Correction to: Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments.
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ABSTRACT:
SUBMITTER: Werme J
PROVIDER: S-EPMC8032730 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.
| S-EPMC7985503 | biostudies-literature
Project description:Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.
| S-EPMC5070904 | biostudies-other
Project description:Despite the importance of gene-environment (GxE) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of GxE interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant GxE interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing GxE interactions are discussed.
| S-EPMC2924567 | biostudies-literature
Project description:The G protein-coupled bradykinin B2 receptor (Bdkrb2) plays an important role in regulation of blood pressure under conditions of excess salt intake. Our previous work has shown that Bdkrb2 also plays a developmental role since Bdkrb2(-/-) embryos, but not their wild-type or heterozygous littermates, are prone to renal dysgenesis in response to gestational high salt intake. Although impaired terminal differentiation and apoptosis are consistent findings in the Bdkrb2(-/-) mutant kidneys, the developmental pathways downstream of gene-environment interactions leading to the renal phenotype remain unknown. Here, we performed genome-wide transcriptional profiling on embryonic kidneys from salt-stressed Bdkrb2(+/+) and Bdkrb2(-/-) embryos. The results reveal significant alterations in key pathways regulating Wnt signaling, apoptosis, embryonic development, and cell-matrix interactions. In silico analysis reveal that nearly 12% of differentially regulated genes harbor one or more Pax2 DNA-binding sites in their promoter region. Further analysis shows that metanephric kidneys of salt-stressed Bdkrb2(-/-) have a significant downregulation of Pax2 gene expression. This was corroborated in Bdkrb2(-/-);Pax2(GFP+/tg) mice, demonstrating that Pax2 transcriptional activity is significantly repressed by gestational salt-Bdkrb2 interactions. We conclude that gestational gene (Bdkrb2) and environment (salt) interactions cooperate to impact gene expression programs in the developing kidney. Suppression of Pax2 likely contributes to the defects in epithelial survival, growth, and differentiation in salt-stressed BdkrB2(-/-) mice.
| S-EPMC4154247 | biostudies-literature
Project description:Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.
| S-EPMC4544597 | biostudies-literature
Project description:Little is known about gene-environment interactions in Parkinson disease (PD). We examined potential interactions of smoking and caffeine intake with 10 genome-wide association studies single nucleotide polymorphisms (SNPs) at or near the SNCA, MAPT, LRRK2, and HLA loci among 584 PD patients and 1571 controls. The main effects of these SNPs and environmental exposures were consistent with previous reports. Family history of PD was associated with PD risk (odds ratio = 2.71, 95% confidence interval, 1.97-3.74), which was little affected by further adjustment for these SNPs and environmental exposures. Overall, we did not find significant interactions of either smoking or caffeine intake with these SNPs. However, with a combined smoking and caffeine intake exposure, we found a significant interaction with rs2896905 at SLC2A13, near LRRK2 (p uncorrected = 0.0008). Each A allele was associated with a 35% higher PD risk among never smokers with low caffeine intake, but with a 32% lower risk among smokers with high caffeine intake. This study provides preliminary evidence of a potential gene-environment interaction for PD, which should be investigated in future studies.
| S-EPMC3419385 | biostudies-literature
Project description:It is believed that almost all common diseases are the consequence of complex interactions between genetic markers and environmental factors. However, few such interactions have been documented to date. Conventional statistical methods for detecting gene and environmental interactions are often based on the linear regression model, which assumes a linear interaction effect. In this study, we propose a nonparametric partition-based approach that is able to capture complex interaction patterns. We apply this method to the real data set of hypertension provided by Genetic Analysis Workshop 18. Compared with the linear regression model, the proposed approach is able to identify many additional variants with significant gene-environmental interaction effects. We further investigate one single-nucleotide polymorphism identified by our method and show that its gene-environmental interaction effect is, indeed, nonlinear. To adjust for the family dependence of phenotypes, we apply different permutation strategies and investigate their effects on the outcomes.
| S-EPMC4143762 | biostudies-literature
Project description:BackgroundGene-environment interaction studies using genome-wide association study data are often underpowered after adjustment for multiple comparisons. Differential gene expression in response to the exposure of interest can capture the most biologically relevant genes at the genome-wide level.ObjectiveWe used differential genome-wide expression profiles from the Epidemiology of Home Allergens and Asthma birth cohort in response to Der f 1 allergen (sensitized vs nonsensitized) to inform a gene-environment study of dust mite exposure and asthma severity.MethodsPolymorphisms in differentially expressed genes were identified in genome-wide association study data from the Childhood Asthma Management Program, a clinical trial in childhood asthmatic patients. Home dust mite allergen levels (<10 or ≥10 μg/g dust) were assessed at baseline, and (≥1) severe asthma exacerbation (emergency department visit or hospitalization for asthma in the first trial year) served as the disease severity outcome. The Genetics of Asthma in Costa Rica Study and a Puerto Rico/Connecticut asthma cohort were used for replication.ResultsIL9, IL5, and proteoglycan 2 expression (PRG2) was upregulated in Der f 1-stimulated PBMCs from dust mite-sensitized patients (adjusted P < .04). IL9 polymorphisms (rs11741137, rs2069885, and rs1859430) showed evidence for interaction with dust mite in the Childhood Asthma Management Program (P = .02 to .03), with replication in the Genetics of Asthma in Costa Rica Study (P = .04). Subjects with the dominant genotype for these IL9 polymorphisms were more likely to report a severe asthma exacerbation if exposed to increased dust mite levels.ConclusionsGenome-wide differential gene expression in response to dust mite allergen identified IL9, a biologically plausible gene target that might interact with environmental dust mite to increase severe asthma exacerbations in children.
| S-EPMC4763940 | biostudies-literature
Project description:A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods for GWIS have been previously proposed, aimed at improving efficiency by prioritizing SNPs most likely to be involved in a G × E interaction using a screening step. For a quantitative trait, these include a method that screens on marginal effects [Kooperberg and Leblanc, 2008] and a method that screens on variance heterogeneity by genotype [Paré et al., 2010] In this paper, we show that the Paré et al. approach has an inflated false-positive rate in the presence of an environmental marginal effect, and we propose an alternative that remains valid. We also propose a novel 2-step approach that combines the two screening approaches, and provide simulations demonstrating that the new method can outperform other GWIS approaches. Application of this method to a G × Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.
| S-EPMC5108681 | biostudies-literature
Project description:The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.
| S-EPMC4469559 | biostudies-literature