Associations between electronic devices use and common mental traits: A gene–environment interaction model using the UK Biobank data

2021 ◽  
Author(s):  
Jing Ye ◽  
Shiqiang Cheng ◽  
Xiaomeng Chu ◽  
Yan Wen ◽  
Bolun Cheng ◽  
...  
Keyword(s):  
Electronic Devices ◽  
Interaction Model ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

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.

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 The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank

2019 ◽  
Author(s):  
Judit García-González ◽  
Julia Ramírez ◽  
David M. Howard ◽  
Caroline H Brennan ◽  
Patricia B. Munroe ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Maternal Smoking ◽  
Smoking Status ◽  
Smoking Behaviour ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Psychotic Experiences ◽  
Gene Environment Interaction ◽  
Gene Environment

ABSTRACTWhile psychotic experiences are core symptoms of mental health disorders like schizophrenia, they are also reported by 5-10% of the population. Both smoking behaviour and genetic risk for psychiatric disorders have been associated with psychotic experiences, but the interplay between these factors remains poorly understood. We tested whether smoking status, maternal smoking around birth, and number of packs smoked/year were associated with lifetime occurrence of three psychotic experiences phenotypes: delusions (n=2067), hallucinations (n=6689), and any psychotic experience (delusions or hallucinations; n=7803) in 144818 UK Biobank participants. We next calculated polygenic risk scores for schizophrenia (PRSSCZ), major depression (PRSDEP) and attention deficit hyperactivity disorder (PRSADHD) in the UK Biobank participants to assess whether association between smoking and psychotic experiences was attenuated after adjustment of diagnosis of psychiatric disorders and the PRSs. Finally, we investigated whether smoking exacerbates the effects of genetic predisposition on the psychotic phenotypes in gene-environment interaction models. Smoking status, maternal smoking, and number of packs smoked/year were significantly associated with psychotic experiences (p<1.77×10−5). Except for packs smoked/year, effects were attenuated but remained significant after adjustment for diagnosis of psychiatric disorders and PRSs (p<1.99×10−3). Gene-environment interaction models showed the effects of PRSDEP and PRSADHD(but not PRSSCZ) on delusions (but not hallucinations) were significantly greater in current smokers compared to never smokers (p<0.0028). There were no significant gene-environment interactions for maternal smoking nor for number of packs smoked/year. Our results suggest that both genetic risk of psychiatric disorders and smoking status may have independent and synergistic effects on specific types of psychotic experiences.

A Gene–Environment Interaction Model of Stress-Induced Hypertension

2005 ◽  
Vol 5 (2) ◽  
pp. 109-132 ◽  
Author(s):  
Ikhide G. Imumorin ◽  
Yanbin Dong ◽  
Haidong Zhu ◽  
Joseph C. Poole ◽  
Gregory A. Harshfield ◽  
...  
Keyword(s):  
Interaction Model ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Induced Hypertension
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