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PubMed2025

Sex as a modifier of genetic risk for type 1 diabetes.

Sex differences influence the pathogenesis of type 1 diabetes (T1D), yet most genetic studies have treated sex as a control covariate rather than a dynamic effect modifier. Sex influences immune cell behaviour, including CD4+ and CD8+ T cell activation, regulatory T cell stability, B cell autoantibody production, dendritic cell priming and monocyte/macrophage inflammation. Underlying mechanisms include hormone-responsive enhancers, X-escape gene dosage and sex-biassed chromatin states, intersecting with T1D-associated variants to produce sex-specific immune phenotypes. These insights help explain regional variation in sex ratios of T1D incidence, such as male predominance in high-risk populations and female excess in low-risk populations. Biological sex shapes T1D risk across multiple layers, including polygenic load; environmental exposures such as vitamin D deficiency and enteroviral infection; and sex-specific hormonal, chromosomal and epigenetic influences. An integrative G × E × S (genetic × environmental × sex-specific) liability-threshold framework is thus supported. Clinical and translational implications include developing sex-specific polygenic risk scores, biomarker panels and interventional strategies targeting pathways such as hormone signalling, vitamin D metabolism and the microbiome. Future multi-omic, longitudinal studies are warranted to test genotype-sex interactions, integrate sex as a core effect modifier and enable precision prevention and treatment of T1D in both males and females.

Humans↗

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↗

PubMed2026

Genomic basis of developmental defects of enamel and sex-specific effects.

We conducted a multi-ancestry genome-wide association study (GWAS) of developmental defects of enamel (DDE) in the primary dentition among 6,061 U.S. preschool-aged children (3-5 years). We investigated four DDE phenotypes (demarcated opacities, diffuse opacities, hypoplastic defects, and a combined DDE trait) leveraging main-effect models, joint gene-sex interaction testing (2df), and sex-stratified analyses. SNP-based heritability for the combined DDE trait was estimated at 20%, with concordance analyses robustly supporting a genetic etiology. We identified 39 unique genome-wide significant loci (P<5×10 ), with five surpassing a study-wide Bonferroni-corrected statistical significance criterion (P<1.25×10 9), including Y RNA and ALDH1A1. The main-effect GWAS identified 20 loci, including HBS1L and MYB, genes regulating hematopoiesis with plausible roles in amelogenesis. Joint test and sex-stratified analyses revealed 19 additional loci, including ALDH1A1, TENM2, and DLGAP2, demonstrating sex-specific heterogeneity. Nineteen loci exhibited sex-specific differences after Bonferroni correction (P<2x10-3), including genes involved in retinoic acid signaling (ALDH1A1), odontogenesis (TENM2), and neurodevelopment (DLGAP2, CDH10). Pathway enrichment highlighted ectodermal and synapse organization networks, suggesting shared etiological mechanisms between DDE and systemic conditions like neurofibromatosis and autism spectrum disorder. Notably, no locus generalized in an external GWAS of permanent dentition DDE, underscoring fundamental biological differences in the genetic architectures governing primary versus permanent enamel formation. Crucially, a comprehensive cross-trait pleiotropy lookup against early childhood caries (ECC) revealed no shared genetic architecture, supporting the notion that the established clinical and epidemiological association between DDE and ECC is likely driven by structural defects increasing caries lesion susceptibility rather than genetic pleiotropy. By integrating gene-sex interaction testing, this study offers novel insights into the complex, sexually dimorphic genetic etiology of DDE and augments the biological evidence base that can support the development of precision pediatric dentistry.

developmental defects of enamel↗

PubMed2025

Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.

Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.

Humans↗

PubMed2026

Using high-dimensional environmental covariates to study genotype by environment interaction for reproductive traits in Duroc boars.

We investigated the potential of incorporating grid-cell-based environmental covariates (ECs) in the genetic evaluation of total sperm count (TSC), sperm motility (MOT), and sperm morphology (MOR) for Duroc boars. A total of 188,665 records derived from 3,684 genotyped boars, born between December 2018 and October 2024 and raised in three stud farms located in different U.S. states, were analyzed using multi-trait linear-threshold repeatability models. To account for genotype by environment interactions (GE), we constructed an interaction matrix as the Hadamard product of the genomic relationship matrix and an environmental (co)variance matrix. The environmental groups were defined in three ways: farm, farm-season, and farm-year-season. The (co)variance matrix was constructed based on daily ECs obtained from the NASA POWER database for each environmental group. Of all available ECs, those significantly associated with TSC, MOT, and MOR (temperature, relative humidity, atmospheric pressure, and wind speed and direction) were retained. We evaluated five models with different GE structures: M1 represented the baseline without accounting for GE, in M2 the GE included farm as environmental groups, in M3 the GE included farm-season as environmental groups, in M4 the GE included farm-year-season as environmental groups, and M5 involved M3 with an additional random effect of the farm-season. Estimates of heritability for TSC, MOT, and MOR ranged from 0.03 to 0.04, 0.05 to 0.08, and 0.04 to 0.08, respectively. Corresponding repeatability ranged from 0.15 to 0.23, 0.28 to 0.49, and 0.28 to 0.49. The proportion of phenotypic variance attributed to GE variance ranged from 0.00 to 0.32, 0.00 to 0.44, and 0.00 to 0.44. Lastly, estimates of genetic correlation, TSC-MOT, TSC-MOR, and MOT-MOR ranged from 0.27 to 0.31, 0.24 to 0.31, and 0.98 to 0.99, respectively, with minor differences across models. We assessed the predictive ability of models using the linear regression validation. Across traits and models, bias ranged from -0.05 to 0.02 standard deviations, slope varied from 0.88 to 0.99, the correlation ranged from 0.75 to 0.84, and accuracy from 0.41 to 0.53. Overall, building the GE matrix considering grid-cell-based ECs helped to account for GE, thereby reducing the proportion of phenotypic variance attributed to genetic components; however, it did not improve the validation metrics. Additional on-farm records for ECs may improve the model performance.

Animals↗

PubMed2026

Integrating Genetics and Environment to Find Causal Mechanisms for Multiple Sclerosis.

Genome-wide association studies (GWAS) have identified hundreds of risk loci for multiple sclerosis (MS), but we have limited knowledge of the mechanisms through which genetic variants mediate risk. Similarly, epidemiological studies implicate numerous environmental risk factors in MS risk, but these cannot identify specific causal mechanisms. We review our current knowledge of genetic mechanisms in MS, including the critical role of expression quantitative trait locus (eQTL) mapping in translating genetic risk loci into causal mechanisms. Molecular and functional context has emerged as an important missing component of these studies, and we discuss how environmental risk factors can be modelled in a quantitative genetic context to identify disease mechanisms. In parallel, we highlight recent advances in which quantitative genetic methods establish a causal role for low vitamin D and obesity in MS, and to dissect the mechanisms through which these operate. As genetic, transcriptional, and epigenetic studies continue to expand, further mechanistic insights for MS are likely to come from the integration of genetic and environmental data.

Humans↗

PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗

PubMed2025

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.

KEY MESSAGE: Perennial ryegrass shows extensive genotype x environment interactions at the limit of its ecological niche. Accounting for GxE may improve prediction even when environmental and genetic samples are highly diverse. BACKGROUND: In breeding the aim is to identify and accumulate beneficial variants. However, detection of these variants may be challenging in the presence of extensive genotype x environment interactions (GxE). METHODS: The study assesses the performance of 264 diploid perennial ryegrass accessions in a multi-environment field trial. We investigate the extent of GxE, for yield (total dry matter) and persistence traits under environmental conditions experienced in Nordic and Baltic regions at the limit of the species distribution. Two different approaches to modelling GxE were tested and validated under three different breeding scenarios. RESULTS: Our analysis documented the presence of significant GxE for all traits. Validation showed improvements in prediction accuracy when accounting for GxE: up to 4% for yield when predicting in unobserved environments, and up to 22% and 9% for spring cover and winter kill, respectively, when predicting unobserved germplasm. Genome-wide-association-studies (GWAS) were utilized to detect genetic variants with marginal effects (environment-independent effect) and conditional effects (environment-dependent effects). Results showed the presence of large-effect genetic variants with marginal effects, in addition to few Quantitative Trait Loci (QTL) whose effects were adaptive under specific environmental conditions while neutral or deleterious under different environmental conditions. CONCLUSION: This study demonstrates the usefulness and limitations of genomic prediction models for predicting GxE in highly diverse samples and describes the extent of GxE at the limit of species distribution for perennial ryegrass. Our study points towards adaptive variation which may enhance persistence of perennial ryegrass populations in Nordic and Baltic growing conditions.

Lolium↗

PubMed2026

Genetic and Environmental Influences on Caffeine Intake in Korean Twins.

Caffeine intake may be influenced by both genetic and environmental factors. This study aimed to examine the genetic contribution to variation in coffee and tea drinking, as well as total caffeine intake. A total of 1,106 Korean twins, comprising 456 monozygotic and 97 dizygotic twin pairs aged 30 years or older, from the Healthy Twin Study were included. Structural equation models were used to assess heritability estimates and their 95% confidence intervals (CIs). Heritability (95% CI) was estimated at 0.29 (0.21, 0.37) for coffee drinking, 0.11 (0.02, 0.20) for tea drinking, and 0.27 (0.16, 0.38) for total caffeine intake, with the remaining variance in each trait attributed to unique environmental factors. The unique environmental factors contributed more substantially to coffee drinking than genetic factors during early adulthood, while the genetic contribution to tea drinking remained consistently low across all ages. In conclusion, coffee drinking, tea drinking, and total caffeine intake were partly heritable, with unique environmental factors playing a predominant role.

Humans↗

PubMed2026

Humanizing acidic mammalian chitinase variants establish lung immune conditioning and control environmentally driven inflammation and fibrosis.

Chitin, a widespread environmental particle constituent, triggers lung inflammation but is degraded by chitinases. In humans, single-nucleotide polymorphisms (SNPs) in CHIA (acidic mammalian chitinase; AMCase) are associated with lung disease, suggesting that chitinase variants influence responses to airborne particles. Here, we edit the mouse Chia1 locus to generate humanized (hChia) mice harboring common human SNPs. Compared with controls expressing disease-protective SNPs, hChia mice lack robust chitinase activity and fail to degrade natural chitin substrates. Lung-resident lymphocytes and macrophages are spontaneously primed and sensitive to inflammatory triggering by environmental chitin. Immune cell infiltration correlates with airway chitin following challenge, and hChia mice exhibit exacerbated inflammatory and fibrotic lung disease. In humans with acute respiratory failure, alveolar hemorrhage coincides with environmentally derived chitin particles that are susceptible to chitinase degradation, attenuating inflammatory cell responses. Thus, environmental chitin and chitinase activity are crucial determinants of lung immune conditioning with potential therapeutic applications.

AMCase↗

PubMed2026

Genetic heterogeneity affects the risk of incident depression, comorbidity, and response to environment: A prospective trajectory study.

BACKGROUND: Depression exhibits significant heterogeneity in its genetic underpinnings. The role of genetic components in the development of depression and its comorbidities remains insufficiently explored. METHODS: First, depression risk loci from a large-scale genome-wide meta-analysis were annotated to Gene Ontology (GO) terms by functional enrichment. GO-based polygenic risk scores (GO-PRS) were then calculated for individuals in the UK Biobank. Principal component analysis (PCA) was applied for dimensionality reduction, followed by cluster analysis to identify genetic subtypes of depression. Multistate models were applied to assess the impact of genetic patterns on the trajectory from healthy status to incident depression, and depression to 26 subsequent diseases, as well as the associations between environmental factors and disease trajectories across genetic subtypes. RESULTS: Participants were categorized into three genetic subtypes: immune-dominant, neuro-dominant, and comprehensive-risk. Significant differences in risk of depression and subsequent diseases, and susceptibility to environmental factors were observed across subtypes. Comprehensive-risk subtype showed higher risks of depression compared to immune-dominant (HR: 1.10, 95% CI: 1.05-1.15) and neuro-dominant subtype (HR: 1.12, 95% CI: 1.08-1.16). Comprehensive-risk subtype exhibited higher risks of transition from depression to subsequent diseases, such as anemia compared to immune-dominant subtype, and diseases of the digestive system compared to neuro-dominant subtype. Environmental factors were more strongly associated with the transition from depression to subsequent diseases in immune-dominant and comprehensive-risk subtypes, including cardiovascular, respiratory, and metabolic diseases. CONCLUSIONS: Our findings highlight the genetic heterogeneity of depression and comorbidities, and shed light on how genetic components modulate responses to environmental factors.

Humans↗

PubMed2026

Deconstructing empirical fitness seascapes across scales of granularity.

The fitness landscape metaphor remains resonant in evolutionary theory and has facilitated the birth of newer concepts, like the fitness seascape, that consider the role of environmental context in shaping the dynamics of evolution. Since its emergence, the seascape has appeared in numerous studies examining how different and fluctuating environments shape evolutionary outcomes. Despite growing interest, we lack comprehensive examinations of how environmental context shapes features of fitness seascapes. In this study, we address this gap by deconstructing empirical fitness seascapes across scales of granularity: loci, locus interactions (epistasis), alleles, trajectories, and entire seascapes. For each, we examine how environmental context influences qualitative and quantitative aspects of seascapes, and find that they change appreciably, with patterns specific to individual systems of study. We also quantify how much each scale varies across environments, and find that certain scales tend to be more sensitive to context than others. In summary, we reflect on the implications of the seascape metaphor for the incorporation of environmental effects into theoretical population genetics, for understanding how the environment shapes evolution in disease systems, and for contemporary bioengineering efforts.

Genetic Fitness↗

PubMed2025

Genotype by Environment Interactions in Gene Regulation Underlie the Response to Soil Drying in the Model Grass Brachypodium distachyon.

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called genotype and environment (G × E). Understanding the mechanisms driving G × E is fundamental for ensuring stable crop performance across environments and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, yet the interactions between genotype and environment in gene regulation are rarely considered, particularly at the genome scale. Current frameworks and experimental designs often lack power to explicitly test network rewiring or to systematically compare regulatory networks. Here, we leverage a highly replicated RNA-sequencing dataset to model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We first identified genotypic, environmental, and G × E effects on physiological, metabolic, and gene expression traits. We identify patterns of conservation-or variation-in gene coexpression networks and link these coexpression features to physiological traits. We further develop predictions of gene-gene interactions using causal inference and screen for interactions specific to-or with higher affinity in-a single genotype, treatment, or their interaction, G × E. Our analyses identify variation in candidate gene regulatory networks that may shape the evolution of environmental response in B. distachyon. We highlight the environmentally dependent regulatory control of several metabolic traits shown previously to play a role in drought acclimation. The framework presented here provides a scalable approach for more complex comparisons, particularly with the growing availability of large datasets from technologies such as single-cell transcriptomics.

Brachypodium↗

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↗

PubMed2025

Experimental Validation of Genome-Environment Associations in Arabidopsis.

Identifying the genetic basis of local adaptation is a key goal in evolutionary biology. Allele frequency clines along environmental gradients, known as genotype-environment associations (GEA), are often used to detect potential loci causing local adaptation but are rarely followed by experimental validation. Here, we tested loci identified in three moisture-related GEA studies on Arabidopsis. We studied 42 GEA-identified genes using t-DNA knockout lines under drought and tested effects on flowering time, an adaptive trait, and genotype-by-environment (GxE) interactions for performance and fitness. In total, 16/42 genes had significant effects on traits involved in local adaptation or performance responses to the environment. We found that wrky38 mutants had significant GxE effects for fitness; lsd1 plants had a significant GxE effect for flowering time, and 11 genes showed flowering time effects with no drought interaction. However, most GEA candidates did not exhibit GxE. In the follow-up experiments, wrky38 caused decreased stomatal conductance and specific leaf area under drought, indicating potentially adaptive drought avoidance. Additionally, GEA identified natural putative LoF variants of WRKY38 associated with dry environments, as well as alleles associated with variation in LSD1 expression. While only a few GEA-identified genes were validated for GxE interactions for fitness, we likely overlooked some genes because experiments might not well represent natural environments and t-DNA insertions might not well represent natural alleles. Nevertheless, GEAs apparently identified some genes contributing to local adaptation. GEA and follow-up experiments are straightforward to implement in model systems and demonstrate prospects for GEA discovery of new local adaptations.

Arabidopsis↗

PubMed2025

Identification of a novel heterozygous GPD1 missense variant in a Chinese adult patient with recurrent HTG-AP consuming a high-fat diet and heavy smoking.

BACKGROUND: Glycerol-3-phosphate dehydrogenase 1 (GPD1) gene defect can cause hypertriglyceridemia (HTG), which usually occurs in infants. The gene defect has rarely been reported in adult HTG patients. In the present study, we described the clinical and functional analyses of a novel GPD1 missense variant in a Chinese adult patient with recurrent hypertriglyceridemia‑related acute pancreatitis (HTG-AP), consuming a high-fat diet and smoking heavily. METHODS: Exome sequencing was used to analyze the DNA of the adult patient's blood sample. It was found that there was a new variant of GPD1 gene-p.K327N, which was verified by gold standard-sanger sequencing method. In vitro, the corresponding plasmid was constructed and transfected into human renal HEK-293T cells, and GPD1 protein levels were detected. A biogenic analysis was performed to study the population frequency, conservation, and electric potential diagram of the new variant p.K327N. Finally, the previously reported GPD1 variants were sorted and their phenotypic relationships were compared. RESULTS: A novel heterozygous variant of GPD1, p.K327N (c.981G > C), was found in the proband. Furthermore, the patient's daughter carried this variant, whereas his wife did not carry the variant. The proband with obesity suffered eight episodes of HTG-AP from the age of 36 years, and each onset of AP was correlated to high-fat diet consumption and heavy smoking. In vitro, this variant exerted a relatively mild effect on GPD1 functions, which were associated with its effect upon secretion (~ 25% of secretion decreased compared with that of the wild-type); thus, eventually impairing protein synthesis. Additionally, 36 patients with GPD1 variants found in previous studies showed significant transient HTG in infancy. The proband carrying the GDP1 variant was the first reported adult with recurrent HTG-AP. CONCLUSION: We identified a novel GPD1 variant, p.K327N, in a Chinese adult male patient with recurrent HTG-AP. The variant probably exerted a mild effect on GPD1 functions. The heterozygosity of this GPD1 variant, in addition to high-fat diet consumption and heavy smoking, probably triggered HTG-AP in the patient.

Adult↗

PubMed2026

Genome-wide interactions with cadmium exposure in dysglycemia: Populational effects and molecular insights.

Dysglycemia is a complex metabolic disorder governed by the interplay between environmental exposures and genetic factors, yet the precise molecular mechanisms driving these gene-environment (G×E) interactions remains poorly understood. Here, we characterized the population-level landscape and molecular causality underlying the interactions between cadmium (Cd), a widespread environmental toxicant, and genetic susceptibility loci in dysglycemia. By conducting a Genome-wide Environmental Interaction (GWEI) study within a sub-cohort of the China National Human Biomonitoring (CNHBM) cohort (N = 1298), we identified 29 genetic risk loci that significantly interact with Cd burden to exacerbate elevated fasting plasma glucose levels. Functional enrichment integrated with metabolomic profiling unmasked a profound multi-omics convergence, positioning epigenetic modifications (e.g. H3K27me3) and zinc-finger transcription factors (e.g. OVOL2, KLFs) as central regulatory hubs that disrupt metabolic homeostasis. To establish causality, we demonstrated that the rs11743277 A>T variant at the lead G×E locus functions as a Cd-responsive enhancer element, facilitating recruitment of TEAD3 and upregulating TICAM2 expression in CRISPR/Cas9-edited HepG2 cells, especially upon Cd exposure. This initiates a TICAM2-mediated inflammatory response, with elevated pro-inflammatory cytokines (IFN-β, TNF-α, IL-6) impairing downstream insulin signaling and glucose utilization. Collectively, these findings establish a robust paradigm for G×E interactions in complex metabolic disorders, revealing how environmental stressors reprogram genetic susceptibilities through molecular checkpoints and paving the way for tailored, precision-prevention strategies in environmental health.

CRISPR/Cas9 editing↗

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↗