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Combining GS-assisted GWAS and transcriptome analysis to mine candidate genes for nitrogen utilization efficiency in Populus cathayana

Abstract

Background

Forest trees such as poplar, shrub willow, et al. are essential natural resources for sustainable and renewable energy production, and their wood can reduce dependence on fossil fuels and reduce environmental pollution. However, the productivity of forest trees is often limited by the availability of nitrogen (N), improving nitrogen use efficiency (NUE) is an important way to address it. Currently, NUE genetic resources are scarce in forest tree research, and more genetic resources are urgently needed.

Results

Here, we performed genome-wide association studies (GWAS) using the mixed linear model (MLM) to identify genetic loci regulating growth traits in Populus cathayana at two N levels, and attempted to enhance the signal strength of single nucleotide polymorphism (SNP) detection by performing genome selection (GS) assistance GWAS. The results of the two GWAS analyses identified 55 and 40 SNPs that were respectively associated with plant height (PH) and ground diameter (GD), and 92 and 69 candidate genes, including 30 overlapping genes. The prediction accuracy of the GS model (rrBLUP) for phenotype exceeds 0.9. Transcriptome analysis of 13 genotypes under two N levels showed that genes related to carbon and N metabolism, amino acid metabolism, energy metabolism, and signal transduction were differentially expressed in the xylem of P. cathayana under N treatment. Furthermore, we observed strong regional patterns in gene expression levels of P. cathayana, with significant differences between different regions. Among them, P. cathayana in Longquan region exhibited the highest response to N. Finally, through weighted gene co-expression network analysis (WGCNA), we identified a module closely related to the N metabolic process and eight hub genes.

Conclusions

Integrating the GWAS, RNA-seq and WGCNA data, we ultimately identified four key regulatory genes (PtrNAC123, PtrNAC025, Potri.002G233100, and Potri.006G236200) involved in the wood formation process, and they may affect P. cathayana growth and wood formation by regulating nitrogen metabolism. This study will provide strong evidence for N regulation mechanisms, and reliable genetic resources for growth and NUE genetic improvement in poplar.

Peer Review reports

Background

As a rapidly growing renewable biomass and derived energy source, poplar wood can reduce dependence on fossil fuels, reduce environmental pollution, and have great economic value [1, 2]. Nitrogen (N) availability is often the main factor limiting tree productivity. N deficiency affects N uptake and assimilation, carbohydrate accumulation and distribution, and ultimately affects poplar growth and wood yield [3, 4]. Applying N fertilizers has become the main method to improve poplar wood yield in agricultural production. However, poplars have a low rate of N uptake, and excessive N fertilizer can lead to resource wastage, cause environmental pollution, and reduce economic benefits. Improving poplar NUE and identifying N use-related genes will be crucial for wood production and environmental protection.

Currently, forest tree NUE studies tend to focus on theoretical studies, with few instances of functional validation [2, 5]. The lack of genes related to N use efficiency is the main reason for this phenomenon. In the genomics era, genome-wide association studies (GWAS) with high-density marker coverage and low levels of linkage disequilibrium (LD) have developed into an efficient tool to mine key regulatory genes of target traits in populations [6,7,8]. However, the regulatory genes of most forest traits are often comprised of multiple genes with small effects, and effects of low-frequency alleles may not be detected by GWAS association analysis alone [8]. While increasing the sample size of the GWAS analysis population is feasible, it may not be desirable in certain scenarios. Combining GWAS with multiomics techniques will help to improve the accuracy and accuracy of candidate gene [9,10,11].

NUE refers to the efficiency with which plants obtain and use N [3, 12,13,14]. Poplar NUE research has progressed slowly, with fewer reports compared to model plants such as Arabidopsis, rice, and maize [15, 16]. Most studies have focused on N uptake efficiency by roots, with less attention given to N transport and assimilation efficiencies in xylem and phloem [17,18,19,20]. Variations in NUE can impact wood formation, which is regulated by a transcriptional regulatory network (TRN) consisting of transcription factors (TF) and genes related to the secondary cell wall (SCW) [21]. Among them, some TFs have been proven to have pleiotropic effects and participate in multiple complex metabolic processes [15, 22]. Uncovering the mechanisms underlying xylem gene responses under N treatment will provide a basis for studying the regulatory role of xylem-related genes in nitrogen metabolism.

Plants typically exhibit adaptive evolution in response to environmental changes [23, 24], including N availability, which also serves as a basis for selection in population studies. The Populus cathayana natural population has undergone long-term natural selection in the wild, displaying strong environmental adaptability and rich intraspecific variation. In previous research, we collected 410 genotypic P. cathayana germplasm resources from 34 natural populations [25, 26]. Here, we used GWAS to identify candidate genes that regulate the growth of P. cathayana under two N supplies and attempted to acquire genomic estimated breeding values (GEBV) by genomic selection (GS) to enhance SNPs signaling and improve GWAS mining power. In addition, we selected 13 genotypes of P. cathayana with high N utilization to explore the response of poplar xylem genes to N by xylem RNA-seq. Integration of GWAS and RNA-seq results provides potential genetic resources for studying NUE in poplar and strong evidence for exploring the molecular genetic basis of the response of xylem genes to N.

Results

Population phenotypic analysis under nitrogen treatment

Different genotype of P. cathayana had differences in response to N, with differences in growth indicators for most genotypes. Under two N levels, plant height (PH) and ground diameter (GD) reached significant correlation level with correlation coefficients of 0.80 and 0.85, respectively. The coefficient of variation (CV) was between 0.20–0.31. The analysis of normality using the Shapiro–Wilk test showed that the phenotype data followed a normal distribution (Figure S1). Under fertilization conditions, the average PH, average GD, and heritability remained basically unchanged. The maximum PH and maximum GD increased by 22.47% and 35.11%, respectively (Table 1). Among them, there are 159 genotypes promote PH and 191 genotypes promote GD, respectively. The ratio ranges for Ratio-PH and Ratio-GD were 1.01–2.85 and 1.01–2.62, respectively (Supplementary Table S1). Phenotypic data analysis further indicated the P. cathayana population has rich genetic variation and high selection potential.

Table 1 Statistical results of growth indices of different genotypes of P. cathayana in fertilization trials

GWAS of population growth traits

To identify candidate genes regulating the growth and development of P. cathayana under N treatment, we conducted GWAS analysis on genotypes significantly promoted by Ratio-PH and Ratio-GD (Fig. 1a, c and Fig. S2a, c). Among them, Ratio-PH was associated with 9 significant single nucleotide polymorphism (SNP) loci, and Ratio-GD was associated with 10 significant loci. We also observed multiple clusters of SNP signals in the GWAS analysis and attempted to increase the size of the GWAS population by obtaining GEBV through GS to enhance the strength of the SNP signals. GEBV analysis showed that the predictive power of PH and GD was rPH = 0.92 and rGD = 0.96 (Supplementary Table S1), respectively. Under this condition, the GEBV-PH trait was associated with 50 significant loci, and the GEBV-GD trait was associated with 35 significant SNPs loci (Fig. 1b, d and Fig. S2b, d).

Fig. 1
figure 1

Manhattan plots of GWAS association analysis. a Manhattan plot of Ratio-PH GWAS correlation analysis. b Manhattan plot of GEBV-PH GWAS correlation analysis. c Manhattan plot of Ratio-GD GWAS correlation analysis. d Manhattan plot of GEBV-GD GWAS association analysis. P-values are converted to -log10 (P-value). SNPs above red lines passed the Bonferroni correction test, and SC1-SC15 indicate SNPs signal clusters

Using the candidate intervals of 20 kb upstream and downstream of significant SNPs, a total of 92 and 69 candidate genes were identified for the PH and GD traits, respectively, with 30 candidate genes shared between the two traits. The GWAS statistics are shown in Supplementary Table S2 and S3, and the functional annotations of all genes are shown in Supplementary Table S4. The candidate genes are involved in multiple metabolic pathways, such as amino acid metabolism and biosynthesis, circadian rhythm in plants, carbon fixation in photosynthetic organisms, carbon metabolism, starch and sucrose metabolism, plant hormone signal transduction, carotenoid biosynthesis, and flavonoid biosynthesis (Supplementary Table S5). Based on gene function annotation and homology comparison, we initially identified 12 GWAS candidate genes that may respond to N treatment (Table 2).

Table 2 Important candidate genes and functional annotations identified in GWAS

Analysis of differentially expressed genes

To explore the potential N utilization-related genes involved in the growth process of P. cathayana, we collected the developing xylem of 13 P. cathayana genotypes (consisting of 5 natural populations) under two N levels and performed transcriptome analysis. Among the five natural populations we selected, the Ratio-PH and Ratio-GD populations in the Longquan area have the highest values, which are 1.45 and 1.31, respectively (Supplementary Table S6). Transcriptome analysis revealed that the expression levels of the P. cathayana gene exhibit strong regional pattern, and fertilization leads to differential expression of some genes. Samples from the same regional population showed high correlation, and principal component analysis (PCA) clusters into a single cluster (Fig. 2a, b, c). After quality control of the 13 sample groups (Supplementary Table S7), a total of 12,908 differentially expressed genes (DEGs) were identified (Fig. 2d). The DEGs were significantly enriched in 126 GO terms (Fig. 3a) and 119 KEGG metabolic pathways (Fig. 3b), including multiple N utilization-related metabolic pathways such as amino acid metabolism, carbon and nitrogen metabolism, energy metabolism, and signal transduction.

Fig. 2
figure 2

Clustering of transcriptome samples and statistical results of differentially expressed genes (DEGs). a Principal component analysis (PCA) of 78 samples b Sample correlation coefficients, with redder colors indicating higher correlation coefficients c Hierarchical clustering analysis of DEGs. d The number of upregulated and downregulated DEGs in each comparison group

Fig. 3
figure 3

Enrichment analysis of DEGs at two N levels. a GO enrichment bubble diagram of DEGs. Node color and size indicate the P-value and the number of genes corresponding to the enrichment term, respectively. b Bubble diagram of KEGG enrichment of DEGs. Node color and size indicate the P-value and the number of genes corresponding to the enrichment term, respectively

Gene co-expression network construction

To comprehensively analyze the response mechanism of P. cathayana to N, we conducted a weighted gene co-expression network analysis (WGCNA) using the FPKM value of RNA-seq gene expression in xylem samples to identify hub regulatory genes. After removing outliers (Fig. S3a), we divided all genes into 18 co-expression modules using dynamic shearing (Fig. S3b, S3c). Module-phenotype correlation analysis of the eight traits revealed that plant height, ground diameter, aboveground biomass, and leaf dry weight had similar correlations with the co-expression modules and were consistent with the interphenotype correlations (Fig. 4a, b, c). Among them, two noteworthy modules were identified: the pink module contained 799 genes (Supplementary Table S8), which were highly positively correlated with P. cathayana growth indicators, and the greenyellow module contained 745 genes (Supplementary Table S9), which were significantly positively correlated with xylem and bark carbon content. Furthermore, the negative correlation between xylem N and C content and other indicators indicated the close relationship between xylem carbon and nitrogen metabolism and plant growth and development.

Fig. 4
figure 4

Transcriptome sample phenotypic data analysis and module association analysis. a Analysis of differences between the eight phenotypes data. * indicates the genotype reached a significant difference at the 0.05 level. b Correlations between phenotypes of all samples, * indicates a significant correlation between phenotypes at 0.05 level, *** indicates a significant correlation at 0.001 level. c Correlations between modules and phenotypes were based on Pearson correlation coefficients; color depth indicates the magnitude of correlation coefficients. d Pink, greenyellow module KEGG enrichment chord plot, the arc length corresponding to the pathway indicates the number of enriched genes; the minimum is 3 and the maximum is 32

Module enrichment analysis and screening of hub genes

To elucidate the biological significance of genes in the modules and identify hub genes, we conducted KEGG enrichment analysis. The pink module exhibited significantly enriched in 25 KEGG pathways (p < 0.05), mainly related to carbon and N metabolism, amino acid biosynthesis and metabolism of various amino acids, phenylalanine and fatty acids (Fig. 4d). Subsequently, using Cytoscape_3.7.1, we identified eight central genes from the 799 genes in the pink module, including two NAC transcription factors, Potri.011G058400 (PtrNAC123) and Potri.007G014400 (PtrNAC025) (Fig. 5a). All eight hub genes were upregulated under fertilization conditions, with four genes directly or indirectly involved in the process of N metabolism processes (Fig. 5b). The greenyellow module was enriched in 11 KEGG pathways, mainly related to the biosynthesis of cofactors, biosynthesis of amino acids, fatty acid anabolism, and catabolism (Fig. 4d). The five central genes in the module were expressed at low levels and exhibited less responsive to N, and the functional annotation information is shown in Fig. S4.

Fig. 5
figure 5

Pink module genes interactions network and hub genes analysis. a Visualization of the Pink module gene network, the circled parts were hub genes; the Potri.015G034700 gene regulates all genes except the gray circular parts. b Functional annotation of hub genes and heatmap of gene expression. Heatmap data were derived from normalized of gene expression in each region population in RNA-seq

Predicting candidate genes by GWAS and transcriptome analysis

To further identify the GWAS candidate genes, we overlapped them with DEGs, and obtained 28 overlapping genes (Supplementary Table S10). Subsequently, KEGG enrichment analysis was performed on these overlapping genes to understand their biological functions. The results showed that they were involved in several metabolic pathways, such as carbon metabolism, biosynthesis of amino acids, flavone and flavonol biosynthesis, plant hormone signal transduction, plant-pathogen interaction, flavonoid biosynthesis, and MAPK signaling pathway-plant (Fig. 6a). Furthermore, RNA-seq data was used to evaluate gene expression levels, which revealed that 9 genes were upregulated and 4 genes were downregulated under fertilization conditions (Fig. 6b, Table 3). Of the upregulated genes, three genes are particularly interesting: Potri.002G233100 (encoding amino acid transporter AVT1H isoform X1), Potri.004G140900 (encoding abscisic acid 8'-hydroxylase 4) and Potri.006G236200 (encoding the auxin-responsive protein IAA18-related). The quantitative real-time fluorescence PCR (qRT‒PCR) results basically showed consistency with the gene expression trends observed by RNA-seq, and the expression levels of some genes were slightly different (Fig. 7). qRT-PCR and RNA-seq results were highly correlated (r = 0.74; p < 0.001), further indicating that the transcriptomic data have high reliability.

Fig. 6
figure 6

Overlapping genes KEGG enrichment analysis and expression heatmap. a Overlapping genes KEGG enrichment analysis. b Overlapping gene expression heatmap, data were derived from normalized of gene expression in each region in RNA-seq

Table 3 Overlapping genes with similar expression patterns and functional annotations
Fig. 7
figure 7

Real-time fluorescence quantification versus RNA-seq data comparison results. Three biological replicates and three technical replicates were performed for each genotype, Log2 normalized relative expression and combined with RNA-seq data to verify the accuracy of RNA-seq

Discussion

Evaluation of nitrogen use of P. cathayana populations

Forest tree growth and development are highly dependent on N, but research on N utilization-related genes in forest tree is lacking and genetic transformation has rarely been reported [15, 27]. Therefore, there is an urgent need to identify genetic resources related to N utilization. By long-term natural selection, the P. cathayana population has resulted in a population with rich potential genetic resources related to NUE. However, due to the difficulties in collecting germplasm resources, no systematic evaluation of NUE in P. cathayana has been conducted. Different regions of P. cathayana exhibit varying degrees of response to N, with gene expression levels displaying a strong regional pattern. This pattern is associated with adaptive variation that has arisen from long-term natural selection in P. cathayana. By integrating the GEBV values for growth traits and transcriptomic data, we discovered that among the 34 regional natural populations of P. cathayana, the population in the Longquan area exhibited the greatest response to N. This information will contribute to the study of NUE in P. cathayana.

GS-assisted GWAS enhances the signal strength of SNPs

Most traits involved in forest tree GWAS research are quantitative traits controlled by the microscopic effects of multiple genes, and require high data accuracy and population size [28, 29]. In the present study, the population size was too small under our defined significant facilitation condition, resulting in insufficient GWAS signal intensity. Therefore, it is necessary to consider methods to increase the signal intensity. Previous studies extensively reported on GWAS-assisted GS [30,31,32,33,34,35]. On this basis, Spindel's study [36, 37] confirmed the feasibility of combining the GWAS model with ridge regression best linear unbiased prediction (rrBLUP) to improve the accuracy of the GS model. Therefore, we attempted to extend GWAS population by using GS to improve the detection ability of SNPs. Our results suggest that this is a feasible approach, and the combination of the two can greatly reduce data and false positives of candidate SNPs.

Analysis of N metabolism-related pathways

Carbon and N mutually regulate each other and are crucial for changing plant development and growth [38, 39]. Elucidating the expression patterns and major regulatory networks of N utilization-related genes under N treatment is of great significance for improving NUE and plant growth and development. Here, we extracted DEGs involved in amino acid carbon metabolism, nitrogen metabolism, and biosynthesis of three closely related metabolic pathways. To facilitate presentation, we plotted heatmaps using the average gene expression in each region at two N levels. Most of the DEGs associated with nitrogen metabolism and amino acid biosynthesis were upregulated under N treatment (Fig. S5). Previous studies have shown that gene expression levels [5, 40, 41] and enzyme activities associated with nitrogen metabolism differ significantly at different N levels [27, 42, 43]. We focused on examining the differences gene expression involved in major pathways of N metabolism under fertilization conditions (Fig. 8). Three hub genes (Potri.015G034700, Potri.012G043900, and Potri.015G017500) identified by WGCNA played crucial roles in this process. Based on the protein interaction network of N utilization-related genes that we constructed, we identified the important regulatory role of Potri.012G043900 (Fig. S6). This gene has catalytic activity and is involved in processes such as nitrate reduction, N compound metabolism, and glutamine biosynthesis.

Fig. 8
figure 8

Expression changes in various enzyme-related genes during N metabolism at two N levels. NRT: nitrate transporter; NR: nitrate reductase; NiR: nitrite reductase; GS: glutamine synthetase; GOGAT: glutamate synthase; effectively expressed genes (FPKM value > 1) associated with enzymes in N utilization were screened based on gene annotation information, and heatmaps were drawn based on the difference multiplicity Log2FC

Integrating transcriptome data and GWAS to predict candidate genes

NUE affects the wood yield, and NUE regulatory genes have been shown to have multiple effects [15, 16, 22, 44, 45]. In order to explore potential NUE-related genes in the process of wood formation, we identified a WGCNA co-expression module closely related to nitrogen metabolism based on transcriptome data. The hub genes of this module include four genes directly or indirectly involved in nitrogen metabolism and two NAC transcription factors (PtrNAC025 and PtrNAC12). Previously, Chen et al. [5] constructed a four-layer transcriptional regulatory network (TRN) for poplar wood formation. This network, with PtrSND1 as the top-level regulatory factor and PtrMYB74 and PtrMYB21 as second-level regulatory factors, regulate the expression of 17 target genes and their downstream genes involved in secondary cell wall biosynthesis during wood formation [21, 46, 47]. One of the candidate genes we identified, PtrNAC025 (also known as VND6-C1), has strong interactions with the PtrSND1 family of regulators at the top layer of this TRN [48, 49]. The candidate gene PtrNAC123 is located at the third layer of this TRN, and its regulated downstream gene PtrCCoAOMT1 (Potri.009G099800) has been reported to be highly responsive to N [50], while the PtrCCoAOMT1 gene is also located in the module we identified regulated by two candidate NAC genes.

Integrating GWAS and multi-omics data has improved the accuracy and precision of candidate gene selection to some extent, and has been widely used in maize, soybean and poplar [10, 51, 52]. In this study, two GWAS candidate genes, Potri.002G233100 and Potri.006G236200, were identified by integrating GWAS and transcriptome data (Fig. 9). Potri.002G233100 encodes amino acid transporter family protein, and its homologous gene in Arabidopsis thaliana, AT5G16740 is closely associated with the transmembrane amino acid transport [53]. Potri.006G236200 encodes auxin-responsive protein IAA18-related, and its homologous gene in Arabidopsis thaliana, AT3G16500 encodes phytochrome-associated protein 1, which may regulate the expression of genes associated with the growth hormone response [54].

Fig. 9
figure 9

GWAS and transcriptome data network graph. The size of the dots represents the degree of gene association. The WGCNA core genes are the 8 core genes identified in the pink module, and the NUE-related genes are important genes in the nitrogen metabolism, carbon metabolism, and amino acid biosynthesis pathways

Conclusion

Here, we presented the first genome-wide study on the response of P. cathayana population to N and clarify the differences in N response among different populations of P. cathayana, with the Longquan area showing the greatest response. Differences in N utilization efficiency affect the expression levels of NUE-related genes in the xylem, thereby influencing carbon metabolism and fixation, and ultimately impacting the growth and development of poplar trees. Based on this, by integrating the GWAS, GS and xylem transcriptomic data we identified the important regulatory role of Potri.012G043900 in nitrogen metabolism, and we preliminarily suggested that PtrNAC123, PtrNAC025, Potri.002G233100, and Potri.006G236200 may affect poplar growth by regulating nitrogen metabolism levels in addition to their known functions. This study provides new genetic resources and strong evidence to explore the molecular genetic basis of NUE in forest trees, and attempts to validate the functions of these genes in subsequent research.

Materials and methods

Plant material and experimental design

The P. cathayana population included 408 genotypes from natural populations in 34 different regions of China. In early April 2021, stem cuttings were used for clonal propagation in the greenhouse, with 30 plants of each genotype. After one month of growth, they were transplanted to the Yuquan Mountain Nursery of the Chinese Academy of Forestry. They were used for field fertilization experiments in a completely randomized block design, with a total of four blocks, two N-applied and two non-N-applied. Each block included three plants of each genotype, with a spacing of 30 cm × 50 cm. N-applied blocks were fertilized four times with 4 g/plant/15 days (CON2H4, N content ≥ 46.0%), and non-N-applied blocks were left untreated. The test plots were brown soils with 1.275 g/kg total N, 0.797 g/kg total P and 16.0 g/kg total K.

Population phenotype data determination and analysis

After growth ceased in October, the PH and GD of each tree were measured, and the phenotypic data were tested for normality using SPSS 20.0 software. The mean (Mean), standard deviation (SD), coefficient of variation (CV) and heritability (H2) of PH and GD were calculated for each genotype under fertilized and unfertilized conditions. Based on the trait measurements, we calculated the trait ratios of PH and GD at two N levels for GWAS analysis. To identify phenotype-associated SNPs, we selected significantly promoted genotypes for GWAS analysis. Among them, there were 159 and 191 genotypes for Ratios-PH and Ratios-GD, respectively.

SNPs quality control and GWAS analysis

Based on the whole-genome resequenced SNPs data, we obtained a total of 575,472 SNPs data after using the quality control of Plink 1.9 software (minor allele frequency > 0.05, missing genotype < 0.05 and r2 > 0.20). To reduce false positives of significant SNPs in GWAS association analysis, Q matrix and K matrix were controlled in GEMMA (0.98.3) software [55] and mixed linear model (MLM). Using GEMMA (0.98.3) software to calculate Kinship (K matrix), GTAC software to calculate PCA (Q matrix). Manhattan and Q-Q plots were drawn using the package "CMplot" package in R. The significance threshold for SNP markers was corrected by Bonferroni P = 0.05/n, where n indicates the number of valid independent SNPs. The GWAS model was Y = Wα + xβ + u + e, where Y is an n-vector of quantitative traits (or binary disease labels) for n individuals; W is a n × c matrix of covariates (fixed effects), it should contain a first column with an intercept of all 1 s; α is a c-vector of the corresponding coefficients including the intercept; x is an n-vector of marker genotypes; β is the effect size of the marker; u is an n-vector of random effects; and e is an n-vector of errors.

GS-assisted GWAS analysis

In the GWAS analysis, we found the presence of multiple signaling clusters of SNPs, which may have been false negatives after Bonferroni correction. To further identify phenotype-associated SNPs, we attempted to use the GEBV obtained from GS by the rrBLUP method and to use them for GWAS to expand the breeding population and enhance the signal strength of SNPs. The quality control conditions of genotype data for GS analysis are the same as those for GWAS analysis. First, we used the significantly promoted genotypes as the reference population, and the remaining genotypes as the validation population. Based on the ratios of the PH and GD traits at two N levels in the reference population, genome selection analysis was performed using the R package "rrBLUP" to obtain the GEBV value of each genotype. The predictive ability (rgy) was estimated as the correlation between the observed and the GEBV (r (y, GEBV)) [56]. The GS model in the R package "rrBLUP" was Y = Xb + Za + e, where Y is the phenotypic measure of the trait being analyzed; X and Z are incidence matrices for the vectors for parameters b and a, respectively; b is a vector of fixed block effects; a is a vector of random additive effects, and e is the random residual effect.

Identification of significant SNPs and candidate genes

Here, to reduce the false positives of SNPs loci, we defined two categories of loci significantly associated with phenotypes as the final significant SNPs loci. The first category of SNPs are the significant SNPs co-localized with two GWAS results; the other category is SNP clusters (SCs) that reached a significant level in the GS-assisted GWAS. The preassembled P. cathayana genome in the laboratory was used as the reference genome, and genes within 20 kb upstream and downstream of significant SNPs were screened as candidate genes.

Transcriptome sample collection and RNA-seq

To investigate the pattern changes in xylem gene expression at two N levels and to explore potential N utilization-related genes, 13 growth promoting genotypes (A1, A2, A3, B2, C1, C2, C2, C3, D1, E2, E3) were selected from five natural populations (Lantian A, Yixian B, Longquan C, Fengning D, Youyu E). In mid-August, the stem bark at the breast diameter of the selected genotypes was peeled off, and the developing xylem was scraped from the xylem surface using a razor blade and immediately frozen in liquid N. Three biological replicates were performed for each genotype in the N-applied and non-N-applied blocks, respective. The Illumina NovaSeq™ 6000 high-throughput sequencing platform was used to perform RNA-seq on the library. After quality control of the offline data, Populus trichocarpa 3.1 was used as the reference genome (http://plants.ensembl.org), and Hisat2 software was used for sequence alignment analysis [57].

Functional enrichment analysis of DEGs

To investigate the differences in response of different genotypes of P. cathayana to N and the regulatory mechanisms, we used fragments per kilobase million (FPKM) values to evaluate gene expression levels, and used fold change | log2FC |> 1 and significance level p < 0.05 as the screening standard for DEGs. Functional enrichment analysis of the DEGs were performed using Gene Ontology (GO) [58] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [59] databases, with a significance level of p < 0.05.

Gene co-expression network analysis

To further identify genes related to N utilization, we measured 8 traits related to N utilization as WGCNA associated traits, including PH, GD, aboveground biomass, leaf weight, xylem carbon content, xylem N content, bark carbon content, and bark N content. Aboveground biomass was determined after air drying, and leaf weight was the dry weight of each leaf. The C and N content was determined using an elemental analyzer after grinding the stem segments and bark. Before constructing the co-expression network, we removed genes with a total FPKM expression of less than 30 in all samples, and removed outliers by clustering analysis. The soft threshold function was used to calculate the weights, and then the modules were divided according to the dynamic hybrid shearing method, with a minimum of 150 genes per module. Modules with similar expression patterns (similarity = 0.75) were merged, and the merged modules were associated with traits using association analysis. Finally, Cytoscape 3.7.1 software was used to visualize the gene interaction network and screen hub genes.

Identification of GWAS candidate genes and real-time fluorescence quantitative PCR validation

The combination of GWAS and WGCNA significantly enhances the capacity to identify core genes. To further identify the core genes in the GWAS candidate genes, we first homology-matched the P. cathayana genome with the reference genome and then overlapped the GWAS-identified candidate genes with the DEGs. Based on the transcriptome expression data, we evaluated the candidate gene expression patterns under fertilization conditions and further screened the candidate genes by combining gene annotation and enrichment results. To verify the reliability of RNA-Seq analysis and the expression levels of candidate genes, qRT-PCR analysis was performed with β-actin as an internal reference gene, and one genotype was randomly chosen in each of the five regions. Primers were designed using Primer Primer 6.0 software, and primer information for the four differentially expressed genes and the eight candidate genes were shown in Supplementary Table S11. Three technical replicates and three biological replicates were used for each genotype, and 2−ΔΔCt was used to calculate the relative expression. The reaction system and reaction procedure refer to the description of TB Green® Premix Ex Taq™ II (TaKaRa, Dalian, China).

Availability of data and materials

The data in this study will be published publicly. RNA-seq data have been uploaded to the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under Bioproject PRJNA851202. Additional data and materials can be provided by the corresponding author on demand.

Abbreviations

DEGs:

Differentially expressed genes

GD:

Ground diameter

GEBV:

Genomic estimated breeding values

GS:

Genomic selection

GWAS:

Genome-wide association studies

MLM:

Mixed linear model

N:

Nitrogen

NUE:

Nitrogen use efficiency

PCA:

Principal component analysis

PH:

Plant height

qRT‒PCR:

Quantitative real-time fluorescence PCR

rrBLUP:

Ridge regression best linear unbiased prediction

SNP:

Single nucleotide polymorphism

TRN:

Transcriptional regulatory network

WGCNA:

Weighted gene co-expression network analysis

References

  1. Nunes-Nesi A, Fernie AR, Stitt M. Metabolic and signaling aspects underpinning the regulation of plant carbon nitrogen interactions. Mol Plant. 2010;3(6):973–96. https://doi.org/10.1093/mp/ssq049.

    Article  CAS  PubMed  Google Scholar 

  2. Gan H, Jiao Y, Jia J, Wang X, Li H, Shi W, Peng C, Polle A, Luo ZB. Phosphorus and nitrogen physiology of two contrasting poplar genotypes when exposed to phosphorus and/or nitrogen starvation. Tree Physiol. 2016;36(1):22–38. https://doi.org/10.1093/treephys/tpv093.

    Article  CAS  PubMed  Google Scholar 

  3. Plavcová L, Hacke UG, Almeida-Rodriguez AM, Li E, Douglas CJ. Gene expression patterns underlying changes in xylem structure and function in response to increased nitrogen availability in hybrid poplar. Plant, Cell Environ. 2013;36(1):186–99. https://doi.org/10.1111/j.1365-3040.2012.02566.x.

    Article  CAS  PubMed  Google Scholar 

  4. Wang JP, Matthews ML, Williams CM, Shi R, Yang C, Tunlaya-Anukit S, Chen HC, Li Q, Liu J, Lin CY, et al. Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis. Nat Commun. 2018;9(1):1579. https://doi.org/10.1038/s41467-018-03863-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen C, Chu Y, Huang Q, Zhang W, Ding C, Zhang J, Li B, Zhang T, Li Z, Su X. Morphological, physiological, and transcriptional responses to low nitrogen stress in Populus deltoides Marsh. clones with contrasting nitrogen use efficiency. BMC genomics. 2021;22(1):697. https://doi.org/10.1186/s12864-021-07991-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bamba M, Kawaguchi YW, Tsuchimatsu T. Plant adaptation and speciation studied by population genomic approaches. Dev Growth Differ. 2019;61(1):12–24. https://doi.org/10.1111/dgd.12578.

    Article  PubMed  Google Scholar 

  7. Mohanta TK, Bashir T, Hashem A, Abd Allah EF. Systems biology approach in plant abiotic stresses. Plant Physiol Biochemistry. 2017;121:58–73. https://doi.org/10.1016/j.plaphy.2017.10.019.

    Article  CAS  Google Scholar 

  8. Chhetri HB, Macaya-Sanz D, Kainer D, Biswal AK, Evans LM, Chen JG, Collins C, Hunt K, Mohanty SS, Rosenstiel T, et al. Multitrait genome-wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits. New Phytol. 2019;223(1):293–309. https://doi.org/10.1111/nph.15777.

    Article  CAS  PubMed  Google Scholar 

  9. Ma L, Zhang M, Chen J, Qing C, He S, Zou C, Yuan G, Yang C, Peng H, Pan G, et al. GWAS and WGCNA uncover hub genes controlling salt tolerance in maize (Zea mays L.) seedlings TAG Theoretical and applied genetics. Theoretische und angewandte Genetik. 2021;134(10):3305–18. https://doi.org/10.1007/s00122-021-03897-w.

    Article  CAS  PubMed  Google Scholar 

  10. Song Y, Chen P, Xuan A, Bu C, Liu P, Ingvarsson PK, El-Kassaby YA, Zhang D. Integration of genome wide association studies and co-expression networks reveal roles of PtoWRKY 42-PtoUGT76C1-1 in trans-zeatin metabolism and cytokinin sensitivity in poplar. New Phytol. 2021;231(4):1462–77. https://doi.org/10.1111/nph.17469.

    Article  CAS  PubMed  Google Scholar 

  11. Kobayashi Y, Sadhukhan A, Tazib T, Nakano Y, Kusunoki K, Kamara M, Chaffai R, Iuchi S, Sahoo L, Kobayashi M, et al. Joint genetic and network analyses identify loci associated with root growth under NaCl stress in Arabidopsis thaliana. Plant Cell Environ. 2016;39(4):918–34. https://doi.org/10.1111/pce.12691.

    Article  CAS  PubMed  Google Scholar 

  12. Zhao L, Chen P, Liu P, Song Y, Zhang D. Genetic effects and expression patterns of the nitrate transporter (NRT) gene family in Populus tomentosa. Front Plant Sci. 2021;12:661635. https://doi.org/10.3389/fpls.2021.661635.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pitre FE, Pollet B, Lafarguette F, Cooke JE, MacKay JJ, Lapierre C. Effects of increased nitrogen supply on the lignification of poplar wood. J Agric Food Chem. 2007;55(25):10306–14. https://doi.org/10.1021/jf071611e.

    Article  CAS  PubMed  Google Scholar 

  14. Camargo EL, Nascimento LC, Soler M, Salazar MM, Lepikson-Neto J, Marques WL, Alves A, Teixeira PJ, Mieczkowski P, Carazzolle MF, et al. Contrasting nitrogen fertilization treatments impact xylem gene expression and secondary cell wall lignification in Eucalyptus. BMC Plant Biol. 2014;14:256. https://doi.org/10.1186/s12870-014-0256-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shen C, Li Q, An Y, Zhou Y, Zhang Y, He F, Chen L, Liu C, Mao W, Wang X, et al. The transcription factor GNC optimizes nitrogen use efficiency and growth by driving the expression of nitrate uptake and assimilation genes in poplar. J Exp Bot. 2022. https://doi.org/10.1093/jxb/erac190.

    Article  PubMed  Google Scholar 

  16. Wei M, Zhang M, Sun J, Zhao Y, Pak S, Ma M, Chen Y, Lu H, Yang J, Wei H, et al. PuHox52 promotes coordinated uptake of nitrate, phosphate, and iron under nitrogen deficiency in Populus ussuriensis. J Integr Plant Biol. 2022. https://doi.org/10.1111/jipb.13389.

    Article  PubMed  Google Scholar 

  17. Dash M, Yordanov YS, Georgieva T, Kumari S, Wei H, Busov V. A network of genes associated with poplar root development in response to low nitrogen. Plant Signal Behav. 2016;11(8):e1214792. https://doi.org/10.1080/15592324.2016.1214792.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ceccon C, Tagliavini M, Schmitt AO, Eissenstat DM. Untangling the effects of root age and tissue nitrogen on root respiration in Populus tremuloides at different nitrogen supply. Tree Physiol. 2016;36(5):618–27. https://doi.org/10.1093/treephys/tpw022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jiao Y, Chen Y, Ma C, Qin J, Nguyen THN, Liu D, Gan H, Ding S, Luo ZB. Phenylalanine as a nitrogen source induces root growth and nitrogen-use efficiency in Populus × canescens. Tree Physiol. 2018;38(1):66–82. https://doi.org/10.1093/treephys/tpx109.

    Article  CAS  PubMed  Google Scholar 

  20. Li S, Tian Y, Wu K, Ye Y, Yu J, Zhang J, Liu Q, Hu M, Li H, Tong Y, et al. Modulating plant growth-metabolism coordination for sustainable agriculture. Nature. 2018;560(7720):595–600. https://doi.org/10.1038/s41586-018-0415-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu H, Gao J, Sun J, Li S, Zhang B, Wang Z, Zhou C, Sulis DB, Wang JP, Chiang VL, et al. Dimerization of PtrMYB074 and PtrWRKY19 mediates transcriptional activation of PtrbHLH186 for secondary xylem development in Populus trichocarpa. New Phytol. 2022;234(3):918–33. https://doi.org/10.1111/nph.18028.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xin W, Wang J, Li J, Zhao H, Liu H, Zheng H, Yang L, Wang C, Yang F, Chen J, et al. Candidate gene analysis for nitrogen absorption and utilization in japonica rice at the seedling stage based on a genome-wide association study. Front Plant Sci. 2021;12:670861. https://doi.org/10.3389/fpls.2021.670861.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sakakibara H, Takei K, Hirose N. Interactions between nitrogen and cytokinin in the regulation of metabolism and development. Trends Plant Sci. 2006;11(9):440–8. https://doi.org/10.1016/j.tplants.2006.07.004.

    Article  CAS  PubMed  Google Scholar 

  24. Iqbal A, Dong Q, Wang X, Gui H, Zhang H, Zhang X, Song M: Variations in nitrogen metabolism are closely linked with nitrogen uptake and utilization efficiency in cotton genotypes under various nitrogen supplies. Plants (Basel, Switzerland). 2020;9(2). https://doi.org/10.3390/plants9020250

  25. Cao D: Molecular genetic basis of leaf phenotypic traits in natural germplasm of Populus cathayana. Chinese Acad Forest. 2021.

  26. Cao D, Zhang Y, Cheng X, Xiang X, Zhang L, Hu J. Genetic variation of leaf phenotypic traits in different populations of Populus cathayana. Scientia Silvae Sinicae. 2021;57(08):56–67.

    Google Scholar 

  27. Li H, Li M, Luo J, Cao X, Qu L, Gai Y, Jiang X, Liu T, Bai H, Janz D, et al. N-fertilization has different effects on the growth, carbon and nitrogen physiology, and wood properties of slow- and fast-growing Populus species. J Exp Bot. 2012;63(17):6173–85. https://doi.org/10.1093/jxb/ers271.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Fernandes SB, Casstevens TM, Bradbury PJ, Lipka AE: A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits. Plant Genome. 2022:e20200. https://doi.org/10.1002/tpg2.20200

  29. Rice BR, Fernandes SB, Lipka AE. Multi-Trait genome-wide association studies reveal loci associated with maize inflorescence and leaf architecture. Plant Cell Physiol. 2020;61(8):1427–37. https://doi.org/10.1093/pcp/pcaa039.

    Article  CAS  PubMed  Google Scholar 

  30. Xiao Y, Jiang S, Cheng Q, Wang X, Yan J, Zhang R, Qiao F, Ma C, Luo J, Li W, et al. The genetic mechanism of heterosis utilization in maize improvement. Genome Biol. 2021;22(1):148. https://doi.org/10.1186/s13059-021-02370-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang X, Lourenco D, Aguilar I, Legarra A, Misztal I. Weighting strategies for single-step genomic BLUP: an Iterative approach for accurate calculation of GEBV and GWAS. Front Genet. 2016;7:151. https://doi.org/10.3389/fgene.2016.00151.

    Article  PubMed  PubMed Central  Google Scholar 

  32. McKown AD, Guy RD, Quamme L, Klápště J, La Mantia J, Constabel CP, El-Kassaby YA, Hamelin RC, Zifkin M, Azam MS. Association genetics, geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and disease resistance trade-offs. Mol Ecol. 2014;23(23):5771–90. https://doi.org/10.1111/mec.12969.

    Article  CAS  PubMed  Google Scholar 

  33. Shi A, Bhattarai G, Xiong H, Avila CA, Feng C, Liu B, Joshi V, Stein L, Mou B, du Toit LJ et al: Genome-wide association study and genomic prediction of white rust resistance in USDA GRIN spinach germplasm. Horticult Res. 2022;9:uhac069. https://doi.org/10.1093/hr/uhac069

  34. Medina CA, Kaur H, Ray I, Yu LX: Strategies to increase prediction accuracy in genomic selection of complex traits in Alfalfa (Medicago sativa L.). Cells. 2021;10(12). https://doi.org/10.3390/cells10123372

  35. Rice B, Lipka AE: Evaluation of rr-BLUP genomic selection models that incorporate peak genome-wide association study signals in maize and sorghum. Plant Genome. 2019;12(1). https://doi.org/10.3835/plantgenome2018.07.0052

  36. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink JL, McCouch SR. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet. 2015;11(2):e1004982. https://doi.org/10.1371/journal.pgen.1004982.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Spindel JE, Begum H, Akdemir D, Collard B, Redoña E, Jannink JL, McCouch S. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity (Edinb). 2016;116(4):395–408. https://doi.org/10.1038/hdy.2015.113.

    Article  CAS  PubMed  Google Scholar 

  38. Baslam M, Mitsui T, Sueyoshi K, Ohyama T: Recent advances in carbon and nitrogen metabolism in C3 plants. Int J Mol Sci. 2020;22(1). https://doi.org/10.3390/ijms22010318

  39. Wu X, Liu J, Meng Q, Fang S, Kang J, Guo Q. Differences in carbon and nitrogen metabolism between male and female Populus cathayana in response to deficient nitrogen. Tree Physiol. 2021;41(1):119–33. https://doi.org/10.1093/treephys/tpaa108.

    Article  CAS  PubMed  Google Scholar 

  40. Chen M, Yin Y, Zhang L, Yang X, Fu T, Huo X, Wang Y. Metabolomics and transcriptomics integration of early response of populus tomentosa to reduced nitrogen availability. Front Plant Sci. 2021;12:769748. https://doi.org/10.3389/fpls.2021.769748.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Song H, Cai Z, Liao J, Tang D, Zhang S. Sexually differential gene expressions in poplar roots in response to nitrogen deficiency. Tree Physiol. 2019;39(9):1614–29. https://doi.org/10.1093/treephys/tpz057.

    Article  CAS  PubMed  Google Scholar 

  42. Lasa B, Jauregui I, Aranjuelo I, Sakalauskiene S, Aparicio-Tejo PM. Influence of stage of development in the efficiency of nitrogen fertilization on poplar. J Plant Nutr. 2016;39(1):87–98. https://doi.org/10.1080/01904167.2015.1024323.

    Article  CAS  Google Scholar 

  43. Han M, Xu M, Wang S, Wu L, Sun S, Su T. Effects of exogenous L-Glutamine as a sole nitrogen source on physiological characteristics and nitrogen use efficiency of poplar. Plant Physiol Biochem. 2022;172:1–13. https://doi.org/10.1016/j.plaphy.2021.12.032.

    Article  CAS  PubMed  Google Scholar 

  44. Shen C, Zhang Y, Li Q, Liu S, He F, An Y, Zhou Y, Liu C, Yin W, Xia X. PdGNC confers drought tolerance by mediating stomatal closure resulting from NO and H2O2 production via the direct regulation of PdHXK1 expression in Populus. New Phytol. 2021;230(5):1868–82. https://doi.org/10.1111/nph.17301.

    Article  CAS  PubMed  Google Scholar 

  45. Wei M, Liu Q, Wang Z, Yang J, Li W, Chen Y, Lu H, Nie J, Liu B, Lv K, et al. PuHox52-mediated hierarchical multilayered gene regulatory network promotes adventitious root formation in Populus ussuriensis. New Phytol. 2020;228(4):1369–85. https://doi.org/10.1111/nph.16778.

    Article  CAS  PubMed  Google Scholar 

  46. Chen H, Wang JP, Liu H, Li H, Lin YJ, Shi R, Yang C, Gao J, Zhou C, Li Q, et al. Hierarchical transcription factor and chromatin binding network for wood formation in black cottonwood (Populus trichocarpa). Plant Cell. 2019;31(3):602–26. https://doi.org/10.1105/tpc.18.00620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang Z, Mao Y, Guo Y, Gao J, Liu X, Li S, Lin YJ, Chen H, Wang JP, Chiang VL, et al. MYB transcription factor161 mediates feedback regulation of secondary wall-associated NAC-Domain1 family genes for wood formation. Plant Physiol. 2020;184(3):1389–406. https://doi.org/10.1104/pp.20.01033.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lin YJ, Chen H, Li Q, Li W, Wang JP, Shi R, Tunlaya-Anukit S, Shuai P, Wang Z, Ma H, et al. Reciprocal cross-regulation of VND and SND multigene TF families for wood formation in Populus trichocarpa. Proc Natl Acad Sci USA. 2017;114(45):E9722-e9729. https://doi.org/10.1073/pnas.1714422114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhou J, Zhong R, Ye ZH. Arabidopsis NAC domain proteins, VND1 to VND5, are transcriptional regulators of secondary wall biosynthesis in vessels. PloS one. 2014;9(8):e105726. https://doi.org/10.1371/journal.pone.0105726.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhao H, Qu C, Zuo Z, Cao L, Zhang S, Xu X, Xu Z, Liu G. Genome identification and expression profiles in response to nitrogen treatment analysis of the Class I CCoAOMT Gene Family in Populus. Biochem Genet. 2022;60(2):656–75. https://doi.org/10.1007/s10528-021-10112-4.

    Article  CAS  PubMed  Google Scholar 

  51. Guo J, Li C, Zhang X, Li Y, Zhang D, Shi Y, Song Y, Li Y, Yang D, Wang T. Transcriptome and GWAS analyses reveal candidate gene for seminal root length of maize seedlings under drought stress. Plant Sci. 2020;292:110380. https://doi.org/10.1016/j.plantsci.2019.110380.

    Article  CAS  PubMed  Google Scholar 

  52. Wen Z, Tan R, Zhang S, Collins PJ, Yuan J, Du W, Gu C, Ou S, Song Q, An YC, et al. Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean. Plant Biotechnol J. 2018;16(11):1825–35. https://doi.org/10.1111/pbi.12918.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hanada K, Sawada Y, Kuromori T, Klausnitzer R, Saito K, Toyoda T, Shinozaki K, Li WH, Hirai MY. Functional compensation of primary and secondary metabolites by duplicate genes in Arabidopsis thaliana. Mol Biol Evol. 2011;28(1):377–82. https://doi.org/10.1093/molbev/msq204.

    Article  CAS  PubMed  Google Scholar 

  54. Dreher KA, Brown J, Saw RE, Callis J. The Arabidopsis aux/iaa protein family has diversified in degradation and auxin responsiveness. Plant Cell. 2006;18(3):699–714. https://doi.org/10.1105/tpc.105.039172.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–4. https://doi.org/10.1038/ng.2310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Müller BSF, Neves LG, de Almeida Filho JE, Resende MFR Jr, Muñoz PR, Dos Santos PET, Filho EP, Kirst M, Grattapaglia D. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics. 2017;18(1):524. https://doi.org/10.1186/s12864-017-3920-2.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. https://doi.org/10.1038/nmeth.1226.

    Article  CAS  PubMed  Google Scholar 

  58. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9. https://doi.org/10.1038/75556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. https://doi.org/10.1093/nar/28.1.27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the National Key Research and Development Program of China (2021YFD2200201) and the National Natural Science Foundation (32071797).

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X.Z., X.X., L.Z. and J.H. designed and conducted the research. X.Z. conducted the data and wrote the manuscript. X.X., M.Z., D.C. and C.D. planted the trees in the field and collected the samples. X.Z., L.Z. and J.H. contributed to discussion and manuscript revision. All authors read the data and approved the final manuscript.

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Correspondence to Lei Zhang or Jianjun Hu.

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Supplementary Information

Additional file 1:

Fig. S1. Normality test of phenotypic data. (a) Normal distribution of PH in the N-applied area. (b) Normal distribution of GD in the N-applied area. (c) Normal distribution of PH in the no-N-applied area. (d) Normal distribution of GD in the no-N-applied area. Fig. S2. QQ plots of GWAS association analysis. (a) QQ plot of Ratio-PH GWAS correlation analysis. (b) QQ plot of GEBV-PH GWAS correlation analysis. (c) QQ plot of Ratio-GD GWAS correlation analysis. (d) QQ plot of GEBV-GD GWAS association analysis. Fig. S3. WGCNA quality control results. (a) Sample clustering tree; outlier samples C12 and D12 were eliminated. (b) Determination of the soft threshold; the soft threshold used in this study is β = 9. (c) Cluster tree and network heatmap, divided into 18 co-expression modules. Fig. S4. Greenyellow module gene interaction network and hub gene analysis. (a) Greenyellow module gene network visualization. (b) Hub gene function annotation and expression heatmap. Heatmap data were derived from normalized gene expression in each region population in RNA-seq. Fig. S5. Heatmap of DEG expression in N metabolism-related pathways. (a) Heatmap of carbon metabolism pathways. (b) Heatmap of N metabolism pathways. (c) Heatmap of the amino acid biosynthesis pathway. Heatmap data were derived from the normalized gene expression in each region population in RNA-seq. Fig. S6. The protein interaction network diagram of the N metabolism-related genes; the pink mark indicates the hub gene.

Additional file 2:

Supplementary Table S1. GS anlysis of phenotypic data and GEBV values. Supplementary Table S2. GWAS analyzes statistical information. Supplementary Table S3. GWAS association loci and candidate gene information. Supplementary Table S4. Functional annotation information of GWAS candidate genes in the P. cathayana genome. Supplementary Table S5. KEGG enrichment results of GWAS candidate genes. Supplementary Table S6. The average Ratio-GD and Ratio-PH of the 34 regional Populus cathayana populations. Supplementary Table S7. Transcriptome sample quality control results. Supplementary Table S8. Gene ID and functional annotation of the pink module. Supplementary Table S9. gene ID and functional annotation of the greenyellow module. Supplementary Table S10. GWAS and transcriptome results overlap gene ID and expression level. Supplementary Table S11. qRT-RCR primer sequences.

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Zhou, X., Xiang, X., Zhang, M. et al. Combining GS-assisted GWAS and transcriptome analysis to mine candidate genes for nitrogen utilization efficiency in Populus cathayana. BMC Plant Biol 23, 182 (2023). https://doi.org/10.1186/s12870-023-04202-1

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