Genome-wide association mapping and functional annotation of loci conferring resistance to Pyrenophora teres f. teres in barley

Abstract

Introduction:

Net form net blotch (NFNB), caused by Pyrenophora teres f. teres (Ptt), is a major constraint to barley production. However, the genetic basis of adult plant resistance (APR) and seedling resistance remains incompletely understood. This study aimed to dissect the genetic architecture of NFNB resistance in a diverse panel of 273 spring barley accessions.

Methods:

APR was evaluated in two contrasting field environments in Kazakhstan, whereas seedling resistance was assessed under greenhouse conditions using two Ptt races. Genotyping with the 50K SNP array yielded 31,834 high-quality SNPs. Genome-wide association analyses were performed using four models – MLM, MLMM, FarmCPU, and BLINK – that accounted for population structure and kinship. Candidate genes within QTL intervals were prioritized using transcriptomic data from 16 barley tissues and co-expression network analysis.

Results:

Substantial phenotypic variation was observed, with moderate heritability for APR (h₂ = 50.6%) and seedling resistance (h₂ = 41.3%), together with strong genotype × environment and genotype × race interactions. In total, 275 marker–trait associations were detected for APR and 48 for seedling resistance. These associations were consolidated into 57 genome-wide significant (P < 1.57E–6) or multi-model-supported QTLs across all seven barley chromosomes, including 39 APR and 18 seedling-resistance QTLs. Forty QTLs co-localized with known resistance genes (Rpt1, Rpt2, Rpt3, Rpt4, Rpt6, Rpt8, Rpt9, and SPN1) or previously reported net blotch QTLs, whereas 17 were potentially novel. Transcriptomic integration identified 87 highly expressed genes within APR QTL regions and 42 within seedling-resistance QTLs. The potentially novel QTLs Q_NB_1H.6, Q_NB_2H.3, and Q_NB_3H.1 harbored genes encoding proteins previously associated with pathogen resistance and stress responses. Co-expression analysis revealed stage-specific transcriptional patterns, with APR-associated genes enriched in regulatory functions and seedling-resistance genes enriched in metabolic and structural functions.

Discussion:

The results demonstrate that NFNB resistance is polygenic and developmentally stage-dependent, with partly distinct mechanisms underlying adult plant and seedling resistance. The identified QTLs and prioritized candidate genes provide targets for independent validation, functional characterization, and the development of molecular markers to support breeding for durable NFNB resistance in barley.

1 Introduction

Barley (Hordeum vulgare L.) ranks as the fourth most important cereal crop globally after wheat, maize, and rice, with more than 50 million hectares cultivated and annual production exceeding 150 million metric tons (Lammas and Shitikova, 2024). Due to its tolerance to adverse environmental conditions, including drought, poor soils, and temperature extremes, barley plays a critical role in agriculture in marginal environments where other cereals perform poorly. The crop also has considerable economic importance, with the global barley market valued at approximately USD 22.8 billion in 2024 (Global Market Insights, 2024). In Central Asia, Kazakhstan is a major producer, ranking 13^th globally, with production reaching approximately 3.3 million metric tons in 2022 and 3.8 million metric tons in 2024 (FAOSTAT, 2022; Bureau of National Statistics, 2025).

Barley has diverse applications that underline its economic importance. Globally, about 70% of barley grain is used for animal feed, providing a valuable and cost-effective nutrient source for livestock and poultry (Fortune Business Insights, 2025; Perera et al., 2022). Approximately 21% is utilized for malting in the brewing and distilling industries, supporting the large global beer and whiskey markets (SkyQuest Technology, 2024). Human consumption accounts for roughly 6%, mainly in the form of flour, porridges, and health foods, particularly in Asia and North Africa due to barley’s high fiber and micronutrient content (Tricase et al., 2018). Additional uses, including biofuels and functional foods, further contribute to the crop’s economic value.

Despite its adaptability, barley production faces significant biotic constraints, among which net blotch (NB) disease, caused by the fungal pathogen Pyrenophora teres Drechsler (anamorph: Drechslera teres), is a major threat (Tomić et al., 2024). This disease manifests in two distinct forms: the net form net blotch (NFNB), incited by P. teres f. teres (Ptt), and the spot form net blotch (SFNB), caused by P. teres f. maculata (Ptm). NFNB symptoms appear as elongated lesions with dark brown blotches and net-like striations on leaves, while SFNB presents as circular to elliptical dark brown spots surrounded by chlorotic halos (Tomić et al., 2024). The pathogen thrives under cool, humid conditions but can also occur in warmer, drier areas, making it prevalent in barley-growing regions worldwide, including North America (Steffenson et al., 1996; Akhavan et al., 2016), Europe (Jørgensen et al., 2000; Tini et al., 2022), and Australia (Hassett et al., 2023). In Central Asia, NB affects key production areas; in Kazakhstan, it manifests as both net- and spot-type symptoms in northern fields, with studies highlighting genotypic correlations and variability in resistance in local landraces (Rsaliyev et al., 2016; Pakhratdinova et al., 2017). Neighboring Russia reports NFNB as a significant foliar disease in southern regions, with polymorphic microsatellite markers linked to resistance genes in winter barley varieties (Dontsova et al., 2018). Surveys in Morocco from 2015 to 2018 reported a 100% incidence of leaf area damage, ranging from 40% to 80%, highlighting its destructive potential (Rehman et al., 2025). The fungus survives on seed, crop residues, and wild hosts, with ascospores and conidia serving as primary and secondary inoculum sources.

NB has a profound impact on barley yield and quality, leading to substantial economic and agronomic losses. Under favorable conditions, yield reductions typically range from 10% to 40%, with extreme cases resulting in near-total crop failure (Tomić et al., 2024). In Kazakhstan, where barley is a major crop, NFNB contributes to yield variability, which is compounded by abiotic stresses such as drought and poor grain quality resulting from weather extremes, potentially reducing yields in affected seasons (Rsaliyev et al., 2016; Genievskaya et al., 2026). Beyond quantitative losses, the disease impairs grain quality by reducing kernel size, plumpness, and bulk density, which diminishes malting and feed value. Toxin production by the pathogen exacerbates these effects, resulting in downgraded grain that is for premium markets (Friis et al., 1991). Fungicide applications and cultural practices offer partial control but increase production costs and risk the development of resistance, underscoring the need for host resistance as a sustainable solution.

Current knowledge on barley resistance to NB reveals a spectrum of mechanisms, encompassing both qualitative and quantitative traits. Qualitative resistance, often governed by major genes, follows a gene-for-gene model where dominant resistance alleles interact with specific pathogen avirulence factors. Notable major loci include Rpt1 on chromosome 3H, Rpt2 on 1H, Rpt3 on 2H, Rpt4 on 7H, Rpt5 on 6H, Rpt6 on 5H, and Rpt7 on 4H, many of which confer seedling or adult plant resistance (APR) (Manninen et al., 2006; Clare et al., 2024), as well as minor genes Rpt8, Rpt9, Rpt10, Rpt11, and SPN1 (Clare et al., 2024). However, evidence also supports inverse gene-for-gene interactions, where dominant susceptibility genes in the host enable pathogen virulence (Liu et al., 2015). Quantitative resistance, mediated by polygenic effects, involves multiple quantitative trait loci (QTLs) that provide partial, durable protection. Over 280 QTLs have been identified across all seven barley chromosomes (Vatter et al., 2017; Richards et al., 2017; Amezrou et al., 2018; Adhikari et al., 2019; Esmail et al., 2023; Clare et al., 2024; Rehman et al., 2025), with prominent clusters on 6H and 7H conferring broad-spectrum effects.

Barley breeding for NB resistance has historically relied on conventional approaches, such as bi-parental crosses and phenotypic selection, to introgress resistance from diverse germplasm, including landraces from Ethiopia and Eritrea (Yitbarek et al., 1998; Manninen et al., 2006). These methods have successfully deployed varieties with enhanced resistance, as demonstrated by the yield advantages of 39–56% in resistant lines under field conditions. However, conventional breeding faces several limitations: low allelic diversity in mapping populations limits QTL detection, recombination events are limited, and identified loci are often population-specific and exhibit variable expression due to environmental interactions. The genetic complexity of NB resistance further complicates progress, as multiple QTLs interact epistatically and are influenced by genotype-by-environment (G×E) effects, while populations of P. teres exhibit substantial genetic and virulence diversity that can lead to rapid breakdown of host resistance (Novakazi et al., 2019).

Genome-wide association studies (GWAS) have emerged as a powerful approach to overcome some of these limitations by exploiting natural genetic variation and historical recombination in diverse germplasm panels. In Kazakhstan, GWAS has been successfully applied in barley to dissect the genetic basis of agronomic traits (Genievskaya et al., 2025a), grain quality (Genievskaya et al., 2023a), and resistance to major diseases (Genievskaya et al., 2023b, 2025), enabling the identification of numerous loci associated with important breeding targets. Despite progress in identifying loci for NB resistance, important gaps remain in understanding its genetic architecture, particularly in underexplored germplasm such as landraces and wild barley. Limited use of genome-wide approaches in some breeding programs may lead to the overlooking of small-effect loci and allele interactions, constraining the development of durable resistance. GWAS provides an effective strategy to address these gaps by capturing historical recombination events across unrelated accessions, enabling high-resolution detection of resistance loci. Recent studies have identified numerous QTLs associated with NB resistance across the barley genome, including both previously known and novel loci (Grewal et al., 2008; Vatter et al., 2017; Rozanova et al., 2019). However, comprehensive GWAS analyses integrating multi-stage phenotyping remain limited for NB resistance in barley, particularly in Kazakhstan.

In this study, a genome-wide association analysis was conducted using a diverse barley panel to identify loci associated with resistance to NFNB. Seedling resistance was evaluated using two pathogen races, whereas APR was assessed under field conditions in two environments. Particular emphasis was placed on identifying highly expressed genes within QTL regions, functionally characterizing them using Gene Ontology (GO) annotations, and analyzing potential interactions among them.

2 Materials and methods

2.1 Plant material and SNP genotyping

A panel comprising 273 accessions of spring two-row barley from the USA, Kazakhstan, Europe, Africa, and the Middle East (Supplementary Table 1) was grown in field trials during 2024 at two locations in Kazakhstan: the Research Institute of Biological Safety Problems (RIBSP; southern Kazakhstan, 43.576476° N, 75.213618° E) and the Kazakh Research Institute of Agriculture and Plant Growing (KRIAPG; southeastern Kazakhstan, 43.229402° N, 76.699168° E).

Genomic DNA was extracted from 5-day-old etiolated barley seedlings using the DNeasy Plant Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Genotyping of these accessions was conducted with the 50K Illumina Infinium iSelect SNP array (Bayer et al., 2017) by TraitGenetics GmbH (Gatersleben, Germany). The resulting genotypic data served as the basis for investigating population structure, assessing linkage disequilibrium (LD), and conducting subsequent GWAS. Physical positions of the SNPs were obtained from the Morex v3 reference genome assembly (The Triticeae Toolbox – Barley, 2025).

2.2 Assessment of race-specific seedling resistance to NFNB

Race-specific resistance of barley accessions was evaluated at the seedling stage under greenhouse conditions at the RIBSP. Seeds were sown in 200 mL plastic pots (five seeds per pot) filled with a soil:sand:compost mixture (2:1:1, v/v/v). The experiment was conducted in two independent replicates. Seedlings were grown to the two-leaf stage under controlled conditions of 20/18 °C (day/night), a 16 h photoperiod, and a light intensity of 180 µmol m^–2 s^–1.

To comprehensively evaluate seedling resistance, spring barley accessions were inoculated with two races of Ptt (Race 100 and Race 500) (Rsaliyev et al., 2018). Seedling resistance was assessed using the detached leaf segment assay (Afanasenko et al., 2009). Leaf segments were incubated in a 0.004% benzimidazole solution to maintain viability. Conidia of Ptt were obtained by flooding sporulating fungal cultures with sterile distilled water and gently scraping the surface with a sterile wire loop. The resulting suspension was adjusted to a concentration of 5, 000 conidia per mL using a Goryaev counting chamber. Tween 20 (Sigma-Aldrich, St. Louis, MI, USA) was added to the suspension (one drop per 100 mL) to reduce surface tension and ensure uniform distribution of the inoculum.

Inoculation was performed by applying a 0.2 mL droplet of the conidial suspension to each leaf segment using a micropipette. After inoculation, trays were covered with glass plates and incubated at the same temperature and under the same light conditions used for seedling growth. Disease symptoms were evaluated 7–9 days after inoculation. The reactions of barley accessions were classified using a 9-point infection response scale (Tekauz, 1985). For each accession, three seedlings were evaluated, and the final disease reaction was determined as the highest (most severe) score across replicates to avoid underestimating susceptibility in heterogeneous genotypes during preliminary screening (Ahn et al., 2022).

2.3 Characterization of APR to NFNB at two locations

NFNB severity in the barley collection was evaluated at two locations in 2024, with two replicates per site. At RIBSP, accessions were grown in experimental field plots of 0.4 m². Each plot consisted of two rows spaced 20 cm apart and 100–200 cm in length, with 55 seeds sown per row. The spring barley cultivar “Arna” was planted between experimental plots as a susceptible spreader to facilitate pathogen accumulation and disease spread within the nursery. Artificial infection was established by augmenting infected plant segments with a targeted conidial suspension of Ptt (1.0 × 10⁴ conidia/mL) applied to the spreader rows at the tillering stage to ensure uniform and high disease pressure. To optimize spore adhesion and guarantee uniform coverage across the hydrophobic leaf surfaces, the inoculum was supplemented with 0.05% (v/v) Tween 20 as a surfactant.

At KRIAPG, NFNB resistance was evaluated under natural infection conditions. Accessions were sown in 1 m² plots with 10 cm spacing between rows. The same susceptible cultivar “Arna” was randomly sown five times across the field to promote uniform disease development. Despite differences in plot size between locations, sowing rates were adjusted to ensure comparable plant density per m².

At both locations, disease assessments were performed three times during the growing season: at the early booting stage, during ear emergence, and at the flowering stage. Disease severity was scored using a 9-point infection response scale (Tekauz, 1985). Broad-sense heritability (h²) and ANOVA were calculated for APR and seedling resistance using the lme4 package in R.

Prior to harvest, phenological traits, including heading time (HT) and heading-to-maturity time (HMT), as well as morphometric parameters, namely plant height (PH) and peduncle length (PL), were recorded according to CIMMYT protocols (Pask et al., 2012). After natural grain drying, grain yield per 1 m² (YM2) and thousand-kernel weight (TKW) were determined using the same methodology. For correlation analysis, the corrplot package in R was used.

Meteorological data, including precipitation, relative humidity, sunshine hours per month, and temperature, from April to August 2024, were recorded and are presented in Supplementary Table 2.

2.4 Population structure and association analysis

Population structure was evaluated by computing pairwise kinship coefficients and performing principal component analysis (PCA). The kinship matrix was generated using GAPIT v3 (Wang and Zhang, 2021) and represented as a heatmap via the heatmap3 package in R, whereas PCA results, including eigenvalues, were visualized with ggplot2.

To detect marker-trait associations (MTAs) related to NFNB resistance, four GWAS models implemented in GAPIT v3 were applied: MLM, MLMM, BLINK, and FarmCPU. In each model, PCA.total=3 was incorporated to account for and correct population stratification effects. The significance of associations was assessed by P-value and their consistency across these models. A P-value threshold of 1.00E–4 was used to identify potential associations, recognizing that conventional genome-wide thresholds may overlook genuine signals in datasets featuring low-frequency variants or limited sample sizes owing to reduced statistical power (Fadista et al., 2016). Prior research indicates that adopting a more lenient P-value cutoff can enhance the identification of variants with modest effects, thereby revealing a wider array of authentic genetic associations (Chen et al., 2021).

2.5 Identification of QTLs, analysis of candidate Rpt genes and expression profiling

To consolidate closely linked marker-trait associations (MTAs) into discrete QTLs, the critical LD decay distance corresponding to r² = 0.1 was determined and employed as the criterion for merging adjacent MTAs. Within the defined QTL intervals, the SNP with the most significant P-value was selected as the lead (peak) SNP.

Candidate genes were identified by mapping the physical coordinates of previously characterized Rpt barley resistance genes onto the QTL regions. A physical map illustrating the genomic positions of the detected QTLs alongside the aligned Rpt genes was constructed with MapChart v2.3 (Voorrips, 2002). A ±2 Mb physical window around each QTL peak marker was used to identify candidate QTLs and genes reported in the literature based on their genomic coordinates. In addition, protein-coding genes located within a ±1 Mb interval surrounding the physical positions of aligned Rpt genes were considered candidate genes. To pinpoint protein-coding genes potentially implicated in NFNB resistance within these QTL intervals, gene IDs residing in the regions were extracted from EnsemblPlants (EnsemblPlants, 2026). Expression profiles were evaluated using BarleyExpDB (Barleyexpdb, 2026) and RNA-Seq data from 16 tissues of the Morex cultivar (Mascher et al., 2017). Genes with expression >200 transcripts per million (TPM) were considered robust. Functional characterization of the proteins encoded by these genes was conducted through UniProt (The UniProt Consortium, 2025) and QuickGO (QuickGO, 2025), with expression patterns across tissues visualized using the heatmap3 package in R. Gene ontology (GO) enrichment analysis for the highly expressed genes was performed using the online g:Profiler platform (Kolberg et al., 2023). Significantly enriched GO terms were identified through over-representation analysis (ORA), utilizing the total set of annotated barley genes as the background population. Statistical significance was determined using an adjusted P-value threshold of < 0.05. To explore developmental co-expression relationships among highly expressed genes (TPM > 200), a weighted gene co-expression network was constructed using RNA-seq expression profiles across 16 healthy barley tissues and organs. The resulting networks therefore reflect tissue- and developmental-stage-related co-expression patterns rather than pathogen-induced defense responses. A Pearson correlation coefficient threshold of r ≥ 0.7 was applied to preserve biologically relevant connections. The correlation matrix was transformed into an undirected weighted network with the igraph package in R, and community structure was identified via the Walktrap algorithm (Pons and Latapy, 2006). To assess whether the observed network topology differed from random expectations, 100 degree-preserving randomized networks were generated using the igraph package in R, and topological metrics, including clustering coefficient, modularity, assortativity, and average path length, were compared between the observed and randomized networks.

3 Results

3.1 Evaluation of adult and seedling resistances to NFNB

The phenotypic evaluation of NFNB resistance in the 273 spring two-row barley accessions, conducted across two field environments and controlled greenhouse seedling assays using two pathogen races, uncovered substantial genetic variation in disease responses at both adult plant and seedling stages (Supplementary Table 3; Figure 1). Field-based APR assessments revealed contrasting disease pressures between the two environments. At KRIAPG, disease pressure was low, with many immune accessions (0 points) and a distribution strongly skewed toward resistance (mean = 0.6 ± 0.6, CV = 149.4%; Figure 1A). In contrast, RIBSP showed a broader range of infection levels, including highly susceptible lines scoring 8–9 points (mean = 2.8 ± 2.4, CV = 86.0%). BLUP values normalized these environmental extremes, showing a major frequency at 3 points (mean = 2.8 ± 0.9, CV = 30.8%), reflecting predominantly moderate-to-low infection across the population. Seedling assays under controlled greenhouse conditions showed distinct virulence patterns for the two pathogen races. Race 100 exhibited lower virulence, with 66 accessions remaining symptom-free and a secondary frequency peak at 3 points (mean = 2.2 ± 1.8, CV = 84.5%). By contrast, race 500 was more aggressive, shifting the population toward susceptibility, with the most frequent score at 6 points (mean = 5.8 ± 1.6, CV = 27.4%; Figure 1B). ANOVA indicated that all primary sources of variation were significant (P < 0.001; Table 1).

Figure 1

For APR, genotype contributed substantially to phenotypic variance, while environment had the largest mean square, highlighting the strong influence of field conditions. The genotype × environment (G×E) interaction was also significant, indicating that accession performance varied between locations. Broad-sense heritability (h²) for APR was 50.6%. In seedling assays, both genotype and pathogen race significantly affected NFNB severity. The high F-value for race confirmed a major difference in virulence among isolates, and the significant genotype × race (G×R) interaction reflected differential responses across accessions. Heritability for seedling resistance was 41.3%. Correlations between NFNB resistance and agronomic traits were evaluated using Pearson coefficients (Figure 2). At RIBSP, NB severity negatively correlated with yield per square meter (YM2; r = −0.48), indicating reduced productivity under higher disease pressure. Weak positive correlations were observed with plant height (PH; r = 0.13) and thousand kernel weight (TKW; r = 0.15), whereas phenological traits (HT, HMT) were not significantly associated with NB severity (Figure 2A). At KRIAPG, NB severity had minimal impact on agronomic traits, with only a weak negative correlation observed for TKW (r = −0.17; Figure 2B). BLUP-based correlations across environments confirmed significant negative associations of NB severity with peduncle length (PL; r = −0.33) and YM2 (r = −0.23; Figure 2C).

Figure 2

Relationships among resistance responses across growth stages and pathogen races were also examined (Figure 2D). BLUP values showed strong correlation with APR at RIBSP (r = 0.70) and moderate correlation at KRIAPG (r = 0.35), as well as with seedling resistance to race 500 (r = 0.35). Resistance to race 500 correlated moderately with race 100 (r = 0.76) and APR at RIBSP (r = 0.48), indicating that some accessions maintain resistance across both seedling and adult stages.

3.2 Genotyping and population structure analysis

Genotyping with the 50K SNP array yielded 44, 050 markers. All genotyped accessions had passed the missing data threshold of 10%. After quality filtering (call rate > 0.9; MAF ≥ 0.05), 31, 834 polymorphic SNPs were retained for downstream analyses (Supplementary Table 4). These markers were used to characterize the genomic architecture of the barley panel, including marker distribution and linkage disequilibrium (LD) decay across the genome (Figure 3).

Figure 3

SNP markers were distributed across all seven barley chromosomes (1H–7H), providing broad coverage of the genome. Density plots (Figure 3A) revealed higher marker concentrations in telomeric regions, whereas centromeric regions had lower marker density, reflecting reduced recombination and the heterochromatic nature of barley centromeres. Overall, the markers spanned ~4.5 Gb of the barley genome, with marker density varying among chromosomes: the highest on 5H (average spacing 137 Kb) and the lowest on 4H (233 Kb). Genome-wide LD decayed to r² = 0.1 at ~2.15 Mb (Figure 3B).

Population structure was assessed using the 31, 834 filtered SNPs (Figure 4). The scree plot of principal components (Figure 4A) indicated that PC1, PC2, and PC3 explained 10.32%, 6.04%, and 5.59% of the total genetic variation, respectively. The PCA scatter plot (Figure 4B) revealed three partially overlapping groups, corresponding to USA germplasm, African accessions, and the remaining genotypes. Overall, the results suggested two major genetic clusters that separated the USA accessions from the rest of the collection. Consistently, the kinship heatmap (Figure 4C) highlighted three main clusters with smaller subgroups of closely related accessions within them, confirming the patterns observed in PCA.

Figure 4

3.3 GWAS summary and identification of QTLs

To investigate genetic loci associated with NFNB resistance in barley, a GWAS was conducted using APR data from two field environments, their BLUP values, seedling resistance to two pathogen races, and genotypic data comprising 31, 834 SNPs, analyzed using four GWAS models. In total, 275 MTAs (P < 1.00E-4) associated with APR to NFNB were identified across the four GWAS models (Supplementary Table 5). For seedling resistance, 48 MTAs were detected using the same models. Detailed information regarding MTAs, their positions within QTLs, P-values, phenotypic variance explained (PVE) values, effects, QQ- and Manhattan plots are provided in Supplementary Table 5.

To consolidate overlapping signals, MTAs located in close proximity and with r² > 0.1 were grouped into single QTL intervals, with QTL-significance determined by the most significant MTA within each interval. The identified QTLs were subsequently classified into three categories. QTLs exceeding the Bonferroni significance threshold (P = 1.57E−6) were considered genome-wide significant QTLs, QTLs detected by two or more GWAS models and/or in two or more environments, but below the Bonferroni threshold were considered multi-model-supported QTLs, and QTLs failing to reach the Bonferroni threshold and detected by only a single GWAS model were considered tentative QTLs. QTLs from the latter category were entirely excluded from subsequent analyses. In total, 57 QTLs were retained for further analysis, including 39 associated with APR, 13 were detected for seedling resistance to the race 500, and 5 QTLs for the resistance to the race 100 (Table 2). Among them, 17 (all APR) represented genome-wide significant QTLs, while 40 were classified as multi-model-supported QTLs.

Of the 57 QTLs, 18 were detected under greenhouse conditions (seedling resistance), 12 were identified using BLUPs of APR data, 13 were detected in both RIBSP and BLUP analyses, 7 were found at KRIAPG, and 4 were detected in both KRIAPG and BLUP datasets. Additionally, one QTL each was detected exclusively in RIBSP, jointly in KRIAPG and RIBSP, and across all three APR datasets (KRIAPG, RIBSP, and BLUP) (Figure 5A). Most of the QTLs identified at the seedling stage under greenhouse conditions were distinct from field-based APR QTLs, indicating potential plant stage-specific genetic control of NFNB resistance.

Figure 5

Considering GWAS models, 54 QTLs were consistently identified across multiple models and environments. Twenty-five QTLs were detected by all four models, 13 by BLINK and FarmCPU, 8 by MLM and MLMM, 7 by BLINK, FarmCPU, and MLMM, 2 by BLINK alone, 1 by FarmCPU alone, and 1 by MLM and FarmCPU (Figure 5B). Analysis of GWAS model contributions revealed that multi-locus models (BLINK and FarmCPU) frequently captured additional QTLs, particularly for seedling resistance, whereas loci detected by MLM were generally nested within multi-locus results (Supplementary Table 5).

The QTLs were distributed across all chromosomes, with chromosomes 1H, 2H, 3H, and 5H harboring the most loci, and two QTLs with unknown positions. Among the most significant ones, Q_NB_6H.2 (474.16 Mb) exhibited the strongest association (P = 1.76E–22), followed by Q_NB_1H.6 (504.91–516.33 Mb; P = 2.47E–21) and Q_NB_1H.2 (81.50 Mb; P = 3.47E–20), highlighting these regions as key targets for further functional validation.

Based on PVE values, QTLs were classified into two categories: minor-effect (1% ≤ PVE < 5%) and very small-effect (PVE < 1%) QTLs. According to this classification, 39 loci were identified as minor-effect QTLs, and 18 as very small-effect QTLs (Table 2). The largest PVEs were observed for Q_NB500_7H.1 (4.935%), Q_NB500_5H.3 (4.835%), and Q_NB500_7H.3 (4.835%), while the remaining QTLs had PVE values ranging from 0.291% to 4.672%.

3.4 Mapping QTLs to known NB resistance genes and literature

All identified QTLs were mapped onto the barley genome (Figure 6) and the largest number of APR QTLs was detected on chromosome 2H (n = 7), followed by chromosome 3H (n = 7), while seedling resistance QTLs were most frequent on chromosome 5H (n = 5). The positions of 55 identified QTLs with known physical locations were compared with previously reported NB resistance loci, including 12 characterized NB resistance genes (Rpt) and NB QTLs from the literature (Table 3). Of these 55 QTLs, 9 coincided exclusively with known Rpt genes, 8 overlapped with both Rpt genes and previously reported NB QTLs, and 23 matched only with NB QTLs from the literature. The remaining 15 QTLs were located in genomic regions distinct from all known resistance loci, suggesting potentially novel sources of NFNB resistance for further investigation. Two QTLs with unknown positions in the genome were also considered to be presumably novel.

Figure 6

Clare et al., 2024.

3.5 Developmental expression profiling and GO analysis of genes located within NFNB resistance QTLs

Genes located within the identified QTL regions were analyzed for expression across 16 barley tissues and organs representing different developmental stages. In total, 3, 492 and 2, 041 protein-coding genes were located within the 31 APR and 17 seedling resistance QTLs, respectively (Supplementary Table 6). Seven QTLs were positioned in genomic regions lacking coding genes, and the positions of two QTLs were unknown.

Filtering for moderate-to-high expression (TPM > 200) revealed 87 highly expressed genes within 18 APR QTLs (Supplementary Figure 1). The number of expressed genes varied among tissues and developmental stages, with the highest counts observed in grain at 5 days post-anthesis (CAR5, 26 genes), embryos (EMB, 23 genes), and senescing leaves (SEN, 22 genes). Similarly, roots from 10 cm seedlings (ROO2) contained 22 genes exceeding the TPM threshold, followed by rachis (RAC) with 21 genes, whereas developing tillers (NOD) included 20 genes, and young inflorescences (INF1) and lemma (LEM) each contained 19 genes. Applying a higher expression threshold (TPM ≥ 2, 000) identified 6 genes in APR QTLs with strong expression in specific tissues: four genes in the fifteen-days-old epidermis (CAR15), two genes in each of INF1 and INF2, and one gene in CAR5. The highest expression level (TPM = 22, 387.12) was detected for HORVU.MOREX.r3.3HG0230270 within QTL Q_NB_3H.1 in CAR15.

For seedling resistance QTLs, 42 genes were expressed above TPM > 200 across tissues (Supplementary Figure 2). Epidermis (EPI) exhibited the highest number of expressed genes (n = 18), followed by etiolated 10-day-old seedlings (ETI), 10 cm shoots (LEA), and NOD, each containing 16 genes. Only eight genes displayed very high expression (TPM > 2, 000), primarily in EPI (6 genes), with fewer in CAR5, INF1, and INF2. Functional annotation of highly expressed genes (TPM > 200) provided insights into potential molecular functions, biological processes, and cellular localization (Supplementary Table 7; Figure 7).

Figure 7

In the Molecular Function category, genes located within APR QTLs were more highly represented across all functional classes (Figure 7A). The dominant group in both resistance types comprised structural, ribosomal, and cofactor-binding proteins. APR QTL genes were further enriched in nucleic acid binding and gene regulation, primary metabolism enzymes, as well as proteolysis-related and defense/redox-associated functions. In contrast, genes within seedling resistance QTLs were mainly represented by structural and primary metabolism categories. Molecular functions remained unclassified (NA) for 20 APR genes and 13 seedling resistance genes.

For Biological Processes, APR QTL genes were strongly associated with stress and stimulus responses, as well as gene expression, RNA-related processes, and amino acid metabolism (Figure 7B). Both resistance types showed comparable involvement in primary carbon metabolism and energy production. A substantial number of genes had unknown biological functions (24 in APR and 15 in seedling resistance).

In terms of Cellular Components, APR QTL genes were predominantly localized to the cytoplasm and nucleus, suggesting enhanced transcriptional activity, whereas seedling resistance genes were mainly associated with the cytoplasm and chloroplasts (Figure 7C). Plasma membrane and extracellular components were well represented in both groups.

Organ/tissue-specific expression analysis revealed the highest gene counts for APR QTLs in the EPI, with strong expression also observed in CAR5, EMB, and SEN. In contrast, seedling resistance QTL genes were primarily expressed in EPI, NOD, LEA, and ETI (Figure 7D).

To further elucidate the functional significance of highly expressed genes identified within both APR and seedling resistance QTL regions, a GO enrichment analysis was performed to identify overrepresented functional categories associated with resistance mechanisms (Figure 8).

Figure 8

In the Molecular Function category, the most significantly enriched term was ribulose-bisphosphate carboxylase activity (P = 2.507E–06), alongside monooxygenase activity, ribonuclease T2 activity, structural molecule activity, 5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase activity, and serine-type endopeptidase inhibitor activity. Biological Process enrichment highlighted metabolic compound salvage (P = 8.813E–06), the reductive pentose-phosphate cycle (P = 1.218E–05), amino acid biosynthesis, and actin filament depolymerization. In the Cellular Component category, fewer terms reached significance, with intracellular membraneless organelle identified as enriched (P = 3.967E–02).

3.6 Developmental co-expression patterns of highly expressed genes within QTLs

Gene co-expression network analysis of highly expressed genes (TPM > 200) within APR-associated QTLs revealed a modular organization comprising 10 clusters of developmentally co-expressed genes across barley tissues (Figure 9). Comparison with degree-preserving randomized networks demonstrated that the observed co-expression networks exhibited substantially higher clustering coefficients, modularity, and assortativity than expected under a random topology, supporting the presence of non-random, biologically structured transcriptional organization (Supplementary Table 8). Among these, five clusters (1–5) were particularly prominent, forming the largest and most interconnected modules.

Figure 9

Cluster 1 was dominated by histone variants (H2A, H2B, H4), elongation factor 1-alpha, and transcription factors with GATA- and C2H2-type domains, potentially associated with chromatin organization, nucleosome assembly, and protein synthesis. Genes in this cluster were distributed across seven QTLs. Cluster 2 contained metabolic and proteostasis-related proteins from six QTLs, including Peptidase A1 domain-containing proteins and SKP1-related factors, suggesting involvement in protein turnover and cellular homeostasis. Notably, Clusters 1 and 2 were interconnected via Alcohol dehydrogenase 1 and an uncharacterized protein, suggesting co-expression relationships between genes associated with chromatin-related and metabolic functions.

Cluster 3 was enriched for defense- and storage-related proteins, such as Serpin-domain and cupin-type proteins, oleosin, and Glucose-1-phosphate adenylyltransferase, mapped to five QTLs, suggesting possible roles in energy storage, structural organization, and stress-related processes. Cluster 4 included cytoskeletal and biosynthetic enzymes, such as Tubulin beta chain, shikimate kinase, and delta-1-pyrroline-5-carboxylate synthase, possibly reflecting cell wall organization and secondary metabolite biosynthesis. Cluster 5 integrated defense- and stress-responsive genes across 13 QTLs, including chitinase, pathogen-related proteins, catalase, and lipoxygenase. Smaller clusters (6–10) contained stress-protective proteins and uncharacterized factors, suggesting auxiliary roles in mitigating oxidative stress. Collectively, these developmental co-expression patterns indicate that candidate genes located within APR-associated QTLs are enriched in regulatory, metabolic, and stress-associated functional categories across barley tissues.

In contrast, the co-expression network for highly expressed genes within seedling resistance QTLs exhibited a more fragmented topology, comprising seven specialized clusters, possibly highlighting stage-specific differences in resistance strategies (Figure 10). Cluster 1, anchored by elongation factor 1-alpha and ribosomal protein L19, included tubulin alpha chain (tua4) and dirigent proteins, may reflect coordinated protein synthesis and cytoskeletal organization, and was primarily associated with Q_NB500_2H.1 and Q_NB500_5H.3. Cluster 2, the largest and most densely connected module, was dominated by multiple Ribulose-bisphosphate carboxylase (Rubisco) small subunit variants, germin-like proteins, non-specific serine/threonine protein kinases, and glycine-rich proteins, suggesting coordinated expression of genes associated with photosynthesis, signaling, and primary metabolism.

Figure 10

The smaller clusters (3–7) were enriched for structural, cell wall, and metabolic functions, including COBRA-like and tubulin proteins, proline-rich and agglutinin domain-containing proteins, Rubisco subunits, and enzymes such as 5-methyltetrahydropteroyltriglutamate–homocysteine S-methyltransferase and UDP-glucuronic acid decarboxylase. These clusters reflect coordinated roles in cell wall biosynthesis, structural integrity, carbon fixation, and primary metabolism. Overall, the developmental co-expression patterns observed for candidate genes within seedling resistance QTLs were enriched in genes associated with photosynthesis, cytoskeletal organization, cell wall-related functions, and primary metabolism. In contrast, APR-associated candidate genes showed stronger representation of chromatin-related, regulatory, and stress-associated functional categories. These differences likely reflect distinct developmental expression contexts across barley tissues. However, because the underlying RNA-seq datasets were generated from healthy tissues, these observations should be interpreted with caution and do not constitute direct evidence of stage-specific NFNB resistance mechanisms.

4 Discussion

4.1 Overview of adult NFNB resistance variation in barley

The evaluation of NFNB resistance across 273 spring barley accessions revealed substantial phenotypic variation at both adult plant and seedling stages, supporting a quantitative architecture of resistance to Ptt. The skew toward resistant phenotypes under low disease pressure at KRIAPG may reflect the presence of favorable alleles or lower pathogen virulence (Figure 1A), as further suggested by contrasting responses to the two races at the seedling stage (Figure 1B). In contrast, the broader distribution of disease scores at RIBSP enabled clearer discrimination among genotypes, consistent with typical field conditions under higher infection pressure (Steffenson et al., 1991). Environmental factors likely contributed to these differences, as disease development in NB is driven by interactions among temperature, humidity, and leaf wetness rather than precipitation alone (Rowlandson, 2011; Van den Berg and Rossnagel, 1991).

ANOVA revealed significant effects of genotype, environment, and their interaction, with environment having the strongest influence on APR (Table 1). The pronounced G×E interaction indicates instability of resistance across environments, complicating selection. Moderate heritability estimates for APR (50.6%) and seedling resistance (41.3%) further support multi-locus control, in agreement with earlier reports (Burlakoti et al., 2017; Rehman et al., 2025). At the seedling stage, strong race effects and significant G×R interaction emphasize the importance of race-specific resistance. The higher aggressiveness of race 500 suggests broader virulence, consistent with gene-for-gene interactions in Ptt (Lai et al., 2007). However, seedling resistance in our study was evaluated using a detached-leaf assay, which may not fully capture systemic defense responses in intact plants, such as systemic acquired resistance. Nevertheless, detached leaf assays are widely used in barley–Ptt interaction studies because they provide controlled and reproducible conditions for evaluating race-specific responses (Boydom et al., 2013).

The impact of disease on agronomic traits was environment-dependent. A moderate negative correlation between NFNB severity and yield at RIBSP (Figure 2A) confirms its detrimental effect under high disease pressure (Jayasena et al., 2007; Tomić et al., 2024), whereas weak associations at KRIAPG indicate limited impact under low infection levels (Figure 2B). Weak correlations with plant height and TKW suggest that resistance is largely independent of these traits. Moderate correlations between seedling and adult resistance (Figure 2D), particularly for race 500, indicate partial genetic overlap, although the incomplete correspondence between stages suggests distinct genetic control at different developmental phases (Neupane et al., 2015; Rehman et al., 2025).

These results demonstrate that NFNB resistance is shaped by environmental conditions, race-specific interactions, and multi-locus genetic control, while the consistency of phenotypic patterns across environments supports the robustness of the dataset for GWAS.

4.2 Population structure, LD, and their implications for GWAS

The filtered SNP set provided dense genome-wide coverage suitable for association mapping, consistent with previous barley studies using high-density platforms (Muñoz-Amatriaín et al., 2014; Bayer et al., 2017). The enrichment of markers in telomeric regions and reduced density in centromeric regions (Figure 3A) reflects the recombination landscape of barley, where gene-rich distal regions exhibit higher recombination rates (Mascher et al., 2017). The observed LD decay (2.15 Mb at r² = 0.1) is considerably broader than that reported in high-resolution barley GWAS panels (e.g., 177.76 kb at r² = 0.2 in Babiye et al., 2026), which likely reflects stronger population structure and reduced historical recombination in the present breeding material.

Population structure analysis revealed two major genetic groups, separating USA germplasm from the rest of the collection. The concordance between PCA and kinship analyses supports the robustness of this structure (Figure 4) and is consistent with patterns shaped by geographic origin and breeding history (Genievskaya et al., 2025a, 2025). The combination of moderate LD and clear population structure underscores the importance of models accounting for both kinship and structure to ensure reliable GWAS results.

4.3 QTL discovery and comparison with previous studies

The combined use of single-locus (MLM, MLMM) and multi-locus (BLINK, FarmCPU) GWAS models improved QTL detection power and robustness, consistent with previous studies demonstrating the advantage of multi-locus approaches in identifying small-effect loci while reducing false positives (Hu et al., 2018; Paire et al., 2024). In agreement, loci identified by MLM in this study were largely nested within multi-locus results, while BLINK and FarmCPU detected additional associations, particularly for seedling resistance (Table 2). The identification of 57 genome-wide significant and multi-model-supported QTLs across two environments, BLUPs and seedling resistance (Table 2, Figure 6) confirms the complex, polygenic nature of NFNB resistance, as widely reported (Burlakoti et al., 2017; Rehman et al., 2025). The integration of multi-environment field data with BLUP estimates likely contributed to the stability of detected QTLs by reducing environmental noise. However, the predominance of minor- and very small-effect QTLs in our study contrasts with earlier reports where several major-effect loci were detected (Amezrou et al., 2018; Adhikari et al., 2019; Novakazi et al., 2019), likely reflecting differences in population structure, environmental conditions, and, probably, phenotyping strategies. The relatively low PVE values (<5%) observed for all loci further support a highly polygenic architecture, consistent with quantitative resistance governed by multiple small-effect genes rather than major R genes (Rehman et al., 2025). From a breeding perspective, the predominance of small-effect loci suggests that genomic selection or pyramiding of multiple QTLs may be more effective than relying on individual loci (Buerstmayr et al., 2020).

Importantly, co-localization analysis revealed that a subset of QTLs overlapped with known Rpt genes (Rpt1, Rpt2, Rpt3, Rpt4, Rpt6, Rpt8, Rpt9, and SPN1) (Table 3), confirming the validity of the GWAS results and consistency with previously characterized resistance loci. At the same time, overlap with previously reported QTLs across multiple genomic regions further supports the reliability of the key loci identified in this study. However, 17 QTLs were located outside known Rpt genes and previously reported NB-resistance regions, suggesting the presence of potentially novel resistance loci for NFNB, which represent promising targets for fine-mapping and the identification of highly expressed genes. Especially, four highly significant APR QTLs Q_NB_1H.3, Q_NB_1H.4, and Q_NB_2H.3 (Table 3).

Overall, compared to previous studies, these results emphasize both the reproducibility of genomic regions controlling NFNB resistance and the added value of this study in uncovering new QTLs, thereby expanding the genetic basis available for barley resistance breeding.

4.4 Highly expressed genes within QTLs and functional insights

The integration of GWAS with developmental expression profiling enabled the prioritization of potentially relevant genes within large genomic intervals. It should be noted that the transcriptomic dataset used in this study was derived from non-infected barley tissues and organs. Therefore, the observed expression patterns reflect developmental and tissue-specific transcriptional activity rather than pathogen-induced responses. In total, 129 highly expressed protein-coding genes were identified, among which 27 encode proteins (Supplementary Table 7) previously associated with barley disease resistance and responses to fungal pathogens, including responses to NB (Lorang et al., 2007; Akram et al., 2008; Scharte et al., 2009; Feurtado et al., 2011; Hoi et al., 2011; Mohan et al., 2025). Among the identified loci, QTLs Q_NB_1H.6, Q_NB_2H.3, and Q_NB_3H.1 were particularly notable, all showing high significance (P-values from 6.73E–07 to 2.47E–21) (Table 2). Q_NB_2H.3 and Q_NB_3H.1 likely represent novel loci harboring highly expressed genes encoding Glucose-6-phosphate 1-dehydrogenase, Chitinase, GATA-type domain-containing protein, and Bifunctional inhibitor/lipid transfer protein – proteins previously implicated in barley disease resistance or stress-related responses. In contrast, Q_NB_1H.6, which contains a Pathogen-related protein and a C2H2-type domain-containing protein, overlaps previously reported resistance regions (Table 3).

The enrichment of genes expressed in epidermal and photosynthetically active tissues (Figure 7D) is consistent with Ptt’s infection strategy, which initially colonizes the leaf surface and mesophyll (Bogacki et al., 2008). Notably, QTLs associated with seedling resistance were enriched for genes linked to chloroplast function and primary metabolism, including multiple ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO) small subunits and RuBisCO large subunit-binding proteins, suggesting a possible association between photosynthesis-related processes and stress-related physiological responses during early developmental stages. This may indicate that seedling resistance partly relies on rapid metabolic adjustments and chloroplast-mediated signaling, which are known to contribute to early immune responses.

In contrast, APR-associated QTLs exhibited a broader functional spectrum, with enrichment of genes involved in transcriptional regulation, redox homeostasis, proteolysis, and structural defense (Figure 7A). Among these, several highly expressed genes encode proteins previously associated with plant immunity and stress responses, including L-ascorbate peroxidase, which plays a key role in the detoxification of reactive oxygen species (ROS), and germin-like proteins, which are associated with the oxidative burst and pathogen-induced defense responses. The identification of ROS-related enzymes is consistent with previous studies that suggest an important role for redox balance in NB resistance (Able, 2003; Ismail and Able, 2016).

APR QTLs also included genes involved in cell wall biosynthesis and reinforcement, such as cellulose synthase and COBRA-like proteins, as well as proline- and glycine-rich proteins, which are structural components associated with cell wall strengthening and the formation of physical barriers against pathogen penetration (Rehman et al., 2025). The presence of dirigent proteins further suggests the involvement of lignin and phenolic compound biosynthesis, potentially contributing to cell wall reinforcement and structural integrity. In addition, signaling components such as non-specific serine/threonine protein kinases and TIFY proteins may indicate the involvement of complex regulatory networks, including jasmonate-mediated defense pathways (Vatter et al., 2017).

Biological process analysis revealed strong associations with stress responses, RNA-related processes, and amino acid metabolism (Figure 7B), potentially reflecting transcriptional and metabolic functions relevant to stress adaptation and defense-related processes. The identification of cysteine synthase and methionine metabolism-related enzymes suggests a role for sulfur-containing compounds in defense, while malate dehydrogenase points to the importance of cellular redox balance and energy metabolism.

At the cellular level, APR genes were predominantly localized to the nucleus and cytoplasm (Figure 7C), consistent with regulatory and signaling functions, whereas SR genes showed a stronger association with chloroplast-localized proteins, supporting a possible association between early resistance and photosynthesis-related processes. Despite these differences, both resistance types shared enrichment in primary metabolism, indicating common energetic and biosynthetic requirements during stress-related responses.

GO enrichment further emphasized the contribution of primary metabolism and redox-related pathways (Figure 8), which are essential for maintaining cellular homeostasis under stress (Rojas et al., 2014). Additionally, genes such as UDP-glucuronic acid decarboxylase suggest a possible role of cell wall polysaccharide biosynthesis in defense, while agglutinin domain-containing proteins suggest potential involvement in pathogen recognition or binding.

Overall, these findings suggest that seedling resistance may be associated with chloroplast-related and metabolic functions, whereas adult plant resistance may involve a broader range of redox-, structural-, and signaling-related functions. These observations are consistent with the concept that APR may represent a multilayered and durable form of quantitative resistance, potentially involving coordinated physiological and molecular networks rather than single major resistance genes. Because the expression data were derived from healthy tissues, these analyses primarily describe tissue-specific transcriptional activity of genes located within QTL intervals. Further transcriptomic studies under Ptt infection are required to determine whether these genes are directly involved in NFNB resistance responses.

4.5 Developmental co-expression organization of highly expressed genes within NFNB-associated QTLs

The highly interconnected and modular structure observed for APR-associated genes indicates coordinated regulation across multiple biological processes. The prominence of clusters associated with chromatin organization, transcriptional regulation, and proteostasis (Figure 9) suggests that epigenetic regulation and dynamic reprogramming of gene expression play a central role in mediating durable resistance at later growth stages. Similar associations between histone dynamics and stress-responsive transcription have been reported in plant immunity (Kang et al., 2022; Xie and Duan, 2023).

In contrast, the seedling resistance network exhibited a more fragmented and specialized topology, indicating a more targeted and rapid response system (Figure 10). The dominance of clusters associated with photosynthesis, cytoskeletal organization, and cell wall biosynthesis aligns with previous findings that early-stage resistance often relies on structural barriers and primary metabolic processes to restrict pathogen establishment (Hardham et al., 2007; Underwood, 2012). The strong representation of Rubisco-related and germin-like proteins further suggests tight coupling between carbon metabolism and defense signaling during early stages of infection.

These contrasting co-expression architectures indicate differences in the organization of developmental expression between candidate genes associated with seedling resistance and APR-associated QTLs. APR-associated genes exhibited stronger representation of chromatin-related, regulatory, and stress-associated functional categories, whereas seedling-associated genes were more enriched in photosynthesis-, cytoskeleton-, and primary metabolism-related functions. However, because the underlying RNA-seq datasets were generated under non-infected conditions, these observations should be interpreted with caution and do not constitute direct evidence of stage-specific NFNB resistance mechanisms. Therefore, the potential involvement of these genes in NFNB resistance requires validation under Ptt infection conditions.

5 Conclusion

This study investigated the genetic architecture of NFNB resistance in 273 spring two-row barley accessions by integrating adult-plant and seedling phenotypes with high-density genotyping. Phenotypic evaluation revealed extensive variation, with APR skewed toward resistance at KRIAPG (mean = 0.6 ± 0.6) and broader susceptibility at RIBSP (mean = 2.8 ± 2.4). Seedling responses were strongly race-dependent, with race 500 showing greater aggressiveness (mean = 5.8 ± 1.6). Significant genotype × environment and genotype × race interactions, together with moderate heritabilities (50.6% for APR; 41.3% for seedling resistance), confirm the polygenic and stage-specific nature of NFNB resistance. GWAS identified 57 genome-wide significant and multi-model-supported QTLs, including 39 for APR and 18 for seedling resistance. While 40 QTLs co-localized with known resistance loci, 17 QTLs were novel. Transcriptomic profiling of non-infected tissues identified 129 highly expressed genes located within QTL regions, including 27 genes previously associated with disease resistance or stress-related responses. Among the most notable were highly significant QTLs Q_NB_1H.6, Q_NB_2H.3, and Q_NB_3H.1, containing genes encoding proteins previously associated with pathogen resistance-related functions in barley. Co-expression analysis suggested distinct transcriptional patterns between developmental stages: seedling-associated genes were enriched in metabolic functions, and APR-associated genes were enriched in regulatory and stress-related functions. These findings provide a comprehensive framework for NFNB resistance and support the use of QTL pyramiding and genomic selection in barley breeding.

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