scholarly journals Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Josefin Werme ◽  
Sophie van der Sluis ◽  
Danielle Posthuma ◽  
Christiaan A. de Leeuw
Keyword(s):  
Exploratory Study ◽  
Predictive Ability ◽  
Risk Scores ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Sets ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk ◽  
Main Effects

AbstractGene-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.

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.

Set-Based Gene × Environment Interaction Tests for Complex Diseases with Application to Genome-Wide Association and Sequencing Studies

2016 ◽  
Author(s):  
Shuo Jiao
Keyword(s):  
Ratio Test ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Type 1 Error ◽  
Gene Environment ◽  
Genome Wide ◽  
Component Test ◽  
Sequencing Studies ◽  
Main Effects

This chapter presents set-based approaches that focus on identifying G X E interactions rather than set-based approaches that are based primarily on detecting G main effects (e.g., via marginal effects). The author reviews both his own research and the development of his Set Based Gene EnviRonment InterAction test (SBERIA), as well as another set-based G X E approach referred to as GESAT. GESAT extends the variance component test of the SNP-set Kernel Association Test (SKAT) to evaluate G x E effects while incorporating the main SNP effects as covariates. While both of these approaches (SBERIA and GESAT) have outperformed other benchmark methods (e.g., likelihood ratio test) and have been demonstrated to retain the appropriate Type 1 error rate, in this chapter the author conducts simulation studies to compare findings for SBERIA and GESAT approaches, and identifies associated strengths and limitations of the respective methods.

scholarly journals Effect of polygenic risk scores on depression in childhood trauma

2014 ◽  
Vol 205 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Wouter J. Peyrot ◽  
Yuri Milaneschi ◽  
Abdel Abdellaoui ◽  
Patrick F. Sullivan ◽  
Jouke J. Hottenga ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Meta Analysis ◽  
Genetic Effect ◽  
Risk Scores ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Direct Genetic Effect

BackgroundResearch on gene×environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic.AimsTo test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma.MethodThe study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium.ResultsThe polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves.ConclusionsThe interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD.

scholarly journals A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

2020 ◽  
Author(s):  
Arunabha Majumdar ◽  
Kathryn S. Burch ◽  
Sriram Sankararaman ◽  
Bogdan Pasaniuc ◽  
W. James Gauderman ◽  
...  
Keyword(s):  
Genetic Effect ◽  
Single Step ◽  
Environment Interaction ◽  
Step Method ◽  
Gene Environment Interaction ◽  
Gxe Interaction ◽  
Multiple Phenotypes ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk

AbstractWhile gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18% – 43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two independent genome-wide significant signals of an overall GxE effect on the vector of lipids.

scholarly journals Maximum likelihood method quantifies the overall contribution of gene-environment interaction to continuous traits: an application to complex traits in the UK Biobank

2019 ◽  
Author(s):  
Jonathan Sulc ◽  
Ninon Mounier ◽  
Felix Günther ◽  
Thomas Winkler ◽  
Andrew R. Wood ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Complex Traits ◽  
Maximum Likelihood Method ◽  
Scale Effects ◽  
Likelihood Method ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Gene Environment ◽  
The Uk

AbstractAs genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance is significantly (P < 10−56) larger than it would be expected for a similarly correlated variable . We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures.

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