scholarly journals Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons

2011 ◽  
Vol 175 (3) ◽  
pp. 177-190 ◽  
Author(s):  
Bhramar Mukherjee ◽  
Jaeil Ahn ◽  
Stephen B. Gruber ◽  
Nilanjan Chatterjee
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Case Control ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Control Association

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

Association Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China

2021 ◽  
Author(s):  
Chunbao Mo ◽  
Tingyu Mai ◽  
Jiansheng Cai ◽  
Haoyu He ◽  
Huaxiang Lu ◽  
...  
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Case Control Study ◽  
Case Control ◽  
Environment Interaction ◽  
Additive Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Control Study

Abstract Background: Fatty liver disease (FLD) is a serious public health problem that is rapidly increasing. Evidences indicated that the transcription factor EB (TFEB) gene may be involved in the pathophysiology of FLD; however, whether TEFB polymorphism is association with FLD remains unclear.Objectives: To explore the association among TFEB polymorphism, gene–environment interaction, and FLD and provide epidemiological evidence for clarifying the genetic factors of FLD.Methods: This study is a case–control study. Sequenom MassARRAY was applied in genotyping. Logical regression was used to analyze the association between TFEB polymorphism and FLD, and the gene–environment interaction in FLD was evaluated by multiplication and additive interaction models.Results: (1) The alleles and genotypes of each single nucleotide polymorphism of TFEB in the case and control groups were evenly distributed; no statistically substantial difference was observed. (2) Logistic regression analysis indicated that TFEB polymorphism is not remarkably associated with FLD. (3) In the multiplicative interaction model, rs1015149, rs1062966, and rs11754668 had remarkable interaction with smoking amount. Rs1062966 and rs11754668 also had a considerable interaction with body mass index and alcohol intake, respectively. However, no remarkable additive interaction was observed.Conclusion: TFEB polymorphism is not directly associated with FLD susceptibility, but the risk can be changed through gene–environment interaction.

scholarly journals Case-Control Studies of Gene-Environment Interaction: Bayesian Design and Analysis

Biometrics ◽  
2009 ◽  
Vol 66 (3) ◽  
pp. 934-948 ◽  
Author(s):  
Bhramar Mukherjee ◽  
Jaeil Ahn ◽  
Stephen B. Gruber ◽  
Malay Ghosh ◽  
Nilanjan Chatterjee
Keyword(s):  
Case Control ◽  
Environment Interaction ◽  
Case Control Studies ◽  
Bayesian Design ◽  
Gene Environment Interaction ◽  
Gene Environment

Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

2011 ◽  
Vol 38 (3) ◽  
pp. 564-566 ◽  
Author(s):  
PROTON RAHMAN
Keyword(s):  
Psoriatic Arthritis ◽  
Annual Meeting ◽  
Association Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

scholarly journals Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Mike Schmidt ◽  
Elizabeth R Hauser ◽  
Eden R. Martin ◽  
Silke Schmidt
Keyword(s):  
Association Studies ◽  
Real Data ◽  
Environment Interaction ◽  
Data Generation ◽  
Environmental Covariates ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Linkage And Association Analysis ◽  
Complex Inheritance ◽  
Disease Loci

We have previously distributed a software package, SIMLA (SIMulation of Linkage and Association), which can be used to generate disease phenotype and marker genotype data in three-generational pedigrees of user-specified structure. To our knowledge, SIMLA is the only publicly available program that can simulate variable levels of both linkage (recombination) and linkage disequilibrium (LD) between marker and disease loci in general pedigrees. While the previous SIMLA version provided flexibility in choosing many parameters relevant for linkage and association mapping of complex human diseases, it did not allow for the segregation of more than one disease locus in a given pedigree and did not incorporate environmental covariates possibly interacting with disease susceptibility genes.Here, we present an extension of the simulation algorithm characterized by a much more general penetrance function, which allows for the joint action of up to two genes and up to two environmental covariates in the simulated pedigrees, with all possible multiplicative interaction effects between them. This makes the program even more useful for comparing the performance of different linkage and association analysis methods applied to complex human phenotypes. SIMLA can assist investigators in planning and designing a variety of linkage and association studies, and can help interpret results of real data analyses by comparing them to results obtained under a user-controlled data generation mechanism.A free download of the SIMLA package is available at http://wwwchg.duhs.duke.edu/software.

scholarly journals SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Jocelyn T. Chi ◽  
Ilse C. F. Ipsen ◽  
Tzu-Hung Hsiao ◽  
Ching-Heng Lin ◽  
Li-San Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Exact Algorithm ◽  
Large Sample Size ◽  
Environment Interaction ◽  
Sample Sizes ◽  
Computationally Efficient ◽  
Test Statistic ◽  
Large Sample ◽  
Gene Environment Interaction ◽  
Gene Environment

The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.

scholarly journals Environment and Lifestyle: Their Influence on the Risk of RA

2020 ◽  
Vol 9 (10) ◽  
pp. 3109
Author(s):  
Carine Salliot ◽  
Yann Nguyen ◽  
Marie-Christine Boutron-Ruault ◽  
Raphaèle Seror
Keyword(s):  
Large Scale ◽  
Complex Disease ◽  
Dietary Factors ◽  
Environment Interaction ◽  
Food Groups ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Increased Risk ◽  
Environmental Agents ◽  
Hormonal Exposures

Background: Rheumatoid arthritis (RA) is a complex disease in which environmental agents are thought to interact with genetic factors that lead to triggering of autoimmunity. Methods: We reviewed environmental, hormonal, and dietary factors that have been suggested to be associated with the risk of RA. Results: Smoking is the most robust factor associated with the risk of RA, with a clear gene–environment interaction. Among other inhalants, silica may increase the risk of RA in men. There is less evidence for pesticides, pollution, and other occupational inhalants. Regarding female hormonal exposures, there is some epidemiological evidence, although not consistent in the literature, to suggest a link between hormonal factors and the risk of RA. Regarding dietary factors, available evidence is conflicting. A high consumption of coffee seems to be associated with an increased risk of RA, whereas a moderate consumption of alcohol is inversely associated with the risk of RA, and there is less evidence regarding other food groups. Dietary pattern analyses (Mediterranean diet, the inflammatory potential of the diet, or diet quality) suggested a potential benefit of dietary modifications for individuals at high risk of RA. Conclusion: To date, smoking and silica exposure have been reproducibly demonstrated to trigger the emergence of RA. However, many other environmental factors have been studied, mostly with a case-control design. Results were conflicting and studies rarely considered potential gene–environment interactions. There is a need for large scale prospective studies and studies in predisposed individuals to better understand and prevent the disease and its course.

scholarly journals HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

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