Gene-environment Interaction Analysis Research Articles

Secondhand tobacco smoke and risk of atrial fibrillation: an observational epidemiologic and gene-environment interaction analysis

Abstract Background Second-hand tobacco smoke (SHS) is related to coronary artery disease and increases susceptibility to arrhythmia inducibility including atrial fibrillation (AF). However, epidemiologic evidence on the relationship between SHS and the risk of incident AF remains controversial. Purpose To evaluate the effect of SHS exposure on the long-term risk of AF and assess whether gene-environment interaction (synergism) exists by utilising the population-based data from the UK biobank. Methods We performed a nationwide population-based cohort study using the UK Biobank database. Participants with previous AF or current smokers were excluded. Participants were categorised into SHS-exposure and non-exposure groups. The risk of incident AF was assessed using the multivariable Cox proportional hazards model. We also evaluated the synergistic effect of SHS exposure and the polygenic risk score (PRS) of AF. Results Out of 400,493 participants, AF occurred in 23,471 (5.9%) during a median follow-up of 12.5 years. The SHS-exposure group (mean exposure 2.2 ± 5.2 hours/week) had a higher proportion of male participants with a younger age. The risk of incident AF was significantly increased in the SHS-exposure group (multivariable-adjusted hazard ratio [aHR] 1.06, 95% confidence interval 1.03-1.10, P <0.001) compared to the non-exposure group, suggesting a dose-dependent manner (aHR of severe SHS-exposure group, 1.11 [1.03-1.20], P = 0.005). The risk of AF increases regardless of the exposure location (home, outside, or workplace). When SHS-exposure was combined with the high genetic risk of AF, significant additive interaction was observed on the risk AF (Synergy Index of SHS-exposure with 3rdtertile of AF PRS, 1.13 [1.02-1.25], P = 0.024). Conclusion Exposure to SHS is a significant predictor for an increased risk of AF, with a synergistic effect when combined with genetic predisposition. Minimising SHS exposure, especially in individuals with a heightened genetic susceptibility to AF, may hold potential benefits for reducing the future risk of AF.

NRF2 genetic polymorphisms modify the longitudinal association of serum polychlorinated biphenyls with glucose homeostasis damage: A gene-environment interaction analysis among Chinese general adults

NRF2 genetic polymorphisms modify the longitudinal association of serum polychlorinated biphenyls with glucose homeostasis damage: A gene-environment interaction analysis among Chinese general adults

GWAS of Folate Metabolism With Gene-environment Interaction Analysis Revealed the Possible Role of Lifestyles in the Control of Blood Folate Metabolites in Japanese: The J-MICC Study.

The present genome-wide association study (GWAS) aimed to reveal the genetic loci associated with folate metabolites, as well as to detect related gene-environment interactions in Japanese. We conducted the GWAS of plasma homocysteine (Hcy), folic acid (FA), and vitamin B12 (VB12) levels in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study participants who joined from 2005 to 2012, and also estimated gene-environment interactions. In the replication phase, we used data from the Yakumo Study conducted in 2009. In the discovery phase, data of 2,263 participants from four independent study sites of the J-MICC Study were analyzed. In the replication phase, data of 573 participants from the Yakumo Study were analyzed. For Hcy, MTHFR locus on chr 1, NOX4 on chr 11, CHMP1A on chr 16, and DPEP1 on chr 16 reached genome-wide significance (P < 5 × 10-8). MTHFR also associated with FA, and FUT2 on chr 19 associated with VB12. We investigated gene-environment interactions in both studies and found significant interactions between MTHFR C677T and ever drinking, current drinking, and physical activity >33% on Hcy (β = 0.039, 0.038 and -0.054, P = 0.018, 0.021 and <0.001, respectively) and the interaction of MTHFR C677T with ever drinking on FA (β = 0.033, P = 0.048). The present GWAS revealed the folate metabolism-associated genetic loci and gene-environment interactions with drinking and physical activity in Japanese, suggesting the possibility of future personalized cardiovascular disease prevention.

The Association of Urinary Sodium Excretion with Glaucoma and Related Traits in a Large United Kingdom Population

Excessive dietary sodium intake has known adverse effects on intravascular fluid volume and systemic blood pressure, which may influence intraocular pressure (IOP) and glaucoma risk. This study aimed to assess the association of urinary sodium excretion, a biomarker of dietary intake, with glaucoma and related traits, and determine whether this relationship is modified by genetic susceptibility to disease. Cross-sectional observational and gene-environment interaction analyses in the population-based UK Biobank study. Up to 103 634 individuals (mean age: 57 years; 51% women) with complete urinary, ocular, and covariable data. Urine sodium:creatinine ratio (UNa:Cr; mmol:mmol) was calculated from a midstream urine sample. Ocular parameters were measured as part of a comprehensive eye examination, and glaucoma case ascertainment was through a combination of self-report and linked national hospital records. Genetic susceptibility to glaucoma was calculated based on a glaucoma polygenic risk score comprising 2673 common genetic variants. Multivariable linear and logistic regression, adjusted for key sociodemographic, medical, anthropometric, and lifestyle factors, were used to model associations and gene-environment interactions. Corneal-compensated IOP, OCT derived macular retinal nerve fiber layer and ganglion cell-inner plexiform layer (GCIPL) thickness, and prevalent glaucoma. In maximally adjusted regression models, a 1 standard deviation increase in UNa:Cr was associated with higher IOP (0.14 mmHg; 95% confidence interval [CI], 0.12-0.17; P<0.001) and greater prevalence of glaucoma (odds ratio, 1.11; 95% CI, 1.07-1.14; P<0.001) but not macular retinal nerve fiber layer or ganglion cell-inner plexiform layer thickness. Compared with those with UNa:Cr in the lowest quintile, those in the highest quintile had significantly higher IOP (0.45 mmHg; 95% CI, 0.36-0.53, P<0.001) and prevalence of glaucoma (odds ratio, 1.30; 95% CI, 1.17-1.45; P<0.001). Stronger associations with glaucoma (P interaction=0.001) were noted in participants with a higher glaucoma polygenic risk score. Urinary sodium excretion, a biomarker of dietary intake, may represent an important modifiable risk factor for glaucoma, especially in individuals at high underlying genetic risk. These findings warrant further investigation because they may have important clinical and public health implications. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Impact of ambient air pollution on colorectal cancer risk and survival: insights from a prospective cohort and epigenetic Mendelian randomization study

This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. Using a newly developed Air Pollutants Exposure Score (APES), we utilized a prospective cohort study (UK Biobank) to investigate the associations of individual and combined air pollution exposures with CRC incidence and survival, followed by an up-to-date systematic review with meta-analysis to verify the associations. In epigenetic two-sample Mendelian randomization analyses, we examine the associations between genetically predicted DNA methylation related to air pollution and CRC risk. Further genetic colocalization and gene-environment interaction analyses provided different insights to disentangle pathogenic effects of air pollution via epigenetic modification. During a median 12.97-year follow-up, 5767 incident CRC cases among 428,632 participants free of baseline CRC and 533 deaths in 2401 patients with CRC were documented in the UK Biobank. A higher APES score was associated with an increased CRC risk (HR, 1.03, 95% CI= 1.01-1.06; P=0.016) and poorer survival (HR, 1.13, 95% CI= 1.03-1.23; P=0.010), particularly among participants with insufficient physical activity and ever smokers (Pinteraction > 0.05). A subsequent meta-analysis of seven observational studies, including UK Biobank data, corroborated the association between PM2.5 exposure (per 10μg/m3 increment) and elevated CRC risk (RR,1.42, 95% CI= 1.12-1.79; P=0.004; I2=90.8%). Genetically predicted methylation at PM2.5-related CpG site cg13835894 near TMBIM1/PNKD and cg16235962 near CXCR5, and NO2-related cg16947394 near TMEM110 were associated with an increased CRC risk. Gene-environment interaction analysis confirmed the epigenetic modification of aforementioned CpG sites with CRC risk and survival. Our study suggests the association between air pollution and CRC incidence and survival, underscoring the possible modifying roles of epigenomic factors. Methylation may partly mediate pathogenic effects of air pollution on CRC, with annotation to epigenetic alterations in protein-coding genes TMBIM1/PNKD, CXCR5 and TMEM110. Xue Li is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001), the National Nature Science Foundation of China (No. 82204019) and Healthy Zhejiang One Million People Cohort (K-20230085). ET is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). MGD is supported by the MRC Human Genetics Unit Centre Grant (U127527198).

Gene-environment interaction analysis of school quality and educational inequality

We study to what extent schools increase or decrease environmental and genetic influences on educational performance. Building on behavioral genetics literature on gene-environment interactions and sociological literature on the compensating and amplifying effects of schools on inequality, we investigate whether the role of genes and the shared environment is larger or smaller in higher-quality school environments. We apply twin models to Dutch administrative data on the educational performance of 18,384 same-sex and 11,050 opposite-sex twin pairs, enriched with data on the quality of primary schools. Our results show that school quality does not moderate genetic and shared-environmental influences on educational performance once the moderation by SES is considered. We find a gene-environment interplay for school SES: genetic variance decreases with increasing school SES. This school SES effect partly reflects parental SES influences. Yet, parental SES does not account for all the school SES moderation, suggesting that school-based processes play a role too.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Hierarchical false discovery rate control for high-dimensional survival analysis with interactions

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, which includes a set of low-dimensional covariates, a set of high-dimensional covariates, and their interactions. This model has been motivated by gene-environment (G-E) interaction analysis, where the E variables have a low dimension, and the G variables have a high dimension. For such a model, there has been extensive research on estimation and variable selection. Comparatively, inference studies with a valid false discovery rate (FDR) control have been very limited. The existing high-dimensional inference tools cannot be directly applied to interaction models, as interactions and main effects are not “equal”. In this article, for high-dimensional survival analysis with interactions, we model survival using the Accelerated Failure Time (AFT) model and adopt a “weighted least squares + debiased Lasso” approach for estimation and selection. A hierarchical FDR control approach is developed for inference and respect of the “main effects, interactions” hierarchy. The asymptotic distribution properties of the debiased Lasso estimators are rigorously established. Simulation demonstrates the satisfactory performance of the proposed approach, and the analysis of a breast cancer dataset further establishes its practical utility.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

Analysis of exposome and genetic variability suggests stress as a major contributor for development of pancreatic ductal adenocarcinoma

BackgroundThe current knowledge on pancreatic ductal adenocarcinoma (PDAC) risk factors is limited and no study has comprehensively tested the exposome in combination with the genetic variability in relation to the disease susceptibility. AimThe aim of this study was to analyze the exposome and its interaction with known genetic susceptibility loci, in relation to PDAC risk. MethodsA case-control study nested in UK Biobank cohort was conducted on 816 PDAC cases and 302,645 controls. A total of 347 exposure variables, and a polygenic risk score (PRS) were analyzed through logistic regression. Gene-environment interaction analyses were conducted. ResultsA total of 52 associations under the Bonferroni corrected threshold of p < 1.46 × 10−4 were observed. Known risk factors such as smoking, pancreatitis, diabetes, PRS, heavy alcohol drinking and overweight were replicated in this study. As for novel associations, a clear indication for length and intensity of mobile phone use and the stress-related factors and stressful events with increase of PDAC risk was observed. Although the PRS was associated with PDAC risk (P = 2.09 × 10−9), statistically significant gene-exposome interactions were not identified. ConclusionIn conclusion, our results suggest that a stressful lifestyle and sedentary behaviors may play a major role in PDAC susceptibility independently from the genetic background.

Statistical methods for gene-environment interaction analysis.

Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.

Interactive effects between CDHR3 genotype and rhinovirus species for diagnosis and severity of respiratory tract infections in hospitalized children.

Rhinovirus (RV) is the leading pathogen causing childhood wheezing, with rhinovirus C (RV-C) species reported to cause asthma exacerbation. Allele A of single-nucleotide polymorphism (SNP) CDHR3_rs6967330 upregulates epithelial expression of RV-C receptors which results in more severe asthma exacerbations in children. Nevertheless, there are limited data on interactions between CDHR3 variants and their impact on severity of RV-related pediatric respiratory tract infections (RTIs). Medical records of RV-related RTIs in children aged below 18 years who were hospitalized in two public hospitals in 2015-2016 were independently reviewed by two paediatricians. Archived nasopharyngeal aspirates were retrieved for RV detection and sequencing as well as CDHR3 genotyping. HaploView v.5.0 and generalized multifactor dimensionality reduction (GMDR) analysis were employed for haplotypic assignment and gene-environment interaction analyses. Among 1019 studied cases, our results confirmed the relationship between RV-C species and more severe RTIs. Besides the top risk variant rs6967330-A, we identified rs140154310-T to be associated with RV-C susceptibility under the additive model [odds ratio (OR) 2.53, 95% CI 1.15-5.56; P = 0.021]. Rs140154310 was associated with wheezing illness (OR 2.38, 95% CI 1.12-5.04; P = 0.024), with such association being stronger in subjects who wheezed due to RV-C infections (OR 2.71, 95% CI 1.32-5.58; P = 0.007). Haplotype GAG constructed from rs4730125, rs6967330, and rs73195665 was associated with increased risk of RV-C infection (OR 1.71, 95% CI 1.11-2.65; P = 0.016) and oxygen supplementation (OR 1.93, 95% CI 1.13-3.30; P = 0.016). GMDR analyses revealed epistatic interaction between rs140154310 and rs6967330 of CDHR3 for RV-C infection (P = 0.001), RV-C-associated lower RTI (P = 0.004), and RV-C-associated wheeze (P = 0.007). There was synergistic gene-environmental interaction between rs3887998 and RV-C for more severe clinical outcomes (P < 0.001). To conclude, rs140154310-T is another risk variant for RV-C susceptibility and more severe RTIs. Synergistic epistatic interaction is found between CDHR3 SNPs and RV-C for RTI severity, which is likely mediated by susceptibility to RV-C. Haplotypic analysis and GMDR should be included in identifying prediction models of CDHR3 for childhood asthma and RTIs. IMPORTANCE This case-control study investigated the interaction between CDHR3 genotypes and rhinovirus (RV) species on disease severity in Hong Kong children hospitalized for respiratory tract infection (RTI). There were synergistic effects between RV-C and CDHR3 SNPs for RTI severity, which was mainly driven by RV-C. Specifically, rs6967330 and rs140154310 alone and their epistatic interaction were associated with RV-C-related and severe RTIs in our subjects. Therefore, genotyping of CDHR3 SNPs may help physicians formulate prediction models for severity of RV-associated RTIs.

Dissecting the pathogenic effects of smoking and its hallmarks in blood DNA methylation on colorectal cancer risk.

Tobacco smoking is suggested as a risk factor for colorectal cancer (CRC), but the complex relationship and the potential pathway are not fully understood. We performed two-sample Mendelian randomisation (MR) analyses with genetic instruments for smoking behaviours and related DNA methylation in blood and summary-level GWAS data of colorectal cancer to disentangle the relationship. Colocalization analyses and prospective gene-environment interaction analyses were also conducted as replication. Convincing evidence was identified for the pathogenic effect of smoking initiation on CRC risk and suggestive evidence was observed for the protective effect of smoking cessation in the univariable MR analyses. Multivariable MR analysis revealed that these associations were independent of other smoking phenotypes and alcohol drinking. Genetically predicted methylation at CpG site cg17823346 [ZMIZ1] were identified to decrease CRC risk; while genetically predicted methylation at cg02149899 would increase CRC risk. Colocalization and gene-environment interaction analyses added further evidence to the relationship between epigenetic modification at cg17823346 [ZMIZ1] as well as cg02149899 and CRC risk. Our study confirms the significant association between tobacco smoking, DNA methylation and CRC risk and yields a novel insight into the pathogenic effect of tobacco smoking on CRC risk.

Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses

BackgroundDiabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis.MethodsWe used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test).ResultsBased on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 – ORAA: 1.62, 95% CI: 1.34–1.96; ORAG: 1.41, 95% CI: 1.30–1.54; ORGG: 1.22, 95% CI: 1.13–1.31; p-value3-d.f.: 5.46 × 10−11) and 13q14.13 (rs9526201, LRCH1 – ORGG: 2.11, 95% CI: 1.56–2.83; ORGA: 1.52, 95% CI: 1.38–1.68; ORAA: 1.13, 95% CI: 1.06–1.21; p-value2-d.f.: 7.84 × 10−09).DiscussionThese results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.

The Association of Physical Activity with Glaucoma and Related Traits in the UK Biobank

To examine the association of physical activity (PA) with glaucoma and related traits, to assess whether genetic predisposition to glaucoma modified these associations, and to probe causal relationships using Mendelian randomization (MR). Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia. UK Biobank participants with data on self-reported or accelerometer-derived PA and intraocular pressure (IOP; n= 94 206 and n= 27 777, respectively), macular inner retinal OCT measurements (n=36 274 and n= 9991, respectively), and glaucoma status (n= 86 803 and n= 23 556, respectively). We evaluated multivariable-adjusted associations of self-reported (International Physical Activity Questionnaire) and accelerometer-derived PA with IOP and macular inner retinal OCT parameters using linear regression and with glaucoma status using logistic regression. For all outcomes, we examined gene-PA interactions using a polygenic risk score (PRS) that combined the effects of 2673 genetic variants associated with glaucoma. Intraocular pressure, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and glaucoma status. In multivariable-adjusted regression models, we found no association of PA level or time spent in PA with glaucoma status. Higher overall levels and greater time spent in higher levels of both self-reported and accelerometer-derived PA were associated positively with thicker mGCIPL (P < 0.001 for trend for each). Compared with the lowest quartile of PA, participants in the highest quartiles of accelerometer-derived moderate- and vigorous-intensity PA showed a thicker mGCIPL by+0.57 μm (P < 0.001) and+0.42 μm (P= 0.005). No association was found with mRNFL thickness. High overall level of self-reported PA was associated with a modestly higher IOP of+0.08 mmHg (P= 0.01), but this was not replicated in the accelerometry data. No associations were modified by a glaucoma PRS, and MR analyses did not support a causal relationship between PA and any glaucoma-related outcome. Higher overall PA level and greater time spent in moderate and vigorous PA were not associated with glaucoma status but were associated with thicker mGCIPL. Associations with IOP were modest and inconsistent. Despite the well-documented acute reduction in IOP after PA, we found no evidence that high levels of habitual PA are associated with glaucoma status or IOP in the general population. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk.

This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

AF4/FMR2 and IL-10 gene single nucleotide polymorphisms are correlated with disease susceptibility and immune infiltration in ankylosing spondylitis

To explore the correlation of polymorphisms of AF4/FMR2 family genes and IL-10 gene with genetic susceptibility to ankylosing spondylitis (AS) and identify the high-risk factors of AS. This case-control study was conducted among 207 AS patients and 321 healthy individuals. The tag single nucleotide polymorphisms (SNPs) rs340630, rs241084, rs10865035, rs1698105, and rs1800896 of the AF4/FMR2 family gene and IL-10 gene of the AS patients were genotyped, and the distribution frequencies of the genotypes and alleles were analyzed to explore the relationship between different genetic models and AS and the gene-gene and gene-environment interactions. Gender ratio, smoking history, drinking history, hypertension, erythrocyte sedimentation rate and C-reactive protein differed significantly between the case group and the control group (P < 0.05). The dominant model and recessive model of AFF1 rs340630, the recessive model of AFF3 rs10865035, and the recessive model of IL-10 rs1800896 were significantly different between the two groups (P=0.031, 0.010, 0.031, and 0.019, respectively). Gene-environment interaction analysis suggested that the interaction model incorporating AFF1 rs340630, AFF2 rs241084, AFF3 rs10865035, AFF4 rs1698105, IL-10 rs1800896, smoking history and drinking history was the best model. The genes related with AF4/FMR2 and IL-10 were enriched in the biological processes of AF4 super extension complex, interleukin family signal transduction, cytokine stimulation and apoptosis. The expression levels of AF4/FMR2 and IL-10 were positively correlated with immune infiltration (r > 0). The SNPs of AF4/FMR2 and IL-10 genes are associated with the susceptibility to AS, and the interactions of AF4/FMR2 and IL-10 genes with the environmental factors contributes causes AS through immune infiltration.

Pathological imaging-assisted cancer gene-environment interaction analysis.

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance andstability.

Gene–environment interaction analysis under the Cox model

Gene–environment interaction analysis under the Cox model

Effects of residential greenness and genetic predisposition on hemoglobin A1c and type 2 diabetes: Gene-environment interaction analysis from a nationwide study

BackgroundCurrent evidence on the relations of residential greenness with glucose homeostasis and type 2 diabetes (T2D) remained largely uncertain. Most importantly, no prior studies have investigated whether genetic predisposition modifies the above associations. MethodsWe leveraged data from the UK Biobank prospective cohort study, with participants enrolled between 2006 and 2010. Residential greenness was assessed by using the Normalized Difference Vegetation Index, and the weighting T2D-specific genetic risk score (GRS) was constructed based on previously published genome-wide association studies. Linear regression models and logistic regression models were used to investigate associations of residential greenness with glycated hemoglobin (HbA1c) and T2D prevalence, respectively. Interaction models explored whether genetic predisposition modifies greenness-HbA1c/T2D associations. ResultsAmong 315,146 individuals (mean [SD] age, 56.59 [8.09] years), each one-unit increase in residential greenness was associated with reduction in HbA1c (β: −0.87, 95% CI: −1.16 to −0.58) and a 12% decrease in odds of T2D (OR: 0.88, 95% CI: 0.79 to 0.98), respectively. Additionally, interaction analyses further demonstrated that residential greenness and genetic risk had cumulative effects on HbA1c and T2D. Compared with individuals who were exposed to low greenness and had high GRS, participants with low GRS and high greenness had a significant decline in HbA1c (β: −2.96, 95% CI: −3.10 to −2.82, P for interaction = 0.04) and T2D (OR: 0.47, 95% CI: 0.45 to 0.50, P for interaction = 0.09). ConclusionsWe add novel evidence that residential greenness has protective effects on glucose metabolism and T2D, and those beneficial effects can be amplified by low genetic risk. Our findings may facilitate the improvement of the living environment and the development of prevention strategies by considering genetic susceptibility to T2D.