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A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis.

ABSTRACT: The influence and effect of cigarette smoking in sarcoidosis is unclear. Here, we evaluated gene-environment interaction between multiple genetic variants including HLA genes and smoking in sarcoidosis defined by two clinical phenotypes, Löfgren's syndrome (LS) and patients without Löfgren's syndrome (non-LS). To quantify smoking effects in sarcoidosis, we performed a gene-environment interaction study in a Swedish population-based case-control study consisting of 3,713 individuals. Cases and controls were classified according to their cigarette smoking status and genotypes by Immunochip platform. Gene-smoking interactions were quantified by an additive interaction model using a logistic regression adjusted by sex, age and first two principal components. The estimated attributable proportion (AP) was used to quantify the interaction effect. Assessment of smoking effects with inclusion of genetic information revealed 53 (in LS) and 34 (in non-LS) SNP-smoking additive interactions at false discovery rate (FDR) below 5%. The lead signals interacting with smoking were rs12132140 (AP?=?0.56, 95% CI?=?0.22-0.90), p?=?1.28e-03) in FCRL1 for LS and rs61780312 (AP?=?0.62, 95% CI?=?0.28-0.90), p?=?3e-04) in IL23R for non-LS. We further identified 16 genomic loci (in LS) and 13 (in non-LS) that interact with cigarette smoking. These findings suggest that sarcoidosis risk is modulated by smoking due to genetic susceptibility. Therefore, patients having certain gene variants, are at a higher risk for the disease. Consideration of individual's genetic predisposition is crucial to quantify effects of smoking in sarcoidosis.

SUBMITTER: Rivera NV

PROVIDER: S-EPMC6901455 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis.

Rivera Natalia V NV Patasova Karina K Kullberg Susanna S Diaz-Gallo Lina Marcela LM Iseda Tomoko T Bengtsson Camilla C Alfredsson Lars L Eklund Anders A Kockum Ingrid I Grunewald Johan J Padyukov Leonid L

Scientific reports 20191209 1

The influence and effect of cigarette smoking in sarcoidosis is unclear. Here, we evaluated gene-environment interaction between multiple genetic variants including HLA genes and smoking in sarcoidosis defined by two clinical phenotypes, Löfgren's syndrome (LS) and patients without Löfgren's syndrome (non-LS). To quantify smoking effects in sarcoidosis, we performed a gene-environment interaction study in a Swedish population-based case-control study consisting of 3,713 individuals. Cases and co ...[more]

PMID: 31819081

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Project description:The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected sub-sample. It is natural to apply such a strategy for collecting genetic data in a sub-sample enriched for exposure to environmental factors for gene-environment interaction (G × E) analysis. In this paper, we consider two-phase studies of G × E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phase I and phase II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade-off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the non-parametric Bayes construction of Dunson and Xing (2009). We carry out a small-scale simulation study to compare the proposed Bayesian method with weighted likelihood and pseudo likelihood methods that are standard choices for analyzing two-phase data. The motivating example originates from an ongoing case-control study of colorectal cancer, where the goal is to explore the interaction between the use of statins (a drug used for lowering lipid levels) and 294 genetic markers in the lipid metabolism/cholesterol synthesis pathway. The sub-sample of cases and controls on which these genetic markers were measured is enriched in terms of statin users. The example and simulation results illustrate that the proposed Bayesian approach has a number of advantages for characterizing joint effects of genotype and exposure over existing alternatives and makes efficient use of all available data in both phases.

Project description:IntroductionThere has been rapid growth in research exploring gene-environment interaction (G×E) contributing to smoking behaviors. Yet, no systematic review exists to date.MethodsThis article aims to review evidence on the contribution of G×E to the risk of smoking. Through a search of electronic databases (ie, Google Scholar, PubMed, ScienceDirect, and Elsevier) up until May 2014, 16 studies of G×E focused on smoking behaviors were identified. These studies were compared in terms of: research design and sample studied, measure of smoking behavior and environments used, genes explored, and G×E in relation to these factors.ResultsThirteen of 16 studies (81.2%) found at least one significant G×E association. Wide variation in analytic methods was found across studies, especially with respect to the phenotypes of interest, environmental measures used, and tests conducted to estimate G×E. Heterogeneity across studies made it difficult to compare findings and evaluate the strength of evidence for G×E.ConclusionsG×E research on smoking contains studies that are methodologically different, making it difficult to assess the current state of the evidence. To decrease heterogeneity, we offer recommendations related to: (1) choice of measurement for environmental variables, (2) testing and reporting of main and interaction effects, (3) treatment of covariates, (4) reporting gene-environment correlation, and (5) conducting sensitivity analyses and checking for scaling artifacts. Continued study is needed to identify mechanisms by which genes and environmental factors combine to influence smoking behaviors.ImplicationsNo comprehensive review of G×E studies of smoking behavior has previously been published. The present article seeks to fill this gap by providing a comprehensive review of: how G×E has been defined, how twin and molecular genetic methodologies have been used to test for G×E, and which genes and environmental factors are associated with smoking behaviors. Variations in methodological approaches make it difficult to interpret and summarize findings, so recommendations for future research are provided as a means to more easily compare and replicate findings across studies.

Project description:The genetic and environmental influences on human personality and behaviour are a complex matter of ongoing debate. Accumulating evidence indicates that short tandem repeats (STRs) in regulatory regions are good candidates to explain heritability not accessed by genome-wide association studies.We tested for associations between the genotypes of four selected repeats and 18 traits relating to personality, behaviour, cognitive ability and mental health in a well-studied longitudinal birth cohort (n?=?458-589) using one way analysis of variance. The repeats were a highly conserved poly-AC microsatellite in the upstream promoter region of the T-box brain 1 (TBR1) gene and three previously studied STRs in the activating enhancer-binding protein 2-beta (AP2-?) and androgen receptor (AR) genes. Where significance was found we used multiple regression to assess the influence of confounding factors.Carriers of the shorter, most common, allele of the AR gene's GGN microsatellite polymorphism had fewer anxiety-related symptoms, which was consistent with previous studies, but in our study this was not significant following Bonferroni correction. No associations with two repeats in the AP2-? gene withstood this correction. A novel finding was that carriers of the minor allele of the TBR1 AC microsatellite were at higher risk of conduct problems in childhood at age 7-9 (p?=?0.0007, which did pass Bonferroni correction). Including maternal smoking during pregnancy (MSDP) in models controlling for potentially confounding influences showed that an interaction between TBR1 genotype and MSDP was a significant predictor of conduct problems in childhood and adolescence (p?<?0.001), and of self-reported criminal behaviour up to age 25 years (p???0.02). This interaction remained significant after controlling for possible confounders including maternal age at birth, socio-economic status and education, and offspring birth weight.The potential functional importance of the TBR1 gene's promoter microsatellite deserves further investigation. Our results suggest that it participates in a gene-environment interaction with MDSP and antisocial behaviour. However, previous evidence that mothers who smoke during pregnancy carry genes for antisocial behaviour suggests that epistasis may influence the interaction.

Dataset Information

A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis.

Publications

A Gene-Environment Interaction Between Smoking and Gene polymorphisms Provides a High Risk of Two Subgroups of Sarcoidosis.

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