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At least 19 recordsLinked to original sources
PubMed2026

Methods for modeling gene-environment interplay using polygenic risk scores.

Polygenic risk scores (PRS) are increasingly recognized as pivotal tools for quantifying disease risk through the aggregation of multiple genetic variants. As sample sizes in genome-wide association studies (GWAS) continue to expand and PRS become more powerful, they are set to play a key role in translational research and personalized medicine. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in diverse contexts. This interplay manifests in two forms: PRS-by-environment interaction (PRS × E) and gene-environment correlation (rGE). However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS × E interaction analyses while controlling for rGE, which can lead to inconsistencies across studies and misinterpretation of results. Here we provide a review of different methods for performing PRSxE interaction in various epidemiological study designs, propose recommendations for best-practice, and discuss future challenges.

Gene-Environment Interaction↗
PubMed2024

Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Humans↗
PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗
PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗
PubMed2026

Gene-environment interaction between perinatal oxytocin exposure and Pten mutation shapes epigenetic reprogramming of oxytocin signaling and behavior in mice.

Synthetic oxytocin (Pitocin) is the most commonly used pharmacologic agent for induction and augmentation of labor. Beyond its uterotonic effects, oxytocin plays a critical role in neurodevelopment and social behavior. Dysregulated oxytocin signaling has been implicated in autism spectrum disorder (ASD), raising concern that perinatal exposure to exogenous oxytocin may have lasting neurodevelopmental consequences. This study aimed to determine whether offspring harboring a genetic predisposition for ASD are differentially impacted by perinatal oxytocin exposures, with a focus on long-term oxytocin signaling and autism-like behavior. Pregnant mice carrying offspring with heterozygous mutations in phosphatase and tensin homolog deleted on chromosome ten (Pten), a well-established monogenic risk factor for ASD, received continuous oxytocin versus phosphate-buffered saline (PBS) control via micro-osmotic pumps during late gestation. Wild-type (WT) offspring exposed to each treatment served as a secondary control. Adult offspring were assessed for oxytocin receptor (Oxtr) methylation in the frontal cortex and hippocampus, oxytocin expression in the hypothalamus, serum oxytocin levels, and were subject to a battery of social and anxiety-related behavior tests. Perinatal oxytocin exposure produced genotype-dependent effects in offspring. Epigenetic analyses revealed bidirectional remodeling of Oxtr methylation in the frontal cortex and hippocampus, with increased exon 1 methylation in WT mice and decreased methylation in Pten-mutant mice, resulting in significant genotype-treatment interactions. Hypothalamic oxytocin expression increased following treatment regardless of genotype, though baseline levels were higher in Pten-mutant mice. Neither oxytocin treatment nor genotype impacted long-term serum oxytocin levels. Behavioral outcomes were modest but context-specific: repetitive behaviors and cognition performance were unchanged, but oxytocin-treated Pten-mutant mice exhibited increased anxiety-like behavior alongside improved social memory. In contrast, oxytocin-treated WT mice showed reduced social novelty preference. Exploratory analyses suggested potential sex-dependent trends. Our findings support a model in which genetic susceptibility shapes the epigenetic encoding of early-life hormonal signals, thereby recalibrating oxytocin system function and downstream behavioral outcomes. Together, these data highlight the context-dependent effects of perinatal oxytocin exposure and argue against uniformly beneficial or detrimental effects, emphasizing the importance of gene-environment interactions in neurodevelopmental trajectories.

Animals↗
PubMed2025

Gene-environment interaction analysis in atopic eczema: evidence from large population datasets and modelling in vitro.

BACKGROUND: Environmental factors play a role in the pathogenesis of complex traits including atopic eczema (AE) and a greater understanding of gene-environment interactions (G*E) is needed to define pathomechanisms for disease prevention. We analysed data from 16 European studies to test for interaction between the 24 most significant AE-associated loci identified from genome-wide association studies and 18 early-life environmental factors. We tested for replication using a further 10 studies and in vitro modelling to independently assess findings. RESULTS: The discovery analysis showed suggestive evidence for interaction (p<0.05) between 7 environmental factors (antibiotic use, cat ownership, dog ownership, breastfeeding, elder sibling, smoking and washing practices) and at least one established variant for AE, 14 interactions in total (maxN=25,339). In replication analysis (maxN=252,040) dog exposure*rs10214237 (on chromosome 5p13.2 near IL7R) was nominally significant (ORinteraction=0.91 [0.83-0.99] P=0.025), with a risk effect of the T allele observed only in those not exposed to dogs. A similar interaction with rs10214237 was observed for siblings in the discovery analysis (ORinteraction=0.84[0.75-0.94] P=0.003), but replication analysis was under-powered ORinteraction=1.09[0.82-1.46]). Rs10214237 homozygous risk genotype is associated with lower IL-7R expression in human keratinocytes, and dog exposure modelled in vitro showed a differential response according to rs10214237 genotype. CONCLUSIONS: Interaction analysis and functional assessment provide evidence that early-life dog exposure may modify the genetic effect of rs10214237 on AE via IL7R, supporting observational epidemiology showing a protective effect for dog ownership. The lack of evidence for other G*E studied here implies that only weak effects are likely to occur.

Atopic eczema↗
PubMed2026

Variance Polygenic Scores (vPGS) as a Tool for Studying Gene-Environment Interactions Associated With Refractive Error.

PURPOSE: Conventional polygenic scores predict an individual's phenotype based on their genetics. By contrast, variance polygenic scores (vPGS) quantify genetic predisposition to phenotypic variance. We tested the hypothesis that a vPGS for refractive error can identify individuals with increased susceptibility to environmental risk factors for myopia. METHODS: Six vPGS construction strategies were evaluated in UK Biobank participants: three variance heterogeneity genome-wide association study (vGWAS) methods and two reweighting schemes. vPGS performance was assessed using two metrics: (i) "Diff"-difference in phenotypic variance in vPGS decile ten versus one; (ii) Spearman correlation of phenotypic variance versus vPGS decile. The optimal vPGS was used to test for vPGS &#xd7; time spent reading or vPGS &#xd7; time spent outdoors interactions in children aged 15 years (ALSPAC cohort; n = 3471). RESULTS: Of the vGWAS methods, conditional quantile regression outperformed SCAMPI and Levene's Test. Of the re-weighting schemes, LDpred2 outperformed pruning and thresholding. In an independent sample of UK Biobank participants (n = 19,470), the top-performing vPGS successfully stratified individuals into groups with increasing variance in refractive error, even after adjusting for a conventional PGS (Diff: 2.55, 95% confidence interval [CI], 1.64-3.47; Spearman correlation = 0.87; 95% CI, 0.43-0.93). However, in ALSPAC participants, there was minimal support for vPGS interactions with time reading (P = 0.80) or time outdoors (P = 0.89). CONCLUSIONS: A novel vPGS successfully stratified individuals into groups with relatively high or low genetic susceptibility to refractive error variance. However, the vPGS could not identify individuals at enhanced risk from lifestyle risk factors for myopia.

Humans↗
PubMed1978

Research designs for the study of gene-environment interactions in psychiatric disorders. Report of a Foundations Fund for Research in Psychiatry Panel.

Understanding the genetic and environmental contributions (and their interactions, which are likely to be complex) to the etiology of psychiatric disorders requires research designs incorporating many basic principles of genetics. Genetic variation is likely to contribute to psychiatric disorders and genetic heterogeneity is likely to exist for any single disorder, ie, completely different genetic variants may each be capable of increasing an individual's susceptibility to the disorder. Thus, it is important to define phenotypes that may more closely reflect each individual genetic variant rather than to rely solely on the psychiatric diagnosis. Research should be undertaken with the goal of testing specific hypotheses that can be excluded. Research designs can include studies of unrelated individuals, twins, separated relatives, nuclear families, or extended pedigrees. Not all hypotheses can be tested on one type of data, and appropriate analytic methods vary. Because genetic hypotheses cannot be tested on studies of unrelated individuals, it is important that data be collected on families instead of unrelated individual patients and/or controls. Studies should include traits that bridge the gap between the genotype and the diagnostic phenotype. Such studies should be multidisciplinary, and the best statistical-genetics methodology should be used for data analysis.

Adoption↗
PubMed2026

Genetic risk scores, perceived neighborhood disorder, and sleep duration.

STUDY OBJECTIVES: Most studies of neighborhood context and sleep health emphasize direct effects and fail to account for the role of genetics. In this paper, we draw on the socioecological model to examine the interplay of genetics, neighborhood context, and sleep health. We specifically examine the independent and joint effects of genetic risk scores (GRS) and perceived neighborhood disorder on sleep duration. METHODS: We combine genomic and cross-sectional survey data from the All of Us Research Program, a non-probability sample of 22&#x2009;575 adults of European ancestry living in the United States. We use the sleep duration-increasing risk allele count for 78 genome-wide single nucleotide polymorphisms (SNPs) to construct weighted genetic risk scores. Our analyses include an index of perceived neighborhood disorder and an objective measure of sleep duration based on wrist actigraphy. RESULTS: Genetic risk scores are inversely associated with neighborhood disorder, positively associated with continuous sleep duration, and inversely associated with the odds of short sleep. Neighborhood disorder is inversely associated with continuous sleep duration and positively associated with the odds of short and long sleep. The association between genetic risk scores and sleep duration (continuous and categorical) is invariant across levels of neighborhood disorder. CONCLUSIONS: Our analyses confirm the independent direct effects of genetic risk scores and neighborhood disorder on sleep duration. Our findings extend the socioecological model by assessing the role of genetics in the study of neighborhood context and sleep health. Although we observed a gene-environment correlation between genetic risk scores and perceived neighborhood disorder, there was little indication of genetic confounding and no evidence of gene-environment interaction.

Humans↗
PubMed2025

Complex and Dynamic Gene-by-Age and Gene-by-Environment Interactions Underlie Functional Morphological Variation in Adaptive Divergence in Arctic Charr (Salvelinus alpinus).

The evolution of adaptive phenotypic divergence requires heritable genetic variation. However, it is underappreciated that trait heritability is molded by developmental processes interacting with the environment. We hypothesized that the genetic architecture of divergent functional traits was dependent on age and foraging environment. Thus, we induced plasticity in full-sib families of Arctic charr (Salvelinus alpinus) morphs from two Icelandic lakes by mimicking prey variation in the wild. We characterized variation in body shape and size at two ages and investigated their genetic architecture with quantitative trait locus (QTL) analysis. Age had a greater effect on body shape than diet in most families, suggesting that development strongly influences phenotypic variation available for selection. Consistent with our hypothesis, multiple QTL were detected for all traits and their location depended on age and diet. Many of the genome-wide QTL were located within a subset of duplicated chromosomal regions suggesting that ancestral whole genome duplication events have played a role in the genetic control of functional morphological variation in the species. Moreover, the detection of two body shape QTL after controlling for the effects of age provides additional evidence for genetic variation in the plastic response of morphological traits to environmental variation. Thus, functional morphological traits involved in phenotypic divergence are molded by complex genetic interactions with development and environment.

Animals↗
PubMed2026

Gene by environment interaction effects on the metabolic subtype of Polycystic Ovary Syndrome in Hispanic Community Health Study/Study of Latinos.

Polycystic Ovary Syndrome (PCOS) is a common polygenic endocrine disorder that is heterogenous in clinical presentation across genetic ancestry groups. PCOS is characterized by an array of symptoms such as hyperandrogenism, impaired mental health, and metabolic dysregulation. Studying the interaction of environmental factors (such as diet, physical activity, anxiety, and depression) with genetic variants on PCOS and its subtypes in populations with high cardiometabolic burden, e.g., Hispanic/Latinas, could aid in unraveling pathophysiological and genetic pathways through which PCOS functions. We sought to study gene by environment interactions with PCOS and its metabolic subtype (mPCOS) in a sample of US Hispanic/Latina female adults from the Hispanic Community Heath Study/Study of Latinos. In this large community-based study, we derived PCOS using self-reported condition and menstrual cycle information. We classified females with PCOS as having mPCOS if they had high metabolic impairment (fasting glucose, fasting insulin, or body mass index higher than the 75th percentile). There were 451 individuals with PCOS and 221 of them had mPCOS in our sample. We found that PCOS and mPCOS were significantly associated with hyperglycemia and high triglycerides in this population. While a polygenic risk score derived in European ancestry did not generalize to Hispanic/Latina females with PCOS, we identified the best proxy genetic variants in this population in known PCOS regions and investigated their interactions with four environment variables (diet, physical activity, anxiety and depression). Associations with known PCOS loci were generalized in our study at STAG3L4 and CACNA1G genomic regions. We observed GxE interactions between variants in/near three genes and physical activity on PCOS and mPCOS, including FGGY, FAT1, and PTHLH. Additionally, we noted interactions between diet and a variant in FANCC on PCOS, and diet and a variant near CMAS on both PCOS and mPCOS. We also detected GxE interactions between anxiety and depression and a variant in FGGY on PCOS, and depression and a variant near FBP1 on mPCOS. Our results point to potential protective effects of physical activity in females with PCOS and could inform future research on the mitigating effects of lifestyle management on PCOS genetic risk in Hispanic/Latino populations.

GxE↗
PubMed2026

Epigenetics and childhood obesity: DNA methylation coordinates environment and gene regulation.

Childhood obesity is a complex disorder which results from the combined contribution of genetics, the environment, and development, which is programmed and coordinated by epigenetic mechanisms. Of them, DNA methylation has emerged as an important molecular interface between environmental inputs and changes in gene expression. In this review, we provide an overview of the role of DNA methylation in childhood obesity during the key developmental stages, from prenatal life and childhood to adolescence. We also highlight the available evidence from candidate genes and genome-wide association studies implicating critical loci involved in energy homeostasis and adipogenesis, where DNA methylation is altered. Further, we also provide an overview of how maternal obesity, nutritional status, and bariatric surgery shape offspring's methylation profiles and contribute to the increased risk of programming obesity across generations. Although aberrant methylation patterns are consistently associated with altered metabolic phenotypes, disentangling causality remains a significant challenge. Herein, we highlight emerging approaches, such as rigorous longitudinal cohorts, epigenetic Mendelian randomization, and CRISPR-based epigenome editing, that are beginning to provide the analytical clarity needed to move beyond association. Finally, we examine the potential of DNA methylation signatures to inform early risk stratification and prevention possibilities. Although yet to be clinically validated, whole-genome methylation profiling is increasingly integrated with systems biology and multi-omics frameworks, making the identification of robust, clinically actionable markers more promising. A more precise understanding of how epigenetic processes shape susceptibility to childhood obesity could ultimately support strategies capable of altering lifelong metabolic trajectories.

Humans↗
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

Heterokairic Genes and the Eco-Evo-Devo of Timing.

Concepts of developmental timing have traditionally been framed under heterochrony as evolved (genetically based) differences in timing, while environmentally induced shifts in timing within genotypes have been treated more loosely. In this article, heterokairy is presented as plasticity in the timing of developmental events, and the term "heterokairic genes" is proposed for environmentally modulated heterochronic genes that underlie this plasticity. Evidence from nematodes, insects, plants, and vertebrates is assembled, with emphasis placed on systems where environmental cues are relayed through endocrine or metabolic pathways to known timing modules/genes. On this basis, a distinction is drawn between validated heterokairic genes, supported by direct mechanistic data, and a broader set of candidates inferred from gene-environment interactions in developmental timing. The eco-evolutionary consequences of such genes are considered, and experimental and genomic strategies for their identification are outlined. It is argued that heterokairic genes provide a useful bridge between environmental variation, developmental mechanisms, and evolutionary change in timing.

Animals↗
PubMed1976

Resolution of cultural and biological inheritance by path analysis.

Analysis of family resemblance is developed in terms of three genetic parameters, six parameters for cultural inheritance, and one parameter for an index estimating family environment. With efficient use of nuclear families the model is fully determinate. Other biological and social relationship provide additional degrees of freedom for testing goodness of fit. Performance of the model is satisfactory on simulated data with extreme gene-environment interaction. Applied to a large body of published data on I.Q., neither genetic assortative mating nor gene-environment covariance is significant by a likelihood ratio test, but heritability is less and cultural inheritance is greater for adults than children. Whereas family resemblance of children is largely genetic, for adults it is largely due to their childhood environments, presumably acting on occupational aspirations. Further resolution is more likely to come from nuclear families than from the rare relationships that were favored by classical human genetics.

Culture↗