Gene-environment Interactions Research Articles

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.

Genome-wide association study for metabolic syndrome reveals APOA5 single nucleotide polymorphisms with multilayered effects in Koreans

Background and purposeGenome-wide association studies (GWAS) of metabolic syndrome (MetS) have predominantly focused on non-Asian populations, with limited representation from East Asian cohorts. Moreover, previous GWAS analyses have primarily emphasized the significance of top single nucleotide polymorphisms (SNPs), poorly explaining other SNP signals in linkage disequilibrium. This study aimed to reveal the interaction between rs651821 and rs2266788, the principal variants of apolipoprotein A5 (APOA5), within the most significant loci identified through GWAS on MetS.MethodsGWAS on MetS and its components was conducted using the data from the Korean Genome and Epidemiology Study (KoGES) city cohort comprising 58,600 individuals with available biochemical, demographic, lifestyle factors, and the most significant APOA5 locus was analyzed further in depth.ResultsAccording to GWAS of MetS and its diagnostic components, a significant association between the APOA5 SNPs rs651821/rs2266788 and MetS/triglycerides/high-density lipoprotein phenotypes was revealed. However, a conditional analysis employing rs651821 unveiled a reversal in the odds ratio for rs2266788. Therefore, rs651821 and rs2266788 emerged as independent and opposing signals in the extended GWAS analysis, i.e., the multilayered effects. Further gene-environment interaction analyses regarding lifestyle factors such as smoking, alcohol consumption, and physical activity underscored these multilayered effects.ConclusionThis study unveils the intricate interplay between rs651821 and rs2266788 derived from MetS GWAS. Removing the influence of lead SNP reveals an independent protective signal associated with rs2266788, suggesting a multilayered effect between these SNPs. These findings underline the need for novel perspectives in future MetS GWAS.

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.

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

A genome-wide methylation analysis of Chinese Han patients with chronic insomnia disorder.

As the most common sleep disorder, chronic insomnia disorder (CID) has become a global health burden to the public. However, it remains unclear about the pathogenesis of this disease. Epigenetic changes may provide important insights into the gene-environment interaction in CID. Therefore, this study was conducted to investigate the DNA methylation pattern in CID and reveal the epigenetic mechanism of this disease. In this study, whole blood DNA was extracted from 8 CID patients (the CID group) and 8 healthy controls (the control group), respectively. Besides, genome-wide DNA methylation was detected by Illumina Human Methylation 850K Beadchip. Moreover, the sleep quality and insomnia severity were evaluated by the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI), respectively. A total of 369 differentially methylated positions (DMPs) and 23 differentially methylated regions (DMRs) were identified between the CID and control groups. LHX6 was identified as the most important differentially methylated gene (DMG). The Gene Ontology (GO) analysis results corroborated that DMPs were significantly enriched in 105 GO terms, including cell signaling, homogenous cell adhesion of plasma membrane adhesion molecules, nervous system development, cell adhesion, and calcium ion binding. In addition, it was demonstrated that DMPs were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including the hippo signaling pathway, Ras signaling pathway, and vitamin B6 metabolism. The DMR-related GO analysis results revealed the positive regulation of protein kinase activities. DNA methylation plays a critical role in the development of CID, and LHX6 is validated to be an important DMG.

Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub

Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their impact on health remain incompletely understood. In this study, we utilized genome-wide association study (GWAS) data from the UK Biobank, expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and protein quantitative trait loci (pQTL) data from the Atherosclerosis Risk in Communities (ARIC) study to conduct comprehensive analyses using the Unified Test for Molecular Signatures (UTMOST), Transcriptome-wide Association Studies (TWAS), and Proteome-wide Association Studies (PWAS). To integrate and synthesize these analyses, we developed the AirSigOmniTWP Hub, a specialized platform designed to consolidate and interpret the results from UTMOST, TWAS, and PWAS. TWAS analysis identified a significant association between PM10 exposure and the gene INO80E in females (P = 4.37×10⁻⁵, FDR = 0.0383), suggesting a potential role in chromatin remodeling. PWAS analysis revealed a significant association between NOx exposure and the gene PIP in females (P = 2.28×10⁻⁵, FDR = 0.0299), implicating its involvement in inflammatory pathways. Additionally, UTMOST analyses uncovered significant associations between various pollutants and genes including NCOA4P3 and SPATS2L with PM2.5 exposure, indicating potential mechanisms related to transcriptional regulation and gene-environment interactions.

The Value Proposition of Coordinated Population Cohorts Across Africa.

Building longitudinal population cohorts in Africa for coordinated research and surveillance can influence the setting of national health priorities, lead to the introduction of appropriate interventions, and provide evidence for targeted treatment, leading to better health across the continent. However, compared to cohorts from the global north, longitudinal continental African population cohorts remain scarce, are relatively small in size, and lack data complexity. As infections and noncommunicable diseases disproportionately affect Africa's approximately 1.4 billion inhabitants, African cohorts present a unique opportunity for research and surveillance. High genetic diversity in African populations and multiomic research studies, together with detailed phenotyping and clinical profiling, will be a treasure trove for discovery. The outcomes, including novel drug targets, biological pathways for disease, and gene-environment interactions, will boost precision medicine approaches, not only in Africa but across the globe.

Class, genes, and rationality: A gene–environment interaction approach to ideology

Class, genes, and rationality: A gene–environment interaction approach to ideology

Exposure-associated DNA methylation among people exposed to multiple industrial pollutants

BackgroundCurrent research on the epigenetic repercussions of exposure to a combination of pollutants is limited. This study aims to discern DNA methylation probes associated with exposure to multiple pollutants, serving as early effect markers, and single-nucleotide polymorphisms (SNPs) as surrogate indicators for population susceptibility. The investigation involved the analysis of urine exposure biomarkers for 11 heavy metals (vanadium, arsenic, mercury, cadmium, chromium, nickel, lead, manganese, copper, strontium, thallium), polycyclic aromatic hydrocarbon (PAHs) (1-hydroxypyrene), genome-wide DNA methylation sequencing, and SNPs array on all study participants. The data were integrated with metabolomics information and analyzed both at a community level based on proximity to home addresses relative to the complex and at an individual level based on exposure biomarker concentrations.ResultsOn a community level, 67 exposure-related CpG probes were identified, while 70 CpG probes were associated with urine arsenic concentration, 2 with mercury, and 46 with vanadium on an individual level. These probes were annotated to genes implicated in cancers and chronic kidney disease. Weighted quantile sum regression analysis revealed that vanadium, mercury, and 1-hydroxypyrene contributed the most to cg08238319 hypomethylation. cg08238319 is annotated to the aryl hydrocarbon receptor repressor (AHRR) gene, and AHRR hypomethylation was correlated with an elevated risk of lung cancer. AHRR was further linked to deregulations in phenylalanine metabolism, alanine, aspartate, and glutamate metabolism, along with heightened oxidative stress. Additionally, three SNPs (rs11085020, rs199442, and rs10947050) corresponding to exposure-related CpG probes exhibited significant interaction effects with multiple heavy metals and PAHs exposure, and have been implicated in cancer progression and respiratory diseases.ConclusionOur findings underscore the pivotal role of AHRR methylation in gene-environment interactions and highlight SNPs that could potentially serve as indicators of population susceptibility in regions exposed to multiple heavy metals and PAHs.

Acacia seeds: compositional variation based on species, growing locations and harvest years

SummaryThree different Acacia seeds (A. retinodes, A. provincialis and A. tenuissima) harvested from different locations and in different years were analysed for their variations in proximate and mineral composition. Results showed no one species had the highest content across all proximate and mineral analyses, for example, A. retinodes Harmans 2020 had the highest ash (3.7%), A. retinodes Harmans 2022 had the highest protein content (31.3%), A. tenuissima Hindmarsh 2020 had the highest fat content (18.5%) and A. provincialis Tarrington 2022 had the highest magnesium content (469 mg/100 g). Principle component analysis was carried out to determine the effect of species, harvest locations and years on the chemical composition. A biplot of the first two principal components with a total of 60.5% variation showed clustering based on harvest years. The compositions of the Acacia seeds were determined to be affected by species, harvest location and year differences. However, a complete gene–environment interaction study is needed to validate this.

Strong genotype-by-environment interaction across contrasted sites for summer mortality syndrome in the Pacific oyster Crassostrea gigas

Genotype-by-environment (GxE) interaction in aquaculture is usually estimated for continuous traits and based on data from a limited number of 2–3 rearing sites. Here we report the results of a GxE study for resistance to Pacific oyster mortality syndrome (POMS), a multi-factorial disease that severely impacts Pacific oyster production worldwide. The syndrome is largely associated with Ostreid Herpes Virus 1 (OsHV-1). Resistance to OsHV-1 in Crassostrea gigas has been shown to be heritable, meaning that selective breeding is a suitable strategy for reducing mortalities. However, limited information was available about GxE interaction or the possible need to consider it in selective breeding. Survival of two cohorts (C1 and C2), consisting of a total of 104 full-sib families, was evaluated during the summer of 2013 in 7 sites along the French Atlantic, Channel and Mediterranean coasts. Mean survivals in autumn 2013 were 12.6% ± 10.9 and 4.6% ± 6.4 for C1 and C2, respectively. Genetic parameters were computed by MCMC, which is suitable for binary data like survival. Heritability estimates ranged from 0.16 to 0.42 depending on site and cohort, with a mean of 0.24 [0.20; 0.27] when including all data. GxE interactions were estimated by the genetic correlations between pairs of sites. Genetic correlations were high (ρ > 0.80) for C1 between most tidal Atlantic and Channel sites, and intermediate between tidal sites and a Mediterranean lagoon site, while they were lower and more variable for C2 (0.21–0.77). Expected genetic gains were maximal when production site was the same as selection site. They were closed to this expected maximum when the selection site was different from the production site along the Atlantic or Channel coast. Limited GxE interaction along the French Atlantic coast is favorable to wide dissemination of genetically improved oysters along this coast. Limited potential improvement was shown in the Mediterranean site if selection was carried out elsewhere, confirming the specificity of this environment. Consequently, a specific strategy such as dedicated breeding should be used to achieve genetic progress for this site.

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.

Research 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. The 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. First, 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). Despite 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.

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study’s objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone’s reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone’s reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

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.