2022, 13, 853601. [Google Scholar] [CrossRef]
  5. Maqbool, S.; Hassan, M.A.; Xia, X.; York, L.M.; Rasheed, A.; He, Z. Root system architecture in cereals: Progress, challenges and perspective. Plant J. 2022, 110, 23–42. [Google Scholar] [CrossRef]
  6. Shaheen, A.; Li, Z.; Yang, Y.; Xie, J.; Zhu, L.; Li, C. Genetic regulation of wheat plant architecture and future prospects for its improvement. New Crops 2024, 14, 100048. [Google Scholar] [CrossRef]
  7. Ma, S.C.; Wang, T.C.; Guan, X.K.; Zhang, X. Effect of sowing time and seeding rate on yield components and water use efficiency of winter wheat by regulating the growth redundancy and physiological traits of root and shoot. Field Crops Res. 2018, 221, 166–174. [Google Scholar] [CrossRef]
  8. Fang, Y.; Xu, B.C.; Turner, N.C.; Li, F.M. Grain yield, dry matter accumulation and remobilization, and root respiration in winter wheat as affected by seeding rate and root pruning. Eur. J. Agron. 2010, 33, 257–266. [Google Scholar] [CrossRef]
  9. Nagar, C.K.; Bharati, A.; Sinha, S.K.; Venkatesh, K.; Mandal, P.K. Nitrogen stress induced changes in root system architecture (RSA) in diverse wheat (T. aestivum L.) genotypes at seedling stage. J. Cereal Sci. 2018, 80, 10–16. [Google Scholar] [CrossRef]
  10. Středa, T.; Hajzlerová, J.; Alba-Mejía, J.; Jovanović, I.; Frantová, N.; Středová, H. Quo vadis, breeding for an efficient root system, in the era of climate change? Czech. J. Genet. Plant Breed. 2024, 60, 181–211. [Google Scholar] [CrossRef]
  11. Khan, M.K.; Pandey, A.; Hamurcu, M.; Vyhnánek, T.; Zargar, S.M.; Kahraman, A.; Gezgin, S. Exploring strigolactones for inducing abiotic stress tolerance in plants. Czech. J. Genet. Plant Breed. 2024, 60, 55–69. [Google Scholar] [CrossRef]
  12. Rasheed, A.; Wen, W.; Gao, F.; Zhai, S.; Jin, H.; Liu, J. Development and validation of KASP assays for genes underpinning key economic traits in bread wheat. Theor. Appl. Genet. 2016, 129, 1843–1860. [Google Scholar] [CrossRef]
  13. Liu, J.; He, Z.; Rasheed, A.; Wen, W.; Yan, J.; Zhang, P.; Tao, Y.; Li, B.; Zhang, M. Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.). BMC Plant Biol. 2017, 17, 220. [Google Scholar] [CrossRef] [PubMed]
  14. Jin, Y.; Wang, Y.; Liu, J.; Wang, F.; Qiu, X.; Liu, P. Genome-wide linkage mapping of root system architecture-related traits in common wheat (Triticum aestivum L.). Front. Plant Sci. 2023, 14, 1274392. [Google Scholar] [CrossRef] [PubMed]
  15. Ragland, D.I. The Evaluation of Root System Architecture (RSA) As a New Breeding Target for Climate-Resilient Winter Wheat (Triticum aestivum L.). Ph.D. Dissertation, Virginia Tech, Blacksburg, VA, USA, 2024. [Google Scholar]
  16. Wang, H.; Yang, B.; Zhao, X.; Chen, H.; Liu, F.; Ru, Y. Identification of novel QTL for seedling root architectural traits in the D genome of natural and resynthetic allohexaploid wheat. Agronomy 2024, 14, 608. [Google Scholar] [CrossRef]
  17. Rufo, R.; Salvi, S.; Royo, C.; Soriano, J.M. Exploring the genetic architecture of root-related traits in Mediterranean bread wheat landraces by genome-wide association analysis. Agronomy 2020, 10, 613. [Google Scholar] [CrossRef]
  18. Maccaferri, M.; El-Feki, W.; Nazemi, G.; Salvi, S.; Canè, M.A.; Colalongo, M.C. Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J. Exp. Bot. 2016, 67, 1161–1178. [Google Scholar] [CrossRef] [PubMed]
  19. Soriano, J.M.; Alvaro, F. Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis. Sci. Rep. 2019, 9, 10537. [Google Scholar] [CrossRef]
  20. Liu, P.; Jin, Y.; Liu, J.; Liu, C.; Yao, H.; Luo, F.; Gao, Y.; Guo, F.; Wanjiru, B. Genome-wide association mapping of root system architecture traits in common wheat (Triticum aestivum L.). Euphytica 2019, 215, 121. [Google Scholar] [CrossRef]
  21. Mehrabi, A.A.; Pour-Aboughadareh, A.; Mansouri, S.; Hosseini, A. Genome-wide association analysis of root system architecture features and agronomic traits in durum wheat. Mol. Breed. 2020, 40, 55. [Google Scholar] [CrossRef]
  22. Alemu, A.; Feyissa, T.; Maccaferri, M.; Sciara, G.; Tuberosa, R.; Ammar, K.; Ghedini, M.; Jordi, M.; Drinić, F. Genome-wide association analysis unveils novel QTLs for seminal root system architecture traits in Ethiopian durum wheat. BMC Genom. 2021, 22, 20. [Google Scholar] [CrossRef] [PubMed]
  23. Jia, Y.; Xu, N.; Zhang, J.; Ren, K.; Wu, J.; Wang, C.; Luo, Y.; Hu, X.; Fan, N.; Zhang, Y. Combined genome-wide association studies (GWAS) and linkage mapping identifies genomic regions associated with seedling root system architecture (RSA) under different nitrogen conditions in wheat (Triticum aestivum L.). Agriculture 2024, 14, 1652. [Google Scholar] [CrossRef]
  24. Saini, D.K.; Chopra, Y.; Pal, N.; Chahal, A.; Srivastava, P.; Gupta, P.K. Meta-QTLs, ortho-MQTLs and candidate genes for nitrogen use efficiency and root system architecture in bread wheat (Triticum aestivum L.). Physiol. Mol. Biol. Plant. 2021, 27, 2245–2267. [Google Scholar] [CrossRef] [PubMed]
  25. Sallam, A.; Awadalla, R.A.; Elshamy, M.M.; Börner, A.; Heikal, Y.M. Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population. Comput. Struct. Biotechnol. J. 2024, 23, 870–882. [Google Scholar] [CrossRef]
  26. Siddiqui, N.; Gabi, M.T.; Kamruzzaman, M.; Ambaw, A.M.; Teferi, T.J.; Dadshani, S. Genetic dissection of root architectural plasticity and identification of candidate loci in response to drought stress in bread wheat. BMC Genom. 2023, 24, 38. [Google Scholar] [CrossRef]
  27. Zaman, Z.; Iqbal, R.; Jabbar, A.; Zahra, N.; Saleem, B.; Kiran, A.; Ullah, S.; Akbar, T.; Khan, M.R. Genetic signature controlling root system architecture in diverse spring wheat germplasm. Physiol. Plant. 2024, 176, e14183. [Google Scholar] [CrossRef] [PubMed]
  28. Yang, M.J.; Wang, C.R.; Hassan, M.A.; Wu, Y.Y.; Xia, X.C.; Shi, S.B.; Liu, Y.L.; Xie, J.Y.; He, Z.H. QTL mapping of seedling biomass and root traits under different nitrogen conditions in bread wheat (Triticum aestivum L.). J. Integr. Agric. 2021, 20, 1180–1192. [Google Scholar] [CrossRef]
  29. Liu, R.; Wu, F.; Xin, Y.; Yu, L.; Wang, Z.; Liu, S.; Cheng, Y.; Wang, L.; Zhao, G. Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions. J. Integr. Agric. 2020, 19, 1947–1960. [Google Scholar] [CrossRef]
  30. Lu, Y.; Xing, L.; Xing, S.; Hu, P.; Cui, C.; Zhang, M. Characterization of a putative new semi-dominant reduced height gene, RHT_NM9, in Wheat (Triticum aestivum L.). J. Genet. Genom. 2015, 42, 685–698. [Google Scholar] [CrossRef]
  31. Ren, T.; Hu, Y.; Tang, Y.; Li, C.; Yan, B.; Ren, Z. Utilization of a wheat 55K SNP array for mapping of major QTL for temporal expression of the tiller number. Front. Plant Sci. 2018, 9, 333. [Google Scholar] [CrossRef]
  32. Gasperini, D.; Greenland, A.; Hedden, P.; Dreos, R.; Harwood, W.; Griffiths, S. Genetic and physiological analysis of RHT8 in bread wheat: An alternative source of semi-dwarfism with a reduced sensitivity to brassinosteroids. J. Exp. Bot. 2012, 63, 4419–4436. [Google Scholar]
  33. Chai, L.; Chen, Z.; Bian, R.; Zhai, H.; Cheng, X.; Peng, H.; Liu, X.; Sun, G.; Yuan, Y. Dissection of two quantitative trait loci with pleiotropic effects on plant height and spike length linked in coupling phase on the short arm of chromosome 2D of common wheat (Triticum aestivum L.). Theor. Appl. Genet. 2019, 132, 1815–1831. [Google Scholar] [CrossRef]
  34. Chai, L.; Xin, M.; Dong, C.; Chen, Z.; Zhai, H.; Zhuang, J.; Feng, H.; Zhang, Y. A natural variation in Ribonuclease H-like gene underlies RHT8 to confer Green Revolution trait in wheat. Mol. Plant 2022, 15, 377–380. [Google Scholar] [CrossRef]
  35. Haque, M.; Martinek, P.; Watanabe, N.; Kuboyama, T. Genetic mapping of gibberellic acid-sensitive genes for semi-dwarfism in durum wheat. Cereal Res. Commun. 2011, 39, 171–178. [Google Scholar] [CrossRef]
  36. Duan, S.; Cui, C.; Chen, L.; Yang, Z.; Hu, Y.G.; Ghedini, M.; Rossi, L. Fine mapping and candidate gene analysis of dwarf gene RHT14 in durum wheat (Triticum durum). Funct. Integr. Genom. 2022, 22, 141–152. [Google Scholar] [CrossRef]
  37. Liu, H.; Shi, Z.; Ma, F.; Xu, Y.; Han, G.; Zhang, J.; Liu, D.; An, D. Identification and validation of plant height, spike length and spike compactness loci in common wheat (Triticum aestivum L.). BMC Plant Biol. 2022, 22, 568. [Google Scholar] [CrossRef]
  38. Lo, S.F.; Yang, S.Y.; Chen, K.T.; Hsing, Y.I.; Zeevaart, J.A.; Yu, S.M. A novel class of gibberellin 2-oxidases control semi-dwarfism, tillering, and root development in rice. Plant Cell 2008, 20, 2603–2618. [Google Scholar] [CrossRef] [PubMed]
  39. Emenecker, R.J.; Strader, L.C. Auxin-abscisic acid interactions in plant growth and development. Biomolecules 2020, 10, 281. [Google Scholar] [CrossRef] [PubMed]
  40. Kondhare, K.R.; Patil, A.B.; Giri, A.P. Auxin: An emerging regulator of tuber and storage root development. Plant Sci. 2021, 306, 110854. [Google Scholar] [CrossRef] [PubMed]
  41. Iqbal, M.; Ashraf, M.; Jamil, A. Seed enhancement with cytokinins: Changes in growth and grain yield in salt stressed wheat plants. Plant Growth Regul. 2006, 50, 29–39. [Google Scholar] [CrossRef]
  42. Lopez-Valdivia, I.; Perkins, A.C.; Schneider, H.M.; Vallebueno-Estrada, M.; Burridge, J.D.; Vielle-Calzada, J.P. Gradual domestication of root traits in the earliest maize from Tehuacán. Proc. Natl. Acad. Sci. USA 2022, 119, e2110245119. [Google Scholar] [CrossRef]
  43. Li, H.; Liu, H.; Hao, C.; Li, T.; Liu, Y.; Wang, X.; Ma, F.; Zhang, J.; Shi, Z. The auxin response factor TaARF15-A1 negatively regulates senescence in common wheat (Triticum aestivum L.). Plant Physiol. 2023, 191, 1254–1271. [Google Scholar] [CrossRef]
  44. Cho, H.; Ryu, H.; Rho, S.; Hill, K.; Smith, S.; Audenaert, D.; Peng, J.; Yang, Y.; Park, J.Y.; Jang, S.C. A secreted peptide acts on BIN2-mediated phosphorylation of ARFs to potentiate auxin response during lateral root development. Nat. Cell Biol. 2014, 16, 66–76. [Google Scholar] [CrossRef] [PubMed]
  45. Ober, E.S.; Alahmad, S.; Cockram, J.; Forestan, C.; Hickey, L.T.; Kant, J.; Watt, M. Wheat root systems as a breeding target for climate resilience. Theor. Appl. Genet. 2021, 134, 1645–1662. [Google Scholar] [CrossRef] [PubMed]
  46. Vysotskaya, L.B.; Timergalina, L.N.; Simonyan, M.V.; Veselov, S.Y.; Kudoyarova, G.R. Growth rate, IAA and cytokinin content of wheat seedling after root pruning. Plant Growth Regul. 2001, 33, 51–57. [Google Scholar] [CrossRef]
  47. Li, Z.; Zhang, X.; Zhao, Y.; Li, Y.; Zhang, G.; Peng, Z.; Zhang, J. Enhancing auxin accumulation in maize root tips improves root growth and dwarfs plant height. Plant Biotechnol. J. 2018, 16, 86–99. [Google Scholar] [CrossRef] [PubMed]
  48. Chen, L.; Wang, R.; Hu, X.; Wang, D.; Wang, Y.; Xue, R.; Duan, G.; Zhai, H.; Zhang, Y. Overexpression of wheat C2H2 zinc finger protein transcription factor TaZAT8-5B enhances drought tolerance and root growth in Arabidopsis thaliana. Planta 2024, 260, 126. [Google Scholar] [CrossRef]
  49. Feng, L.; Gao, Z.; Xiao, G.; Huang, R.; Zhang, H.; Li, Q. Leucine-rich repeat receptor-like kinase FON1 regulates drought stress and seed germination by activating the expression of ABA-responsive genes in rice. Plant Mol. Biol. Rep. 2014, 32, 1158–1168. [Google Scholar] [CrossRef]
  50. Meng, L.; Li, H.; Zhang, L.; Wang, J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J. 2015, 3, 269–283. [Google Scholar] [CrossRef]
Plants 14 00490 g001
Figure 1. Frequency distributions for wheat seedling root traits in the Wp-072/Wp-119 RIL population. (a) RL, total root length (cm); (b) RA, total root surface area (mm2); (c) RV, total root volume (mm3); (d) RT, number of root tips; (e) RW, total root dry weight (g).
Figure 1. Frequency distributions for wheat seedling root traits in the Wp-072/Wp-119 RIL population. (a) RL, total root length (cm); (b) RA, total root surface area (mm2); (c) RV, total root volume (mm3); (d) RT, number of root tips; (e) RW, total root dry weight (g).
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Figure 2. The identified QTLs for wheat seedling root traits in the Wp-072 × Wp-119 RIL population. The red filled area represents the position of QTL.
Figure 2. The identified QTLs for wheat seedling root traits in the Wp-072 × Wp-119 RIL population. The red filled area represents the position of QTL.
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Figure 3. Validating the efficiency of KASP markers for root traits in the natural population. Lowercase letters a and b indicate significant differences at p = 0.05 level; “AA”, “CC”, and “GG” refer to the different base pairs; whiskers show the standard deviation (SD).
Figure 3. Validating the efficiency of KASP markers for root traits in the natural population. Lowercase letters a and b indicate significant differences at p = 0.05 level; “AA”, “CC”, and “GG” refer to the different base pairs; whiskers show the standard deviation (SD).
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Figure 4. qRT-PCR of the nine candidate genes identified in Wp-072/Wp-119 RIL population.
Figure 4. qRT-PCR of the nine candidate genes identified in Wp-072/Wp-119 RIL population.
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Table 1. QTL for wheat seedling root traits in Wp-072/Wp-119 RIL population.
Table 1. QTL for wheat seedling root traits in Wp-072/Wp-119 RIL population.
QTLGenetic IntervalPhysical
Position (Mb)		LODPVEAdd aFavorable
Allele		Unfavorable Allele
QRL.gau-1DSExcalibur_c17152_454~
RAC875_c1471_566		7.1~7.92.75.2−6.175.8 cm72.4 cm
QRL.gau-1DLRAC875_c103613_441~
Excalibur_c1236_840		424.7~431.84.28.4−9.276.0 cm72.7 cm
QRL.gau-4ALBS00059454_51~
Kukri_c19883_816		731.4~732.52.75.4−6.175.8 cm72.3 cm
QRA.gau-1DKukri_c18350_151~
wsnp_Ex_rep_c70574_69491038		294.5~302.84.18.2−0.710.6 mm29.6 mm2
QRA.gau-2DLBobWhite_c6770_617~
BS00026262_51		439.6~446.04.38.40.710.7 mm29.8 mm2
QRV.gau-6ASCAP11_c4727_205~
Tdurum_contig11539_81		31.0~45.33.75.1−58.2389.2 mm3325.6 mm3
QRW.gau-2ASBS00039489_51~
Kukri_c15325_1360		203.0~208.54.78.2−0.01860.032 g0.027 g
QRW.gau-2DLRAC875_c66820_684~
Ku_c22718_1072		623.3~629.24.28.10.01820.032 g0.027 g
QRT.gau-3ASRAC875_c55313_89~
Kukri_c64788_552		60.1~70.22.64.5−6.0224.6196.9
QRT.gau-6DLEx_c31468_763
tplb0055d02_624		462.6~468.33.87.2−7.5218.7190.2
a ‘−’ indicates the effects originating from Wp-072.
Table 2. The primers of the developed KASP markers for wheat seedling root traits.
Table 2. The primers of the developed KASP markers for wheat seedling root traits.
KASP MarkerQTLPrimerSequence
KASP-RL-1DLQRL.gau-1DLFAMAGGTGGGTTCTTCAAAGGAAT
HEXAGGTGGGTTCTTCAAAGGAAC
CommonGAGAATGCAAATGAATCCTCTGG
KASP-RA-1DQRA.gau-1DFAMGCTGACGCATTTGAAAAAGATACA
HEXGCTGACGCATTTGAAAAAGATACG
CommonCACATTTCCTGCACGGAGAA
KASP-RA-2DLQRA.gau-2DLFAMTCCATGTCGTTTTATAACATTGACA
HEXTCCATGTCGTTTTATAACATTGACG
CommonGTGAACACTAAGTTGTTTGTGGTTA
KASP-RW-2ASQRW.gau-2ASFAMTGTTAGAATCTTACCTACCAGCATA
HEXTGTTAGAATCTTACCTACCAGCATG
CommonTCCGAGGATGGGTATTTAACATG
KASP-RW-2DLQRW.gau-2DLFAMATGGTCGTCAACTCCATACAA
HEXATGGTCGTCAACTCCATACAG
CommonGCATCAGTTCAACAAGGCTG
Table 3. Effects of developed KASP markers for wheat seedling root traits in the natural population.
Table 3. Effects of developed KASP markers for wheat seedling root traits in the natural population.
Marker NameQTLGenotype aNumber of LinesPhenotypep-Value
KASP-RL-1DLQRL.gau-1DLTT56276.6 cm0.048 *
CC77230.5 cm
KASP-RA-1DQRA.gau-1DAA5612.5 mm20.014 *
CC8411.2 mm2
KASP-RA-2DLQRA.gau-2DLAA5310.7 mm20.007 **
GG8912.1 mm2
KASP-RW-2ASQRW.gau-2ASAA660.037 g0.001 **
GG790.032 g
KASP-RW-2DLQRW.gau-2DLAA410.037 g0.002 **
GG1020.033 g
Bold indicates the favorable allele; * Significant at p < 0.05; ** Significant at p < 0.01.
Table 4. The candidate genes for wheat seedling root traits identified in the Wp-072/Wp-119 RIL population.
Table 4. The candidate genes for wheat seedling root traits identified in the Wp-072/Wp-119 RIL population.
QTLCandidate GeneChr.Start (bp)End (bp)Annotation
QRL.gau-1DSTraesCS1D01G0182001D7,913,1297,917,136Zinc finger family protein
QRA.gau-1DTraesCS1D01G2165001D302,349,108302,352,162Auxin canalization protein
QRL.gau-1DLTraesCS1D01G3369001D426,869,492426,870,633Gibberellin 2-oxidase
QRW.gau-2ASTraesCS2A01G2205002A208,242,970208,243,194Leucine-rich repeat receptor-like protein kinase
QRA.gau-2DLTraesCS2D01G3446002D440,633,947440,634,603Leucine-rich repeat receptor-like protein kinase
QRW.gau-2DLTraesCS2D01G5489002D624,361,783624,369,474Auxin response factor
QRT.gau-3ASTraesCS3A01G1014003A65,952,81565,953,444E3 ubiquitin protein ligase
QRV.gau-6ASTraesCS6A01G0703006A38,453,85538,454,175F-box family protein
QRT.gau-6DLTraesCS6D01G3836006D462,483,712462,487,539Ethylene receptor
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Ma, X.; Wang, J.; Zhang, H.; Yao, L.; Si, E.; Li, B.; Meng, Y.; Wang, H. Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping. Plants 2025, 14, 490. https://doi.org/10.3390/plants14030490

AMA Style

Ma X, Wang J, Zhang H, Yao L, Si E, Li B, Meng Y, Wang H. Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping. Plants. 2025; 14(3):490. https://doi.org/10.3390/plants14030490

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Ma, Xiaole, Juncheng Wang, Hong Zhang, Lirong Yao, Erjing Si, Baochun Li, Yaxiong Meng, and Huajun Wang. 2025. "Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping" Plants 14, no. 3: 490. https://doi.org/10.3390/plants14030490

APA Style

Ma, X., Wang, J., Zhang, H., Yao, L., Si, E., Li, B., Meng, Y., & Wang, H. (2025). Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping. Plants, 14(3), 490. https://doi.org/10.3390/plants14030490

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