Vol.: (0123456789) Euphytica (2025) 221:152 https://doi.org/10.1007/s10681-025-03602-8 RESEARCH Genome‑wide association analysis of type II resistance to Fusarium head blight in Pakistani spring wheat germplasm Rafi Ullah · Fahim Ullah Khan   · Inam Ullah · Valentina Spanic   · Katarina Sunic Budimir · Attiq ur Rehman  Received: 13 January 2025 / Accepted: 12 August 2025 / Published online: 28 August 2025 © The Author(s) 2025 spread along the spike (Type II resistance) was evalu- ated under controlled conditions, and the genotypes were categorized based on FHB scores ranging from 0 (highly resistant) to 9 (highly susceptible). We found statistically significant variation (p ≤ 0.01) for resistance to type II FHB in the tested panel including some promising genotypes with high levels of resist- ance to the infection. To dissect the genetic basis of FHB resistance, multi-model GWAS were performed using 14,800 SNP markers from the 50 K SNP array. Population structure and kinship were accounted for to control false positives, using principal compo- nents and a kinship matrix. Our study identified eight quantitative trait loci (QTL) regions associated with Type II FHB resistance, distributed across six chro- mosomes (1A, 1B, 2A, 2B, 7B, and 7D). Among these, q_Fhb_1B on chromosome 1B was consist- ently detected across multiple models, underscoring its potential as a key resistance locus based on the top SNP 1B_667978743. Haplotype analysis fur- ther revealed favorable allele combinations linked to resistance, providing additional insights for marker- assisted selection. These findings offer valuable insights for genome-based breeding strategies aimed at enhancing FHB resistance while maintaining agronomic performance, thereby contributing to the development of more resilient wheat varieties suitable for FHB-prone regions. Keywords  Fusarium head blight · Spring wheat · QTL · GWAS · Type II resistance · MAS Abstract  Fusarium head blight (FHB) is a devas- tating fungal disease of wheat, causing significant losses in grain yield and quality. Understanding the genetic basis of FHB resistance is crucial for devel- oping resistant varieties. This study aimed to charac- terize the genetic architecture of FHB resistance in a diverse panel of Pakistani spring wheat germplasm, consisting of 150 recognized varieties, 45 landraces/ lines, and two check varieties. Resistance to fungal Supplementary Information  The online version contains supplementary material available at https://​doi.​ org/​10.​1007/​s10681-​025-​03602-8. R. Ullah · I. Ullah  Department of Biotechnology and Genetic Engineering, Hazara University Mansehra, Mansehra 21300, Pakistan F. U. Khan (*)  Department of Agriculture, Hazara University Mansehra, Mansehra 21300, Pakistan e-mail: fahimbiotech@hu.edu.pk V. Spanic (*) · K. S. Budimir  Agricultural Institute Osijek, Juzno Predgradje 17, 31000 Osijek, Croatia e-mail: valentina.spanic@poljinos.hr A. u. Rehman (*)  Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland e-mail: attiq.rehman@helsinki.fi A. u. Rehman  Natural Resources Institute (Luke), Helsinki, Finland http://orcid.org/0000-0003-3712-0738 http://orcid.org/0000-0002-7684-5419 http://orcid.org/0000-0002-0131-3928 http://crossmark.crossref.org/dialog/?doi=10.1007/s10681-025-03602-8&domain=pdf https://doi.org/10.1007/s10681-025-03602-8 https://doi.org/10.1007/s10681-025-03602-8 Euphytica (2025) 221:152152  Page 2 of 17 Vol:. (1234567890) Introduction Wheat, rice, and maize are the major cereal grain crops. Among cereals, bread wheat (Triticum aesti- vum L.) accounts for 15% of the world’s total arable land. Bread wheat, also called common wheat, is the second most productive crop in the world in terms of grain yield, whereas Central Asia is the most suitable region for bread wheat production (Soto-Gómez and Pérez-Rodríguez 2022). Pakistan is one of the world’s largest wheat producers, and the crop plays a central role in the country’s agriculture, economy, and food security (Abbas and Alnafrah 2024). Current wheat production is sufficient to meet present food demands but may not be adequate for future generations. How- ever, global wheat-producing regions, including Pakistan, are threatened by several biotic (pathogens, weeds, and insects) and abiotic (temperature, salinity, and drought) factors. Rust diseases, powdery mildew, and Fusarium head blight are among the most impor- tant diseases (Prasad et al. 2021). Fusarium head blight (FHB), a major fungal dis- ease of wheat, barley, and maize, is primarily caused by two predominant species Fusarium graminearum and F. culmorum but other species can also cause the disease depending on environmental conditions (Becher et al. 2013). The FHB species complex pro- duces mycotoxins that reduce grain quality and pose significant health risks to both humans and animals. (Haile et  al. 2023). The weather conditions directly affect the concentration of Fusarium mycotoxins that accumulate in grains. It has been reported that pro- longed periods of high humidity, starting from the flowering stage and continuing until harvest, signifi- cantly increase the risk of infection with Fusarium spp. and, consequently, the concentration of myco- toxins in grain (Brennan et  al. 2005; Van der Burgt et  al. 2011). At flowering FHB causes wheat spikes to become blighted, leading to significant yield losses due to flower abortion, reduced grain number, and poorly developed grains. In susceptible varieties the whole spike can become bleached within 7–10 days of infection (Sunic et al. 2023). FHB poses a significant threat to the future wheat production in Pakistan, particularly in the northern belt of Khyber Pakhtunkhwa, including the Hazara division, where unexpected climatic events, favora- ble temperatures, and high humidity during the wheat growing season create ideal conditions for the disease (Gul et al. 2022). Fungicide use can reduce the sever- ity of FHB, but the actual reductions are highly variable. Moreover, when weather conditions are unfavorable for FHB occurrence, fungicide applica- tion can be both costly and environmentally harmful (Matengu et al. 2024). In some cases, FHB losses can be reduced by proper agronomic practices or biologi- cal control. Given the high risk of future FHB epi- demics, implementing preemptive measures, such as cultivation of FHB-resistant genotypes, represents the most effective and sustainable strategy for combat- ing the disease (Spanic and Sarcevic 2023). However, FHB resistance is a complex trait comprising multiple genetic components, each controlled by various quan- titative trait loci (QTL) with small to medium effects. The Type I component reflects resistance to initial infection, the Type II component indicates resistance to the spread of symptoms, and the Type III com- ponent represents resistance to toxin accumulation in the grains (Mesterházy et  al. 1999). Additionally, two more types of resistance have been identified: Type IV, which pertains to resistance against kernel infection, and Type V, which focuses on grain yield tolerance. FHB-resistant genotypes are critial for dis- ease management, as they are expected to accumulate significantly lower levels of mycotoxins compared to susceptible ones (Spanic et al. 2023). Phenotyping for FHB resistance is often challeng- ing due to strong genotype-by-environment (G × E) interactions, making marker-assisted selection (MAS) a valuable tool in many breeding programs. QTL for FHB resistance has been reported on all wheat chro- mosomes and molecular markers have been devel- oped for most of the mapped QTL. This includes seven cataloged FHB genes, Fhb1–Fhb7 (Zhu et  al. 2020). Dai et  al. (2022) reported that Sumai 3 har- bors three different genes Fhb1, Fhb2, and Fhb5 for resistance to FHB on chromosome 3BS, 4BL and 6BL, respectively. Fhb1 is the most important and widely studied one (Buerstmayr et al. 2009) as it was fine mapped to a 1.1 Mb genomic region containing 28 candidate genes. Many spring wheat varieties con- tain the FHB resistance derived from Sumai 3. In a spring wheat mapping population [Surpresa × Whea- ton], four QTL for type II resistance on chromosomes 3A, 5A, 6A, and 7A, explained 10.4–14.4% of the total phenotypic variation (Poudel et  al. 2022). Zhu et al. (2020) evaluated Chinese wheat germplasm for FHB resistance using mixed linear model and found Euphytica (2025) 221:152 Page 3 of 17  152 Vol.: (0123456789) five QTL on chromosomes 1, 2, 5, and 7. Notably, QTL are often specific to the populations, meaning not transferable to other populations and are often not stable across different environments (Würschum 2012). On the other hand, landraces with high or moderate FHB resistance could not be used directly as the breeding parents in modern breeding programs due to their inferior agronomic performances (Zhang et al. 2021). Therefore, incorporating their resistance into elite varieties requires a more gradual breeding approach, such as backcrossing or introgression. This process can be accelerated by identifying loci associ- ated with FHB resistance, which can then be used in MAS to streamline breeding efforts. Genome-wide association studies (GWAS) are promising approach for identifying QTL associ- ated with various simple and complex traits of inter- est. This method leverages recombination events in the genome to achieve a higher mapping resolution, allowing for more precise identification of QTL related to the trait under investigation. Zhu et  al. (2020) observed that very few GWAS on wheat have addressed FHB type II resistance. Recent studies have demonstrated the utility of GWAS in identify- ing genomic regions associated with FHB resistance, such as Shi et al. (2023), who detected novel loci on chromosomes 1B and 5A using high-density SNP arrays, and Gaire et  al. (2021), who identified QTL from diverse sources contributing to FHB resistance traits in soft red winter wheat. Further, more powerful methods like FarmCPU and BLINK can improve the ability of GWAS to detect loci with smaller effects (Huang et  al. 2019). Markers identified through GWAS can be used for MAS directly or after conver- sion to utility markers (Radecka-Janusik et al. 2022). The current research was conducted to study pheno- typic variance among Pakistani wheat germplasm for type II resistance and to determine the genomic regions controlling FHB resistance in the diversity panel. Methods and material Plant material A set of 197 spring wheat genotypes including 150 released varieties, 45 landraces/lines and two checks including Sumai 3 and Gamenya was tested in the greenhouse at Agriculture Research Institute, Croatia (Supplementary Dataset S1). Inoculum preparation The inoculum was prepared at a concentration of 50,000 spores mL−1 by mixing spores equally from two pathogenic species of F. graminearum (PIO 31) collected from East Croatia and F. culmorum (IFA 104) collected from Austria. For a mass production of the conidia of each species in the proportion 1:1., two discs (5 mm diam.) from a well-grown colony at syn- thetic low-nutrient (SNA) medium were transferred to the mixture of wheat and oat (3:1), previously soaked in water overnight, and autoclaved (Spanic et  al. 2021). Growth conditions, inoculation, and evaluation of reaction to Fusarium head blight In greenhouse environments at Agricultural Institute Osijek (Croatia), the spring wheat material was grown in 2.5  L pots filled with soil (pH: 5.5–7.0, organic matter: 70.0–85.0%, N (1/2 vol.): 100–200  mg L−1, P2O5 (1/2 vol.): 100–150  mg L−1, K2O (1/2 vol.): 200–400  mg L−1) in randomized complete block design. In each pot four plants were planted in two replications. The greenhouse (Gis Impro d.o.o., Vrbovec, Croatia) was supplemented with artificial light for a 14 h photoperiod, with temperature main- tained between 22 and 25 °C, and irrigation applied twice per week. Nitrogen was applied at the two-leaf development stage (GS12) using calcium ammo- nium nitrate (CAN) (27% N). Plants were protected against pests with the insecticide Vantex (gamma- cyhalothrin 60 g L−1) (GS31). At the flowering stage (GS61), plants were inoculated with a mixture of F. graminearum and F. culmorum using the single- spikelet inoculation method (Sunic et al. 2023). 20 μL of inoculum was injected into the central spikelet of a spike using an automatic pipette (Eppendorf, Wien, Austria). To facilitate disease development, the mist- ing system was turned on for the next 36 h keeping the spraying with foggers every hour for a period of 2 minutes. Disease ratings were conducted at 21 days after inoculation in one plant per pot in two replica- tions. Type II resistance was assessed by counting the blighted spikelets (Sunic et al. 2023). Euphytica (2025) 221:152152  Page 4 of 17 Vol:. (1234567890) Genomic DNA extraction and genotyping Seeds were surface sterilized using 3% NaOCl and were sown in plastic trays containing peat moss. Seedlings were harvested 15  days after sowing and genomic DNA was extracted from fresh leaves using phenol–chloroform method (Ahmed et  al. 2009). A 50 μl aliquot of DNA (50–100 ng/μl) for each sam- ple was used for 50  K (Triticum TraitBreed array) SNP array genotyping (Rasheed and Xia 2019). The samples were sent to Chinese Academy of Agricul- tural Sciences (CAAS), Beijing, China for genotyping using 50 K SNP array. For quality control, SNP mark- ers with heterozygosity > 0.2 and minor allele fre- quency (MAF) < 5% were filtered using TASSEL 5.2. software and the finally retained 14,800 SNP markers for 146 genotypes were used for population structure and association analysis (Supplementary Dataset S1). Statistical analysis for FHB disease severity Statistical analysis of FHB disease severity was performed using Statistica 12.0 software (StatSoft Inc., Tulsa, OK, USA). Variation of disease sever- ity was computed using one-way analysis of vari- ance (ANOVA), followed by Fisher LSD post hoc test (p < 0.05). Broad-sense heritability (H2) for FHB severity was estimated as H2 = Vg/(Vg + Ve), follow- ing the method described by Buerstmayr et al. (2000) (Supplementary Dataset S1). Population structure analysis The principal component analysis (PCA) and Kinship matrix (VanRaden 2008) were performed using high quality 14,800 SNP markers in Genomic Association and Prediction Integrated Tool (GAPIT) version 3 (Wang and Zhang 2021). Genome‑wide association analyses We implemented a comprehensive analytical strategy by integrating five GWAS methods: Generalized Lin- ear Model (GLM), Mixed Linear Model (MLM), Compressed Mixed Linear Model (CMLM), Fixed and random model Circulating Probability Unifica- tion (FarmCPU), and Bayesian-information and Link- age-disequilibrium Iteratively Nested Keyway (BLINK) within the GAPIT version 3 (Wang and Zhang 2021). Our association analyses were con- ducted with careful consideration of population struc- ture and individual relationships, utilizing both the Q + K matrices. Q was the principal component from PCA while the K matrix was computed using the Van Raden method (VanRaden, 2008). Both GLM and MLM are single locus models and can be described as following formula: Y = X� + Zu + e , where Y is the vector of observed phenotypes; β is an unknown vector containing fixed effects, including the genetic marker, population structure (PCA in both GLM and MLM), and the intercept; u is an unknown vector of random additive genetic effects from multiple mark- ers for individuals; X and Z are the known design matrices; and e is the unobserved vector of residuals. The u and e vectors are assumed to be normally dis- tributed with a null mean and a variance of: Var ( u e ) = ( G 0 0 R )  , where G = �2 a K while �2 a as additive genetic variance and K as kinship matrix (included only in MLM). Whereas CMLM is an improved form of am MLM model in which individu- als are grouped using clustering algorithms, including the un-weighted pair group method with arithmetic mean (UPGMA) and the statistical power is increased (Li et  al. 2014). The first ten principal components were included in the analysis to account for popula- tion stratification, with the number chosen based on Bayesian information criterion (BIC)-based model selection procedure, indicating that these components capture the majority of population structure present in the genetic data (Supplementary Dataset S1). Among the multi-locus models, the FarmCPU model itera- tively uses both random and fixed models to estimate pseudo-quantitative trait nucleotides (QTNs) and test- ing markers by using pseudo-QTNs as covariates, respectively. BLINK model replaces the binning method of FarmCPU with linkage disequilibrium to increase statistical power and decrease the computa- tion time. Both FarmCPU and BLINK are considered particularly powerful methods to conduct GWAS by controlling false-negative rate (Liu et  al. 2016). To identify significant marker-trait associations (MTAs), initially, a stringent Bonferroni corrected p-value threshold was considered for significance; however, due to the highly conservative nature of this correc- tion in the context of linked markers and the poly- genic nature of FHB resistance, no significant MTAs were detected at this level. Consequently, as a method Euphytica (2025) 221:152 Page 5 of 17  152 Vol.: (0123456789) of correction for multiple testing issues and to iden- tify candidate loci, a more relaxed threshold value of −log (p) > 3.0 was applied (Saripalli et  al. 2023). QTLs were given names based on the relative posi- tion on the chromosome where significant MTAs are identified. A QTL was considered stable if the associ- ated SNP was detected by at least two of the five tested GWAS models. To estimate the effect of each top SNP within identified QTL, we calculated the proportion of phenotypic variance explained (R2) using the formula: R2 = 2 × MAF × (1−MAF) × β2, where β beta the additive effect of the SNP, MAF is the minor allele frequency (You et al. 2021). Results Variation in FHB resistance The analysis of variance showed significant dif- ferences in Fusarium Head Blight (FHB) severity among the 197 wheat genotypes (Supplementary Dataset S1). This suggests that the observed varia- bility in FHB severity is primarily driven by genetic differences among the genotypes, underscoring the presence of substantial genetic diversity for FHB resistance within the tested wheat germplasm. Simi- larly, broad-sense heritability (H2) for FHB score was estimated at 0.51, based on a genetic vari- ance (Vg) of 1.68 and an environmental variance (Ve) of 1.60. FHB scores for 197 wheat genotypes were categorized (Fig.  1) into four groups: resist- ant (0–2), moderately resistant (2.1–3.0), mod- erately susceptible (3.1–4.9), and susceptible (5 and above). Notable genotypes include Johar-16, Nowshera-96, NIA-Zakheera, Ghazi-19, MH-97, Gomal-08, and SKD-1, Punjab-1, NIFA-Insaf, Pun- jab-2011, Shafaq-2006, 10,810, 18,678, and 24,002 fall into resistant category with scores of 2 or below. Moderately resistant genotypes, with scores between 2.1 and 3.0, included varieties like Shali- mar-88, Barani-17, Khyber-87, NIFA-Awaz, and Faisalabad-2008 and landraces viz. 11,154, 11,395, 18,671, 18,675, 10,865, 11,062, 11,154, 11,180, 11,255, 11,529, and 18,677. Genotypes classified as moderately susceptible, with scores between 3.1 and 4.9, included varieties like NIFA-Aman, Pirsabak-2004, NN-Gandum1, Auqab-2000, and NARC-09. Finally, the FHB susceptible genotypes, with scores of 5 and above, exhibited severe FHB symptoms and included varieties like Rohtas-90, Chakwal-86, Shalakot-13, and Inqilab-91. The most susceptible genotype in the study was 11,706, with a maximum score of 9. Population structure revealed clear grouping of landraces and varieties Although the inclusion of diverse varieties and lan- draces in this study was apparent, both PCA and Kinship matrix using 14,800 SNP markers also showed clear separation between varieties and the set of landraces used in this study, with some over- lap indicating the presence of population structure in the data (Fig. 2a, b). As most of the variance was explained by the first three components (Fig.  2c), with PC1 explaining approximately 9.8%, PC2 explaining 8.5%, and PC3 explaining 5.8%, how- ever, we included all ten principal components dur- ing the subsequent GWAS analysis when the inflec- tion began to become clearer. Fig. 1   Frequency distribution of the tested spring wheat panel based on FHB phenotype categories, separately displayed for varieties (a) and landraces (b)  Euphytica (2025) 221:152152  Page 6 of 17 Vol:. (1234567890) GWAS identified multiple QTL for FHB type II resistance We performed GWAS with five different models, including three single-locus (GLM, MLM, CMLM) and two multi-locus models (FarmCPU, and BLINK) that resulted in eight unique marker-trait associa- tions (MTAs) on six different wheat chromosomes. These MTAs were clustered to eight QTL regions as detailed in Table  1. Notably, multi-locus models proved particularly effective in dissecting FHB resist- ance, not only identifying the majority of unique marker-trait associations but also demonstrating supe- rior control of population stratification based on QQ plots (Fig. 3). Specifically, while single-locus models (GLM, MLM, CMLM) exhibited p-value deflation, indicative of over-correction, the multi-locus models closely adhered to the expected null distribution until approximately −log10(p) = 3.0, beyond which a clear upward deviation indicated the presence of significant associations. All the single-locus models (GLM, MLM, and CMLM) detected only one SNP on chromosome 1B, whereas both the multi-locus models (FarmCPU and BLINK) identified seven additional significant SNPs each, exceeding the threshold value of −log (p) > 3.0 (Fig.  4a). The multi-locus models identified seven unique regions in addition to the one on chromosome 1B, indicating superior detecting power than the tested single-locus models. These eight MTAs cor- respond to eight different QTL localized on six dif- ferent chromosomes, each confirmed by at least two of the tested GWAS models (Fig.  4b, c). Only one single MTA (1B_667978743) on chromosome 1B was detected by all the tested GWAS models, thereby making the q_Fhb_1B a promising QTL for FHB resistance in the evaluated wheat panel. We also searched the literature for previously reported QTL on the same chromosomes and found that nearly all the discovered QTL were close to the previously reported markers, suggesting a Fig. 2   a Population structure of wheat germplasm based on first three principal components, b A heatmap of the kinship matrix of the tested wheat panel, c Proportions of explained variance for 10 principal components indicated on x-axis (c)  Euphytica (2025) 221:152 Page 7 of 17  152 Vol.: (0123456789) possibility of similar resistance sources present in different plant materials (Table  2). Notably, the QTL on chromosomes 1B and 2B were in close vicinity of the previously published studies on FHB resistance. Allele effects of significant SNPs The distribution of different allelic classes of the significant SNPs for FHB resistance showed vari- ability across the tested panel (Fig.  5). The SNP located on chromosome 7B (7B_690048233) Table 1   QTL names, SNP IDs, R2, chromosome positions, −log(p) values, minor allele frequencies for respective GWAS model QTL SNP R2 Chr Pos −log10 (p) MAF Model q_Fhb_1A 1A_173966594 0.10 1A 173,966,594 3.11 0.12 FarmCPU 1A 173,966,594 3.11 0.12 BLINK q_Fhb_1B 1B_667978743 0.06 1B 667,978,743 3.21 0.09 CMLM 1B 667,978,743 3.21 0.09 MLM 1B 667,978,743 3.21 0.09 GLM 1B 667,978,743 3.21 0.09 FarmCPU 1B 667,978,743 3.21 0.09 BLINK 1B 667,933,358 3.05 0.12 FarmCPU 1B 667,933,358 3.05 0.12 BLINK q_Fhb_2A.1 2A_115142667 0.12 2A 115,142,667 3.35 0.24 FarmCPU 2A 115,142,667 3.35 0.24 BLINK q_Fhb_2A.2 2A_707010276 0.08 2A 707,010,276 3.17 0.27 FarmCPU 2A 707,010,276 3.18 0.27 BLINK q_Fhb_2B 2B_694612619 0.07 2B 694,612,619 3.56 0.49 FarmCPU 2B 694,612,619 3.57 0.49 BLINK q_Fhb_7B 7B_690048233 0.09 7B 690,048,233 3.54 0.13 FarmCPU 7B 690,048,233 3.54 0.13 BLINK q_Fhb_7D.1 7D_397567807 0.16 7D 397,567,807 3.05 0.34 FarmCPU 7D 397,567,807 3.06 0.34 BLINK q_Fhb_7D.2 7D_581139373 0.11 7D 581,139,373 3.03 0.19 FarmCPU 7D 581,139,373 3.03 0.19 BLINK Fig. 3   Quantile–Quantile (QQ) plots of observed (y-axis) ver- sus expected (x-axis) p-values for each of the tested GWAS models. The red diagonal line indicates the expected null dis- tribution of p-values, with each SNP represented as red dot. The gray shaded area indicates the 95% confidence interval for each plot Euphytica (2025) 221:152152  Page 8 of 17 Vol:. (1234567890) demonstrated the clearest allelic trend, where geno- types carrying the ‘TT’ allele were associated with lower FHB scores, indicative of moderate resist- ance, while the ‘AA’ allele was more frequently observed in moderately susceptible genotypes. Conversely, the ‘TT’ allele at SNPs on chromo- somes 2A was associated with moderate suscepti- bility. However, visual differences in FHB scores between alleles were not statistically significant based on two-sample t-tests (p > 0.05 for all top SNPs). The R2 values for these SNPs ranged from 0.06 to 0.16 (Table  1), indicating minor contribu- tions to phenotypic variance. Despite the lack of strong statistical support, the consistent directional trends observed across genotypes suggest additive effects, supporting the classification of these loci as minor-effect QTLs. Haplotype analysis Haplotype analysis, which involves combining SNPs located on the same chromosome that can possibly co-segregate, proved to be more effective at identi- fying favorable allele combinations than individual SNP analysis. In this study, several haplotypes were tested, with some showing clear associations with either susceptibility or resistance to FHB (Fig. 6). For example, on chromosome 1B, the haplotype ‘CCTT’ was linked to susceptible phenotypes, as were the haplotypes ‘TTGG’ and ‘TTTT’ on chromosome 2A. Similarly, the haplotype ‘AGGC’ and ‘AGGG’ on chromosome 7D demonstrated a strong connection to FHB susceptibility as well. In contrast, the haplotype ‘GGCC’ on chromosome 1B was consistently asso- ciated with FHB resistance across the tested GWAS Fig. 4   a Venn diagram showing single shared marker-trait association between five GWAS models, b Stacked Manhattan plots for GWAS using single-locus GLM, MLM, and CMLM models, c and multi-locus FarmCPU and BLINK models for FHB resistance in wheat. The x-axis represents different chro- mosomes, and y-axis represents −log(p) values of the SNPs. Horizontal black line shows cutoff at −log(p) = 3. Significant SNPs and respective FHB QTL names are represented in red Euphytica (2025) 221:152 Page 9 of 17  152 Vol.: (0123456789) panel. These haplotypes can be further investigated for potential use in haplotype-based selection for FHB resistance in spring wheat. Discussion Fusarium head blight (FHB) continues to be a major challenge in wheat production, with disease sever- ity varying based on environmental conditions, crop management practices, and varietal susceptibility. The severity of FHB can range from subtle symp- toms that may go unnoticed to devastating epidemics resulting in severe grain yield losses and quality dete- rioration (Shaner 2003). Fusarium spp., the primary causal agents of FHB, can survive as saprophytes on plant residues or exist on plant surfaces without ini- tially causing disease. They transition into opportun- istic pathogens during anthesis, characterized by a narrow infection window and a short parasitic period (Shaner 2003). Key environmental factors contribut- ing to FHB outbreaks include moisture, humidity, and moderate temperatures around anthesis (Cowger et al. 2020). Breeding for FHB-resistant varieties is crucial, with a focus on developing stable resistance across different environments. Effective control strategies integrate genetic resistance with agronomic prac- tices such as crop rotation and residue management (Bai and Shaner 2004). In this study, we assessed 197 spring wheat genotypes, including released varieties, landraces, and checks like Sumai 3 (resist- ant) and Gamenya (susceptible). To date, over 50 QTL related to FHB resistance have been mapped across all 21 wheat chromosomes, with particularly strong and stable QTL identified on chromosomes 3BS, 5AS, 6BS, 3A, and 2D (Buerstmayr et  al. 2009; Liu et  al. 2009; Bai et  al. 2018). Meta-anal- yses identified novel loci, enriching our understand- ing of the genetic architecture of FHB resistance (Liu et al. 2009). QTL are often specific to the pop- ulations in which they are identified, which limits the transferability of markers into unrelated popula- tions. Additionally, these markers may exhibit insta- bility across environments, underscoring the need to conduct association studies to confirm existing sources of resistance and potentially discover new ones. Table 2   QTL co-segregating with previously reported FHB resistance QTL. The approximate positions of the QTL with reference to the previously reported ones are also mentioned in parenthesis in the SNP column QTL SNP Chr Pos MAF GWAS Model References q.Fhb-1A Xgwm153 (≈675 Mb) 1A 528,309,856 0.15 BLINK Bourdoncle and Ohm (2003) Xgwm153 (≈675 Mb) 1A 528,309,876 0.12 BLINK Aviles et al. (2020) q.Fhb-1B Xgwm153 (≈39.15 Mb) 1B 628,826,245 0.14 FarmCPU Buerstmayr et al. (2009) XEtcg.Magc-7 (≈39.15 Mb) 1B 628,826,264 0.16 FarmCPU Zhang et al. (2004); Buerstmayr et al. (2009) q.Fhb-2A.1 RAC875_rep_c78744_228 (≈83.18 Mb) 2A 31,957,675 0.12 BLINK Wang et al. (2019) q.Fhb-2A.2 – 2A 31,957,775 0.13 BLINK Wang et al. (2023) q.Fhb-2B Xwmc149 (≈84.5 Mb) 2B 779,109,515 0.15 BLINK Liu et al. (2007); Gilsinger et al. (2005) Xcn16-2B (≈46.19 Mb) 2B 740,802,332 0.14 BLINK Sari et al. (2018) q.Fhb-7B – 7B – – MIXED Wang et al. (2019); Bourdoncle and Ohm (2003) q.Fhb-7D.1 GBS979/GBS20328 (≈124–290 Mb) 7D 107.1–521.7 Mb 0.1 BLINK Cativelli et al. (2013) q.Fhb-7D.2 Xwmc405 (≈381.14 Mb) 7D 200,000,000 0.12 BLINK Ren et al. (2019) Euphytica (2025) 221:152152  Page 10 of 17 Vol:. (1234567890) Despite the identification of well-characterized QTL, FHB resistance is quantitatively inherited, involving multiple minor-effect genes, which com- plicates breeding efforts. Genotype-environment interactions further influence resistance expression, emphasizing the importance of multi-environment tri- als (Zhang et  al. 2020). The challenge of balancing disease resistance with other agronomic traits, such as grain yield and quality, adds to the complexity of developing FHB-resistant varieties (Spanic et  al. 2021). Recent studies have increasingly focused on locally adapted germplasm to avoid potential draw- backs associated with exotic resistance sources, such as linkage drag (Bohra et al. 2022). Thus, our study Fig. 5   Box plots of MTAs associated with FHB resistance in wheat. Chromosome and SNP information is given on the top of each box-and-whisker. Boxes represent the first quar- tile, the median, and the third quartile, respectively. The thick horizontal lines correspond to the median. Whiskers represent variability outside the upper and lower quartiles. p-values from t-tests comparing genotype groups are shown above each plot Euphytica (2025) 221:152 Page 11 of 17  152 Vol.: (0123456789) utilized locally developed varieties and landraces, well-adapted to diverse agro-ecological zones of Pakistan. This approach ensured genetic variation for successful GWAS and identified potential resistance sources for local breeding use. Artificial inoculation methods used in this study created uniform and moderate-to-high disease pres- sure, ensuring accurate assessment of FHB resist- ance. The use of mixed Fusarium isolates with vary- ing levels of aggressiveness ensured sufficient disease pressure while minimizing reliance on environmental conditions (Mesterházy et  al. 2012). FHB resistance is not necessarily specific to Fusarium species or isolates, highlighting the need for standardized test- ing environments and inoculation protocols (Steiner et al. 2009). Parameters for evaluating FHB resistance include visual scoring, grain yield loss, grain qual- ity traits, and quantification of Fusarium biomass or deoxynivalenol (DON) content. Our results showed substantial variation in Type II resistance among genotypes. The genotypes were categorized into four groups: 54 were highly resistant (scores of 0–2), 65 showed moderate resistance (scores of 2.1–3.0), 48 were moderately susceptible (scores of 3.1–4.9), and 30 were susceptible (scores of 5 and above). These results underscore the substantial genetic diversity for FHB resistance in the tested wheat germplasm, which is valuable for breeding programs targeting improved resistance. The selection of appropriate models and statistical methods in GWAS is paramount for obtaining reliable results, especially considering the nature of the trait under investigation. FHB resistance, being a poly- genic and multifactorial complex trait, is regulated by numerous small-effect loci. Utilizing multi-locus methods proves to be more effective and efficient in capturing these small-effect loci, as demonstrated by previous studies not only in wheat but other crops as well (Segura et al. 2012; Zhang et al. 2019; Rehman et  al. 2024, 2025). However, for increased detection power and robustness, it is recommended to comple- ment multi-locus methods with single-locus models (Li et  al. 2017a, b). Furthermore, the integration of multiple GWAS methods is advantageous, serving as a cross-check mechanism to enhance the confi- dence of identified QTL (Sorrells et  al. 2010). We Fig. 6   Box plots of haplotypes on three different chromo- somes associated with FHB resistance in wheat. Boxes rep- resent the first quartile, the median, and the third quartile, respectively. The thick horizontal lines correspond to the mean. Whiskers represent variability outside the upper and lower quartiles Euphytica (2025) 221:152152  Page 12 of 17 Vol:. (1234567890) found multi-locus models advantageous in particular as most of the unique QTL were detected with this approach, in addition to detecting the same QTL identified by traditional single-locus models. Fur- thermore, our analysis indicated that while single- locus models exhibited p-value deflation, suggestive of potential over-correction and a loss of statistical power, the multi-locus models demonstrated more robust statistical performance, with their SNP distri- bution closely adhering to the expected null distri- bution before showing clear deviations indicative of potential genetic associations. It is important to note that these associations were identified using a sig- nificance threshold of −log10(p) = 3.0. This approach was necessitated as a more stringent Bonferroni cor- rection, which, as widely discussed in the context of GWAS, is considered a conservative method for selecting a threshold due to its assumption that every genetic variant tested is independent of the rest (Kaler and Purcell 2019), did not yield any significant asso- ciations. This highlights the challenge of detecting complex trait loci under such conservative multiple testing adjustments. Our study identified QTL for FHB resistance on chromosomes 1A, 1B, 2A, 2B, 7B, and 7D. These findings contribute to the broader understanding of the genetic architecture of FHB resistance in wheat (McMullen et al. 2012). While the well-known FHB resistance loci Fhb1 on chromosome 3B (Cuthbert et  al. 2006) and Fhb2 on chromosome 6BS (Cuth- bert et  al. 2007) are critical, the QTLs identified in our study represent minor-effect loci, with relatively lower R2 values. These loci did not show strong sta- tistical support in allele-wise comparisons but exhib- ited additive trends that may be relevant under poly- genic selection. Importantly, the newly detected loci, particularly on chromosomes 1A, 2A, and 7B, may play supporting roles in Type II resistance (resistance to spread within the spike), and warrant further vali- dation in larger or multi-environment populations.. When combined with known loci such as Fhb1 and Fhb2, the loci identified in this study may also have the potential to contribute to overall FHB resist- ance in wheat. However, given that these loci were detected at a LOD threshold of 3, further validation and fine mapping are necessary to confirm their sig- nificance and utility in breeding programs (Bai and Shaner 2004; McMullen et al. 2012; Kage et al. 2017) QTL. Our study estimated a broad-sense heritability (H2) of 0.51 for FHB severity, reflecting a moder- ate genetic contribution. This aligns with previous reports, though values vary widely with conditions. For example, Buerstmayr et  al. (2000) found high heritability (H2 > 0.75) under controlled environ- ments, while Ghimire et al. (2022) reported a range of 0.36–0.85 across environments. Similarly, Urrea et  al. (2002) observed H2 of 0.65 for severity and 0.46 for DON in barley, confirming moderate herit- ability in cereals. In the current study, four out of 43 landraces showed resistance with FHB scores between 0 and 2, while 11 landraces exhibited moderate resistance to with FHB score less than 3.0. Significant progress in identifying FHB resist- ance loci has been made, but translating these find- ings into commercial varieties remains challenging. Resistance derived from exotic sources often comes with undesirable agronomic traits, necessitating careful selection during backcrossing (Steiner et al. 2009). Marker-assisted selection has been essential in accelerating the introgression of key QTL into elite lines. However, the polygenic nature of FHB resistance implies that MAS alone may be insuf- ficient. Integrating genomic selection approaches, which account for multiple small-effect loci, can improve prediction accuracy and breeding effi- ciency (Otto et al. 2002; Wang et al. 2025). Chromosome‑specific findings Chromosome 1A Chromosome 1A has been identified as a significant contributor to FHB resistance in several studies. Buerstmayr et  al. (2009) identified multiple QTL on 1A, emphasizing its role in providing resistance against FHB. Similarly, Buerstmayr et  al. (2020); Yuan et  al. (2013) confirmed the importance of this chromosome in controlling resistance, particu- larly in relation to deoxynivalenol accumulation. Fine mapping efforts by He et  al. (2019) have fur- ther refined these QTL, by pinpointing their precise locations and contributing to a better understand- ing of their functional roles. The characterization of these QTL offers valuable insights for selecting resistant varieties and improving FHB resistance through breeding. Euphytica (2025) 221:152 Page 13 of 17  152 Vol.: (0123456789) Chromosome 1B Chromosome 1B also plays a crucial role in FHB resistance. Zhang et  al. (2004) first mapped signifi- cant QTL on this chromosome, which was further confirmed by more recent studies by Hu et al. (2020). These studies demonstrated the presence of multiple QTL, suggesting a complex genetic architecture for resistance on 1B. Guo et al. (2015) utilized high-den- sity SNP markers to enhance the mapping resolution, which improved our understanding of the QTL’ con- tributions to resistance. These findings underscore the potential of chromosome 1B for breeding programs aimed at enhancing FHB resistance in wheat. Chromosome 2A Chromosome 2A has been recognized for its role in FHB resistance through various studies. Wang et  al. (2019); Li et  al. (2016) identified and characterized QTL on this chromosome, highlighting its contribu- tion to resistance mechanisms. The fine mapping per- formed by McCartney et al. (2005); He et al. (2018) has been instrumental in pinpointing QTL locations with greater precision. Studies such as those by Wang et al. (2023); Cao et al. (2022) have provided further insights into the genetic control of resistance on chro- mosome 2A. The identification and validation of QTL on this chromosome are critical for developing wheat varieties with enhanced FHB resistance. Chromosome 2B QTL for FHB resistance on chromosome 2B have been extensively studied. Li et  al. (2017a, b) and Gilsinger et al. (2005) reported significant QTL asso- ciated with resistance, which were further character- ized by Liu et al. (2007); Wang et al. (2023). These studies reveal a diverse set of QTL that contribute to resistance, supported by high-density SNP mapping and meta-analysis (Sari et al. 2018). The characteriza- tion of these QTL provides valuable information for improving resistance through genetic selection and breeding. Chromosome 7B Chromosome 7B has been identified as a key region for FHB resistance. Wang et  al. (2019); Schmolke et  al. (2005) reported significant QTL on this chro- mosome, which were further characterized by stud- ies such as those by Liu et al. (2019). These findings, including high-density SNP mapping by Mellers et al. (2020), highlight the importance of chromosome 7B in developing resistant wheat varieties. The character- ization of QTL on this chromosome offers promising targets for enhancing FHB resistance. Chromosome 7D Chromosome 7D has also been associated with sig- nificant QTL for FHB resistance. Ren et  al. (2019); Cativelli et  al. (2013) identified key QTL on this chromosome, with further characterization provided by McCartney et al. (2016). These studies, along with those by Ren et al. (2019) have enhanced our under- standing of the genetic factors contributing to resist- ance. The mapping and validation of QTL on chro- mosome 7D are crucial for developing wheat varieties with improved FHB resistance. Conclusions Rapid climate changes pose significant challenges to cereal crops worldwide, including Pakistan. Contin- ued exploration of diverse genetic backgrounds and refinement of resistance mechanisms are crucial for developing wheat cultivars with improved resistance to Fusarium head blight. This study advances our understanding on the genetic architecture underlying FHB resistance in a diverse panel of spring wheat from Pakistan, highlighting the potential of integrated breeding strategies that incorporate marker-assisted selection and facilitate genomic selection in local breeding programs. However, we acknowledge obvi- ous limitations in our study. The GWAS was con- ducted under controlled greenhouse conditions due to the currently low and inconsistent incidence of FHB in field settings across Pakistan, which limited our ability to assess resistance under natural infection pressures. Moreover, while our results offer impor- tant preliminary insights, further validation under field conditions is essential to confirm the effective- ness of the identified loci across environments. The identified loci represent minor-effect QTLs, as evi- denced by their modest R2 values and non-significant allelic effects in t-tests. The use of a more relaxed Euphytica (2025) 221:152152  Page 14 of 17 Vol:. (1234567890) significance threshold was employed to capture these minor-effect loci, which might otherwise be over- looked by more stringent corrections (e.g., Bonfer- roni) that can be overly conservative for polygenic traits. Although individually small, these minor-effect loci may collectively contribute to durable resistance and can be valuable targets when pyramided through genomic selection. Despite these constraints, this study provides a foundational dataset that can guide future multi-environment studies and supports the long-term goal of genome-informed breeding for FHB resistance in Pakistan. Acknowledgements  This paper is part of the PhD disserta- tion of Rafi Ullah, PhD scholar Department of Biotechnology and Genetic Engineering, Hazara University Mansehra, Paki- stan. The authors are also thankful to Dr. Awais Rasheed for data availability and quality control. Author contributions  All authors contributed to the study conception and design. The study was conceptualized by Rafi Ullah, Fahim Ullah Khan, Inam Ullah, Valentina Spanic, and Katarina Sunic Budimir. Material preparation, data collection, and recording were performed by Valentina Spanic and Kata- rina Sunic Budimir, while the experiments were conducted by Rafi Ullah, Valentina Spanic, and Katarina Sunic Budimir. Data analysis was carried out by Fahim Ullah Khan and Attiq ur Rehman. The first draft of the manuscript was written by Rafi Ullah and Fahim Ullah Khan and was finally revised by Attiq ur Rehman. All authors reviewed and approved the final manuscript. Funding  Open Access funding provided by University of Helsinki (including Helsinki University Central Hospital). This research was supported by the International Foundation for Science (IFS) project (Grant No. C/6481-1). We gratefully acknowledge IFS for their financial support, which was instru- mental in conducting this study and advancing our understand- ing the genetic basis of FHB resistance in Pakistani wheat germplasm. Data availability  No datasets were generated or analysed during the current study. Declarations  Conflict of interest  The authors declare no conflict of inter- ests. Open Access  This article is licensed under a Creative Com- mons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Crea- tive Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Abbas S, Alnafrah I (2024) Food security in Pakistan: investi- gating the spillover effect of Russia-Ukraine conflict. 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Front Plant Sci. https://​doi.​org/​10.​3389/​fpls.​2020.​00206 Publisher’s Note  Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.3389/fcvm.2021.736001 https://doi.org/10.3389/fcvm.2021.736001 https://doi.org/10.3389/fpls.2019.00100 https://doi.org/10.3389/fpls.2019.00100 https://doi.org/10.3389/fpls.2020.00532 https://doi.org/10.3389/fpls.2020.00206 Genome-wide association analysis of type II resistance to Fusarium head blight in Pakistani spring wheat germplasm Abstract Introduction Methods and material Plant material Inoculum preparation Growth conditions, inoculation, and evaluation of reaction to Fusarium head blight Genomic DNA extraction and genotyping Statistical analysis for FHB disease severity Population structure analysis Genome-wide association analyses Results Variation in FHB resistance Population structure revealed clear grouping of landraces and varieties GWAS identified multiple QTL for FHB type II resistance Allele effects of significant SNPs Haplotype analysis Discussion Chromosome-specific findings Chromosome 1A Chromosome 1B Chromosome 2A Chromosome 2B Chromosome 7B Chromosome 7D Conclusions Acknowledgements References