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Article

Identification of QTL and Candidate Genes Controlling Plant Height and Internode Length in a Newly Characterized Bread Wheat Recombinant Inbred Population

by
Zidong Wan
1,†,
Shuai Ge
1,†,
Mengxin Li
1,
Xinyan Wang
1,
Dongjie Cui
1,
Qing Chi
1,
Bing Li
1,
Hangbo Xu
1,
Jialing Lu
1,
Zhen Jiao
1,
Wenhui Wei
2,* and
Panfeng Guan
1,*
1
Henan Key Laboratory of Ion-beam Green Agriculture Bioengineering, School of Agriculture and Biomanufacturing, Zhengzhou University, Zhengzhou 450001, China
2
College of Agronomy and Life Sciences, Zhaotong University, Zhaotong 657000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2026, 17(5), 567; https://doi.org/10.3390/genes17050567 (registering DOI)
Submission received: 18 March 2026 / Revised: 30 April 2026 / Accepted: 14 May 2026 / Published: 17 May 2026
(This article belongs to the Special Issue Genetic Architecture of Wheat Domestication- and Improvement-Related Traits)
Browse Figures
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Abstract

Background: Internode length (IL), a key component of plant height (PH), plays an important role in achieving the optimal architecture in wheat. However, the genetic mechanisms underlying internode elongation are not well understood. Methods: In this study, a recombinant inbred line (RIL) population derived from a cross between Bainong 4199 (BN4199) and Zhengyinmai 2 (ZYM2) was evaluated for PH and five ILs across two field locations over two years and genotyped using a 120 K liquid-phase chip. Results: A total of 141 quantitative trait loci (QTL) associated with PH and the five ILs were mapped onto 20 chromosomes, except for chromosome 5D. Among these, 37 stable QTL were identified on chromosomes 1B, 2B, 2D, 4B, 5A, 7A, 7B and 7D, accounting for 3.86–25.97% of the phenotypic variation. Meanwhile, 23 co-localized QTL associated with at least two traits were detected, with QTL cluster regions on chromosomes 2D, 4B, 5A, 7A, and 7B. Moreover, the total additive effects of the QTL combinations increased with the number of QTL, which indicates the effectiveness of pyramid breeding. Additionally, based on gene function annotation, the cloning and characterization of rice orthologs, and analysis via the QTG miner module of the wheat integrative gene regulatory network (wGRN) platform, 63 candidate genes (e.g., Rht1, Rht8, TB1 and ZnF-B) were prioritized within the stable QTL intervals, and their tissue expression patterns were analyzed. Conclusions: Collectively, these findings not only deepen our understanding of the genetic basis of PH and ILs in wheat but also lay a foundation for the further validation and functional characterization of candidate genes, enabling the optimization of plant architecture through marker-assisted selection (MAS) to ultimately improve agronomic performance and yield potential.

1. Introduction

Bread wheat (Triticum aestivum L.) is one of the world’s most important cereal crops, and increasing its yield is crucial for addressing the challenges of global population growth and the food crisis [1]. During the first ‘Green Revolution’, dwarf breeding established the semi-dwarf ideotype of wheat, dramatically increasing wheat yield and harvest index [2,3,4,5]. The ideotype can improve light energy conversion efficiency, reduce pest and disease incidence, and enhance seed yield in crops [6]. Plant height (PH) is a key trait in ideotype improvement in wheat [6,7]. However, the widely used ‘Green Revolution’ genes, Rht1 (Rht-B1b) and Rht2 (Rht-D1b), not only reduce plant height but also decrease coleoptile length, grain weight, and nitrogen-use efficiency [8,9,10]. Therefore, the discovery and utilization of new dwarfing loci and genes have long been a research hotspot in wheat breeding.
Wheat plant height is morphologically divided into two components: spike length (SL) and the total length of the elongated internodes above ground [11,12]. The desirable PH in wheat breeding programs is, therefore, achieved by optimizing these components. Previous studies have shown that cultivars with shorter basal internodes are more likely to exhibit lodging resistance [13,14]. Classical genetic studies have demonstrated that PH is a complex quantitative trait influenced by both Mendelian genes and quantitative trait loci (QTL) [12,15]. Up until now, 28 wheat dwarfing genes have been formally catalogued [16,17]. Among them, the cloned and validated genes are Rht1, Rht2, Rht3 (Rht-B1c), Rht8, Rht10 (Rht-D1c), Rht11 (Rht-B1e), Rht12, Rht13, Rht17 (Rht-B1p), Rht18/Rht24, Rht23 (5Dq) and Rht25 [18,19,20,21,22,23,24,25,26,27,28,29,30]. Furthermore, a number of QTL associated with PH have been identified across 21 wheat chromosomes through linkage mapping in biparental populations and association mapping in natural populations [31]. In particular, multivariate conditional QTL frameworks have proven valuable for partitioning PH into spike and internode contributions, thereby separating loci with direct effects on stem elongation from those acting indirectly through correlated components [32]. In addition, increased activity of the TEOSINTE BRANCHED1 (TB1) gene has been shown to restrict stem height and elongation in bread wheat [11].
Genetic variations that cause semi-dwarfism in wheat, barley and rice primarily affect the biosynthesis, metabolism or signal transduction of the plant hormone gibberellin (GA) and brassinosteroids (BRs) [31,32]. The tall alleles Rht-B1a/Rht-D1a encode normal DELLA repressors that are degraded after forming the GA–GID1–DELLA complex, thereby releasing GA-promoted growth [26,28]. By contrast, major “Green Revolution” and related loci—Rht1, Rht2, Rht3, Rht11, and Rht17—produce DELLA variants that cannot be degraded and, thus, constitutively repress GA responses, reducing stem elongation [18,22]; Rht10 (Rht-D1c) further amplifies this effect via tandem duplication of the Rht-D1b region. Other dwarfing mechanisms include reduced bioactive GA via GA2ox genes (Rht12: GA2ox-A13; Rht18/Rht24: GA2ox-A9), GA-pathway modulation by Rht8, altered miR172–AP2 regulation at Rht23 (5Dq), and restricted cell expansion through immune/cell-wall reinforcement at Rht13 [19,20,21,23,27,29,33]. In addition, the Rht25 gene, which encodes a plant-specific AT-rich sequence- and zinc-binding protein (PLATZ), reduces plant height by interacting with DELLA [30], while the GSK3/SHAGGY-like kinase can phosphorylate and stabilize Rht-B1b protein, thereby strengthening dwarfism [34]. Notably, a 4BS semi-dwarf haplotype involving Rht-B1b/EamA-B/ZnF-B highlights GA–BR crosstalk, where ZnF positively regulates BR signaling by directly interacting with TaBRI1 and TaBKI1 [9,35]. Furthermore, natural deletion of the ‘r-e-z’ haploblock not only results in a semi-dwarf phenotype but also increases wheat yield by 6.48–15.25% and improves the low nitrogen (N)-use efficiency (NUE), which establishes a molecular framework for engineering BR-driven cereal ideotypes [9].
In this study, variations in PH and its component traits, internode length (IL), were evaluated in a recombinant inbred line (RIL) population derived from the cross between Bainong 4199 (BN4199) and Zhengyinmai 2 (ZYM2) across multiple field environments. Furthermore, a high-density linkage map was constructed using a 120 K liquid-phase chip to genotype the BN4199/ZYM2-RIL population. The objectives of this study were to detect genetic loci associated with PH and IL and to identify putative candidate genes within QTL regions. These findings will deepen our understanding of the genetic basis of wheat plant height development and facilitate the optimization of plant architecture for developing new varieties with high and stable yields.

2. Materials and Methods

2.1. Plant Materials

The wheat RIL population used in this study consisted of 184 lines derived from a cross between BN4199 and ZYM2. The female parent, BN4199, was developed by the Wheat Research Center of Henan Institute of Science and Technology. It is a high-light-efficient wheat variety widely cultivated in the North China Plain. The male parent, ZYM2, is an exotic wheat line characterized by a tall stem and a high grain number per spike.

2.2. Field Trials and Phenotyping

During the 2023–2024 and 2024–2025 growing seasons, the BN4199/ZYM2-RIL population, along with the two parents, was planted at the Xingyang (XY) and Xuchang (XC) experimental sites in Henan Province, China. These sites are hereafter referred to as E1 (XY24), E2 (XC24), E3 (XY25) and E4 (XC25). The field trials were conducted in a randomized complete block design with three repetitions. Each row is 1.5 m long, with a row spacing of 0.3 m, and each row contains 30 seeds. Crop management, including irrigation, pesticide and fertilizer applications, followed local standard cultivation practices. At maturity, PH and IL were measured for six to ten randomly selected plants per each RIL in each repetition. Plant height was measured from the ground to the tip of the spike, excluding awns. The first internode length (IL1) was measured from the base of the spike to the first node from the top. The second internode length (IL2) was measured from the first node to the second node, and similarly for the third (IL3), fourth (IL4) and fifth (IL5) internode lengths.

2.3. Genotyping and Linkage Map Construction

Genomic DNA was extracted from fresh seedling leaves of the RILs and both parents using the CTAB method [36]. The wheat 120 K liquid-phase (120 K–4 HWA) chip from the Department of Life Science, Tcuni Inc., Chengdu, China (https://www.tcuni.com), was used for genotyping. Redundant markers with missing data (>10%) or distorted segregation (p < 0.001) were filtered using the ‘BIN’ function in QTL IciMapping v4.2 software [37]. A genetic linkage map was then constructed using the ‘MAP’ function. The graphical presentation of the linkage map was generated using Mapchart v.2.3 software [38]. The physical positions of the molecular markers were based on the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.1 genome of Chinese Spring (CS) hexaploid wheat.

2.4. QTL Analysis

QTL analysis was performed using Windows QTL Cartographer version 2.5 [39]. Composite interval mapping (CIM) method with logarithm of odds (LOD) score threshold of 2.5 was used to detect significant QTL. A forward and backward regression model was applied with a window size of 10 cM, a walking speed of 2 cM, five control markers, 1000 permutations, and a significance level of 0.05. QTL detected in two or more environments were considered stable QTL in this study. QTL that were less than 1 cM apart or shared common flanking markers were regarded as a single locus and named according to the International Rules of Genetic Nomenclature adapted for wheat [40,41].

2.5. Identification of Candidate Genes and RT-qPCR Analysis

High-confidence genes within the stable QTL regions were obtained through the WheatOmics 1.0 platform (http://wheatomics.sdau.edu.cn/) based on the annotation of the IWGSC RefSeq v2.1 genome [42]. The ‘Triticeae-GeneTribe’ database (https://wheat.cau.edu.cn/TGT/) was used for homologous gene query and gene ontology (GO) annotation [43]. The China National Rice Data Center (https://www.ricedata.cn/) was employed to retrieve information on the cloning and functional annotation of homologous genes in rice. The potential candidate genes were narrowed down based on their functional annotation, protein family classification, and predicted regulatory roles. Furthermore, candidate genes within the QTL intervals were prioritized using the wheat integrative gene regulatory network (wGRN) platform (http://wheat.cau.edu.cn/wGRN) via the QTG miner module [44]. The tissue- and stage-specific expression data of candidate genes were obtained from the WheatOmics 1.0 platform (http://wheatomics.sdau.edu.cn/) [42]. The reverse transcription–quantitative polymerase chain reaction (RT-qPCR) analysis of candidate genes was performed using the parental stem at the jointing stage.

2.6. Statistical Analysis

Analysis of variance (ANOVA), best linear unbiased estimation (BLUE), and broad-sense heritability (H2) were performed using the ‘AOV’ function in the QTL IciMapping v4.2 software [37]. The Pearson correlation analysis was conducted using the ‘PerformanceAnalytics’ R package (https://CRAN.R-project.org/package=PerformanceAnalytics). The Student’s t-test (p < 0.05) was performed on the phenotype values using IBM SPSS Statistics v22.0 (SPSS Inc., Chicago, IL, USA). Origin 2018 software (OriginLab, Northampton, MA, USA) was used to generate the figures.

3. Results

3.1. Phenotypic Evaluation of RIL Population

The PH and the five ILs of the BN4199/ZYM2-RIL population were evaluated across four different field environments (Supplementary Table S1). Compared to BN4199, ZYM2 had longer internodes, resulting in a greater PH (Figure 1). Transgressive segregation for PH and the five ILs was observed in the RIL population across all environments, with all traits showing considerable variation. Furthermore, the BN4199/ZYM2-RIL population exhibited continuous variation and a nearly normal distribution for PH and the five ILs, consistent with the quantitative trait characteristics and suitable for subsequent QTL mapping (Table S1, Figure 2). The ANOVA of PH and its component traits in the RIL population under different environmental conditions showed that PH and the five ILs were significantly affected by genotype, environment, and their interaction (Table 1). Meanwhile, the H2 of PH and the five ILs in the BN4199/ZYM2-RIL population all exceeded 80%, indicating high genetic stability (Table 1). In addition, significant and positive correlations (p < 0.001) were observed among PH and the five ILs’ traits based on BLUE values, suggesting that these traits are interrelated (Figure 2).

3.2. QTL Mapping for PH and IL Traits

Based on the phenotypic data and BLUE values across all field environments, a total of 141 QTL associated with PH and the five ILs were detected in the BN4199/ZYM2-RIL population across 20 chromosomes, with the exception of chromosome 5D (Supplementary Table S2). The D sub-genome had fewer QTL (41) than the A (49) and B (51) sub-genomes, while the greatest number of QTL (21) were distributed on chromosomes 2D and 7A. The phenotypic variance (PVE) explained by each individual QTL ranged from 3.60% to 25.97%. In this study, QTL detected in two or more environments were considered stable (Table 2, Figure 3), and QTL located within the same interval were referred to as co-localized QTL (Table 3).
For PH, a total of 26 QTL were identified on chromosomes 1D, 2D, 3D, 4A, 4B, 4D, 5A, 7A, and 7B, among which eleven stable QTL were located on chromosomes 2D, 4B, 5A, 7A and 7B. One of these stable QTL, namely, QPh.zzu.2D.1, was detected in all environments and in the BLUE analysis, with LOD scores ranging from 4.60 to 7.39 explaining 7.93% to 12.95% of the PVE. The favorable alleles that reduce PH for the stable QTL QPh.zzu.2D.1, QPh.zzu.2D.2, QPh.zzu.2D.4, and QPh.zzu.5A.1 were contributed by BN4199, whereas ZYM2 contributed the favorable alleles for QPh.zzu.4B.1, QPh.zzu.4B.2, QPh.zzu.4B.3, QPh.zzu.7A.1, QPh.zzu.7A.2, QPh.zzu.7B.1 and QPh.zzu.7B.3.
For IL1, a total of 24 QTL were identified on chromosomes 1D, 2B, 2D, 3D, 4B, 5B, 6A, 6B, 7A, 7B, and 7D. Among them, the stable QTL QIl1.zzu.2B.1, QIl1.zzu.2D.1, QIl1.zzu.2D.2, QIl1.zzu.2D.3, QIl1.zzu.2D.4 and QIl1.zzu.7D.1 carried favorable alleles from BN4199, whereas the favorable allele for the stable QTL QIl1.zzu.7B.1 and QIl1.zzu.7B.3 came from ZYM2. Notably, QIl1.zzu.2D.1 and QIl1.zzu.2D.2 were consistently identified across two different environments and in the BLUE analysis, with QIl1.zzu.2D.2 exhibiting the highest PVE of up to 25.97%.
For IL2, a total of 22 QTL were identified on chromosomes 1A, 1B, 1D, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 6A, 6D and 7B. Six of these QTL were stable and mapped on chromosomes 1B, 2D, 4B and 7B. The increasing allele of QIl2.zzu.2D.2 was contributed by ZYM2, whereas BN4199 contributed the increasing alleles at the remaining five loci. Among these, QIl2.zzu.7B.1 exhibited the strongest signal, with LOD values ranging from 2.79 to 6.38 and explaining 5.27% to 11.33% of the phenotypic variance.
For IL3, a total of 21 QTL were identified on chromosomes 1A, 1B, 1D, 2A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 6D, 7A and 7B. Three stable QTL were detected on chromosomes 4B and 7B, namely, QIl3.zzu.4B.1, QIl3.zzu.4B.2 and QIl3.zzu.7B.1. The increasing alleles at all three loci were contributed by BN4199. Notably, QIl3.zzu.4B.1 was identified as the most robust locus, being consistently detected across all environments and in the BLUE analysis, with LOD values ranging from 3.38 to 7.62 and accounting for 6.14% to 14.17% of the phenotypic variance.
For IL4, a total of 24 QTL were identified on chromosomes 1A, 1B, 2D, 3B, 4A, 4B, 4D, 5A, 6B, 6D, 7A and 7B. Seven of these QTL were stable and located on chromosomes 4B, 5A and 7A. The increasing alleles at QIl4.zzu.5A.2, QIl4.zzu.5A.3 and QIl4.zzu.7A.2 were contributed by ZYM2. By contrast, increasing alleles at QIl4.zzu.4B.1, QIl4.zzu.4B.2, QIl4.zzu.4B.3 and QIl4.zzu.7A.1 were contributed by BN4199. Among these loci, QIl4.zzu.7A.1 displayed the largest effect, with LOD values ranging from 5.14 to 7.31 explaining 9.24% to 13.09% of the phenotypic variance.
For IL5, two stable QTL were identified on chromosome 7A, namely, QIl5.zzu.7A.3 and QIl5.zzu.7A.6. The major locus, QIl5.zzu.7A.3, was detected in E1, E2, E4 and the BLUE analysis, with LOD values ranging from 6.23 to 11.90 and accounting for 11.09% to 20.33% of the phenotypic variance. The increasing allele was contributed by BN4199. The QIl5.zzu.7A.6 was detected in E2, E4 and BLUE, with LOD values ranging from 3.30 to 6.25 and explaining 7.14% to 11.11% of the phenotypic variance. The increasing allele was contributed by ZYM2.

3.3. Additive Effects Analysis of the Stable QTL

To further elucidate the additive effects of the stable QTL, we analyzed and summarized the composition of favorable alleles (referring to reducing PH and IL) in RIL lines based on the closely linked marker genotypes of each identified locus. The favorable alleles of the 37 stable QTL were contributed by both BN4199 (22) and ZYM2 (15), which suggests that both parents provide different favorable alleles for PH and the five ILs (Table 2). Regardless of the interactions among these stable loci and environmental influences, we found that a greater number of favorable alleles was associated with a gradual decrease in PH and the five ILs (Figure 4), which supports the effectiveness of pyramid breeding. These results demonstrated that the total additive effects of the QTL combinations increased with the number of QTL. Additionally, most lines in the BN4199/ZYM2-RIL population carried four to seven favorable alleles for PH, while some individuals harbored up to 11 favorable alleles and could serve as valuable germplasm resources for wheat breeding.

3.4. Profiling and Identification of Candidate Genes

To identify putative candidate genes within the stable QTL intervals, the high-confidence annotated genes were screened based on the IWGSC CS RefSeq v2.1 genome assembly using the WheatOmics 1.0 platform. To further evaluate the functions of the annotated genes, we examined the cloning and functional analysis of homologous genes in rice. Furthermore, the QTG miner tool on the wGRN platform was used to prioritize high-confidence candidate genes, based on the previously reported genes associated with PH and IL in wheat. Additionally, the candidate gene was expressed in stem tissue during at least one distinct developmental stage. Collectively, a total of 63 high-confidence genes, including Rht1, Rht8, TB1 and ZnF-B, were identified as potential candidates within the stable QTL regions in this study (Table 4).

3.5. Expression Pattern Analysis of Candidate Genes

The expression profiles of these 63 candidate genes in different tissues and developmental stages were further analyzed using a publicly available CS wheat gene expression dataset from the WheatOmics 1.0 platform (Figure 5, Table S3). Among these, three genes (TraesCS2B03G0223300, TraesCS4B03G0343200, and TraesCS7D03G0068000) showed high expression in various tissues, whereas two genes (TraesCS2D03G0047500 and TraesCS4B03G0092100) exhibited low expression across multiple tissues. Moreover, TraesCS2B03G0205100 was mainly expressed in stem (Z65) and leaf (Z10 and Z23). As the stem develops, the expression levels of TraesCS5A03G0778700, TraesCS7A03G0482500, and TraesCS7A03G0510200 increased markedly, while those of TraesCS4B03G0324700, TraesCS4B03G0389300, TraesCS7A03G0479300, and TraesCS7D03G0081400 decreased obviously. Additionally, we selected the top ten candidate genes with high expression in the stems for RT-qPCR analysis (Table S4). The results showed that six of them (TraesCS2B03G0223300, TraesCS7A03G0263900, TraesCS7A03G0479300, TraesCS7A03G0485700, TraesCS7D03G0068000, and TraesCS7D03G0081400) exhibited significantly different expression levels between the two parents in the stem at the jointing stage (p < 0.05).

4. Discussion

4.1. QTL Cluster and Co-Localized QTL

As an important agronomic trait, PH plays a critical role in determining plant architecture and grain yield. Regardless of spike length, the final PH is biologically determined by the length of each internode. Moreover, plant architecture and lodging resistance in crops are greatly influenced by internode characteristics [58,59]. In this study, correlation analysis indicated that PH was significantly positively correlated with the length of each internode (Figure 2). Therefore, genetic dissection and identification of stable QTL and candidate genes for internode length will facilitate the improvement of the optimal plant height in wheat.
For closely related traits in crops, the co-localization of QTL or QTL clusters has been reported in various studies, which suggests potential pleiotropy or a tight linkage [60,61,62,63]. These regions, known as QTL hotspots, are critical for breeding and understanding the genetic architecture of main agronomic traits. In this study, a total of 23 co-localized QTL associated with at least two traits were identified on chromosomes 1B, 1D, 2D, 4A, 4B, 4D, 5A, 6B, 6D, 7A, and 7B (Table 3). Most co-localized QTL were associated with PH and specific ILs, particularly those of the two nodes below the spike. Furthermore, four co-localized QTL on chromosomes 2DS, 4BS, and 7BS simultaneously regulate PH and four ILs, which is consistent with the results of the correlation analysis between PH and ILs (Figure 2). Notably, seven co-localized QTL on chromosomes 1B, 2D, 4B, 5A, 6B, 6D, and 7A were associated only with internode length, and these loci were detected under specific environmental conditions (Table 3 and Table S2), which suggests that environmental factors significantly impact the expression of certain genes. In a previous study, conditional QTL mapping for plant height with respect to spike and internode length showed that spike length contributed the least to PH among the internode lengths considered at the QTL level [12]. In addition, the QTL cluster regions were found on chromosomes 2D, 4B, 5A, 7A, and 7B (Table 3, Figure 3), consistent with previous reports using different genetic populations [45,62,64]. These results indicate that these chromosomal regions may contain multiple key genes controlling PH and ILs, warranting further investigation.

4.2. QTL Comparison Analysis and Novel Loci Identification

As plant height in wheat is a complex quantitative trait controlled by multiple genes and QTL, many studies have been conducted to dissect its genetic basis [12,31,59]. In the present work, a total of 37 stable QTL associated with PH and ILs were identified, 26 of which were consistent with QTL regions previously reported (Table 2). For QIl2.zzu.1B.2, QPh.zzu.5A.1, QIl4.zzu.5A.2, QIl4.zzu.7A.2, and QIl5.zzu.7A.5, genomic loci associated with PH were also identified in a genome-wide association study using a panel of 287 wheat accessions collected over the past 100 years [55]. Furthermore, two QTL (QPht/Sl.cau-2D.1 and QPht/Sl.cau-2D.2) linked in the coupling phase on chromosome 2DS with pleiotropic effects on PH and SL were separated and characterized using two near-isogenic line (NIL) pairs in a previous study [64]. Similarly, this study detected a QTL cluster on chromosome 2DS, which includes three co-localized stable QTL intervals (Table 3, Figure 3). Compared to previous findings, we speculate that this co-localized region (QPh.zzu.2D.1 and QIl1.zzu.2D.1) may be a new genomic locus for PH and IL. In addition, 65 QTL-rich clusters (QRC) for PH were curated by thoroughly summarizing dwarfing loci from QTL linkage analyses and genome-wide association studies published from 2003 to 2022 [31]. By comparing with the QRC locations according to the IWGSC RefSeq v2.1, two co-localized stable QTL intervals on chromosomes 4BS (QPh.zzu.4B.1, QIl2.zzu.4B.1, QIl3.zzu.4B.1 and QIl4.zzu.4B.1) and 7BL (QPh.zzu.7B.1, QIl1.zzu.7B.1, QIl2.zzu.7B.1 and QIl3.zzu.7B.1) were identified as potentially novel loci in this study.

4.3. Promising Candidate Genes Associated with PH and ILs

With the rapid development of wheat genomics research, including genetic and physical mapping, whole-genome sequencing, and the advent of pan-omics technologies, comparative genomics research and gene discovery in wheat have been greatly facilitated [65,66]. In this work, based on gene function annotation, the cloning and identification of rice orthologous genes, analysis using the QTG miner module, and gene expression pattern analysis, a total of 63 potential candidate genes were prioritized within the stable QTL intervals (Table 4, Figure 5). For QPh.zzu.2D.4 and QPh.zzu.4B.3, the ‘Green Revolution’ gene Rht1 and the alternative gene Rht8 were identified in the QTL regions of chromosomes 4BS and 2DS, respectively [21,29]. Furthermore, it was demonstrated that the TB1 gene with the QPh.zzu.4B.3 region regulates PH and stem internode length in bread wheat using pVRN1:TB-D1 transgenic lines [11]. The ZnF-B gene, which encodes a RING-type E3 ligase within the r-e-z haploblock, regulates PH via the BR signaling pathway [9]. Notably, the candidate gene TraesCS4B03G0091100, located near the TB1 gene, encodes a phosphatidylinositol 4-phosphate 5-kinase 1. Its orthologous rice gene Os03g0705300 (OsPIP5K1) has been reported to act with DWT1 and/or DWL2 to co-ordinately regulate the uniform growth of rice shoots through nuclear phosphoinositide signals [67]. Likewise, multiple candidate genes within the stable QTL intervals on chromosome 4B were listed (Table 4).
As one of the largest families of transcription factors in plants, the basic helical–loop–helical (bHLH) proteins play an important role in modulating BR signaling [68,69]. In addition to the Rht8 gene, TraesCS2D03G0198200, a candidate gene within the QPh.zzu.2D.4 interval, encodes the transcription factor bHLH148. A recent study showed that knockout mutants of the orthologous rice gene Os03g0311600 (OsAIF1; OsbHLH176) had lower PH than the wild type but an elongated fifth internode [68]. Furthermore, the candidate gene TraesCS7A03G0263900 within the QPh.zzu.7A.1 interval encodes mitogen-activated protein kinase 1. Mitogen-activated protein kinase (MAPK) cascades are key signaling modules associated with numerous responses, including abiotic and biotic stress, hormonal changes, and developmental processes [70,71]. A dwarf and small grain1 (dsg1) mutant in rice was identified and found to encode the mitogen-activated protein kinase OsMAPK6, which affects BR homeostasis and signaling [72]. For QPh.zzu.5A.1, a candidate gene that encodes a respiratory burst oxidase homolog protein C was identified. The orthologous rice gene Os11g0537400 (OsrbohI; Osrboh8) was demonstrated to regulate rice growth via the jasmonic acid (JA) synthesis and signaling pathways [73]. Overall, further studies are needed to confirm the roles of the candidate genes identified in this study through functional approaches such as mutant screening, gene editing, and virus-induced gene silencing (VIGS).

5. Conclusions

In the present study, PH and its component ILs were evaluated in a set of 184 RILs derived from BN4199/ZYM2 across two field locations over two years, revealing significantly positive correlations among PH and the five ILs’ traits. The H2 of PH and the five ILs in the RIL population all exceeded 80%. A total of 37 stable QTL were identified on chromosomes 1B, 2B, 2D, 4B, 5A, 7A, 7B and 7D, accounting for 3.86–25.97% of the phenotypic variation. Meanwhile, 23 co-localized QTL associated with at least two traits were detected, and the QTL cluster regions were found on chromosomes 2D, 4B, 5A, 7A, and 7B. Additionally, based on gene function annotation, the cloning and identification of rice orthologous genes, and analysis using the QTG miner module, a total of 63 potential candidate genes (e.g., Rht1, Rht8, TB1 and ZnF-B) were prioritized within the stable QTL intervals, and their tissue expression patterns were analyzed. These results deepen our understanding of the genetic basis of PH and ILs in wheat and provide a foundation for the further validation and functional characterization of candidate genes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes17050567/s1, Table S1: Statistical analysis of plant height (PH) and five internode lengths (ILs) for parents and the RIL population across different environments; Table S2: QTL for plant height (PH) and five internode lengths (ILs) identified in the BN4199/ZYM2-RIL population across different environments; Table S3: Expression of candidate genes in various tissues; Table S4: List of primers for reverse transcription–quantitative polymerase chain reaction (RT-qPCR) analysis. Figure S1: RT-qPCR analysis of ten candidate genes in the stems of the parents ZYM2 and BN4199 during the jointing stage. *, and **: significant differences at the p < 0.05, and p < 0.01 levels, respectively. ns: not significant.

Author Contributions

W.W. and P.G. conceived the project; W.W. provided seeds of the genotypes; Z.W. and S.G. performed statistical and QTL analyses and data visualization; M.L., X.W. and D.C. participated in field experiments and phenotypic assessments; W.W., S.G. and P.G. wrote the manuscript; Q.C., B.L., H.X. and J.L. provided extensive revisions and assisted in revising the manuscript; and P.G. and Z.J. coordinated and supervised the activities. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Postdoctoral Science Foundation (2024M752946), the National Natural Science Foundation of China (32201862), the Scientific and Technological Research Project of Henan Province of China (242102111135), and the Natural Science Foundation of Henan Province of China (242300421543).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are contained within the article and the supplementary materials.

Acknowledgments

We are grateful to Wenhui Wei for providing the seeds of the genetic population.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PHPlant height
ILInternode length
SLSpike length
RILRecombinant inbred line
QTLQuantitative trait loci
IWGSCInternational Wheat Genome Sequencing Consortium
CSChinese Spring
MASMarker-assisted selection

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Genes 17 00567 g001
Figure 1. Phenotype comparison of plant height (PH, (A,B)) and five internode lengths (IL1–IL5, (C–G)) between the parents ZYM2 and BN4199. *, **, ***: significant differences at the p < 0.05, p < 0.01 and p < 0.001 levels, respectively.
Figure 1. Phenotype comparison of plant height (PH, (A,B)) and five internode lengths (IL1–IL5, (C–G)) between the parents ZYM2 and BN4199. *, **, ***: significant differences at the p < 0.05, p < 0.01 and p < 0.001 levels, respectively.
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Genes 17 00567 g002
Figure 2. Correlation analysis of plant height-related traits in the ZYM2/BN4199-RIL population using BLUE values. ***: Significant at the p < 0.001 level.
Figure 2. Correlation analysis of plant height-related traits in the ZYM2/BN4199-RIL population using BLUE values. ***: Significant at the p < 0.001 level.
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Figure 3. Distribution of stable QTL on chromosomes.
Figure 3. Distribution of stable QTL on chromosomes.
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Figure 4. Aggregation effect analysis of stable QTL for plant height (PH, (A)) and five internode lengths (IL1–IL5, (B–F)).
Figure 4. Aggregation effect analysis of stable QTL for plant height (PH, (A)) and five internode lengths (IL1–IL5, (B–F)).
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Figure 5. Heatmap of candidate gene expression across different tissues and developmental stages.
Figure 5. Heatmap of candidate gene expression across different tissues and developmental stages.
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Table 1. Heritability (H2) for plant height (PH) and five internode lengths (ILs) in the BN4199/ZYM2-RIL population.
Table 1. Heritability (H2) for plant height (PH) and five internode lengths (ILs) in the BN4199/ZYM2-RIL population.
TraitSourceDFSSMSF-ValuePCVGCVH2
PHGenotype183289,475.18751581.831754.1062 ***16.95%14.23%96.57%
Environment325,489.68958496.5635290.6229 ***
GE_interaction53029,199.064555.09261.8844 ***
Block/Env85115.3491639.418621.8712 ***
Error136739,965.195329.2357
IL1Genotype18337,321.4844203.942536.316 ***20.70%13.98%92.82%
Environment316,399.98055466.6602973.4479 ***
GE_interaction5308132.642615.34462.7324 ***
Block/Env81014.4549126.806922.5805 ***
Error13677676.75835.6158
IL2Genotype18314,107.592877.090731.8138 ***20.05%14.77%91.28%
Environment31555.4806518.4935213.9721 ***
GE_interaction5303781.95617.13582.9448 ***
Block/Env8154.564319.32057.9732 ***
Error13673312.49122.4232
IL3Genotype18311,218.974661.305933.6283 ***23.23%18.82%95.19%
Environment3545.6771181.892499.774 ***
GE_interaction5301593.63113.00691.6494 ***
Block/Env8144.964618.12069.9397 ***
Error13672492.10111.823
IL4Genotype1837947.225643.427527.1927 ***29.64%22.54%93.62%
Environment3758.905252.9683158.3999 ***
GE_interaction5301510.57692.85011.7847 ***
Block/Env8155.567519.445912.1764 ***
Error13672183.13131.597
IL5Genotype1832958.838916.168511.8842 ***43.03%26.28%88.27%
Environment3714.0789238.0263174.9542 ***
GE_interaction5301042.5281.9671.4458 ***
Block/Env8143.523317.940413.1866 ***
Error13241801.30991.3605
DF: Degree of freedom; SS: Sum of square; MS: Mean sum of square; PCV: Phenotypic coefficients of variation; GCV: Genotypic coefficients of variation. H2: Heritability. ***: Significant at the p < 0.001 level.
Table 2. Stable QTL for plant height (PH) and five internode lengths (ILs) in the BN4199/ZYM2-RIL population.
Table 2. Stable QTL for plant height (PH) and five internode lengths (ILs) in the BN4199/ZYM2-RIL population.
TraitQTL NameChromosomeGenetic Interval (cM)Physical Interval (Mb)LODAdditive EffectR2EnvironmentReference
PHQPh.zzu.2D.12D1.3~7.89.7~12.94.60~7.393.28~4.717.93~12.95%E1, E2, E3, E4, BLUE
QPh.zzu.2D.22D10.8~16.214.5~16.75.10~6.513.70~4.149.47~10.91%E2, E4, BLUE[45]
QPh.zzu.2D.42D33.9~65.526.8~50.43.07~3.623.20~3.445.14~5.39%E1, E2[21,29]
QPh.zzu.4B.14B2.0~15.412.2~16.83.16~3.60−3.12~−2.655.14~5.38%E1, E3
QPh.zzu.4B.24B16.3~24.116.8~22.42.62~3.14−2.96~−2.464.61~4.62%E1, E3[46]
QPh.zzu.4B.34B30.7~4826.0~54.02.90~4.40−3.23~−2.624.64~7.13%E2, E4, BLUE[2]
QPh.zzu.5A.15A136.3~141.8503.7~513.22.79~4.352.42~3.593.96~6.63%E1, E2, BLUE[32,47,48]
QPh.zzu.7A.17A99.2~116.768.0~86.52.58~2.87−2.80~−2.484.16~4.31%E2, E4[49,50]
QPh.zzu.7A.27A116.7~128.886.5~99.92.57~2.68−2.74~−2.453.86~4.02%E2, E4[51]
QPh.zzu.7B.17B121.8~135.7714.0~717.13.53~5.61−4.13~−2.905.59~9.66%E1, E2, E4, BLUE
QPh.zzu.7B.37B139.3~149.1719.1~735.93.94~4.24−3.62~−3.206.84~7.92%E1, E4, BLUE[19,52]
IL1QIl1.zzu.2B.12B67.4~78.748.2~69.93.60~5.490.94~1.405.83~8.72%E3, E4[53]
QIl1.zzu.2D.12D0.2~4.38.8~10.33.91~14.111.49~2.165.51~23.60%E1, E2, BLUE
QIl1.zzu.2D.22D10.9~14.214.5~16.55.55~13.241.67~2.6510.76~25.97%E1, E2, BLUE[45]
QIl1.zzu.2D.32D18.7~23.516.5~19.93.51~5.050.98~1.735.91~7.18%E1, E3[45]
QIl1.zzu.2D.42D24.8~46.520.4~35.23.75~6.271.32~1.756.03~13.05%E1, E3, E4[21,29]
QIl1.zzu.7B.17B124.4~134.3714.0~719.24.43~5.01−1.37~−1.067.35~8.09%E3, E4
QIl1.zzu.7B.37B138.6~148.6719.1~735.93.13~6.14−1.64~−0.925.62~11.59%E2, E3, E4[19,52]
QIl1.zzu.7D.17D8.6~23.313.0~283.03~4.660.91~1.315.60~7.76%E3, E4[54]
IL2QIl2.zzu.1B.21B146.7~155.8569.1~572.73.49−0.80~−0.656.50~6.70%E2, E4[55]
QIl2.zzu.2D.22D8.6~16.213.4~16.52.53~3.330.57~0.735.18~5.30%E1, E4[45]
QIl2.zzu.4B.14B5.1~15.912.2~16.83.66~5.00−0.87~−0.716.50~7.89%E1, E2, BLUE
QIl2.zzu.4B.24B17.9~23.917.4~22.72.73~4.25−0.82~−0.645.03~6.89%E1, E2, BLUE[46]
QIl2.zzu.4B.34B64.1~68.8162.1~310.53.65~4.83−0.94~−0.646.12~9.27%E3, E4, BLUE[56,57]
QIl2.zzu.7B.17B120.8~135.7714.0~718.72.79~6.38−1.00~−0.695.27~11.33%E1, E3, BLUE
IL3QIl3.zzu.4B.14B4.7~15.712.2~16.83.38~7.62−1.02~−0.596.14~14.17%E1, E2, E3, E4, BLUE
QIl3.zzu.4B.24B18.1~32.517.7~28.04.17~6.94−1.04~−0.677.96~12.72%E1, E2, E3, E4, BLUE[46]
QIl3.zzu.7B.17B124.8~135.7714.0~718.72.98~6.32−0.76~−0.525.10~10.77%E1, E4, BLUE
IL4QIl4.zzu.4B.14B7.6~16.312.2~16.83.20~4.72−0.70~−0.515.65~8.00%E1, E3
QIl4.zzu.4B.24B19.2~23.917.7~22.43.37~3.74−0.65~−0.525.97~6.86%E1, E3[46]
QIl4.zzu.4B.34B63.9~68.4144.0~357.53.10~4.12−0.55~−0.465.16~7.15%E2, E4[56,57]
QIl4.zzu.5A.25A136.6~141.8503.7~513.23.92~5.330.57~0.776.92~9.09%E1, E3[32,47,48]
QIl4.zzu.5A.35A145.4~148.9533.0~535.83.64~4.050.55~0.676.40~7.01%E1, E3
QIl4.zzu.7A.17A97.8~107.468.0~82.75.14~7.31−0.73~−0.639.24~13.09%E2, E4[49,50]
QIl4.zzu.7A.27A159.8~171.9180.6~206.72.53~5.150.41~0.634.31~9.17%E2, E4[55]
IL5QIl5.zzu.7A.37A99.2~107.978.5~82.76.23~11.90−0.55~−0.4511.09~20.33%E1, E2, E4, BLUE[49,50]
QIl5.zzu.7A.67A160.3~169.3168.7~218.53.30~6.250.32~0.447.14~11.11%E2, E4, BLUE[55]
Note: A positive additive effect indicates that the increasing allele of the corresponding QTL is contributed by Zhengyinmai 2 (ZYM2), whereas a negative additive effect indicates that the increasing allele is contributed by Bainong 4199 (BN4199).
Table 3. Summary of co-localized QTL regions identified for plant height (PH) and five internode lengths (ILs).
Table 3. Summary of co-localized QTL regions identified for plant height (PH) and five internode lengths (ILs).
ChromosomeTraitGenetic Interval (cM)Physical Interval (Mb)QTL Name
1BIL3, IL4159.9~170.8578.4~592.8QIl3.zzu.1B.1; QIl4.zzu.1B.1
1DPH, IL1, IL3145.4~154.6401.3~414.1QPh.zzu.1D; QIl1.zzu.1D.2; QIl3.zzu.1D
2DPH, IL1, IL2, IL4, IL50~7.88.8~12.9QPh.zzu.2D.1; QIl1.zzu.2D.1; QIl2.zzu.2D.1; QIl4.zzu.2D.1; QIl5.zzu.2D.1
2DPH, IL1, IL2, IL58.5~16.213.4~16.5QPh.zzu.2D.2; QIl1.zzu.2D.2; QIl2.zzu.2D.2; QIl5.zzu.2D.2
2DPH, IL1, IL518.7–29.516.5~26.8QPh.zzu.2D.3; QIl1.zzu.2D.3; QIl5.zzu.2D.3
2DPH, IL1, IL4, IL523.5–65.526.8~50.4QPh.zzu.2D.4; QIl1.zzu.2D.4; QIl4.zzu.2D.2; QIl5.zzu.2D.4
2DIL4, IL578.1~91.958.5~67.7QIl4.zzu.2D.3; QIl5.zzu.2D.5
4APH, IL2, IL3, IL485.5~98.6598.7~610.9QPh.zzu.4A.1; QIl2.zzu.4A; QIl3.zzu.4A; QIl4.zzu.4A
4BPH, IL1, IL2, IL3, IL42~16.312.2~16.8QPh.zzu.4B.1; QIl1.zzu.4B.1; QIl2.zzu.4B.1; QIl3.zzu.4B.1; QIl4.zzu.4B.1
4BPH, IL1, IL2, IL3, IL416.3~32.516.8~28QPh.zzu.4B.2; QIl1.zzu.4B.2; QIl2.zzu.4B.2; QIl3.zzu.4B.2; QIl4.zzu.4B.2
4BPH, IL330.3~4828.0~54.0QPh.zzu.4B.3; QIl3.zzu.4B.3
4BIL1, IL2, IL3, IL463.1~68.9144.0~357.5QIl1.zzu.4B.3; QIl2.zzu.4B.3; QIl3.zzu.4B.4; QIl4.zzu.4B.3
4DPH, IL3, IL40~1.437.3~42.5QPh.zzu.4D.1; QIl3.zzu.4D.1; QIl4.zzu.4D
5APH, IL3, IL4136.3~141.8503.7~513.2QPh.zzu.5A.1; QIl3.zzu.5A.1; QIl4.zzu.5A.2
5AIL4, IL5145.4~148.9525.6~535.8QIl4.zzu.5A.3; QIl5.zzu.5A.2
5APH, IL2, IL3151.2~157539.6~550.0QPh.zzu.5A.3; QIl2.zzu.5A; QIl3.zzu.5A.2
6BIL1, IL3, IL4195.7~206.6703.6~714.7QIl1.zzu.6B.3; QIl3.zzu.6B; QIl4.zzu.6B
6DIL2, IL3, IL4156.1~172.3454.6~469.8QIl2.zzu.6D; QIl3.zzu.6D; QIl4.zzu.6D
7APH, IL4, IL596.2~116.768.0~86.5QPh.zzu.7A.1; QIl4.zzu.7A.1; QIl5.zzu.7A.2
7APH, IL1, IL3, IL4, IL5159.8~176.8143.2~237.1QPh.zzu.7A.3; QIl1.zzu.7A.1; QIl3.zzu.7A; QIl4.zzu.7A.2; QIl5.zzu.7A.6
7APH, IL4203.3~210.5613.2~639.1QPh.zzu.7A.6; QIl4.zzu.7A.4
7BPH, IL1, IL2, IL3, IL4120.7~135.7714.8~717.7QPh.zzu.7B.1; QIl1.zzu.7B.1; QIl2.zzu.7B.1; QIl3.zzu.7B.1; QIl4.zzu.7B
7BPH, IL1, IL2, IL3139.3~149.1719.1~735.9QPh.zzu.7B.3; QIl1.zzu.7B.3; QIl2.zzu.7B.3; QIl3.zzu.7B.2
Table 4. The putative candidate genes identified within the stable QTL regions.
Table 4. The putative candidate genes identified within the stable QTL regions.
Gene IDGene NameLocation (IWGSC RefSeqv2.1)DescriptionOryza Sativa
TraesCS2B03G0185500 chr2B:51952971-51956065 (−)Transcription factor MYB36 Os08g0433400
TraesCS2B03G0197800 chr2B:56466313-56470936 (−)GDT1-like protein 2, chloroplastic Os11g0544500
TraesCS2B03G0199900 chr2B:57026094-57027547 (+)UDP-glycosyltransferase 79 Os04g0206700
TraesCS2B03G0205100 chr2B:58334563-58335201 (+)Germin-like protein 8–14 Os08g0460000
TraesCS2B03G0222800 chr2B:63367187-63468933 (−)Two-component response regulator-like PRR37 Os07g0695100
TraesCS2B03G0223300 chr2B:63771237-63775184 (+)L-ascorbate peroxidase 2, cytosolic Os07g0694700
TraesCS2B03G0230000 chr2B:66373336-66376648 (−)Omega-3 fatty acid desaturase, chloroplastic Os07g0693800
TraesCS2D03G0047500 chr2D:11057729-11060001 (−)Probably inactive receptor-like protein kinase At2g46850 Os10g0351500
TraesCS2D03G0058300 chr2D:12785126-12799210 (−)ABC transporter B family member 25, mitochondrial Os06g0128300
TraesCS2D03G0076700 chr2D:15339456-15341024 (+)S-adenosylmethionine synthase 1 Os01g0323600
TraesCSU03G0022100Rht8chrUn:18733736-18737027 (−)Ribonuclease H-Like 1Os04g0261400
TraesCS2D03G0099000 chr2D:20858057-20860133 (+)Tricetin 3′,4′,5′-O-trimethyltransferase Os08g0157500
TraesCS2D03G0198200 chr2D:49296329-49297483 (+)Transcription factor bHLH148 Os03g0311600
TraesCS4B03G0039500 chr4B:15737018-15740036 (−)Fe(2+) transport protein 1 Os03g0667500
TraesCS4B03G0041000 chr4B:15848203-15853551 (−)Guanine nucleotide-binding protein subunit beta Os03g0669200
TraesCS4B03G0045300 chr4B:17485639-17488430 (+)Endo-1,4-beta-xylanase 1 Os03g0672900
TraesCS4B03G0053300 chr4B:19889929-19895453 (−)BEL1-like homeodomain protein 6 Os03g0680800
TraesCS4B03G0060700 chr4B:23432237-23437771 (+)Magnesium transporter MRS2-A, chloroplastic Os03g0684400
TraesCS4B03G0061400 chr4B:23847772-23850308 (+)Ferredoxin C 2, chloroplastic Os03g0685000
TraesCS4B03G0062000 chr4B:23945006-23948825 (+)Gamma-glutamyl peptidase 5 Os03g0685300
TraesCS4B03G0091100 chr4B:32026724-32033370 (−)Phosphatidylinositol 4-phosphate 5-kinase 1 Os03g0705300
TraesCS4B03G0092100TB1chr4B:33121434-33122498 (+)Transcription factor TB1 Os03g0706500
TraesCS4B03G0092600ZnF-Bchr4B:33478324-33487967 (−)RING-type E3 ligaseOs03g0706900
TraesCS4B03G0093100Rht1chr4B:33614435-33616890 (+)DELLA protein RHT-1 Os03g0707600
TraesCS4B03G0096900 chr4B:34988758-34989243 (−)Chemocyanin Os03g0709100
TraesCS4B03G0110700 chr4B:42801164-42805060 (−)ATP-dependent DNA helicase DDM1 Os03g0722400
TraesCS4B03G0112200 chr4B:43560903-43567074 (−)Phytochrome A type 3 Os03g0719800
TraesCS4B03G0116600 chr4B:45598303-45600496 (−)SCARECROW-LIKE protein 7 Os03g0723000
TraesCS4B03G0117300 chr4B:45930921-45934993 (−)Uncharacterized sugar kinase slr0537 Os01g0105900
TraesCS4B03G0121800 chr4B:48378594-48380754 (+)7-methyl-GTP pyrophosphatase Os03g0724700
TraesCS4B03G0290400 chr4B:155152087-155157174 (−)OBERON-like protein Os12g0514400
TraesCS4B03G0317200 chr4B:181393820-181403278 (−)E3 SUMO-protein ligase SIZ2 Os03g0719100
TraesCS4B03G0317800 chr4B:182163392-182169863 (+)Protein DWARF 53 Os11g0104300
TraesCS4B03G0324700 chr4B:186822081-186825362 (−)GDSL esterase/lipase CPRD49 Os11g0708400
TraesCS4B03G0343200 chr4B:202475060-202477534 (+)UDP-arabinopyranose mutase 1 Os03g0599800
TraesCS4B03G0364300 chr4B:224505364-224506622 (−)Protein ELF4-LIKE 4 Os11g0621500
TraesCS4B03G0389300 chr4B:254960827-254974470 (−)Villin-2 Os03g0356700
TraesCS4B03G0404200 chr4B:277078935-277090963 (−)Ubiquitin-like-specific protease ESD4 Os03g0344300
TraesCS4B03G0422100 chr4B:306200080-306210987 (+)Histone chaperone domain CHZOs11g0544600
TraesCS4B03G0422400 chr4B:306823340-306826867 (−)Inactive purple acid phosphatase-like proteinOs11g0586300
TraesCS4B03G0437700 chr4B:319887960-319893701 (+)RNA polymerase II C-terminal domain phosphatase-like 3 Os11g0521900
TraesCS4B03G0445600 chr4B:327792210-327794025 (+)Probable serine/threonine-protein kinase PBL15 Os03g0364400
TraesCS4B03G0449300 chr4B:335351963-335359341 (−)Phosphatidate phosphatase PAH2 Os11g0615000
TraesCS4B03G0459300 chr4B:346726523-346748266 (−)ATP-dependent Clp protease proteolytic subunit 6, chloroplastic Os03g0411500
TraesCS5A03G0721600 chr5A:505345139-505349040 (+)Hsp70 nucleotide exchange factor fes1 Os09g0512700
TraesCS5A03G0734800 chr5A:511126101-511128863 (−)Respiratory burst oxidase homolog protein C Os11g0537400
TraesCS5A03G0778700 chr5A:534224114-534227378 (−)Protein ODORANT1 Os09g0532900
TraesCS7A03G0262100 chr7A:70990291-70993190 (−)F-box/LRR-repeat MAX2 homolog Os06g0154200
TraesCS7A03G0263900 chr7A:71403697-71411075 (−)Mitogen-activated protein kinase 1 Os06g0154500
TraesCS7A03G0293700 chr7A:83704628-83709372 (+)Protein disulfide isomerase-like 1-5 Os06g0163400
TraesCS7A03G0479300 chr7A:172295262-172296871 (−)Ribonucleoside-diphosphate reductase small chain Os06g0257450
TraesCS7A03G0482500 chr7A:174101472-174102770 (+)Zinc finger protein CONSTANS-LIKE 16 Os06g0264200
TraesCS7A03G0482900 chr7A:174308926-174314065 (+)Protein NRT1/ PTR FAMILY 3.1 Os06g0264500
TraesCS7A03G0485700 chr7A:175963414-175964402 (−)Gibberellin-regulated protein 4 Os06g0266800
TraesCS7A03G0490700 chr7A:178822117-178827345 (+)Aldehyde dehydrogenase family 2 member B4, mitochondrial Os06g0270900
TraesCS7A03G0510200 chr7A:191343804-191349225 (−)Zinc finger protein CONSTANS-LIKE 9 Os06g0298200
TraesCS7A03G0539500 chr7A:209112211-209132047 (+)Ubiquitin-like-specific protease 1D Os06g0487900
TraesCS7B03G1197700 chr7B:717798428-717806399 (−)Zinc finger CCCH domain-containing protein 7 Os06g0638000
TraesCS7B03G1209100 chr7B:720834549-720838034 (−)Receptor-like cytoplasmic kinase 176 Os05g0110900
TraesCS7B03G1226700 chr7B:725133900-725140584 (+)Protein MEMO1 Os08g0299000
TraesCS7D03G0068000 chr7D:16298182-16301159 (−)6-phosphogluconate dehydrogenase, decarboxylating 1 Os06g0111500
TraesCS7D03G0068100 chr7D:16346353-16351684 (+)Extra-large guanine nucleotide-binding protein 3 Os06g0111400
TraesCS7D03G0081400 chr7D:19316635-19322955 (−)Protein argonaute 1B Os04g0566500
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Wan, Z.; Ge, S.; Li, M.; Wang, X.; Cui, D.; Chi, Q.; Li, B.; Xu, H.; Lu, J.; Jiao, Z.; et al. Identification of QTL and Candidate Genes Controlling Plant Height and Internode Length in a Newly Characterized Bread Wheat Recombinant Inbred Population. Genes 2026, 17, 567. https://doi.org/10.3390/genes17050567

AMA Style

Wan Z, Ge S, Li M, Wang X, Cui D, Chi Q, Li B, Xu H, Lu J, Jiao Z, et al. Identification of QTL and Candidate Genes Controlling Plant Height and Internode Length in a Newly Characterized Bread Wheat Recombinant Inbred Population. Genes. 2026; 17(5):567. https://doi.org/10.3390/genes17050567

Chicago/Turabian Style

Wan, Zidong, Shuai Ge, Mengxin Li, Xinyan Wang, Dongjie Cui, Qing Chi, Bing Li, Hangbo Xu, Jialing Lu, Zhen Jiao, and et al. 2026. "Identification of QTL and Candidate Genes Controlling Plant Height and Internode Length in a Newly Characterized Bread Wheat Recombinant Inbred Population" Genes 17, no. 5: 567. https://doi.org/10.3390/genes17050567

APA Style

Wan, Z., Ge, S., Li, M., Wang, X., Cui, D., Chi, Q., Li, B., Xu, H., Lu, J., Jiao, Z., Wei, W., & Guan, P. (2026). Identification of QTL and Candidate Genes Controlling Plant Height and Internode Length in a Newly Characterized Bread Wheat Recombinant Inbred Population. Genes, 17(5), 567. https://doi.org/10.3390/genes17050567

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