Search Results (793)

Genomic selection (GS) may improve the adaptation of white lupin to drought or moderately calcareous soil, enabling to realize its potential as a high-protein crop. This study aimed to (a) verify breeders’ ability to identify the top-, mid-, and bottom-performing genotypes of published GS models of breeding lines and landrace genotypes for adaptation to drought and moderately calcareous soil; and (b) compare the top-performing materials produced by GS and phenotypic selection. Twelve selected genotypes were evaluated in four managed environments obtained through combining two soils (non-calcareous; moderately calcareous) with two water treatments (moderate terminal drought; moisture-favorable). GS based on the genotyping-by-sequencing of independent material was challenged by validation conditions that were partly different from the training ones and an imposed similarity of genomically predicted genotype phenology (to exploit drought resistance rather than drought escape). Grain yield reduction relative to favorable conditions averaged 19% for drought and 23% for calcareous soil. GS correctly identified the top-performing material for drought-prone or moderately calcareous soil, except for one model based on a small training set. The best GS lines performed comparably to the best phenotypically selected material. A higher harvest index was associated with better adaptation to drought and calcareous soil. Crossover genotype × water treatment interaction underpinned the selection for adaptation to drought. Full article

This study was conducted in Northwest Ethiopia in 2025 to estimate genetic parameters for dairy cattle hybrids under a group-based mass selection scheme. The objective was to investigate lactation milk yield (MY), lactation length (LL), and key fitness traits across varying breed compositions, aligned with suitable agro-ecological zones and milkshed systems. The findings may then serve as a framework to develop economically efficient and sustainable dairy genotypes tailored to the region. Data were collected from 355 dairy households using semi-structured questionnaires and monthly monitoring of MY. A mass selection scheme was applied to evaluate the productive and reproductive performance of Holstein-Friesian (HF) and Jersey hybrids across varying levels of exotic breed compositions. To identify superior genotypes, a total merit index (TMI) was developed, utilizing economic weights of +0.20 for production traits and −0.12 for reproductive traits. General liner model (GLM) analyses were performed to evaluate the performance of different breeds and exotic breed composition. Realized genetic parameters including genetic correlations (rg) as an indicator of pleiotropy, genetic gain (GG) per trait, and aggregate genetic response (AGG) were estimated for each group using specialized procedures in R software. Breed type (stratified by exotic breed composition), agro-ecology zone, and milkshed system were defined as the main and sub-fixed effects. The genetic contribution to the performance of hybrids indicated that the Holstein-Friesian (HF) hybrid baseline scheme achieved significantly higher efficiency, with an aggregate genetic gain) (AGG) of 155.50, compared with 136.03 for the Jersey hybrid schemes. Specifically, the >75% HF hybrid group exhibited the highest predicted AGG (183.00), a result primarily underpinned by significant gains in MY (182.53 L) and extended LL (0.28 months). This indicated that higher exotic breed composition in HF crosses maximizes the genetic gain when selection is weighted toward productivity. Conversely, the 62.5% Jersey hybrid exhibited the lowest AGG (110.38) and GG for MY (109.86 L), indicating that intermediate Jersey breed compositions may be suboptimal under the studied conditions. Analysis of interaction effects revealed environment-specific superiorities: in the Bahir Dar midland milkshed, the >75% HF hybrids achieved the highest genetic gains in MY (182.53 L) and a superior AGG (181.34). In contrast, within the Gondar midland milkshed, >75% Jersey hybrids reached the highest overall AGG (177.11), with a corresponding GG for MY of 178.75 L per lactation. The observed variance in MY (δ² = 362.44) indicated significant potential for genetic improvement through group-based selection. Pleiotropy was identified between MY and LL (r_g = 0.14), whereas an antagonistic trade-off was observed between maturity and conception efficiency (r_g = −0.34). The consistent upward trend in the performance of hybrids as breed composition increased from 50% to >75% across both main and sub-effects suggests that these genotypes are suited to the environment. In conclusion, single- and multiple-trait predictions based solely on breed and breed comparisons were suboptimal; instead, selection strategies incorporating genotype-by-environment (G × E) interactions offered the most effective alternative for regional dairy selection alternatives. Full article

Cucurbit crops—including cucumber (Cucumis sativus), watermelon (Citrullus lanatus), and melon (Cucumis melo)—are of major economic and nutritional importance worldwide. Yet their productivity and quality are severely compromised by foliar fungal diseases, particularly powdery mildew (PM), downy mildew (DM), and target leaf spot (TLS). While PM and DM have been extensively studied, TLS has emerged as an increasingly prevalent and damaging disease in key production regions, yet it remains comparatively understudied—especially with respect to its molecular basis and comparative pathobiology relative to PM and DM. Current reliance on chemical fungicides is hampered by escalating pathogen resistance and concerns over residual toxicity, whereas conventional breeding approaches face inherent limitations in pyramiding durable, broad-spectrum resistance against multiple pathogens. In this context, cucumber has emerged as a pivotal model species for dissecting foliar disease resistance mechanisms in cucurbits, supported by a high-quality reference genome, extensive resequencing datasets, diverse germplasm collections, and an efficient Agrobacterium-mediated transformation system. Despite these advantages, existing reviews predominantly address PM or DM resistance in isolation; comprehensive syntheses integrating TLS resistance advances—and critically, cross-disease comparisons of genetic architecture, transcriptional reprogramming, and defense signaling—are notably scarce. Furthermore, the translational pipeline—from gene discovery and functional validation to deployment in marker-assisted or genome-edited breeding—lacks systematic evaluation. Here, we provide a focused, cucumber-centered review that (i) synthesizes recent progress in mapping QTLs and GWAS loci, and characterizing key resistance-associated gene families (such as NLRs, RLKs, PR genes) conferring resistance to PM, DM, and TLS; (ii) integrates transcriptomic, epigenomic, and proteomic evidence to delineate conserved versus pathogen-specific host responses; (iii) highlights breakthroughs and unresolved questions in TLS resistance research, including the roles of novel susceptibility factors and non-canonical immune regulators; and (iv) critically assesses bottlenecks in translating resistance genes into practical breeding outcomes—such as linkage drag, functional redundancy, and genotype-by-environment interactions—and proposes empirically grounded strategies for accelerating molecular design of multi-disease-resistant cultivars. Collectively, this review aims to bridge fundamental insights with applied breeding goals, offering a conceptual and strategic framework for integrated management of foliar fungal diseases and the development of durable, broad-spectrum resistance in cucurbits. Full article

White clover (Trifolium repens L.) is a major legume in Mediterranean agroecosystems. This study systematically evaluates 15 autochthonous white clover populations from the Trikala region of Greece, focusing on chemical composition and derived nutritional indices relevant for germplasm characterization and breeding. Fifteen local populations were evaluated under controlled pot cultivation over two consecutive years. Clonal plants were harvested at the early flowering stage. Key traits—crude protein (CP), Ash, Fat, crude fibre (FIBRE), acid detergent fibre (ADF), neutral detergent fibre (NDF), digestible dry matter (DDM), dry matter intake (DMI), and relative feed value (RFV)—were measured. Combined ANOVA revealed significant differences among populations for all traits (p ≤ 0.001), while genotype × year interactions were present but generally minor compared to genotypic effects. Broad-sense heritability was high across most traits (H² = 90.8–99.4%), demonstrating strong genetic control. CP showed positive correlations with DDM, DMI, and RFV, whereas ADF and NDF were negatively correlated with intake and digestibility. Canonical and discriminant analyses showed that a reduced set of traits (CP, Ash, FIBRE, RFV) contributed strongly to differentiation among populations. Hierarchical clustering (heatmap) confirmed these groupings based on fibre and digestibility-related traits. Populations such as Dendrochori and Gorgogyri consistently showed favorable chemical and nutritional profiles, while Fiki and Dendrochori showed the highest stability across years. The present study highlights substantial genetic variability among local white clover populations and identifies trait structures of relevance for germplasm characterization. These findings enhance the characterization of genetic diversity in Trifolium repens and support its potential use in future breeding research under Mediterranean environments. Full article

This study aimed to evaluate the aromatic potential of four new Monastrell-derived white grapevine genotypes (MC180, MC69, MT103, MV67) compared with Verdejo over four consecutive seasons (2020–2023), with particular emphasis on both free and glycosidically bound volatile compounds. This approach provided novel insight into the aromatic composition of emerging cultivars under warm climate conditions and their potential suitability for future viticultural use. Free and glycosidically bound volatile compounds were extracted and analyzed using Gas Chromatography–Mass Spectrometry (GC-MS). Differences in aroma profiles were observed among genotypes and seasons. MV67 and MC69 showed higher levels of monoterpenes and volatile phenols, suggesting enhanced floral and complex aromatic potential. Seasonal effects strongly influenced C6 compounds and norisoprenoids, highlighting the importance of climatic conditions in shaping grape aroma. Multifactorial analysis revealed that season had the greatest impact on most compound families, although genotype and its interaction with season were also significant. These results demonstrate that genotype–environment interactions play a key role in determining aromatic composition. The elevated levels of aroma precursors, particularly glycosidically bound compounds, indicate promising enological potential for producing fresh, aromatic white wines. Therefore, these new cultivars represent suitable alternatives for white wine production in warm climates. Full article

Serum gamma-glutamyltransferase (GGT) is a biomarker for cardiovascular disease, but the role of its encoding gene family in ischemic stroke (IS) is unknown. This pilot study of 1288 individuals (600 cases and 688 controls) investigated GGT1, GGT5, GGT6, and GGT7 polymorphisms using the MassARRAY-4 system. Conventional single-variant, haplotype, and diplotype analyses were complemented by Model-Based Multifactor Dimensionality Reduction (MB-MDR) with stability assessment and model prioritization. Conventional analysis identified female-specific associations for three GGT5 variants (rs8140505, rs2275984, and rs2267073; P_perm < 0.05). A common GGT5 haplotype was protective in females (P_perm = 0.02). Diplotype analysis revealed joint effects of GGT genotypes on IS risk in females (FDR < 0.05). MB-MDR uncovered complex higher-order interactions (P_perm < 0.0001): in women, 12 models represented second-order interactions between smoking and individual GGT variants. In men, 8 models centered on GGT1 rs5751909 spanning second- to fourth-order interactions with alcohol, smoking, and other GGT family members. All prioritized models passed FDR correction (q < 0.05) and achieved higher weighted composite scores. eQTL data linked these variants to regulatory networks controlling glutathione metabolism, oxidative stress, and inflammation. This study supports a novel hypothesis on the combined involvement of GGT gene family polymorphisms and pro-oxidant environmental factors in ischemic stroke predisposition, demonstrating that disease risk is shaped by sex-specific gene–environment interactions. The pronounced sexual dimorphism highlights the need for sex-specific personalized approaches: smoking cessation may be particularly impactful in women carrying GGT5 risk variants, while alcohol moderation could be prioritized in men with GGT1 risk variants. Full article

Plant defences are assumed to evolve in response to the negative effects of virus infection on plant fitness (virulence), and to drive plant–virus coevolution. However, viruses are not always antagonistic symbionts of plants, and the expression of defence traits is environment-dependent. Thus, understanding plant–virus interactions requires analysing the expression of defence traits in the host’s native habitat. Here we analyse the effect of cucumber mosaic virus (CMV) infection, and the expression of resistance and tolerance in the native habitat of a wild Arabidopsis thaliana population. Plants from ten genotypes from that population, which have been shown to differ in resistance and tolerance to CMV in a greenhouse, were inoculated with an Arabidopsis isolate of CMV and transplanted to their habitat. Resistance was rated based on virus accumulation in leaves, and tolerance was rated based on the effect of infection on plant fecundity relative to virus accumulation. Consistent with the greenhouse assays, virulence depended on the host genotype, and polymorphisms for resistance and tolerance were expressed in the field, supporting the validity of the conclusions from the greenhouse assays. Our results also support theoretical predictions on the relationships between pathogen multiplication and virulence and between resistance and tolerance. Full article

The field production of tomato faces challenges regarding abiotic stress factors, which unfavorably impact fruit quality traits. Hedgerows, a form of agroforestry, offer a climate-resilient strategy to buffer temperatures and reduce the impact of direct wind stress on crop production. This study assessed the impact of hedgerow microclimate modulation effects on open-field tomato fruit quality, employing three genotypes (Roma, Ace55, and Szentlőrinckáta). Key quality traits (Total Soluble Solids (TSS), Titratable Acidity (TA), Sugar–Acid Ratio (SAR), Ferric-Reducing Antioxidant Power (FRAP), Total Phenolic Content (TPC), Chroma (C*), and Hue (h^o)) were measured over two harvests per season, in two consecutive years (2023–2024). Plots were positioned at five distances (3, 6, 9, 12, and 15 m from the hedge) on both windy and protected sides (W1–W5 and P1–P5, respectively, with 1 showing the closest position). We observed that the microclimate of the protected side was consistently warmer, with an average deviation from the reference temperature of +3.54 °C at mid-distances and +0.38 °C higher overall across both growing seasons. Results show that mid-distance zones (P3–P4, W3–W4) consistently exhibited the highest C* (up to 39.44) at W4 and TSS values at W1 (7.00 °Bx). Protected sides favored higher TA at P3 (0.70%) and Hue (h^o) values at P3 with (53.06 ± 0.30) with Ace55 and SAR at P3 (16.35) with Szentlőrinckáta. Windy sides significantly enhanced FRAP and TPC, with the Szentlőrinckáta genotype exhibiting the highest antioxidant capacity at W1 (23.67 mg AAE 100 g⁻¹, FRAP) and TPC (244.17 mg GAE 100 g⁻¹). At W4, Roma showed a 9.4% increase in TPC in the second harvest, while Ace55 showed the highest FRAP values during late-season sampling, highlighting genotype-specific antioxidant resilience under contrasting microclimates. These findings suggest that mid-distance zones and microclimatic variation between windy and protected sides remarkably influence fruit quality traits and antioxidant profiles. Full article

Chrysanthemum contains numerous active compounds, including flavonoids and phenolic acids, with its dried capitula widely used for tea and medicinal applications. The content of functional compounds is readily influenced by environmental factors, and the use of varieties with high-level and stable bioactive compounds is essential for sustainable cultivation. However, a key challenge is identifying genotypes that consistently perform well for functional-component traits in contemporary breeding activities. This study aimed to evaluate the performance and stability of functional components in tea chrysanthemums across multiple years. Total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were investigated in 24 tea chrysanthemum accessions across three growing years of 2018, 2021, and 2022. The additive main effects and multiplicative interaction (AMMI) model analysis revealed significant genotype (G), environment (E), and genotype-by-environment interaction (GEI) effects for all functional traits across three growing years. The GEI accounted for 63.58% to 80.82% of the variation across the four components in the AMMI model. Based on the AMMI stability value (ASV) parameter, the tea chrysanthemums showing the most stable concentrations of total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were identified. Based on phenotypic values and stability results, Suju-6, Hongxinju, Wangongju, and Baixiaoxiangju performed relatively well across the functional components investigated, making them promising candidates for future breeding and promotion programs. These findings provide valuable insights into the genetic basis of functional elements in tea chrysanthemum and will contribute to further genetic improvement. Full article

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions. Full article

Grain number per spike (GNS) is a key component of wheat yield, yet its genetic architecture remains incompletely understood. This study phenotyped 610 wheat accessions for GNS in four environments and genotyped them using 429,721 single nucleotide polymorphisms (SNPs). The phenotypes were associated with the SNPs using a three-variance multi-locus random-SNP-effect mixed linear model (3VmrMLM) to identify quantitative trait nucleotides (QTNs), as well as QTN-by-environment (QEI) and QTN-by-QTN (QQI) interactions. These genetic components and residual error explained approximately 18%, 31%, 28%, and 23% of the phenotypic variance, respectively. Two and one previously reported genes were found around QTNs and QEIs, respectively. Bioinformatics and haplotype analyses subsequently yielded 25 candidate genes, 22 gene-by-environment interactions (GEIs), and 24 gene-by-gene interactions (GGIs) around the QTNs, QEIs, and QQIs, respectively. Notably, TraesCS1D01G280000, the wheat homolog of OsRopGEF10, was located near a major QTN explaining over 10% of the total phenotypic variation. A gene interaction network constructed from all identified genes highlighted the central role of GGIs in GNS regulation. Environmental variation may reshape the regulatory network through GEIs. Furthermore, superior haplotypes of 12 candidate genes were identified, providing valuable targets for improving wheat yield. Overall, this study dissects the genetic architecture of GNS and offers practical resources for wheat molecular breeding. Full article

Chickpea (Cicer arietinum L.) is a globally important legume crop whose grain yield is strongly influenced by environmental and agronomic variability. This study aimed to predict chickpea grain yield using artificial neural networks (ANNs) and to identify key traits associated with yield formation across different genotypes under semi-arid conditions. The dataset consisted of 96 chickpea genotypes evaluated over two growing seasons (2022–2023) in Sivas, Türkiye. The results demonstrated that reproductive traits, particularly seed weight per plant, number of pods per plant, and number of seeds per plant, were the most influential factors determining grain yield. Environmental variability also contributed significantly to yield prediction, highlighting the importance of genotype–environment interactions. The developed ANN model showed high predictive accuracy, indicating its robustness in capturing complex relationships among yield-related traits. Beyond prediction, the model provides biologically meaningful insights into trait prioritization, supporting its application in chickpea breeding programs. Overall, the findings suggest that ANN-based approaches can serve as effective decision-support tools in precision agriculture by enabling accurate yield estimation, facilitating the selection of high-performing genotypes, and identifying key breeding traits for sustainable crop improvement. Full article

Spider mites (Oligonychus trichardti) are emerging as a major constraint to Urochloa forage productivity in East Africa; however, knowledge of genotypic variation and tolerance remains limited. Herein, 55 Urochloa genotypes were evaluated under field-infested and non-infested conditions across two seasons using an alpha-lattice design. Agronomic and physiological traits, including plant height (PH), tiller number (TN), the Normalized Difference Vegetation Index (NDVI), total dry weight (TDW), and mite damage indices (visual severity index (VSI) and stress tolerance index (STI)) were assessed. Infestation reduced biomass by 22.4% on average, with reductions of up to 45% in susceptible genotypes. Significant genotypic variation was detected for PH, TN, TDW, and VSI. Heritability estimates under mite infestation were moderate to high for all traits except TDW, suggesting that direct selection of these traits could be effective in breeding programs aimed at improving mite resistance. VSI showed a strong negative correlation with NDVI (r = −0.63), supporting its value as a phenotyping indicator of spider mite response. Additive main effects and multiplicative interaction (AMMI) analysis revealed significant genotype × environment interactions for TDW. The AMMI biplot identified Xaraes, ILRI_13369, and ILRI_14787 as high-yielding and stable genotypes, while the AMMI Stability Value (ASV) and the Weighted Average of Absolute Scores from the Best Linear Unbiased Prediction (WAASB) identified CIAT_16122, CIAT_664, ILRI_14801, ILRI_14787, and ILRI_13266 as the most stable and broadly adapted across environments. STI further highlighted ILRI_13751 (2.71) and ILRI_13531 (2.58) as highly tolerant under stress. Overall, the study reveals substantial exploitable genetic diversity and identifies stable, high-yielding, and mite-tolerant genotypes suitable for breeding to improve Urochloa productivity in East Africa. Full article

Plant architecture-related traits are key agronomic traits affecting crop growth and yield. To unravel the genetic architecture of plant height (PH), ear height (EH), tassel length (TL), and tassel primary branch number (TPBN), 379 DH lines derived from 21 maize hybrids were used for genome-wide association study (GWAS) and genomic selection (GS) analyses. Although plant architecture-related traits were significantly influenced by genotype and genotype-by-environment interactions, moderate to high broad-sense heritability was observed for PH (81.3%), EH (79.6%), TL (86.4%), and TPBN (82.5%). Using six different models for GWAS, seven unique SNPs on chromosomes 1, 2, and 3 were identified for PH, 92 unique SNPs located on chromosomes 1 to 9 were identified for EH, three unique SNPs on chromosome 6 were detected for TL, and 18 unique SNPs located on chromosomes 1, 4, 5, 8, and 10 were identified for TPBN at the p-value threshold of 7.42 × 10⁻⁶. A few hotspot genomic regions conferring plant architecture-related traits were identified, located in bins 2.07, 4.07, 8.03, 6.01, and 10.00. A total of 144 putative candidate genes were identified, which were enriched in endocytosis and lipid biosynthetic process, electron carrier activity, chloroplast stroma, and plastid stroma. The prediction accuracy evaluated through 5-fold cross-validation was 0.44 for PH, 0.43 for EH, 0.31 for TL, and 0.30 for TPBN. When the training population size (TPS) reached 60–70% or marker density (MD) reached 3000, the prediction accuracy tends to stabilize, indicating that the optimum size of TPS and MD were 60–70% and 3000 for GS, respectively. The highest prediction accuracy evaluated by using 30–5000 significant SNPs corresponding to the lowest p-value was 0.70 for PH, 0.85 for EH, 0.58 for TL, and 0.75 for TPBN, with an increase in accuracy of 59.1% to 150.0%. These results demonstrate that integrating GS with a subset of highly significant SNPs can substantially enhance prediction efficiency, thereby facilitating the selection of superior genotypes and accelerating the breeding of maize varieties with optimized plant architecture. This study has further elucidated the genetic basis of maize architecture-related traits and provided valuable information on how to implement GS to breed novel maize varieties with optimized plant types. Full article

Background: Improving grain yield in wheat remains a top priority, requiring integrated breeding and genetic strategies. This complexity poses a major challenge, driven by quantitative polygenic inheritance, environmental influence, and intricate genetic interactions. We investigated genetic factors and their interactions for agronomic and yield traits in two high-yielding winter wheat cultivars adapted to the US Southern Great Plains. Methods: A bi-parental mapping population consisting of 221 F₇ recombinant inbred lines (RIL) derived from ‘TAM 204’ and ‘Iba’ was evaluated for three years in 11 Texas environments. Both parents and RIL population were genotyped on Illumina NovaSeq 6000 and sequences were aligned to IWGSC RefSeq v1.0 using Bowtie2 for SNP calling. For QTL analyses, each trait was analyzed by individual environment, across multiple environments and mega-environments. Results: A total of 86 QTL were mapped for five traits and among them 32 were consistent in more than one environment or analysis. Among consistent QTL, four were pleiotropic to more than one agronomic or yield traits mapped on chromosomes 2B (57.18, 59.47 Mb) and 2D (29.34, 40.64 Mb). The consistent QTL on chromosome 2D (29.34 Mb) was pleiotropic to GYLD, DTH, TW, TKW and explained maximum phenotypic variation for all traits, representing photoperiod gene (Ppd-D1). Another QTL on chromosome 2D (40.64 Mb) was pleiotropic to GYLD and TW and based on the physical position comparisons it likely reflects a unique locus in Iba. The pleiotropic consistent QTL Qgyld.tamu.2B.59 from TAM 204 represents Ppd-B1 gene. Moreover, it is more likely that Qdth.tamu.5B.575 represents the Vrn-B1 gene in Iba. A total of 23 digenic epistatic interactions involved consistent QTL for all traits. Amongst these, epistatic interactions between the consistent QTL on 2B (57.18 Mb) and 2D (29.34 Mb) were observed for GYLD, DTH and TKW. Conclusions: Our findings revealed key allelic diversity and interaction effects in elite wheat cultivars, paving the way for marker development for identified pleiotropic loci and implementation in marker-assisted selection and recombination breeding. Full article