Search Results (665)

Drought stress is a major environmental constraint that significantly reduces wheat productivity worldwide. In this study, seventeen wheat genotypes were evaluated under well-watered and drought-stressed conditions across two consecutive years (2023–2024) in a controlled greenhouse experiment. Twenty morphological and agronomic traits were recorded, and their responses to prolonged water limitation were assessed using multivariate statistical methods, including three-way ANOVA, principal component analysis (PCA), and cluster analysis. Drought stress significantly decreased all traits except the harvest index (HI), with the most severe reductions observed in traits related to secondary spikes (e.g., grain weight reduced by 95%). The ANOVA results confirmed significant genotype × treatment (G × T) interactions for key agronomic traits, with the strongest effect observed for total grain weight (F = 7064.30, p < 0.001). A PCA reduced the 20 original variables to five principal components, explaining 87.2% of the total variance. These components reflected distinct trait groups associated with productivity, spike architecture, and development in phenology. Cluster analysis based on PCA scores grouped genotypes into three clusters with contrasting drought response profiles. A yield-based evaluation confirmed the cluster structure, distinguishing genotypes with a stable performance (average yield loss ~58%) from highly sensitive ones (~70% loss). Overall, the findings demonstrate that drought tolerance in wheat is governed by complex trait interactions. Integrating a trait-based multivariate analysis with a yield stability assessment enables the identification of genotypes with superior adaptation to water-limited environments, providing an excellent genotype background for future breeding efforts. 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

Background/Objectives: Impulsivity is a key psychological construct implicated in the onset and maintenance of behavioural addictions. Dysregulation of impulsivity is central to behavioural addictions, yet its genetic basis remains unclear. This study examined the association between the DAT1 variable number tandem repeat polymorphism and impulsivity in individuals with behavioural addictions. Methods: A total of 328 males (128 with behavioural addictions and 200 controls) completed the Barratt Impulsiveness Scale. DAT1 genotyping was performed via PCR and gel electrophoresis. Statistical analyses included chi-square tests, Mann–Whitney U-tests, and two-way ANOVA. Results: No differences in DAT1 genotype frequencies were found between groups. However, a significant interaction emerged for attentional impulsivity: individuals with behavioural addictions and the 9/9 genotype had the highest BIS-AI scores (F_{2, 322} = 5.48; p = 0.0046). Conclusions: The DAT1 9/9 genotype may increase vulnerability to attentional impulsivity, but only in the context of behavioural addictions. These findings highlight a gene–environment interaction and support the role of dopaminergic mechanisms in cognitive dysregulation. Future studies should validate these findings using longitudinal designs and neurobiological methods. Full article

Canine morbillivirus (CDV), the cause of canine distemper, is a pathogen affecting many hosts. While modified live virus (MLV) vaccines are crucial for controlling the disease in dogs, cases of vaccine-related infections have been found in both domestic and wild animals. Specifically, the America-1 and Rockborn-like vaccine genotypes are concerning due to their spread and ability to transmit between different species. This study conducted a review and analysis of molecular detections of these strains in various carnivores (domestic, captive, synanthropic, and wild species). This study used a conceptual model considering host ecology and the domestic–wild interface to evaluate plausible transmission connections over time using Linear Directional Mean (LDM) and Weighted Mean Centre (WMC) methods. Statistical analyses examined the relationship between how likely a strain is to spread and factors like host type and vaccination status. The findings showed that the America-1 genotype spread in a more organised way, with domestic dogs being the main source and recipient, bridging different environments. Synanthropic mesocarnivores also played this same role, with less intensity. America-1 was most concentrated in the North Atlantic and Western Europe. In contrast, the Rockborn-like strain showed a more unpredictable and restricted spread, residual circulation from past use rather than ongoing spread. Species involved in vaccine-related infections often share characteristics like generalist behaviour, social living, and a preference for areas where domestic animals and wildlife interact. We did not find a general link between a host vaccination status and the likelihood of the strain spreading. The study emphasised the ongoing risk of vaccine-derived strains moving from domestic and synanthropic animals to vulnerable wild species, supporting the need for improved vaccination approaches. Mapping these plausible transmission routes can serve as a basis for targeted surveillance, not only of vaccine-derived strains, but of any other circulating genotype. Full article

Corn smut, caused by Ustilago maydis, significantly threatens maize production. This study evaluated 199 maize inbred lines at the seedling stage under greenhouse conditions for resistance to U. maydis, identifying 39 highly resistant lines. A genome-wide association study (GWAS) using the mrMLM model detected 19 significant single-nucleotide polymorphism (SNP) loci. Based on a linkage disequilibrium (LD) decay distance of 260 kb, 226 candidate genes were identified. Utilizing the significant loci chr1_244281660 and chr5_220156746, two kompetitive allele-specific PCR (KASP) markers were successfully developed. A PCR-based sequence-specific oligonucleotide probe hybridization technique applied to the 199 experimental lines and 60 validation lines confirmed polymorphism for both markers, with selection efficiencies of 48.12% and 43.33%, respectively. The tested materials were derived from foundational inbred lines of domestic and foreign origin. Analysis of 39 highly resistant lines showed that the advantageous alleles carrying thymine/cytosine (T/C) predominated at frequencies of 94.87% and 53.84%, respectively. The genotype TTCC conferred high resistance, while CCTT was highly susceptible. The resistance exhibited high heritability and significant gene-by-environment interaction. This work systematically dissects the genetic basis of common smut resistance in maize, identifies favorable alleles, and provides a novel KASP marker-based strategy for developing disease-resistant germplasm. Full article

Dysregulated inflammatory processes contribute to depression, and gene–environment interactions may influence an individual’s risk and resilience. Reduced brain-derived neurotrophic factor (BDNF) expression increases susceptibility for developing depressive symptoms, and the Val66Met (rs6265) single-nucleotide polymorphism (SNP) on the BDNF gene is linked to mood disorders. However, whether Val66Met confers increased vulnerability to inflammation-induced depressive tendencies is unknown. Here, we tested the hypothesis that the Val66Met SNP increases vulnerability to inflammation-induced depressive symptoms in a mouse model of lipopolysaccharide (LPS)-induced depression-like behavior. Behavior and neuroinflammation, following a 24 h LPS challenge, were measured in mice expressing the human BDNF Val66Met gene variant or Val66Val littermates (control). The Val66Met genotype did not affect the peripheral inflammatory response, acute neuroinflammation, or the acute sickness behavior response. Val66Met mice exhibited anhedonia-like behavioral responses following LPS challenge, and we found increased mRNA expression of IL-1β and TNFα in the cerebrum compared to controls. The mRNA expression of IL-1β and TNFα in the hippocampus and the nucleus accumbens of Val66Met mice was increased following LPS, and a significant genotype × LPS interaction was detected for CD68 expression in the nucleus accumbens. In summary, these data suggest that immune activation in Val66Met mice increased susceptibility to anhedonic behavior and dysregulated negative regulation of inflammation. Full article

The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies—such as genomics, transcriptomics, proteomics, metabolomics, and phenomics—has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility. Full article

Pinus radiata D. Don is planted in South Central Chile on a wide range of sites using genetically improved genotypes for timber production. As drought events are expected to increase with ongoing climatic change, the variability in gas exchange, which could impact growth and water use, needs to be evaluated. In this study, we assessed the genotypic variability of leaf-level light-saturated photosynthesis (A_sat), stomatal conductance (g_s), transpiration (E), intrinsic water use efficiency (iWUE), and Chlorophyll a fluorescence (OJIP-test parameters) among 30 P. radiata genotypes (i.e., full-sib families) from third-cycle parents at age 6 years on three sites in Central Chile. We also evaluated tree height (HT), diameter at breast height (DBH), and stem index volume (VOL). Families were ranked for HT as top-15 and bottom-15. In the OJIP-test parameters we observed differences at the family level for the maximum quantum yield of primary PSII photochemistry (Fv/Fm), the probability that a photon trapped by the PSII reaction center enters the electron transport chain (ψ_Eo), and the potential for energy conservation from photons captured by PSII to the reduction in intersystem electron acceptors (PI_ABS). Fv/Fm, PI_ABS, and ψ_Eo ranged from 0.82 to 0.87, 45 to 95, and 0.57 to 0.64, respectively. Differences among families for growth and not for leaf-level physiology were detected. DBT, H, and VOL were higher in the top-15 families (12.6 cm, 8.4 m, and 0.10 m³, respectively) whereas A_sat, g_s, E, and iWUE were similar in both the top-15 and bottom-15 families (4.0 μmol m⁻² s⁻¹, 0.023 mol m⁻² s⁻¹, 0.36 mmol m⁻² s⁻¹, and 185 μmol mol m⁻² s⁻¹, respectively). However, no family by site interaction was detected for growth and leaf-level physiology. The results of this study suggest that highly improved genotypes of P. radiata have uniformity in leaf-level physiological rates, which could imply uniform water use at the stand-level. The family variation found in PI_ABS suggests that this parameter could be incorporated to select genotypes tolerant to environmentally stressful conditions. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

Climatic variability, particularly fluctuating precipitation and rising temperatures, poses a significant threat to crop productivity and stability. Forage sorghum hybrids are a promising alternative for fodder and bioenergy due to their high level of drought tolerance. This study evaluated genotypic variation and environmental adaptability of 60 forage sorghum genotypes: 13 parental lines, their 40 crosses and seven commercial hybrids, to identify high-yielding, stable hybrids for biomass production under changing agroecological conditions. Field trials conducted over two contrasting years revealed significant genotype-by-environment interactions (p < 0.05), highlighting the need for multi-year evaluations. While favorable rainfall in 2020 enhanced vegetative traits (plant height, stem diameter, leaf area), biomass yield variability increased, emphasizing that favorable vegetative development does not necessarily correlate with yield stability. Principal component analysis indicated that plant height, stem diameter and leaf-related traits contributed most to genotypic differentiation. However, no single trait emerged as a reliable predictor of yield, suggesting complex trait interaction. These findings underscore the importance of integrative breeding strategies that combine phenotypic trait assessment with environmental adaptability to ensure sustainable biomass production. Sorghum’s drought tolerance and resilience make it a promising crop for future food and feed security in regions prone to climatic stress. Full article

Lentil (Lens culinaris Medikus) is a critical food crop offering high protein and essential micronutrients. However, its productivity and nutritional quality are increasingly threatened by climate change. In this study, 36 lentil genotypes were evaluated across two Moroccan locations under normal, heat stress, and combined heat and drought stresses. Significant effects of genotype, environment, and their interactions were observed on seed yield, seed size, cooking time, and nutritional quality. Heat and drought stresses caused substantial reductions in seed yield (up to 40% under combined stress), protein content, iron, and zinc concentration, and increased phytic acid levels, which negatively impacted iron and zinc bioavailability. Cooking time significantly decreased under stress conditions, with up to 54% reduction under combined heat and drought stresses at Annoceur research station. Correlation analysis revealed complex trade-offs among yield, nutritional quality, and cooking traits under stress conditions. Principal component analysis and GGE biplot analyses identified genotypes with superior yield, micronutrient concentration, and cooking time stability across environments. Genotypes such as G32, G3, and G36 combined high iron and zinc levels; G13 and G30 showed low phytic acid, while G 15 exhibited the shortest cooking time. These genotypes also demonstrated adaptability across the tested environment. This study highlights the potential of selecting climate-resilient, nutrient-dense lentil genotypes to support breeding efforts aimed at improving food security in the face of global climate variability. These genotypes can be suggested as elite climate-resilient parental lines to support breeders in enhancing lentil yield, nutritional quality, and stability under multiple stress conditions. Full article

Progressing climate change necessitates the development of drought-tolerant crops, and understanding the temporal dynamics of genotype x environment interactions (GxE) is crucial. This study aimed to test established phenotyping methods (chlorophyll a fluorescence (ChlF) and hyperspectral (HS) imaging) to investigate the variability in 165 inbred maize lines’ responses to progressive drought stress. The inbred maize lines were grown under controlled conditions and were challenged with water withholding. Fifteen ChlF and HS indices were measured at three consecutive time points (M1, M2, and M3). Mixed models were employed to estimate the GxT interaction effects via Best Linear Unbiased Predictors (BLUPs) for each variable. A Principal Component Analysis (PCA) performed on the GxT BLUPs from each time point revealed a highly dynamic interaction structure. While the primary axis of GxT variation (PC1) was consistently associated with HI, which is related to plant vigor, across all measurement times, its importance intensified under severe stress (M3). The secondary axis (PC2) shifted markedly over time: after initial variations at M1, it was dominated by GxT effects in specific ChlF parameters related to photosynthetic regulation under moderate stress (M2), before shifting again under severe stress (M3) to reflect the GxT effects on indices potentially related to pigment degradation and other stress indicators. Full article

This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). Rather than focusing on a model comparison, we illustrate how two IML-compatible models—Random Forests and AMBARTI—can support a clearer interpretation of genotype-by-environment (G×E) interactions and variable importance. Using multi-environment wheat trial data from CREA-DC-Milano across Italian sites, we predicted the yield and protein content while visualizing the performance patterns. Genotype g25 ranked first in protein across both years, while g20 led in yield in Year 1. Tolentino consistently supported higher protein levels; Torino and Tolentino led in yield, varying by year. These insights, made accessible through intuitive IML visualizations, proved valuable in supporting VRO, reinforcing the role of IML as a practical communication tool in regulatory processes, agricultural innovation, and food security. Full article

Flowering represents the most important process in the reproductive stage of fruit trees, including olive trees. Previous studies have demonstrated that the genotype–environment interaction (GEI) has a considerable influence on olive flowering time. This study investigated the GEI and genetic parameters influencing olive flowering phenology in Southwestern China (a non-Mediterranean region), using multi-trait-based stability selection methods. Sixteen olive cultivars from five countries were evaluated over two years in two distinct climatic regions of Southwestern China. Flowering phenology was assessed based on three parameters: full-bloom date (FBD), flowering-period length (FP), and full-bloom-period length (FBP). In the analyses, the best linear unbiased prediction (BLUP) to predict genetic value and genotype + genotype by environment interaction (GGE) biplot methods to visualize and assess stability and performance were employed across four environments. The results showed that genotype, environment, and GEI had highly significant effects on flowering traits, with GEI accounting for 54.12% to 89.62% of the variance. Heritability values were low (0.0589 to 0.262), indicating that genetic factors had limited control over flowering phenology compared to environmental factors. A stability analysis using a mean performance and stability (MPS) index identified genotypes with earlier flowering dates and longer flowering periods. Multi-trait selection using a multi-trait mean performance and stability (MTMPS) index further highlighted six superior genotypes with high performance and stability across environments. The findings emphasize the critical role of environmental factors on olive flowering phenology, highlighting the challenges in breeding for stable flowering traits. This study demonstrates the effectiveness of multi-trait selection methods in identifying genotypes with superior performance and stability under different environmental conditions. These results provide valuable insights for olive breeding programs, particularly in non-Mediterranean regions, suggesting that targeted selection and multi-trait evaluation could enhance the adaptability and productivity of olive cultivars under changing climatic conditions. Full article

Perennial ryegrass (Lolium perenne L.) is a cornerstone forage species in temperate dairy systems worldwide, valued for its high yield potential, nutritive quality, and grazing recovery. However, current regional evaluation systems face challenges in accurately assessing complex traits like seasonal dry matter yield due to polygenic nature, environmental variability, and lengthy evaluation cycles. This review examines the evolution of perennial ryegrass evaluation systems, from regional frameworks—like Australia’s Forage Value Index (AU-FVI), New Zealand’s Forage Value Index (NZ-FVI), and Ireland’s Pasture Profit Index (PPI)—to advanced genomic prediction (GP) approaches. We discuss prominent breeding frameworks—F2 family, Half-sib family, and Synthetic Population—and their integration with high-throughput genotyping technologies. Statistical models for GP are compared, including marker-based, kernel-based, and non-parametric approaches, highlighting their strengths in capturing genetic complexity. Key research efforts include representative genotyping approaches for heterozygous populations, disentangling endophyte–host interactions, extending prediction to additional economically important traits, and modeling genotype-by-environment (G × E) interactions. The integration of multi-omics data, advanced phenotyping technologies, and environmental modeling offers promising avenues for enhancing prediction accuracy under changing environmental conditions. By discussing the combination of regional evaluation systems with GP, this review provides comprehensive insights for enhancing perennial ryegrass breeding and evaluation programs, ultimately supporting sustainable productivity of the dairy industry in the face of climate challenges. Full article