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Results for “genotype by environment interactions”

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PubMed1978

[Intelligence : individual differences, genetic factors, environmental factors and interaction between the genotype and the environment (author's transl)].

The authors undertook a review of published work on the sources of variation in individual differences in intelligence. They should : 1) that methods of quantitative genetics concerning intelligence are not applicable to human populations ; 2) that the results of studies on adoptions and on twins do not permit one to estimate the respective roles of environment and heredity ; 3) that this division of variance had no heuristic value in the study of human intelligence.

Animals↗

PubMed2026

Grain protein and yield stability study in rainfed durum wheat RILs.

Developing durum wheat cultivars with stable grain yield across diverse environments remains a key breeding objective. This study evaluated 118 recombinant inbred lines (RILs) derived from a cross between the drought-adapted cultivar 'Zardak' (Triticum durum) and the landrace 'Iran-249' (T. turanicum) with desirable seed characteristics, across four heterogeneous rainfed environments in Italy and Iran. The assessment focused on grain yield (GY) and grain protein content (GPC) stability. Combined analysis of variance revealed significant (p < 0.01) effects for genotype, environment, and their interaction for both traits. Line ZD-050 showed the highest GY (3.91 t ha⁻¹), while ZD-032 had the highest GPC (14.27%). Stability analysis using parametric and non-parametric methods, along with AMMI and GGE biplot modeling, identified ZD-050 as among the most promising genotypes according to yield-integrating and dynamic-stability approaches. This line showed high grain yield in methods such as the Superiority Index and Kang's rank-sum, although stability rankings differed across the used methods. This line maintained superior yield, demonstrated broad adaptability across environments, and had moderate protein levels, identifying it as an optimal candidate for breeding programs targeting yield stability and wide adaptation under rainfed conditions.

Triticum↗

PubMed2025

Compensatory evolution to DNA replication stress is robust to nutrient availability.

Evolutionary repair refers to the compensatory evolution that follows perturbations in cellular processes. While evolutionary trajectories are often reproducible, other studies suggest they are shaped by genotype-by-environment (GxE) interactions. Here, we test the predictability of evolutionary repair in response to DNA replication stress-a severe perturbation impairing the conserved mechanisms of DNA synthesis, resulting in genetic instability. We conducted high-throughput experimental evolution on Saccharomyces cerevisiae experiencing constitutive replication stress, grown under different glucose availability. We found that glucose levels impact the physiology and adaptation rate of replication stress mutants. However, the genetics of adaptation show remarkable robustness across environments. Recurrent mutations collectively recapitulated the fitness of evolved lines and are advantageous across macronutrient availability. We also identified a novel role of the mediator complex of RNA polymerase II in adaptation to replicative stress. Our results highlight the robustness and predictability of evolutionary repair mechanisms to DNA replication stress and provide new insights into the evolutionary aspects of genome stability, with potential implications for understanding cancer development.

DNA Replication↗

PubMed2025

Dynamic neuro-immune regulation of psychiatric risk loci in human neurons.

The prenatal environment influences neurodevelopment and subsequent clinical trajectories for psychiatric outcomes in childhood and adolescence. Yet it remains unclear if the impact of maternal and fetal immune activation varies with distinct polygenic risk profiles. Therefore, here we catalogue genotype and environment (GxE) interactions, contrasting allele-specific regulatory activity between inflammatory contexts. We report a cue-specific neuronal massively parallel reporter assay (MPRA) of 220 loci from genome-wide association study (GWAS) linked to ten brain traits/disorders, empirically dissecting the impact of interleukin-6 (IL-6) and interferon-alpha (IFNα) on transcriptional activity. Of 1,469 active candidate regulatory risk elements (MPRA-active CRSs) across three conditions, we identify 316 with dynamic variant-specific effects (MPRA-QTLs) in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Broadly, across hundreds of variants, neuronal immune-mediated regulatory activity is driven by differences in transcription factor binding and chromatin accessibility, the gene targets of which show pleiotropic enrichments for brain, metabolic, and immune disorders. Dynamic genetic regulation mediates immune effects, informing our understanding of mechanisms governing pleiotropy and variable penetrance. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and resolve mechanisms mediating prenatal risk.

dynamic expression quantitative trait loci↗

PubMed2025

Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.

With the rapid development of sequencing technology, the application of genomic prediction has become more and more common in breeding schemes of livestocks and crops. Selecting an appropriate statistical model is of central importance to achieve high prediction accuracy. Recently, machine learning models have been expected to upgrade genomic prediction into a new era. However, the perspective still suffers from lack of evidence that machine learning models can generally outperform the traditional ones on empirical data sets. In this study, we compared two machine learning models based on artificial neural network (ANN) and convolutional neural network (CNN) with four traditional models, including genomic best linear unbiased prediction (GBLUP), Bayesian ridge regression (BRR), BayesA and BayesB, using three published data sets for grain yield in wheat. For each model, we considered two variants: modeling and ignoring the genotype-by-environment ([Formula: see text]) interaction. In the comparison, we considered two strategies of cross-validation: predicting genotypes that have not been evaluated in any environment (CV1) and predicting genotypes that have been tested in other environments (CV2). Our results showed that traditional Bayesian models (BayesA, BayesB, and BRR) outperformed GBLUP, ANN and CNN when considering [Formula: see text] interaction. The accuracies of ANN and CNN were higher than traditional models only in CV1 and when [Formula: see text] interaction was ignored. It was also found that the performance of the two machine learning models was significantly affected by the interaction between the CV strategy and the way of treating the [Formula: see text] interaction, while that of the four traditional models was only influenced by whether the [Formula: see text] interaction was considered or not. Thus, machine learning models can be a powerful complementary to the traditional ones and their superiority may depend on the prediction scenario. Among the two machine learning models, we observed that the accuracy of ANN was higher than CNN in most cases, indicating that it is still challenging to adapt complex machine learning models such as CNN to genomic prediction.

ANN↗

PubMed2026

Complex genotype-phenotype relationships in neurodevelopmental disorders.

With the advent of sequencing technologies in recent years, hundreds of high-confidence risk genes have been implicated in neurodevelopmental disorders (NDDs). However, individuals carrying pathogenic variants in the same gene frequently exhibit diverse clinical presentations, including varied symptoms and diagnoses. We propose that this heterogeneity arises from different interacting factors that modulate the phenotypic outcomes of pathogenic variants, including variant-level features, modifying variation across the genome, prenatal and early-life environmental exposures, and developmental noise. Resolving these factors requires integrative approaches that combine population-scale genetics and functional genomics with environmental monitoring and quantitative assessments of stochastic developmental variation. Advancing our understanding of these factors is critical to elucidating the etiology of NDDs and improving diagnostic and personalized therapeutic strategies.

Humans↗

PubMedDate not supplied

[The maturation of the E.E.G. and sleep; genetic and environmental factors (author's transl)].

Whilst studying the post-natal maturation of reflexes, EEG activity, rhythmic activity in Slonaker's wheels and the learning of adult mice, strains showing less pronounced rhythmic activity and more immature EEG and reflexes at birth were discovered. On the other hand some strains having a shorter post-natal period manifest an early maturation of their central nervous system. The results indicate that complex interactions exist between genotype and environment when the different post-natal maturation appearances are considered and that EEG activity may be useful in assessing development.

Aging↗

PubMed1979

Nervous and mental disease. Genetics and the future of psychoanalysis.

Genetics has been associated with psychoanalytic theory since the earliest writings of Freud. Innate factors of constitution and heredity have been recognized in studies of adults and children, both retrospective and prospective. Contemporary genetics has become a science of gene expression and control, not merely of transmission, and its influence will illuminate processes of interaction in the development of affect and personality. As new genetic proposals and practices influence the choices and meanings of human life, they will have a profound effect on man's decisions, values, and self-image, and psychoanalysis will need to reaffirm its interpreative role. Finally, more effective prevention and treatment will be based on better appreciation of the interacting roles of genotype and environment.

Child↗

PubMed2026

MMP-2 rs243865 Polymorphism as a Risk Modifier of Hepatocellular Carcinoma in Taiwanese Males, Smokers and Alcohol Consumers.

BACKGROUND/AIM: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. Matrix metalloproteinase-2 (MMP-2) plays an important role in extracellular matrix remodeling and HCC progression. This study investigated the associations of two functional MMP-2 promoter polymorphisms, rs243865 and rs2285053, with HCC susceptibility in a Taiwanese population and explored their potential interactions with environmental risk factors. MATERIALS AND METHODS: A hospital-based case-control study was conducted at China Medical University Hospital (Taichung, Taiwan), involving 298 HCC patients and 889 age- and sex-matched cancer-free controls recruited in Taiwan. MMP-2 rs243865 and rs2285053 genotypes were determined utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods. Stratified analyses were used to explore their potential interactions between MMP-2 genotypes and environmental factors including sex, smoking and alcohol drinking status. RESULTS: No significant association was observed between HCC susceptibility and either rs243865 (CT+TT versus CC: odds ratio (OR)=1.20, 95% confidence interval (CI)=0.87-1.66, p=0.2944] or rs2285053 (CT+TT versus CC: OR=1.12, 95% CI=0.86-1.46, p=0.4219). Similarly, allelic analyses revealed no significant effects. Interestingly, stratified analyses demonstrated significant gene-environment interactions for rs243865. Male carriers of the TT genotype exhibited an increased HCC risk (OR=3.24, 95% CI=1.12-9.37, p=0.0488). Among smokers, the TT genotype was associated with a markedly elevated risk (OR=6.46, 95% CI=1.65-25.29, p=0.0055), while alcohol drinkers carrying the TT genotype showed the highest susceptibility (OR=9.69, 95% CI=1.99-47.13, p=0.0022). No significant association was found for rs2285053 genotype in any genetic variant and subgroup. CONCLUSION: Although MMP-2 rs243865 and rs2285053 do not independently determine HCC susceptibility, rs243865 T allele may serve as a genetic biomarker for identifying Taiwanese males, smokers, and alcohol drinkers at elevated risk of HCC. These findings highlight the importance of gene-environment interactions in hepatocarcinogenesis and support the incorporation of genetic information into personalized HCC risk assessment and prediction strategies.

Humans↗

PubMed2026

Genetic and environmental interactions outweigh mitonuclear coevolution for complex traits in Drosophila.

The interdependent relationship between mitochondrial and nuclear genomes is a powerful model for understanding how epistasis shapes the architecture and evolution of complex traits. Once considered a neutral marker, mitochondrial DNA variation is now recognized as critical to phenotypic evolution because of its epistatic interactions and history of coevolution with the nuclear genome. A central challenge in evolutionary genetics is to quantify the relative importance of stabilizing and directional selection shaping complex trait distributions within and among species. Both can act on interacting and/or co-evolving genes contributing to quantitative traits, but resolving their relative roles is complicated by the complex architecture of most traits. Here, we use a panel of 90 Drosophila mitonuclear genotypes to quantify the relative contributions of mitochondrial, nuclear, and environmental variation and their interactions to four metabolically demanding complex traits. We sample both within-species and between-species mitochondrial variation and observe stronger interaction effects attributable to within-species variation, consistent with stabilizing selection maintaining mitonuclear function. Additionally, culturing the flies on a mitochondrial Complex I inhibitor, rotenone, reveals significant genotype x environment (G×E and G×G×E) interaction effects, providing insight into how genetic variation can be maintained across changing environments. Our results have broader implications in medicine, where mitochondrial DNA donors with longer purifying selection histories may be safer for mitochondrial replacement therapies.

Journal Article↗

PubMed2025

Order among chaos: High throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system.

The likelihood that a host will be susceptible to infection is influenced by the interaction of diverse biotic and abiotic factors. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between the host, the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, bacterial strain identity and dose, and pathogen dose. Elucidating the interactions between these factors in vivo has been challenging because testing combinations of these variables quickly becomes experimentally intractable. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbe model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and non-pathogenic bacterial dose can overwhelm these effects. Host susceptibility to infection is therefore driven by complex interactions between multiple factors that can both mask and compensate for each other. However, regardless of host or inoculation conditions, the ratio of pathogen to non-pathogen emerged as a consistent correlate of disease. Our results demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host susceptibility to disease. Increasing the scale at which we can screen drivers of disease, such as microbiome community structure, will facilitate both disease predictions and treatments for medicine and agricultural applications.

Arabidopsis↗

PubMed2026

Nonuniform Association of Genetic Risk Scores for Intraocular Pressure.

IMPORTANCE: Elevated intraocular pressure (IOP) is a risk factor for primary open-angle glaucoma, and genetic risk scores hold promise as a tool for screening for ocular hypertension. However, genetic risk scores for IOP have a nonuniform association across the range of IOP, which reduces their accuracy. OBJECTIVE: To test the hypothesis that nonuniform behavior of genetic risk scores for IOP is associated with a specific type of genetic interaction. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional, post hoc genetic association studies were performed using linear and quantile regression in a sample of UK Biobank participants. Data were analyzed from January to September 2025. EXPOSURES: Ninety-eight genetic variants associated with IOP. MAIN OUTCOMES AND MEASURES: Tests were carried out for 98 genetic variants associated with IOP (P < 5.0 ×10-8) to examine (1) dominant or recessive genetic effects, (2) genotype × genotype interactions, (3) genotype × age interactions, and (4) genotype × sex interactions. RESULTS: A total of 98 235 participants (mean [SD] age, 58.1 [7.9] years; 52 168 female [53.1%]) were included in this analysis. More variants exhibited genotype × age interactions than expected by chance (14 of the 98 variants associated with IOP had at least nominal evidence of an interaction with age; P = 3.76 ×10-4). For 12 of these 14 variants, age increased rather than decreased the magnitude of the IOP vs genotype association. However, integrating age interactions into the genetic risk score construction process did not yield improved accuracy (incremental noninteraction model, R2 = 4.05; 95% CI, 3.82-4.31 and interaction model, R2 = 4.04; 95% CI, 3.80-4.27). There was little support for other types of genetic interaction. CONCLUSIONS AND RELEVANCE: In the current work, findings show minimal evidence that nonadditive allelic effects, genotype × genotype interactions, and genotype × sex interactions contributed to the nonuniform association of genetic variants with IOP across quantiles of IOP. Although a genetic risk score for IOP was more accurate in older vs younger individuals, efforts to account for genotype × age interactions in genetic risk score construction did not improve accuracy. These findings suggest other factors, such as gene-environment interactions, contribute to the nonuniform relationship of genetic variants with IOP.

Humans↗

PubMed2026

A cross-sectional study of oxidative stress pathway genotypes and their interactions with environmental pollutant levels identifies associations with gene expression and lung function.

BACKGROUND: Asthma is a heterogeneous disease influenced by genetic and environmental factors. Fine particulate matter (PM2.5) exacerbates asthma, likely through oxidative stress pathways, but whether genetic variation modifies this effect remains unclear. METHODS: We analysed data on 948 adults with asthma from the Severe Asthma Research Program (SARP), linking ZIP-code-level PM2.5 exposure with whole-genome sequencing data. We tested 4337 single nucleotide polymorphisms (SNPs) in 120 oxidative stress pathway genes for gene-environment (GxE) interactions with PM2.5 on lung function (forced expiratory volume in 1 s [FEV1] % predicted) using weighted linear regression. Gene expression data from bronchial epithelial cells (n = 170) were used to assess cis-expression quantitative trait loci (eQTLs). FINDINGS: Higher PM2.5 exposure was associated with lower FEV1% predicted (β per μg/m3 = -0.7, p = 0.01). We identified 20 SNPs across seven genes (OXSR1, PXDN, TPO, LRRK2, APP, MSRA, MSRB2) with significant GxE interactions after multiple-testing correction. Five SNPs were also eQTLs, linking PM2.5-modified gene expression to lung function. Minor alleles in OXSR1 and PXDN were associated with reduced gene expression and worsened FEV1% under high PM2.5 exposure. Conversely, TPO variants were associated with higher baseline expression and lower lung function, but under increasing PM2.5 exposure, minor allele carriers showed suppressed TPO expression and improved FEV1%. INTERPRETATION: This study identified 20 SNPs in oxidative stress pathway genes that modify the effect of PM2.5 on lung function in asthma. These findings highlight the importance of integrating environmental context in genetic studies and suggest potential therapeutic targets for pollution-sensitive asthma phenotypes. FUNDING: Supported by NIH grants.

Cross-Sectional Studies↗

PubMed1979

Comparison of antigen-specific I-region-associated cell interaction factors.

Two basic types of factors reacting with anti-I region (anti-Ia) antisera are compared, those derived from macrophage-like antigen presenting cells and others derived from T-lymphocytes, of either the suppressor or helper type. Despite the common property of reacting with anti-Ia antisera, the two sets of factors differ by many criteria. Macrophages, upon culture with antigen, release complexes of Ia antigen and a fragment of the original immunogen. This material is only produced by responder macrophages and thus appears to be a soluble Ir gene product. The genetic restriction of the T-macrophage interaction was investigated in chimeras, and it was found that the host environment as well as the donor genotype was of importance in determining restrictions, which were thus not really directed to "self." There was no evidence for intrinsic T-cell Ir genes, as nonresponder stem cells developed into responder T-cells in a (responder X nonresponder) F1 environment. However, these cells only responded in the presence of responder macrophages. Specific T-cell factors are different in nature. These all react with anti-Ia antisera, but the nature or function of the T-cell Ia is unknown. The basic structure involves a VARIAble region" responsible for antigen binding which, as it reacts with anti-idiotype antisera and anti-variable region framework antisera is an immunoglobulin variable region. There is also a "constant region," defined by its biological properties as well as by specific rabbit antisera. This two-region nature of specific factors is reminiscent of immunoglobulin structure and it is a reasonable hypothesis that the constant region is linked to the Ig cluster of genes.

Animals↗

PubMed2026

Upper airway microbiome interacts with GSDMB and ORMDL3 asthma risk SNPs to influence early-life wheeze risk.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) in the chromosome 17q12-q21 region and, independently, early-life nasal microbiota dominated by Moraxella, Streptococcus, or Haemophilus (MSH) increase risk of chronic wheeze and asthma development. OBJECTIVE: We sought to determine whether 17q12-q21 risk SNPs and nasal microbiota interact to modulate childhood wheeze risk. METHODS: Nasal wash samples from 12-month-old infants in 2 birth cohorts, COAST (Childhood Origins of Asthma; n = 180) and URECA (Urban Environment and Childhood Asthma; n = 139), underwent 16S ribosomal RNA variable region 4 sequencing. Nasal microbiota dominated by MSH or Corynebacterium, Dolosigranulum, Staphylococcus, or Bacillus (CDSB) were assessed. Paired blood was genotyped for 9 17q12-q21 risk SNPs. Logistic regression tested interactions between 17q12-q21 SNPs and MSH or CDSB on wheeze risk in the first 3 years of life. A549 lung epithelial cells, CRISPR-edited to encode the rs7216389 risk genotype (rs7216389TT) were compared to the heterozygous (rs7216389CT) line using bulk RNA sequencing. RESULTS: SNPs, particularly those in the ORMDL3 (rs8076131; odds ratio [OR]: 1.72; 95% CI: 1.09-2.71; Pint = .031) and GSDMB (rs2305480; OR: 1.72; 95% CI: 1.09-2.71; Pint = 0.042; and rs7216389; OR: 1.73; 95% CI: 1.09-2.70; Pint = .047) genes, interact with MSH microbiota to increase early-life wheeze risk (false discovery rate Pint = .016 for all), while interactions with CDSB reduce risk. A549 airway epithelial cells homozygous for rs7216389TT exhibited decreased expression of genes involved in antimicrobial responses and neutrophil recruitment and evidence increased microbial adherence compared with the heterozygous cell line. CONCLUSION: Airway microbiota interact with SNPs at the 17q12-q21 locus in genes involved in sphingolipid metabolism and intracellular antimicrobial responses, to modulate wheeze risk.

Humans↗

PubMed2025

Deciphering the Impact of Temperature on Pleiotropic Consequences of RNA Polymerase Mutations.

Despite occurring in an essential molecule, mutations in RNA polymerase readily emerge and elicit complex pleiotropic effects across different levels of biological organization, which are all modulated by environment. We investigated the impact of temperature on the effects of six mutations on sequence, structure, transcriptome, and organismal traits. We found temperature altered the transcriptomic response and key organismal traits such as growth rate and biofilm formation in a genotype-specific manner. Critically, mechanistic insights into the possible drivers of mutational effects emerged only when examining the relationships between different levels of organization: location of mutations in the tertiary structure and distance to key interacting molecules partly explained the observed transcriptomic differences, which in turn drove the impact of mutations on organismal traits. While falling short of capturing the full complexity of the system, our findings underscore the benefits of integrating insights across multiple biological levels to understand the relationship between environment and mutational effects in molecules with extensive pleiotropic effects.

Mutation↗

PubMed1977

Genetic-environmental interactions in the variation of blood pressure in Tecumseh, Michigan.

Explore the source record for details and available documents.

Adolescent↗

PubMed2025

Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.

Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resistance alleles, and cereal genomic resources are well advanced due to large-scale sequencing and genotyping efforts. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique to initially discover novel disease resistance loci or alleles in these diverse collections. Traditional disease resistance phenotyping methods are reliant on visual estimation of disease symptom severity and have successfully supported genetic mapping studies either via GWAS or QTL mapping in biparental populations facilitating both marker development and gene cloning efforts. Due to foliar pathogens having a high capacity to evolve, there is a need to pyramid disease resistance genes with diverse mechanisms for durable control. Resistance expressed as a quantitative trait, known as quantitative resistance (QR), is hypothesized to be more durable, unlike major R-gene resistance that is race-specific and can be vulnerable to breaking down without gene stewardship. However, assessing QR visually is challenging, particularly when complicated by complex genotype × environment (G × E) effects in the field. High-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS. In this review, we discuss image-based disease phenotyping based on macroscopic (visible symptoms) and microscopic features during the host-pathogen interaction. Quantitative image analysis approaches using conventional and artificial intelligence (AI) algorithms are also discussed.

Disease Resistance↗