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QTL Mapping and Candidate Gene Identifying for N, P, and K Use Efficiency at the Maturity Stages in Wheat

National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai’an 271018, China
Key Laboratory of Biochemistry and Molecular Biology, College of Biological and Agricultural Engineering, Weifang University, Weifang 261061, China
Jinan Academy of Agricultural Science, Jinan 250316, China
National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Institute of Industrial Crops, Shandong Academy of Agricultural Sciences, Jinan 250108, China
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2023, 14(6), 1168;
Submission received: 5 May 2023 / Revised: 23 May 2023 / Accepted: 25 May 2023 / Published: 27 May 2023
(This article belongs to the Special Issue Wheat Genomics, Genetics and Breeding)


Nitrogen (N), phosphorus (P), and potassium (K) are the three most important mineral nutrients for crop growth and development. We previously constructed a genetic map of unigenes (UG-Map) based on their physical positions using a RIL population derived from the cross of “TN18 × LM6” (TL-RILs). In this study, a total of 18 traits related to mineral use efficiency (MUE) of N/P/K were investigated under three growing seasons using TL-RILs. A total of 54 stable QTLs were detected, distributed across 19 chromosomes except for 3A and 5B. There were 50 QTLs associated with only one trait, and the other four QTLs were associated with two traits. A total of 73 candidate genes for stable QTLs were identified. Of these, 50 candidate genes were annotated in Chinese Spring (CS) RefSeq v1.1. The average number of candidate genes per QTL was 1.35, with 45 QTLs containing only one candidate gene and nine QTLs containing two or more candidate genes. The candidate gene TraesCS6D02G132100 (TaPTR gene) for QGnc-6D-3306 belongs to the NPF (NRT1/PTR) gene family. We speculate that the TaPTR gene should regulate the GNC trait.

1. Introduction

Wheat (Triticum aestivum L.) is a major crop around the world. It is a staple food for 30% of the world’s population [1] and accounts for approximately one-fifth of the total calories consumed by humans [2]. Nitrogen (N), phosphorus (P), and potassium (K) are the most essential and important nutrient elements for plant growth and development [3]. N fertilizer is the main input used to boost grain yield and grain protein content, and agricultural production relies on synthetic N fertilizer to increase productivity [4]. P is an important component of plant biomolecules such as DNA, RNA, ADP, ATP, and NDPH. This makes it the second-most essential nutrient after N for plant development and crop production. As an essential macronutrient, K is involved in most biochemical reactions in plants, such as stomatal regulation, osmoregulation, protein synthesis, carbohydrate metabolism, and enzyme activation. In these processes, K can regulate the rate of generative reactions [5]. In recent decades, global fertilizer application has resulted in a significantly increased application of N, P, and K [6]. In an economic sense, fertilizer application has exceeded the optimal application rate, resulting in a massive waste of resources and economic losses [7]. Reliance on chemical fertilizers is clearly not a long-term solution, as both their production and application cause pollution and threaten environmental sustainability.
Mineral use efficiency (MUE) consists of two components: mineral uptake efficiency (MUpE), the ability of plants to remove minerals from the soil, and mineral utilization efficiency (MUtE), the ability of plants to use minerals to produce grain [8,9,10]. In wheat, the MUE of N, P, and K varies greatly among genotypes by growth stage, and the analysis of the MUE genetic basis is of great value [11,12,13,14,15,16]. The development of new high-yielding crop varieties under relatively low N/P/K environments is an effective way to improve MUE [17,18,19,20].
The traits associated with MUE are quantitative traits. Quantitative trait loci (QTL) analysis provides an efficient way to breakdown complex traits into loci and study their relative effects on specific traits [21]. In wheat, a large number of QTLs related to MUE have been mapped in low and high nutrient environments [22,23,24,25,26,27,28]. For example, Zhang et al. [29] detected 121 and 130 QTLs for N use efficiency traits under hydroponic and field treatments, respectively. Yang et al. [30] detected twelve new QTLs influencing root and biomass-related traits for N uptake under three hydroponic N levels. Su et al. [31] identified seven QTLs related to P utilization efficiency under high and low P treatments. Yuan et al. [14] detected 55 and 68 QTLs related to P use efficiency under hydroponic and field treatments, respectively. Gong et al. [32] detected a total of 127 QTLs related to K use efficiency under 10 different N and P concentrations. Safdar et al. [33] identified two QTLs related to P use efficiency in a genome-wide association study and found that six genes were highly expressed in pyruvate metabolism and the TCA cycle under low phosphorus treatment.
We previously constructed a set of recombinant inbred lines (RILs) derived from the “TN18 × LM6” cross. The objective of the present study was to perform QTL analysis for MUE traits in field trials of RILs at different treatments of N, P, and K and then to find relatively stable QTLs and candidate genes.

2. Materials and Methods

2.1. Plant Materials

The population used for QTL analysis in this study consisted of a set of RILs derived from the cross of “TN18 × LM6” (TL-RILs, 184 lines) [14,29]. The female parent, TN18, is a cultivar developed by our group that was approved by the Crop Variety Approval Committee (CVAC) of Shandong Province in 2008. TN18 was planted on approximately 300,000 hectares per year in the Huang-Huai Winter Wheat Region, China. The male parent, LM6, is an elite wheat line whose mother is a sister line of the famous cultivar Jimai 22. The two parents showed obvious differences in MUE, with the LM6 having a higher MUE than the TN18 as a whole. A total of 184 lines of RILs were randomly selected from the original 305 lines to be studied.

2.2. Experimental Design

We constructed eight nutrient plots of 110 m2 (10 × 11 m) in the experimental station of Shandong Agricultural University for mineral nutrient element experiments. The plots were divided by 1.5-meter-deep cement brick walls. The soil structure was maintained as a natural field with fertile soil [8]. Mineral nutrients were depleted by growing wheat and maize each year until nutrient levels met trial requirements. Three field trials were conducted during the 2012–2013, 2013–2014, and 2014–2015 growing seasons.
Four treatments were designed in the three trials: CK (target grain yield: 9000 kg/ha) and low N (LN), low P (LP), and low K (LK) (target grain yield: 6000 kg/ha for each corresponding element) [14,29]. All P2O5 and K2O and 60% of the N were applied before sowing, and 40% of the N was applied at the stem elongation stage. Each trial was a complete block design with two replications in two nutrient plots. Each line of TL-RILs was sown in two rows, with 1 m in length and 25 cm between rows. Twenty seeds for one row were sown in 2012–2013 and 2013–2014, and 40 seeds for one row were sown in 2014–2015. Seeds were sown on 15 October, and plants were harvested on 13–15 June.

2.3. Trait Measurement

A total of 18 MUE traits were investigated (Table 1), including 12 MUpE traits (GNC, GPC, GKC, StNC, StPC, StKC, GNA, GPA, GKA, StNA, StPA, and StKA) and six MUtE traits (GNUE, GPUE, GKUE, StNUE, StPUE, and StPUE). We randomly harvested the above-ground parts of 10 plants of each line in each replicate, and all traits were determined using mixed samples from the same treatment. Of the investigated data, GNC, StNC, GNA, StNA, GNUE, StNUE, GPC, StPC, GPA, StPA, GPUE, and StPUE from 2012–2013 and 2013–2014 were previously determined [14,29].

2.4. Data Analysis

Analysis of variance (ANOVA) was calculated using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA). Since the traits were measured with mixed samples, a two-factor ANOVA was used. The genotypes and treatments were considered two factors using the average values of three growing seasons. All factors involved were considered sources of random effects. The generalized heritability (hB2) was estimated by the following formula: hB2 = σg2/(σg2 + σe2), where σg2 is the genotype variance and σe2 is the total error variance.

2.5. QTL Detection and Candidate Gene Identification

The genetic map of unigenes (UG-Map) for the TL-RILs was used for QTL analysis. Each line of the TL-RILs was sequenced using RNA-Seq technology. The clean reads were mapped to the Chinese Spring (CS) RefSeq v1.1. A total of 31,445 polymorphic sub-unigenes were obtained, which represented 27,983 unigenes. The sub-unigenes were used to construct the UG-Map based on their physical positions [34]. Three software programs were used for QTL analysis: Windows QTL Cartographer 2.5 ( (accessed on 1 August 2019), IciMapping 4.1 ( accessed on 28 January 2019), and MapQTL 6.0 ( accessed on 1 August 2019) [35]. IciMapping 4.1 adopts the complete interval mapping method. Both the Windows QTL Cartographer 2.5 and MapQTL 6.0 software used the composite interval mapping method. For the three software programs, the LOD value was 3.0 and the step size was 0.5 cM. For a single software program analyzing data from the same treatment, we defined a stable QTL when it was detected over the AV + 1 environments. Finally, we employed the stable QTLs that were detected in at least two of the three software programs.
The unigene(s) covered or rounded (not exceeding the middle distance between two unigenes) by the peak position region of a QTL were regarded as the candidate gene(s) of the corresponding QTLs.

3. Results

3.1. Phenotypic Variation and Heritability

For the parents of the RIL population, TN18 and LM6 showed obvious differences in most investigated traits (Table S1). For the RIL population, a wide range of variations existed. Transgressive segregation was observed for all of the 144 trait-treatment combinations (including AV). All of the 18 investigated traits in each treatment showed continuous distributions, which indicates their quantitative nature. The hB2 for the investigated traits ranged from 61.75% (StKC) to 88.33% (GNC) (Table S1).

3.2. Major Characteristics of the Stable QTLs

Using Windows QTL Mapper 2.5, IciMapping 4.1, and MapQTL 6.0 software, a total of 54 stable QTLs were detected for all 18 traits, distributed across 19 chromosomes except for 3A and 5B (Table 2 and Table S2, Figure 1). Among them, 11 QTLs were detected in all three software programs. Twenty-two QTLs showed positive additive effects with the female parent TN18 increasing the QTL effects, whereas 32 QTLs showed negative additive effects with the male parent LM6 increasing the QTL effects. There were 50 QTLs associated with only one trait, and the other four QTLs were associated with two traits.
For the N/P/K concentration traits, 27 stable QTLs were detected (Table 2, Figure 1). Among them, 8, 6, and 3 QTLs were detected for GNC, GPC, and GKC, respectively, distributed across 11 chromosomes: 1B, 1D, 2A, 2B, 3B, 3D, 5D, 6B, 6D, 7A, and 7B. A number of 5, 2, and 3 QTLs were detected for StNC, StPC, and StKC, respectively, distributed across 10 chromosomes: 1A, 1B, 1D, 2A, 2B, 2D, 4B, 4D, 6A, and 7D.
For the N/P/K accumulation traits, a number of 10 stable QTLs were found (Table 2, Figure 1). Among them, 1, 1, and 3 QTLs were found for GNA, GPA, and GKA, respectively, distributed across 5 chromosomes: 1A, 3D, 4A, 6A, and 7B. A number of 3, 2, and 0 QTLs were found for StNA, StPA, and StKA, respectively, and were distributed across four chromosomes, 2A, 5D, 6A, and 7A.
For the MUtE traits, 13 stable QTLs were detected (Table 2, Figure 1). Among them, 1, 2, and 2 QTLs were found for GNUE, GPUE, and GKUE, respectively, distributed across five chromosomes 1A, 2B, 4D, 5A, and 6B. An amount of 1, 2, and 5 QTLs were found for StNUE, StPUE, and StKUE, respectively, distributed across 7 chromosomes: 2B, 3B, 4A, 4B, 5A, 6B, and 6D.
Four stable QTLs were associated with two traits (Table 2, Figure 1). Of these, QStka/Stkc-2A-12963 and QGpa/Gpc-5A-12924 were associated only with the MUpE traits. QGpa/Stpue-2B-18259 and QGka/Stpue-4B-1222 were associated with both the MUpE and MUtE traits.

3.3. Candidate Genes for Stable QTLs

A total of 73 candidate genes (including ncRNAs) for the 54 stable QTLs were identified (Table 2, Figure 1) that were covered or rounded by the regions of the peak positions of the corresponding stable QTLs. Among these candidate genes, 50 were annotated in Chinese Spring (CS) RefSeq v1.1 [2], eight were annotated in TL-RILs, and 15 were ncRNAs. The average number of candidate genes per QTL was 1.35 (73/54) with 45 QTLs (83.3%) containing only one candidate gene, seven QTLs (13.0%) containing two candidate genes, and two QTLs (3.7%) containing 3–8 candidate genes.
For the N/P/K concentration traits, 31 candidate genes were identified, with 23 genes annotated in RefSeq v1.1, one gene annotated in TL-RILs, and seven ncRNAs (Table 2, Figure 1). Of these, five (TraesCS5D02G261500, TraesCS6D02G132100, TraesCS7A02G-364700, TraesCS7B02G406300, and TraesCS7B02G406400), three (TraesCS5D02G459900, TraesCS-6B02G012600, and TraesCS7A02G476700), and three (TraesCS1D02G024700, TraesCS2B02G564200, and TraesCS6D02G115000) high confidence (HC) genes in RefSeq v1.1 for GNC, GPC, and GKC were found, respectively. For StNC, StPC, and StKC, two (TraesCS2A02G213508 and TraesCS2D02G124400), one (TraesCS2B02G131100), and three (TraesCS4D02G229500, TraesCS6A02G063300, and TraesCS7D02G532100) HC genes were identified, respectively.
For the N/P/K accumulation traits, 11 candidate genes were identified, with four genes annotated in RefSeq v1.1, three genes annotated in TL-RILs, and four ncRNAs (Table 2, Figure 1). Of these, only two HC genes were obtained: TraesCS5D02G295200 for StNA and TraesCS6A02G372600 for StPA.
For the MUtE traits, 16 candidate genes were identified, with 12 genes annotated in RefSeq v1.1, two genes annotated in TL-RILs, and two ncRNAs (Table 2, Figure 1). Of these, two HC genes for GKUE, TraesCS4D02G195400 and TraesCS5A02G383800, were identified. For StNUE, StPUE, and StPUE, two (TraesCS4B02G033800 and TraesCS4B02G-034500), one (TraesCS6B02G440300), and four (TraesCS3B02G008600, TraesCS5A02G-555500, TraesCS6B02G133900, and TraesCS6D02G214800) HC genes were found.
A total of fifteen candidate genes from four QTLs were associated with two traits (Table 2, Figure 1). For QGpa/Gpc-5A-12924 and QGpa/Stpue-2B-18259, TraesCS5A02G556400 and TraesCS2B02G609800 were identified, respectively. For QGka/Stpue-4B-1222, 11 candidate genes were identified, included seven genes annotated in RefSeq v1.1, two genes annotated in TL-RILs, and two ncRNAs. This QTL involved five HC genes: TraesCS4B02G-047600, TraesCS4B02G047700, TraesCS4B02G047900, TraesCS4B02G049300, and TraesCS4B-02G049400. For QStka/Stkc-2A-12963, two LC genes were identified.

4. Discussion

The QTL locations were carried out mainly using the genetic map of DNA markers, and a great number of QTLs for MUE traits of N/P/K were identified [14,22,25,26,27,28,29,30,31,32,33]. Using a genetic map of high-density DNA markers, a QTL could cover dozens of genes [36]. Therefore, it is difficult to identify highly reliable candidate genes for corresponding gene cloning. In this study, we used UG-Map for the RNA sequencing of the RIL population [34], which can directly determine the candidate genes for the QTLs. We identified 73 candidate genes from 54 QTLs of MUE traits, with an average of 1.35 candidate genes per QTL. This should provide convenience for the cloning of related genes and the genetic improvement of wheat breeding programs. In addition, the QTLs and their candidate genes in this study were identified according to the physical positions of the reference genome. It is difficult to accurately compare QTLs obtained based on molecular markers.
Because the cloned genes and their functions for MUE were not reported, the relationship between our candidate genes and MUE needs further verification. An example with phenotypic function is the candidate gene TraesCS6D02G132100 from QGnc-6D-3306 for GNC. The TraesCS6D02G132100 gene is annotated as a peptide transporter (TaPTR gene). NRT1 and PTR belong to the same gene family NPF (NRT1/PTR, nitrate transporter 1/peptide transporter) in terms of sequence homology, i.e., proteins that mediate the transmembrane transport of substances such as nitrate and small molecular peptides of 2–3 amino acids [37]. Therefore, we speculate that the TraesCS6D02G132100 gene should regulate the GNC trait.
TraesCS6A02G372600 was the candidate gene for QStpa-6A-10015 for StPA. This gene is annotated as a phosphate regulon sensor protein (TaPhoR gene). The PhoR protein can act as a protease in the inducible high-affinity phosphate transport system (Pst), which is composed of Pho regulators that accomplish phosphate transport behavior via transmembrane signaling [38]. Therefore, we speculate that the TraesCS6A02G372600 gene may regulate the StPA trait in wheat.
The TraesCS6B02G012600 gene was the candidate gene for QGpc-6B-361 for GPC. This gene is annotated as an F-box family protein (TaPFB gene). In Arabidopsis, FBX2 is a protein containing WD40 and F-box structural domains that can be used to recognize specific ubiquitination targets. For example, under phosphorus-sufficient conditions, the fbx2 mutant has higher levels of phosphoenolpyruvate carbolicase kinase 1 (PPCK1) transcripts and significantly higher root hair numbers and total intracellular phosphorus content than the wild type [39]. Therefore, we speculate that the TraesCS2D02G124400 gene may associate with GPC in wheat.
Using the QTL location of UG-Map for TL-RILs, we previously cloned two genes, the XIP (Xylanase Inhibitor Protein) gene, which regulates the SDS-sedimentation value and dough stability time [40], and the TaDHL (ATP-dependent DNA helicase) gene, which regulates the plant height [41]. These results further showed that the candidate genes in this study are favorable for corresponding gene cloning.
For TL-RILs, the male parent TN18 is derived from the cross “Laizhou137 × Yannong 19” and the female parent LM6 is derived from the cross “924402 × Lvmai13”. The strain 924,402 is one parent of the cultivated variety “Jimai22”. Of these parents, Yannong19 and Jimai22 are the authorized varieties with great area and the core parents in wheat breeding programs in China. Moreover, the parent of TN18 is also a core parent in China. Hundreds of authorized varieties derive from Yannong19, Jimai22, and TN18. Therefore, the QTLs and their candidate genes in this study may be widely identified in the varieties of China.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1: Phenotypic performance of the RILs and their parents; Table S2: Stable QTLs detected in the TL-RILs.

Author Contributions

S.L., F.K. and Y.G. conceived and designed the experiments; X.H., M.Z. and M.G., Y.Y. (Yuanyuan Yuan), G.Z., F.K., Y.A. and Y.G. carried out genetic analysis and data curation; G.Z., M.Z., M.G., Y.Y. (Yapei Yuan), F.K. and Y.G. carried out field evaluations and investigations; X.H., M.Z. and S.L. wrote the manuscript, with assistance from the other authors. All authors have read and agreed to the published version of the manuscript.


This work was supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2020MC151); the Agricultural Variety Program of Shandong Province, China (Grant No. 2019LZGC017).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Locations of the 54 stable QTLs and their 73 candidate genes using the TL-RILs. CS and TL are abbreviations of the gene names “TraesCSxx02G”and “TraesTLxx02G”. 1A~ 7D are the names of the 21 chromosomes.
Figure 1. Locations of the 54 stable QTLs and their 73 candidate genes using the TL-RILs. CS and TL are abbreviations of the gene names “TraesCSxx02G”and “TraesTLxx02G”. 1A~ 7D are the names of the 21 chromosomes.
Genes 14 01168 g001
Table 1. Summary of investigated traits and their measurement methods.
Table 1. Summary of investigated traits and their measurement methods.
AbbreviationNameUnitMeasurement Method
GNCGrain N concentrationmg·g−1Using an NC analyzer (KDY-9820, Tongrunyuan Ltd., Beijing, China)
GPCGrain P concentrationmg·g−1Using a sequential plasma spectrometer (ICPS-7500; Shimadzu Corp., Kyoto, Japan)
GKCGrain K concentrationmg·g−1Using a sequential plasma spectrometer (ICPS-7500; Shimadzu Corp., Kyoto, Japan)
StNCStraw N concentrationmg·g−1Using an NC analyzer (KDY-9820, Tongrunyuan Ltd., Beijing, China)
StPCStraw P concentrationmg·g−1Using a sequential plasma spectrometer (ICPS-7500; Shimadzu Corp., Kyoto, Japan)
StKCStraw K concentrationmg·g−1Using a sequential plasma spectrometer (ICPS-7500; Shimadzu Corp., Kyoto, Japan)
GNAGrain N accumulationg·(m2)−1GNC × GW
GPAGrain P accumulationg·(m2)−1GPC × GW
GKAGrain K accumulationg·(m2)−1GKC × GW
StNAStraw N accumulationg·(m2)−1StNC × StW
StPAStraw P accumulationg·(m2)−1StPC × StW
StKAStraw K accumulationg·(m2)−1StKC × StW
GNUEGrain N use efficiencykg2 GW·g−1 GNAGW2/GNA
GPUEGrain P use efficiencykg2 GW·g−1 GPAGW2/GPA
GKUEGrain K use efficiencykg2 GW·g−1 GKAGW2/GKA
StNUEStraw N use efficiencykg2 StW·g−1 StNAStW2/StNA
StPUEStraw P use efficiencykg2 StW·g−1 StPAStW2/StPA
StKUEStraw K use efficiencykg2 StW·g−1 StKAStW2/StKA
GW—grain weight; StW—straw weight.
Table 2. Stable QTLs and their candidate genes in TL-RILs. “TraesTLxx02G……” are the genes that are annotated in TL-RILs; “STRG……” are the noncoding RNAs (ncRNAs).
Table 2. Stable QTLs and their candidate genes in TL-RILs. “TraesTLxx02G……” are the genes that are annotated in TL-RILs; “STRG……” are the noncoding RNAs (ncRNAs).
TraitChromQTLGeneAnnotation in RefSeq v1.1
GNC3DQGnc-3D-6047TraesCS3D02G618200LCGlutathione S-transferase T3
GNC5DQGnc-5D-3809TraesCS5D02G261500F-box protein
GNC6DQGnc-6D-3306TraesCS6D02G132100Peptide transporter
GNC7AQGnc-7A-9048TraesCS7A02G364700Histone-lysine N-methyltransferase
GNC7BQGnc-7B-7984TraesCS7B02G406300Protein transport protein Sec61 subunit γ
TraesCS7B02G406400B3 domain-containing protein
GPC5DQGpc-5D-7948TraesCS5D02G459900Vesicle-associated membrane protein, putative
GPC6BQGpc-6B-361TraesCS6B02G012600F-box family protein
GPC7AQGpc-7A-11455TraesCS7A02G476700Receptor-kinase, putative
GPC7AQGpc-7A-6017TraesCS7A02G246100LCGlycine-rich family protein
GKC1DQGkc-1D-950TraesCS1D02G024700Dehydration-induced 19-like protein
GKC2BQGkc-2B-16317TraesCS2B02G564200Heme oxygenase 1
GKC6DQGkc-6D-3045TraesCS6D02G115000Receptor kinase
StNC1AQStnc-1A-5191TraesCS1A02G238400LCE3 ubiquitin-protein ligase ORTHRUS 2
StNC2AQStnc-2A-6246TraesCS2A02G213508Pentatricopeptide repeat-containing protein, putative
StNC2DQStnc-2D-2351TraesCS2D02G124400F-box family protein
StNC4BQStnc-4B-629TraesCS4B02G024900LCP-loop containing nucleoside triphosphate hydrolases superfamily protein
StPC1BQStpc-1B-15303TraesCS1B02G741400LChAT transposon superfamily
StPC2BQStpc-2B-4694TraesCS2B02G131100Aspartic proteinase nepenthesin-1
StKC4DQStkc-4D-1882TraesCS4D02G229500Transposon Ty3-I Gag-Pol polyprotein
StKC6AQStkc-6A-2415TraesCS6A02G063300Kinase family protein
StKC7DQStkc-7D-10397TraesCS7D02G715100LCF-box protein
TraesCS7D02G532100Cytochrome P450 family protein, expressed
GKA3DQGka-3D-4973TraesCS3D02G549900LCSignal recognition particle protein
GKA7BQGka-7B-2040TraesCS7B02G163800LCSpindle assembly checkpoint component
StNA5DQStna-5D-4323TraesCS5D02G295200F-box protein
StPA6AQStpa-6A-10015TraesCS6A02G372600Phosphate regulon sensor protein PhoR
GNUE2BQGnue-2B-3336TraesCS2B02G106300LCTetratricopeptide repeat (TPR)-like superfamily protein
GPUE QGpue-6B-5100STRG_6B.000077-
GKUE4DQGkue-4D-1722TraesCS4D02G195400Phosphoinositide phosphatase family protein
GKUE5AGkue-5A-9619TraesCS5A02G383800Heat shock transcription factor
StNUE4BQStnue-4B-1036TraesCS4B02G033800Lysine--tRNA ligase
TraesCS4B02G034500Lysine--tRNA ligase
StPUE4AQStpue-4A-5558TraesCS4A02G475500LCYpt/Rab-GAP domain of gyp1p superfamily protein
StPUE6BQStpue-6B-15642TraesCS6B02G783500LCB3 domain-containing protein
TraesCS6B02G440300Protein DETOXIFICATION
StKUE3BQStkue-3B-1051TraesCS3B02G008600Receptor-like kinase
StKUE5AQStkue-5A-12724TraesCS5A02G555500Autophagy protein 5
StKUE6BQStkue-6B-5408TraesCS6B02G133900Calmodulin-binding family protein, putative, expressed
StKUE6DQStkue-6D-4669TraesCS6D02G214800Bifunctional uridylyltransferase/uridylyl-removing enzyme
GPA/GPC5AQGpa/Gpc-5A-12924TraesCS5A02G556400Protein kinase family protein
GPA/StPUE2BQGpa/Stpue-2B-18259TraesCS2B02G6098003-isopropylmalate dehydrogenase
TraesCS4B02G047600Threonine dehydratase
TraesCS4B02G047900NADH-ubiquinone oxidoreductase subunit
TraesCS4B02G053900LCDNA mismatch repair protein MutS, type 2
TraesCS4B02G054110LCZinc finger CCCH domain protein
TraesCS4B02G049300GPN-loop GTPase-like protein
StKA/StKC2AQStka/Stkc-2A-12963TraesCS2A02G654300LChistone deacetylase 2
TraesCS2A02G654500LCphosphatidyl inositol monophosphate 5 kinase
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Han, X.; Zhang, M.; Gao, M.; Yuan, Y.; Yuan, Y.; Zhang, G.; An, Y.; Guo, Y.; Kong, F.; Li, S. QTL Mapping and Candidate Gene Identifying for N, P, and K Use Efficiency at the Maturity Stages in Wheat. Genes 2023, 14, 1168.

AMA Style

Han X, Zhang M, Gao M, Yuan Y, Yuan Y, Zhang G, An Y, Guo Y, Kong F, Li S. QTL Mapping and Candidate Gene Identifying for N, P, and K Use Efficiency at the Maturity Stages in Wheat. Genes. 2023; 14(6):1168.

Chicago/Turabian Style

Han, Xu, Mingxia Zhang, Minggang Gao, Yuanyuan Yuan, Yapei Yuan, Guizhi Zhang, Yanrong An, Ying Guo, Fanmei Kong, and Sishen Li. 2023. "QTL Mapping and Candidate Gene Identifying for N, P, and K Use Efficiency at the Maturity Stages in Wheat" Genes 14, no. 6: 1168.

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