Gene-environment Interactions Research Articles

Decoding the genetic landscape of autism: A comprehensive review

BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by heterogeneous symptoms and genetic underpinnings. Recent advancements in genetic and epigenetic research have provided insights into the intricate mechanisms contributing to ASD, influencing both diagnosis and therapeutic strategies. AIM To explore the genetic architecture of ASD, elucidate mechanistic insights into genetic mutations, and examine gene-environment interactions. METHODS A comprehensive systematic review was conducted, integrating findings from studies on genetic variations, epigenetic mechanisms (such as DNA methylation and histone modifications), and emerging technologies [including Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 and single-cell RNA sequencing]. Relevant articles were identified through systematic searches of databases such as PubMed and Google Scholar. RESULTS Genetic studies have identified numerous risk genes and mutations associated with ASD, yet many cases remain unexplained by known factors, suggesting undiscovered genetic components. Mechanistic insights into how these genetic mutations impact neural development and brain connectivity are still evolving. Epigenetic modifications, particularly DNA methylation and non-coding RNAs, also play significant roles in ASD pathogenesis. Emerging technologies like CRISPR-Cas9 and advanced bioinformatics are advancing our understanding by enabling precise genetic editing and analysis of complex genomic data. CONCLUSION Continued research into the genetic and epigenetic underpinnings of ASD is crucial for developing personalized and effective treatments. Collaborative efforts integrating multidisciplinary expertise and international collaborations are essential to address the complexity of ASD and translate genetic discoveries into clinical practice. Addressing unresolved questions and ethical considerations surrounding genetic research will pave the way for improved diagnostic tools and targeted therapies, ultimately enhancing outcomes for individuals affected by ASD.

The Environment, Lifestyles and Climate Change: The Many Nongenetic Contributors to The Long and Winding Road to Autoimmune Diseases.

A critical unanswered question is what is causing the increase in the prevalence of autoimmunity and autoimmune diseases around the world. Given the rapidity of change, this is likely the result of major recent alterations in our exposures to environmental risk factors for these diseases. More evidence is becoming available that the evolution of autoimmune disease, years or even decades in the making, results from multiple exposures that alter susceptible genomes and immune systems over time. Exposures during sensitive phases in key developmental or hormonal periods may set the stage for the effects of later exposures. It is likely that synergistic and additive impacts of exposure mixtures result in chronic low-level inflammation. This inflammation may eventually pass thresholds that lead to immune system activation and autoimmunity, and, with further molecular and pathologic changes, the complete clinical syndrome emerges. Much work remains to be done to define the mechanisms and risk and protective factors for autoimmune conditions. However, evidence points to a variety of pollutants, xenobiotics, infections, occupational exposures, medications, smoking, psychosocial stressors, changes in diet, obesity, exercise, and sleep patterns, as well as climate change impacts of increased heat, storms, floods, wildfires, droughts, ultraviolet radiation, malnutrition, and changing infections, as possible contributors. Substantial investments in defining the role of causal factors, in whom and when their effects are most important, the necessary and sufficient gene-environment interactions, improved diagnostics and therapies, and preventative strategies are needed now to limit the many negative personal, societal, and financial impacts that will otherwise occur.

School of ecological selection of academician of the Russian academy of sciences V.F. Pivovarova

The article outlines the main milestones in the formation of the scientific school of Academician of the Russian Academy of Sciences Viktor Fedorovich Pivovarov – a talented organizer of scientific research on vegetable growing, a leading scientist in the field of ecology, introduction, selection and seed production of vegetable crops, Honored Scientist of the Russian Federation, laureate of the State Prize and Government Prizes of the Russian Federation V. Pivovarov. F. has extensive experience both in Russia and abroad in solving theoretical and practical problems in the industry. He developed a new scientific direction of research on ecological selection and genetics of vegetable crops. The article presents the directions of the ecological selection scientific school: increasing the adaptability of agricultural crops to various environmental factors; identifying the peculiarities of the genotype-environment interaction; development of methods for accelerating the breeding process to create highly productive varieties and hybrids resistant to abiotic and biotic stressors; ecological selection for low levels of radionuclides and technologies that reduce the amount of pollutants in vegetable products. School of V.F. Pivovarov Academician of the Russian Academy of Sciences was organized on the basis of common scientific interests, the importance of environmental research for breeding, a high level of scientific results, continuity and great development prospects.

Pathomechanistic Networks of Motor System Injury in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is the most common, adult-onset, progressive motor neurodegenerative disorder that results in death within 3 years of the clinical diagnosis. Due to the clinicopathological heterogeneity, any reliable biomarkers for diagnosis or prognosis of ALS have not been identified till date. Moreover, the only three clinically approved treatments are not uniformly effective in slowing the disease progression. Over the last 15 years, there has been a rapid advancement in research on the complex pathomechanistic landscape of ALS that has opened up new avenues for successful clinical translation of targeted therapeutics. Multiple studies suggest that the age-dependent interaction of risk-associated genes with environmental factors and endogenous modifiers is critical to the multi-step process of ALS pathogenesis. In this review, we provide an updated discussion on the dysregulated cross-talk between intracellular homeostasis processes, the unique molecular networks across selectively vulnerable cell types, and the multisystemic nature of ALS pathomechanisms. Importantly, this work highlights the alteration in epigenetic and epitranscriptomic landscape due to gene-environment interactions, which have been largely overlooked in the context of ALS pathology. Finally, we suggest that precision medicine research in ALS will be largely benefitted from the stratification of patient groups based on the clinical phenotype, onset and progression, genome, exposome, and metabolic identities.

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Gene-environment interaction in functional hypothalamic amenorrhea

Gene-environment interaction in functional hypothalamic amenorrhea

Refining the scope of genetic influences on alcohol misuse through environmental stratification and gene-environment interaction.

Gene-environment interaction (G × E) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we use a series of genome-wide gene-environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. GWEIS models showed few genetic variants with significant interaction effects across gene-environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3 on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.

Genotype and environmental interactions determine the root digging time and essential oil production in vetiver (Chrysopogon zizanioides (L.) Roberty)

Thirty different vetiver genotypes were grown for evaluation in the experiments at CSIR- Central Institute for Medicinal and Aromatic Plants (CSIR-CIMAP), Lucknow, U.P. 226015, India, to assess the impact of genotype × environment interactions (GEI) on essential oil yield and chemical composition in vetiver lines grown in north India, as well as identifying appropriate genotypes and representative root digging times throughout the year for essential oil production. The interplay of genotype and environment is a significant constraint in crop development. The additive main effects and multiplicative interaction (AMMI) and the analysis of variance (ANOVA) revealed significant effects for genotype (G), environment (E), and genotype × environment (GE) (p < 0.01). The primary root transverse section confirmed the changes in cortical sclerenchyma thickness and variety observed in the dug-out roots from different vetiver genotypes after six and twelve months. The quantity and number of sclerenchyma rows in the peripheral cortical region vary throughout vetiver genotypes. Following root digging, one component (Khusol content) had 40 %, while Khusilal content had 20 % for genotype VTR-23. However, after twelve months, Khusol content had 19 %, whereas Khusilal content had 41 %. The shift in essential oil makeup was apparent. The level of essential oil did not alter considerably. Based on the GGE biplot analysis, VTR-1, 11, and 14 were identified as the most stable genotypes with high essential yields; therefore, it is recommended to use these genotypes in India's enormous agriculture.

Genetic predisposition to impaired beta-cell function modifies the association between serum pyrethroid levels and the risk of type 2 diabetes: A gene-environment interaction study

Previous studies suggested that pyrethroid exposure was associated with elevated type 2 diabetes (T2D) risk, while it remains uncertain whether genetic predisposition modifies this association. A nested case-control study within the prospective Dongfeng-Tongji cohort comprised 1832 T2D cases, age- (±5 years) and sex-matched controls with qualified genotyping data. Serum pyrethroids were measured by gas chromatography-tandem mass spectrometry. Overall diabetes-related genetic risk score (GRS) or pathway-specific GRS, including unweighted GRSs (uGRS) and weighted GRSs (wGRS), was developed by genetic variants identified in Asian populations. Higher overall diabetes-related GRS and GRS specific to the pathway of impaired beta cell function (Beta-cell GRS) were associated with a higher incident T2D risk. Beta-cell uGRS significantly modified the association of serum permethrin (Pinteraction=0.04) and deltamethrin (Pinteraction=0.01) with T2D. Specifically, for each doubling increase in serum deltamethrin, the odds ratios (ORs) (95 % confidence intervals [CIs]) for T2D were 1.23 (0.98–1.56) and 0.91 (0.77–1.07) in the highest and lowest Beta-cell uGRS group, as well as 1.23 (1.02–1.47) and 0.95 (0.78–1.15) for Beta-cell wGRS group, respectively. When considering jointly, those with the highest deltamethrin levels and highest Beta-cell GRS had a substantially higher T2D risk, compared with the reference group (OR for uGRS: 3.79 [95 % CI: 2.03–7.07], Pinteraction=0.03 and 3.23 [95 % CI: 1.78–5.87], Pinteraction=0.05 for wGRS). Our findings suggested that genetic susceptibility to impaired beta-cell function should be considered for T2D prevention targeting pyrethroid exposure, particularly deltamethrin.

A Comprehensive Examination of the Role of Epigenetic Factors in Multiple Sclerosis.

According to various research, the risk of multiple sclerosis (MS) is strongly influenced by genetic variations. Population, familial, and molecular studies provide strong empirical support for a polygenic pattern of inheritance, mainly due to relatively common allelic variants in the general population. The strongest MS susceptibility locus, which was unmistakably identified in tested populations, is the major histocompatibility complex on chromosome 6p21.3. However, the effect of a given predisposing variant remains modest, so there is the possibility that multiple gene-gene and/or gene-environment interactions could significantly increase the contribution of specific variants to the overall genetic risk. Furthermore, as is known, susceptibility genes can be subject to epigenetic modifications, which greatly increase the complexity of MS heritability. Investigating epigenetic and environmental factors can provide new opportunities for the molecular basis of the MS, which shows complicated pathogenesis. Although studies of epigenetic changes in MS only began in the last decade, a growing body of literature suggests that these may be involved in the development of MS. Here, we summarize recent studies regarding epigenetic changes related to MS initiation and progression. Furthermore, we discuss how current studies address important clinical questions and how future studies could be used in clinical practice.

Gene-environment interaction between exposure to smoky coal combustion and a polygenic risk score with the risk of lung cancer

Gene-environment interaction between exposure to smoky coal combustion and a polygenic risk score with the risk of lung cancer

Applying an ELSI lens to real-world data and novel genomic insights for personalized mental healthcare.

Improving the understanding of the complex relationship between genetic predispositions, environmental influences, and sociocultural factors in the development and progression of mental illness is crucial for optimizing treatment efficacy and addressing longstanding health disparities. This paper discusses the ethical, legal, and social implications (ELSI) of recent advancements in biomedical research, particularly in genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and genome-wide environment interaction studies (GWEIS). Despite recent scientific progresses, challenges such as inadequate study methodology (e.g., correlational studies) and lack of diversity within study samples persist. Recent discoveries of several genetic variants of diseases, could augment and improve, or even challenge, existing understanding of the onset and management of mental illness. Leveraging real-world data (RWD), including electronic health record data (EHRs) focused on social determinant of health alongside biobank data, offers further opportunities to enhance the understanding of gene-environment interactions and inform efforts for reducing disparities in mental healthcare. Increased knowledge can support timely, holistic, evidence-based, and personalized care. Addressing ELSI considerations and maximizing the use of RWD is essential for advancing ethical and inclusive psychiatric genetics research, ultimately improving patient outcomes and promoting equitable access to evidence-based treatments.

Clinical and cognitive outcomes in first-episode psychosis: focus on the interplay between cannabis use and genetic variability in endocannabinoid receptors

IntroductionResearch data show the impact of the endocannabinoid system on psychosis through its neurotransmission homeostatic functions. However, the effect of the endocannabinoid system genetic variability on the relationship between cannabis use and psychosis has been unexplored, even less in first-episode patients. Here, through a case-only design, we investigated the effect of cannabis use and the genetic variability of endocannabinoid receptors on clinical and cognitive outcomes in first-episode psychosis (FEP) patients.MethodsThe sample comprised 50 FEP patients of European ancestry (mean age (sd) = 26.14 (6.55) years, 76% males), classified as cannabis users (58%) or cannabis non-users. Two Single Nucleotide Polymorphisms (SNP) were genotyped at the cannabinoid receptor type 1 gene (CNR1 rs1049353) and cannabinoid receptor type 2 gene (CNR2 rs2501431). Clinical (PANSS, GAF) and neuropsychological (WAIS, WMS, BADS) assessments were conducted. By means of linear regression models, we tested the main effect of cannabis use and its interaction with the polymorphic variants on the clinical and cognitive outcomes.ResultsFirst, as regards cannabis effects, our data showed a trend towards more severe positive symptoms (PANSS, p = 0.05) and better performance in manipulative abilities (matrix test-WAIS, p = 0.041) among cannabis users compared to non-users. Second, concerning the genotypic effects, the T allele carriers of the CNR1 rs1049353 presented higher PANSS disorganization scores than CC homozygotes (p = 0.014). Third, we detected that the observed association between cannabis and manipulative abilities is modified by the CNR2 polymorphism (p = 0.022): cannabis users carrying the G allele displayed better manipulative abilities than AA genotype carriers, while the cannabis non-users presented the opposite genotype-performance pattern. Such gene–environment interaction significantly improved the overall fit of the cannabis-only model (Δ-R2 = 8.4%, p = 0.019).DiscussionDespite the preliminary nature of the sample, our findings point towards the role of genetic variants at CNR1 and CNR2 genes in the severity of the disorganized symptoms of first-episode psychosis and modulating cognitive performance conditional to cannabis use. This highlights the need for further characterization of the combined role of endocannabinoid system genetic variability and cannabis use in the understanding of the pathophysiology of psychosis.

Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model.

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha-1 (SMH_16417 in Smolice) to 16.89 t ha-1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha-1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Fibrosis and Hepatocarcinogenesis: Role of Gene-Environment Interactions in Liver Disease Progression.

The liver is a complex organ that performs vital functions in the body. Despite its extraordinary regenerative capacity compared to other organs, exposure to chemical, infectious, metabolic and immunologic insults and toxins renders the liver vulnerable to inflammation, degeneration and fibrosis. Abnormal wound healing response mediated by aberrant signaling pathways causes chronic activation of hepatic stellate cells (HSCs) and excessive accumulation of extracellular matrix (ECM), leading to hepatic fibrosis and cirrhosis. Fibrosis plays a key role in liver carcinogenesis. Once thought to be irreversible, recent clinical studies show that hepatic fibrosis can be reversed, even in the advanced stage. Experimental evidence shows that removal of the insult or injury can inactivate HSCs and reduce the inflammatory response, eventually leading to activation of fibrolysis and degradation of ECM. Thus, it is critical to understand the role of gene-environment interactions in the context of liver fibrosis progression and regression in order to identify specific therapeutic targets for optimized treatment to induce fibrosis regression, prevent HCC development and, ultimately, improve the clinical outcome.

Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts

BackgroundThe temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic diseases such as diabetes, and hypertension. While the physiological pathways linking metabolic and cardiovascular diseases have been investigated, the study of the sex and population differences in the CMC have still not been described.MethodsWe present a machine learning approach to model the CMC and investigate sex and population differences in two distinct cohorts: the UK Biobank (17,700 participants) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) (7162 participants). We consider the following CMDs: hypertension (Hyp), diabetes (DM), heart diseases (HD: angina, myocardial infarction, or heart failure), and stroke (STK). For the identification of the CMC patterns, individual trajectories with the time of disease occurrence were clustered using k-means. Based on clinical, sociodemographic, and lifestyle characteristics, we built multiclass random forest classifiers and used the SHAP methodology to evaluate feature importance.ResultsFive CMC patterns were identified across both sexes and cohorts: EarlyHyp, FirstDM, FirstHD, Healthy, and LateHyp, named according to prevalence and disease occurrence time that depicted around 95%, 78%, 75%, 88% and 99% of individuals, respectively. Within the UK Biobank, more women were classified in the Healthy cluster and more men in all others. In the EarlyHyp and LateHyp clusters, isolated hypertension occurred earlier among women. Smoking habits and education had high importance and clear directionality for both sexes. For ELSA-Brasil, more men were classified in the Healthy cluster and more women in the FirstDM. The diabetes occurrence time when followed by hypertension was lower among women. Education and ethnicity had high importance and clear directionality for women, while for men these features were smoking, alcohol, and coffee consumption.ConclusionsThere are clear sex differences in the CMC that varied across the UK and Brazilian cohorts. In particular, disadvantages regarding incidence and the time to onset of diseases were more pronounced in Brazil, against woman. The results show the need to strengthen public health policies to prevent and control the time course of CMD, with an emphasis on women.

Lack of association between TRPV1 gene polymorphisms and risk of migraine chronification: a case-control study and meta-analysis.

To confirm a previously reported association of TRPV1 rs8065080 with the risk of transformation from episodic (EM) to chronic migraine (CM) and to extend knowledge about the role of other TRPV1 single nucleotide polymorphisms (SNPs), we first investigated the impact of three TRPV1 SNPs (rs8065080, rs222747 and rs222749) on the risk of migraine chronification in a case-control study. A systematic review and meta-analysis were then conducted to summarize the accumulated findings. Genotyping of the selected TRPV1 SNPs was performed using TaqMan real-time PCR in 167 EM and 182 CM participants. Crude and adjusted odds ratios with associated 95% confidence intervals were calculated in the log-additive, dominant, and recessive genetic models. A comprehensive literature search was performed in PubMed, Web of Knowledge, Cochrane Library, and OpenGrey until February 2024. In our case-control study, no association was found between TRPV1 SNPs and the risk of migraine chronification, both in the unadjusted logistic regression models and after adjustment for confounding clinical variables. The results of the meta-analysis with a total of 241 participants with EM and 223 with CM confirmed no association between TRPV1 SNPs and the risk of migraine chronification in any of the genetic models tested. The results of the present case-control study and meta-analysis exclude a major role of TRPV1 rs8065080, rs222747, and rs222749 as risk factors for migraine chronification. However, further research is needed to investigate the gene-gene and gene-environment interactions of TRPV1 SNPs on the risk of transformation from episodic to chronic migraine.

Stability Analysis of Yield and Its Components in Snap Melon (Cucumis melo var momordica)

Snap melon, scientifically known as Cucumis melo L. var. momordica (Roxb.), is a crop native to India and belonging to Cucurbitaceae family. This study was carried out to assess the nature and magnitude of genotype-environment interaction and stability among some parameters in snap melon genotypes. Twenty five local cultivars along with three standard checks were evaluated in randomized block design with three replications at three environments during summer 2023 at Navsari. The Eberhart and Russell model of stability analysis was employed which has been proved to be a reliable model. The study revealed that the mean sum of squares due to genotypes when tested against pooled deviation were highly significant for all the traits studied. Environment ´ Genotype (linear) showed significant differences for all characteristics except fruit weight and yield per vine, indicating that prediction could be possible by considering individual genotypes for these traits. A significant non-linear component (pooled deviation) was observed for fruit weight and fruit yield per vine, suggesting that genotypes varied considerably in terms of stable performance for these traits. The G ´ E interaction was only significant for flesh thickness and yield per vine, with all other traits being non-significant. The genotypes T 14 (HUB 18) and T 17 (HUB 21) were relatively stable and can be further used as parents to develop a more robust stable genotype. We identified genotypes which could pave way introducing high performing snap melon varieties.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.