Search Results (81)

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

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

Grain quality in bread wheat is a complex trait determined by multiple genetic factors and their interaction with environmental conditions. This study investigated the genetic architecture of key grain quality traits in the Avalon × Cadenza double haploid (DH) population under contrasting climatic conditions in Kazakhstan. A set of 101 spring-type DH lines was evaluated over three years in three major wheat-growing regions of Kazakhstan, representing northern, central, and southern environments. Grain yield and nine grain quality traits were assessed, including amylose content (Amc, %), test weight per liter (TWL, g/L), grain protein content (GPC, %), gliadin content (Gli, %), glutenin content (Glu, %), grain hardness (GH, %), grain vitreousness (GV, %), falling number (FN, s), and sedimentation value determined in a 2% acetic acid solution (SV, mL). The objectives were to characterize phenotypic variation, examine trait relationships, and identify major and environmentally stable quantitative trait loci (QTLs) controlling grain quality. QTL mapping identified 89 QTLs associated with the nine studied traits, including 82 major QTLs explaining more than 10% of phenotypic variation and 16 stable QTLs detected in two or more environments. The largest numbers of QTLs were found for GPC, SV, and TWL. Stable QTLs were distributed across all three wheat genomes, with important regions detected on chromosomes 1A, 1B, 2D, 4A, 4D, 5A, 6A, and 7D. Several stable QTLs co-localized with genomic regions previously associated with grain quality and developmental regulation, including loci near Wx-B1, Rht-D1, and Ppd-D1, suggesting biologically meaningful links among gluten composition, starch biosynthesis, plant development, and grain physical properties. These results improve understanding of the genetic control of wheat grain quality across diverse environments in Kazakhstan and provide promising targets for marker-assisted selection to combine improved end-use quality with wide environmental adaptation. Full article

Chalkiness is a complex quantitative trait regulated by both genetic and environmental factors. Reducing chalkiness has long been a research focus in rice genetics and breeding. A total of 108 markers on/closely linked to starch biosynthesizing genes, grain shape and chalkiness QTLs were used to detect interactions affecting chalkiness. A total of 30 and 39 marker pairs with significant bigenic epistasis were identified for percentage of grain with chalkiness (PGWC) and degree of endosperm chalkiness (DEC), respectively, of which 16 were commonly found in both traits. Using markers associated with chalkiness and marker pairs with significant epistatic effects as candidate predictors increased the coefficient of determination (R²) of the best multiple regression models for predicting both traits. GBSSI, SSIIa and the interaction between GBSSI and GBSSII were consistently identified in optimal models, indicating their critical roles in regulating rice chalkiness. R² for DEC and PGWC ranged from 36.5% to 42.7% and from 52.9% to 73.8% in two environments, respectively. PGWC decreased significantly from 38.9% to 15.10% after three cycles using marker-assisted recurrent selection (MARS). This study suggests that epistasis contributes substantially to the regulation of chalkiness, and demonstrates that MARS can effectively improve chalkiness without imposing obvious negative impacts on eating quality. 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

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Rice yield is a complex quantitative trait. Although a lot of genes for yield have been cloned, their genetic basis remains unknown. In the present study, a set of chromosome segment substitution line population (CSSL) was developed, derived from the indica variety Huanghuazhan as the recipient parent and the Aus variety N22 as the donor parent, and a high-density bin map containing 609 bins was constructed by resequencing. The CSSL population comprised 155 families with an average background recovery rate of 93.02%. Nine yield-related traits, including plant height, panicle number, panicle length, primary branch number, spikelet number per panicle, grain number per panicle, seed setting rate, 1000-grain weight, and grain yield per plant, were evaluated across four environments. The results showed significant differences in yield-related traits between the two parents across four environments. All nine traits showed continuous distribution with transgressive segregation. Spikelet number per panicle, grain number per panicle and 1000-grain weight showed strong correlations with each other, whereas panicle number had weak correlations with them. A total of 80 main-effect quantitative trait loci (QTLs) affecting yield-related traits were identified, among which 13 QTLs were repeatedly detected in multiple environments, 45 QTLs were located in 8 pleiotropic QTL regions, and 47 QTLs showed significant interactions with environments. In addition, 260 pairs of epistatic QTLs underlying yield-related traits were identified, of which 2 pairs stably expressed across different environments, and 11 pairs controlled more than two traits. These findings provide a theoretical basis for clarifying the genetic differentiation between indica and Aus and cloning yield-related genes, and offer valuable gene resources for molecular breeding of high-yield rice varieties. Full article

The flavor and color profiles of vegetables are crucial in determining their nutritional value, health benefits, taste, and visual appeal. The genomic characteristics of plants control these traits. Components such as sugars, organic acids, amino acids, phenolic compounds, and essential oils, as well as color pigments like anthocyanin, chlorophyll, carotenoid, and betalain, are synthesized in plants based on their genetic structure. Environmental factors like temperature, water, light, and soil can affect the production and intensity of these components. Long-term environmental changes, such as climate change, can significantly alter the dynamics of these components. This comprehensive review focuses on the genetic and environmental interactions underlying the flavor and color profiles of vegetables, with particular emphasis on the analysis of quantitative trait loci (QTL) associated with these traits. The article discusses the identification of genes that regulate taste and color in vegetables and how these genes have been localized in QTL mapping studies. It also discusses the influence of environmental factors on taste and color, as well as gene–environment interactions. Furthermore, it focuses on how this information can be used to improve plant breeding and sustainable agriculture and emphasizes that data from QTL analyses provide valuable insights into the integration of genetic and environmental approaches to improve vegetable quality and meet consumer preferences. In conclusion, the review aims to be a valuable resource for both researchers and professionals interested in the genetic and environmental aspects of taste and color in vegetables. Full article

Climate change in the Pannonian region is accelerating a shift toward autumn sowing of cool-season grain legumes (pea, faba bean, lentil, chickpea, lupine) to achieve higher yields, greater biomass production, enhanced nitrogen fixation, improved soil cover, and superior resource use efficiency compared with spring sowing. However, successful overwintering depends on the availability of robust winter-hardy cultivars. This review synthesizes recent breeding advances, integrating traditional approaches—such as germplasm screening, hybridization, and field-based selection—with genomics-assisted strategies, including genome-wide association studies (GWAS), quantitative trait locus (QTL) mapping, marker-assisted selection (MAS), and CRISPR/Cas-mediated editing of CBF transcription factors. Key physiological mechanisms—LT₅₀ determination, cold acclimation, osmoprotectant accumulation (sugars, proline), and membrane stability—are assessed using field survival rates, electrolyte leakage assays, and chlorophyll fluorescence measurements. Despite challenges posed by genotype × environment interactions, variable winter severity, and polygenic trait control, the release of cultivars worldwide (e.g., ‘NS-Mraz’, ‘Lavinia F’, ‘Ghab series’, ‘Pinklevi’, and ‘Rézi’) and ongoing breeding programs demonstrate substantial progress. Future breeding efforts will increasingly rely on genomic selection (GS), high-throughput phenomics, pangenomics, and G×E modeling to accelerate the development of climate-resilient legume cultivars, ensuring stable and sustainable production under increasingly unpredictable winter conditions. Full article

Background: Beef tenderness is a key quality attribute that significantly influences consumer satisfaction; however, it exhibits considerable variability due to both genetic and environmental factors. While genomic selection based on Expected Progeny Differences (EPDs) has improved the accuracy of predictions, a substantial portion of tenderness variability remains unexplained. Metabolomics has emerged as a valuable approach to address this gap, as metabolites reflect gene–environment interactions and may serve as biomarkers for complex traits such as meat tenderness. Objectives: This study aimed to integrate genomic and metabolomic data to identify genetic loci associated with serum metabolites in Nellore calves, offspring of sires with contrasting EPDs for meat tenderness. Methods: Ninety-five male calves were evaluated and divided into two groups according to the sires’ genetic merit: FA-T (favorable EPD for tenderness, n = 45) and UN-T (unfavorable EPD for tenderness, n = 46). Blood serum samples were analyzed by ¹H NMR spectroscopy to quantify 40 metabolites, and genotyping was performed using a medium-density SNP panel. Metabolite quantitative trait loci (mQTL) were identified using the MatrixEQTL package, and metabolic enrichment analysis was performed in MetaboAnalyst 6.0. Results: In the FA-T group, SNPs were associated with metabolites such as phenylalanine, tyrosine, and succinate, suggesting enhanced oxidative metabolism and preservation of proteolysis. In the UN-T group, associations of pyruvate, creatinine, and glutamine with distinct SNPs indicated greater reliance on anaerobic glycolysis and early ATP consumption, potentially impairing phosphorylation and postmortem proteolytic activity. Conclusions: These findings suggest that genetic selection for tenderness may induce early divergent metabolic profiles, likely leading to persistent differences in postmortem biochemical pathways, with important implications for meat tenderness. Full article

Phenolic compounds contribute significantly to the nutritional and functional properties of wheat, particularly due to their antioxidant activity. In this study, a genome-wide association study was conducted to elucidate the genetic basis of total phenolic content (TPC) and antioxidant activity (AA) in a panel of 144 tetraploid wheat accessions representing diverse subspecies. The panel was evaluated under two different environments, located in Chile and Italy, to assess the influence of genotype, environment, and their interaction. Significant variability was observed for both TPC and AA, with TPC ranging from 0.26 to 0.82 mg gallic acid equivalent (GAE)/g and AA from 0.04 to 0.99 µmol Trolox equivalent (TE)/g. Substantial phenotypic variation and high broad-sense heritability were observed for both traits, underscoring the predominant genetic control. The genome-wide association study, using a mixed linear model (MLM), and the Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) approaches identified 17 significant marker–trait associations, including quantitative trait loci on chromosomes 2B, 3A, 4B, 5A, 5B, and 6B. Notably, QTLs on chromosome 5A were co-localized for both TPC and AA, suggesting potential pleiotropic loci. Candidate genes linked to these loci included flavonol 3-sulfotransferase and peptidylprolyl isomerase, which are involved in phenylpropanoid metabolism and oxidative stress response, respectively. These findings offer valuable insights into the genetic basis of wheat phenolic traits and provide molecular targets for the development of biofortified cultivars through marker-assisted selection. Full article

Improving yield stability under water-limited conditions is a key objective of wheat breeding programmes. One trait of particular interest is carbohydrate accumulation and remobilisation. This study assessed the genetic basis of aspects of yield and flag leaf sugar contents under drought and well-watered conditions using QTL mapping in a population of 90 doubled haploid lines derived from the cross Chinese Spring × SQ1. As well as soluble sugar content, glucose, fructose, sucrose, and maltose, the traits grain yield (Yld), biomass (Bio), and thousand grain weight (TGW) were also analysed. Analysis of variance showed that genotype, environment and their interactions significantly influenced all the traits studied, with environmental effects explaining up to 74.4% of the total variation. QTL analysis identified 40 QTLs for Yld, TGW, and Bio as well as 53 QTLs for soluble carbohydrates, accounting for up to 40% of phenotypic variation. QTLs coincident for more than one trait were identified on 21 chromosome regions, associated with carbohydrate metabolism and yield performance under drought, particularly on chromosomes 2D, 4A, 4B, 5B, 5D, 6B, and 7A. Candidate genes for several yield-related QTLs were identified. These results provide useful genetic markers for the development of more drought-resistant wheat cultivars. Full article

This review synthesizes advances in livestock genomics by examining the interplay between candidate genes, molecular markers (MMs), signatures of selection (SSs), and quantitative trait loci (QTLs) in shaping economically vital traits across livestock species. By integrating advances in genomics, bioinformatics, and precision breeding, the study elucidates genetic mechanisms underlying productivity, reproduction, meat quality, milk yield, fibre characteristics, disease resistance, and climate resilience traits pivotal to meeting the projected 70% surge in global animal product demand by 2050. A critical synthesis of 1455 peer-reviewed studies reveals that targeted genetic markers (e.g., SNPs, Indels) and QTL regions (e.g., IGF2 for muscle development, DGAT1 for milk composition) enable precise selection for superior phenotypes. SSs, identified through genome-wide scans and haplotype-based analyses, provide insights into domestication history, adaptive evolution, and breed-specific traits, such as heat tolerance in tropical cattle or parasite resistance in sheep. Functional candidate genes, including leptin (LEP) for feed efficiency and myostatin (MSTN) for double-muscling, are highlighted as drivers of genetic gain in breeding programs. The review underscores the transformative role of high-throughput sequencing, genome-wide association studies (GWASs), and CRISPR-based editing in accelerating trait discovery and validation. However, challenges persist, such as gene interactions, genotype–environment interactions, and ethical concerns over genetic diversity loss. By advocating for a multidisciplinary framework that merges genomic data with phenomics, metabolomics, and advanced biostatistics, this work serves as a guide for researchers, breeders, and policymakers. For example, incorporating DGAT1 markers into dairy cattle programs could elevate milk fat content by 15-20%, directly improving farm profitability. The current analysis underscores the need to harmonize high-yield breeding with ethical practices, such as conserving heat-tolerant cattle breeds, like Sahiwal. Full article

Aflatoxin contamination, primarily caused by Aspergillus flavus, poses a significant threat to peanut (Arachis hypogaea L.) production, food safety, and global trade. Despite extensive efforts, breeding for durable resistance remains difficult due to the polygenic and environmentally sensitive nature of resistance. Although germplasm such as J11 have shown partial resistance, none of the identified lines demonstrated stable or comprehensive protection across diverse environments. Resistance involves physical barriers, biochemical defenses, and suppression of toxin biosynthesis. However, these traits typically exhibit modest effects and are strongly influenced by genotype–environment interactions. A paradigm shift is underway with increasing focus on host susceptibility (S) genes, native peanut genes exploited by A. flavus to facilitate colonization or toxin production. Recent studies have identified promising S gene candidates such as AhS5H1/2, which suppress salicylic acid-mediated defense, and ABR1, a negative regulator of ABA signaling. Disrupting such genes through gene editing holds potential for broad-spectrum resistance. To advance resistance breeding, an integrated pipeline is essential. This includes phenotyping diverse germplasm under stress conditions, mapping resistance loci using QTL and GWAS, and applying multi-omics platforms to identify candidate genes. Functional validation using CRISPR/Cas9, Cas12a, base editors, and prime editing allows precise gene targeting. Validated genes can be introgressed into elite lines through breeding by marker-assisted and genomic selection, accelerating the breeding of aflatoxin-resistant peanut varieties. This review highlights recent advances in peanut aflatoxin resistance research, emphasizing susceptibility gene targeting and genome editing. Integrating conventional breeding with multi-omics and precision biotechnology offers a promising path toward developing aflatoxin-free peanut cultivars. Full article

Soybeans are a primary source of plant-based protein, with seeds containing approximately 40% protein—a key quality trait. Selecting superior hybrid combinations and managing progeny effectively are crucial for developing high-protein soybean varieties. Using a recombinant inbred line population (RIL3613) derived from Dongnong L13 × Heihe 36 and its previously constructed high-density genetic linkage map, QTLs and QTL × environment interactions (QEIs) associated with seed protein content (SPC) were identified through the bi-parental population (BIP) model and multi-environment trials (MET) model in QTL IciMapping v4.2. Candidate genes were then predicted via sequence alignment and haplotype analysis between the parents. Finally, simulated breeding was conducted using the B4L function in the In Silico Breeding (ISB) module of the Blib platform to determine optimal breeding strategies across diverse environments. The analysis identified 19 QTLs associated with SPC and 97 QEIs linked to SPC. These QTLs collectively explained 84.442% of the phenotypic variance, with four QTLs exhibiting significant contributions. A key candidate gene, Glyma.12G231400, associated with soybean SPC, was predicted within the 38,995,090–39,293,825 bp interval on chromosome 12. Across 11 environments, three to six optimal breeding schemes were selected, all employing modified pedigree selection. These findings enhance our understanding of the genetic basis of soybean protein formation and provide technological support for molecular breeding for seed quality improvement. Full article