A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

AIMS: This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS: A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS: A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS: A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population.

BACKGROUND: Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. METHODS: A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. RESULTS: Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). CONCLUSIONS: Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.

An approach to identify gene-environment interactions and reveal new biological insight in complex traits.

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

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

BACKGROUND: This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. METHODS: 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. FINDINGS: 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 10 µg/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. INTERPRETATION: 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. FUNDING: 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).

Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.

BACKGROUND: Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS: We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS: We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (ß = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (ß = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (ß = 0.088, FDR = 0.010), whereas no such association was found with PRS (ß = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (ß = -0.042, FDR = 6.30 × 10-9), but not with PRS (ß = -0.003, FDR = 0.857). CONCLUSIONS: Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

Insights into the Molecular Mechanisms of Genetic Predisposition to Hematopoietic Malignancies: The Importance of Gene-Environment Interactions.

SUMMARY: The recognition of host genetic factors underlying susceptibility to hematopoietic malignancies has increased greatly over the last decade. Historically, germline predisposition was thought to primarily affect the young. However, emerging data indicate that hematopoietic malignancies that develop in people of all ages across the human lifespan can derive from germline predisposing conditions and are not exclusively observed in younger individuals. The age at which hematopoietic malignancies manifest appears to correlate with distinct underlying biological pathways. Progression from having a deleterious germline variant to being diagnosed with overt malignancy involves complex, multistep gene-environment interactions with key external triggers, such as infection and inflammatory stimuli, driving clonal progression. Understanding the mechanisms by which predisposed clones transform under specific pressures may reveal strategies to better treat and even prevent hematopoietic malignancies from occurring.Recent unbiased genome-wide sequencing studies of children and adults with hematopoietic malignancies have revealed novel genes in which disease-causing variants are of germline origin. This paradigm shift is spearheaded by findings in myelodysplastic syndrome/acute myeloid leukemia (MDS/AML) as well as acute lymphoblastic leukemia, but it also encompasses other cancer types. Although not without challenges, the field of genetic cancer predisposition is advancing quickly, and a better understanding of the genetic basis of hematopoietic malignancies risk affects therapeutic decisions as well as genetic counseling and testing of at-risk family members.

Unveiling challenges in Mendelian randomization for gene-environment interaction.

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

Triple Primary Cancers: An Analysis of Genetic and Environmental Factors.

The occurrence of multiple primary cancers (MPC) is thought to reflect increased cancer susceptibility in patients due to a combination of genetic and environmental factors. Here we conducted a retrospective review of 2,894 consecutive patients evaluated at a single institution and identified 31 (1.14%) individuals with a history of three or more primary cancers, then analyzed the genetic and environmental influences associated with their propensity for developing malignancies. We found that 35.5% of patients had a hereditary cancer syndrome (HCS), with high penetrance HCS in 72.7% of cases, suggesting that monogenic causes underly a significant proportion of triple primary cancer risk. Analysis of cancer frequencies found that the diagnosis of breast cancer was associated with a significantly lower likelihood of HCS, while the diagnosis of colorectal, prostate, and pancreas cancer was associated with a significantly higher likelihood of HCS. Comparison of HCS-positive and HCS-negative patients revealed similar demographic characteristics, mean age at first diagnosis, and family history of cancer. Moreover, no significant differences in lifestyle behaviors, occupational exposures, chronic health conditions, or treatment with chemotherapy and radiation were observed between HCS-positive and -negative groups, though outliers in tobacco smoking, as well as systemic treatment after both first and second primary cancers were observed. These findings indicate a robust contribution of HCS to cancer susceptibility among patients with triple primary cancers while environmental influences were less evident. This emphasizes the need for larger MPC cohorts incorporating additional genetic and environmental factors to more comprehensively characterize drivers of cancer risk. PREVENTION RELEVANCE: In patients with three or more primary cancers, genetic predisposition explained a significant proportion of cases; however, treatment history, lifestyle habits, and other exposures appeared to play a less significant role. This highlights the value of early genetic screening and the need to develop more sensitive markers of cancer susceptibility. See related Spotlight, p. 193.

Genetic and environmental contributions for the relationship between tooth loss and oral potentially malignant disorders and oral squamous cell carcinoma.

BACKGROUND: To investigate the association between tooth loss and oral potentially malignant disorders and oral squamous cell carcinoma, focusing on epidemiological factors and genetic variants. METHODS: Case-control study, including histologically confirmed oral potentially malignant disorders and oral squamous cell carcinoma cases and healthy controls. Unadjusted and adjusted odds ratios for this association were calculated. Single-nucleotides polymorphisms were tested for individuals with and without missing teeth. RESULTS: Case individuals were more edentulous while controls had fewer missing teeth (p = 0.006). There was an increased risk for the outcomes associated with edentulism (OR = 6.95, p = 0.000), even after adjustments for educational level (OR = 4.7, p = 0.034) and smoking habits (OR = 5.01, p = 0.022). Among individuals with tooth loss, rs1533767 (WNT11), rs3923087, and rs11867417 (AXIN2) were associated with the outcomes (OR = 1.67, p = 0.03, OR = 0.53, p = 0.05, and OR = 0.42, p = 0.00, respectively). CONCLUSIONS: Tooth loss could increase the risk for oral potentially malignant disorders and oral squamous cell carcinoma.

Leveraging GWAS: Path to Prevention?

Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7.

CLAVATA signaling in plant-environment interactions.

Plants must rapidly and dynamically adapt to changes in their environment. Upon sensing environmental signals, plants convert them into cellular signals, which elicit physiological or developmental changes that allow them to respond to various abiotic and biotic cues. Because plants can be simultaneously exposed to multiple environmental cues, signal integration between plant cells, tissues, and organs is necessary to induce specific responses. Recently, CLAVATA3/EMBRYO SURROUNDING REGION-related (CLE) peptides and their cognate CLAVATA-type receptors received increased attention for their roles in plant-environment interactions. CLE peptides are mobile signaling molecules, many of which are induced by a variety of biotic and abiotic stimuli. Secreted CLE peptides are perceived by receptor complexes on the surface of their target cells, which often include the leucine-rich repeat receptor-like kinase CLAVATA1. Receptor activation then results in cell-type and/or environment-specific responses. This review summarizes our current understanding of the diverse roles of environment-regulated CLE peptides in modulating plant responses to environmental cues. We highlight how CLE signals regulate plant physiology by fine-tuning plant-microbe interactions, nutrient homeostasis, and carbon allocation. Finally, we describe the role of CLAVATA receptors in the perception of environment-induced CLE signals and discuss how diverse CLE-CLAVATA signaling modules may integrate environmental signals with plant physiology and development.

Gene-Environment Interactions for Parkinson's Disease.

OBJECTIVE: Parkinson's disease (PD) is a neurodegenerative disorder with complex etiology. Multiple genetic and environmental factors have been associated with PD, but most PD risk remains unexplained. The aim of this study was to test for statistical interactions between PD-related genetic and environmental exposures in the 23andMe, Inc. research dataset. METHODS: Using a validated PD polygenic risk score and common PD-associated variants in the GBA gene, we explored interactions between genetic susceptibility factors and 7 lifestyle and environmental factors: body mass index (BMI), type 2 diabetes (T2D), tobacco use, caffeine consumption, pesticide exposure, head injury, and physical activity (PA). RESULTS: We observed that T2D, as well as higher BMI, caffeine consumption, and tobacco use, were associated with lower odds of PD, whereas head injury, pesticide exposure, GBA carrier status, and PD polygenic risk score were associated with higher odds. No significant association was observed between PA and PD. In interaction analyses, we found statistical evidence for an interaction between polygenic risk of PD and the following environmental/lifestyle factors: T2D (p = 6.502 × 10-8), PA (p = 8.745 × 10-5), BMI (p = 4.314 × 10-4), and tobacco use (p = 2.236 × 10-3). Although BMI and tobacco use were associated with lower odds of PD regardless of the extent of individual genetic liability, the direction of the relationship between odds of PD and T2D, as well as PD and PA, varied depending on polygenic risk score. INTERPRETATION: We provide preliminary evidence that associations between some environmental and lifestyle factors and PD may be modified by genotype. ANN NEUROL 2024;95:677-687.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Risk factors for multiple sclerosis in the context of Epstein-Barr virus infection.

Compelling evidence indicates that Epstein Barr virus (EBV) infection is a prerequisite for multiple sclerosis (MS). The disease may arise from a complex interplay between latent EBV infection, genetic predisposition, and various environmental and lifestyle factors that negatively affect immune control of the infection. Evidence of gene-environment interactions and epigenetic modifications triggered by environmental factors in genetically susceptible individuals supports this view. This review gives a short introduction to EBV and host immunity and discusses evidence indicating EBV as a prerequisite for MS. The role of genetic and environmental risk factors, and their interactions, in MS pathogenesis is reviewed and put in the context of EBV infection. Finally, possible preventive measures are discussed based on the findings presented.

Deconvoluting gene and environment interactions to develop an "epigenetic score meter" of disease.

Human health is determined both by genetics (G) and environment (E). This is clearly illustrated in groups of individuals who are exposed to the same environmental factor showing differential responses. A quantitative measure of the gene-environment interactions (GxE) effects has not been developed and in some instances, a clear consensus on the concept has not even been reached; for example, whether cancer is predominantly emerging from "bad luck" or "bad lifestyle" is still debated. In this article, we provide a panel of examples of GxE interaction as drivers of pathogenesis. We highlight how epigenetic regulations can represent a common connecting aspect of the molecular bases. Our argument converges on the concept that the GxE is recorded in the cellular epigenome, which might represent the key to deconvolute these multidimensional intricated layers of regulation. Developing a key to decode this epigenetic information would provide quantitative measures of disease risk. Analogously to the epigenetic clock introduced to estimate biological age, we provocatively propose the theoretical concept of an "epigenetic score-meter" to estimate disease risk.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry.

BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors.

Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.

Does parental separation moderate the heritability of health risk behavior among adolescents?

Social influences on adolescents' health risk behavior are well documented, but little is known about the interaction of parental separation with genetic sensitivities. Using data from a German sample of 1762 twins, this study examines whether family living arrangements moderate the influence of genetic predispositions on health risk behavior. Derived from variance decomposition moderator models, three key findings emerge. Firstly, genetic contributions to drug use are significantly higher in single-mother families, indicating an amplified heritability potentially resulting from triggered genetic sensitivities or challenges in preventing genetic risks from unfolding. Secondly, unique environmental factors have a greater impact on drug use in single-mother families. Lastly, no heritability differences are found in smoking and excessive alcohol consumption between family types. These findings provide novel evidence of increased importance of genetic influences on drug use in single-mother families, shedding light on gene-environment interactions, and informing policy interventions that support vulnerable family arrangements.

[Genotype-environment interaction on arterial stiffness: A pedigree-based study].

OBJECTIVE: To utilized the baseline data of the Beijing Fangshan Family Cohort Study, and to estimate whether the association between a healthy lifestyle and arterial stiffness might be modified by genetic effects. METHODS: Probands and their relatives from 9 rural areas in Fangshan district, Beijing were included in this study. We developed a healthy lifestyle score based on five lifestyle behaviors: smoking, alcohol consumption, body mass index (BMI), dietary pattern, and physical activity. The measurements of arterial stiffness were brachial-ankle pulse wave velocity (baPWV) and ankle-brachial index (ABI). A variance component model was used to determine the heritability of arterial stiffness. Genotype-environment interaction effects were performed by the maximum likelihood methods. Subsequently, 45 candidate single nucleotide polymorphisms (SNPs) located in the glycolipid metabolism pathway were selected, and generalized estimated equations were used to assess the gene-environment interaction effects between particular genetic loci and healthy lifestyles. RESULTS: A total of 6 302 study subjects across 3 225 pedigrees were enrolled in this study, with a mean age of 56.9 years and 45.1% male. Heritability of baPWV and ABI was 0.360 (95%CI: 0.302-0.418) and 0.243 (95%CI: 0.175-0.311), respectively. Significant genotype-healthy diet interaction on baPWV and genotype-BMI interaction on ABI were observed. Following the findings of genotype-environment interaction analysis, we further identified two SNPs located in ADAMTS9-AS2 and CDH13 might modify the association between healthy dietary pattern and arterial stiffness, indicating that adherence to a healthy dietary pattern might attenuate the genetic risk on arterial stiffness. Three SNPs in CDKAL1, ATP8B2 and SLC30A8 were shown to interact with BMI, implying that maintaining BMI within a healthy range might decrease the genetic risk of arterial stiffness. CONCLUSION: The current study discovered that genotype-healthy dietary pattern and genotype-BMI interactions might affect the risk of arterial stiffness. Furthermore, we identified five genetic loci that might modify the relationship between healthy dietary pattern and BMI with arterial stiffness. Our findings suggested that a healthy lifestyle may reduce the genetic risk of arterial stiffness. This study has laid the groundwork for future research exploring mechanisms of arterial stiffness.