scholarly journals GxEMM: Extending linear mixed models to general gene-environment interactions

2018 ◽  
Author(s):  
Andy Dahl ◽  
Na Cai ◽  
Jonathan Flint ◽  
Noah Zaitlen
Keyword(s):  
Major Depression ◽  
Mixed Models ◽  
Environmental Variables ◽  
Mixed Model ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Aggregate Effect ◽  
Additive Inheritance ◽  
Categorical Groups

AbstractGene-environment interaction (GxE) is a well-known source of non-additive inheritance. GxE can be important in applications ranging from basic functional genomics to precision medical treatment. Further, GxE effects elude inherently-linear LMMs and may explain missing heritability. We propose a simple, unifying mixed model for polygenic interactions (GxEMM) to capture the aggregate effect of small GxE effects spread across the genome. GxEMM extends existing LMMs for GxE in two important ways. First, it extends to arbitrary environmental variables, not just categorical groups. Second, GxEMM can estimate and test for environment-specific heritability. In simulations where the assumptions of existing methods do not hold, we show that GxEMM improves estimates of ordinary and GxE heritability and increases power to test for polygenic GxE. We then use GxEMM to prove that the heritability of major depression (MD) is reduced by stress, which we previously conjectured but could not prove with prior methods, and that a tail of polygenic GxE effects remains unexplained by MD GWAS.

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.

Genetic, environmental, and epigenetic factors in the development of personality disturbance

2009 ◽  
Vol 21 (4) ◽  
pp. 1031-1063 ◽  
Author(s):  
Richard A. Depue
Keyword(s):  
Environmental Variables ◽  
Extreme Values ◽  
Environment Interaction ◽  
Epigenetic Factors ◽  
Dimensional Model ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Main Factors ◽  
Personality Disturbance ◽  
Epigenetic Processes

AbstractA dimensional model of personality disturbance is presented that is defined by extreme values on interacting subsets of seven major personality traits. Being at the extreme has marked effects on the threshold for eliciting those traits under stimulus conditions: that is, the extent to which the environment affects the neurobiological functioning underlying the traits. To explore the nature of development of extreme values on these traits, each trait is discussed in terms of three major issues: (a) the neurobiological variables associated with the trait, (b) individual variation in this neurobiology as a function of genetic polymorphisms, and (c) the effects of environmental adversity on these neurobiological variables through the action of epigenetic processes. It is noted that gene–environment interaction appears to be dependent on two main factors: (a) both genetic and environmental variables appear to have the most profound and enduring effects when they exert their effects during early postnatal periods, times when the forebrain is undergoing exuberant experience–expectant dendritic and axonal growth; and (b) environmental effects on neurobiology are strongly modified by individual differences in “traitlike” functioning of neurobiological variables. A model of the nature of the interaction between environmental and neurobiological variables in the development of personality disturbance is presented.

Behavior Genetic Approaches for Situation Research

2017 ◽  
pp. 24-39
Author(s):  
Daniel A. Briley
Keyword(s):  
Environmental Variables ◽  
Behavior Genetics ◽  
Behavioral Genetics ◽  
Situational Factors ◽  
Environment Interaction ◽  
Selection Effects ◽  
Personality Psychology ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
History Of

As a field, behavior genetics has a long and often underappreciated focus on environmental and situational factors. This chapter describes the methodological details and empirical findings of this line of work, as well as what situation research can gain from behavior genetics and vice versa. Genetically informative designs offer tools to quantify the extent to which people actively create their situational experiences as opposed to randomly encountering them, and novel advances in situation research have the potential to clarify the scattered history of environmental variables in behavioral genetics. Current progress in personality psychology will be highlighted. Parallels between behavior genetics and personality work can be found both in terms of mechanisms (e.g., gene-environment correlation and gene × environment interaction contrasting with selection effects and person × situation effects) and explanatory pitfalls. Researchers interested in delineating the pathways from situations to behavior would do well to draw from and build upon work in behavior genetics.

scholarly journals Case-control vs. case-only estimates of gene-environment interactions with common and misclassified clinical diagosis

2019 ◽  
Author(s):  
Iryna Lobach ◽  
Ying Sheng ◽  
Siarhei Lobach ◽  
Lydia Zablotska ◽  
Chiung-Yu Huang
Keyword(s):  
Clinical Diagnosis ◽  
Environmental Variables ◽  
False Negative ◽  
Case Control ◽  
Outcome Variable ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Disease States ◽  
Gene Environment ◽  
Negative Findings

ABSTRACTGenetic studies provide valuable information to assess if the effect of genetic variants varies by the non-genetic (“environmental”) variables, what is traditionally defined to be gene-environment interaction. A common complication is that multiple disease states present with the same set of symptoms, and hence share the clinical diagnosis. Because 1) disease states might have distinct genetic bases; and 2) frequencies of the disease states within the clinical diagnosis vary by the environmental variables, analyses of association with the clinical diagnosis as an outcome variable might result in false positive or false negative findings. We develop estimates for assessment of GxE in a case-only study and compare the case-control and case-only estimates. We report extensive simulation studies that evaluate empirical properties of the estimates and show the application to a study of Alzheimer’s disease.

scholarly journals Efficient gene-environment interaction tests for large biobank-scale sequencing studies

2020 ◽  
Author(s):  
Xinyu Wang ◽  
Elise Lim ◽  
Ching-Ti Liu ◽  
Yun Ju Sung ◽  
Dabeeru C. Rao ◽  
...  
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Environment Interaction ◽  
Whole Genome ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Sequencing Studies ◽  
Related Individuals ◽  
Main Effects

ABSTRACTComplex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

scholarly journals Life events, first depression onset and the serotonin transporter gene

2006 ◽  
Vol 188 (3) ◽  
pp. 210-215 ◽  
Author(s):  
Kay Wilhelm ◽  
Philip B. Mitchell ◽  
Heather Niven ◽  
Adam Finch ◽  
Lucinda Wedgwood ◽  
...  
Keyword(s):  
Risk Factors ◽  
Major Depression ◽  
Life Events ◽  
Serotonin Transporter ◽  
Serotonin Transporter Gene ◽  
Environment Interaction ◽  
Transporter Gene ◽  
Adverse Life Events ◽  
Gene Environment Interaction ◽  
Gene Environment

BackgroundA relationship between the serotonin transporter gene, adverse events and onset of major depression has been reported.AimsTo replicate a gene × environment interaction in a cohort with longitudinal data for life events, experience of depression, parental bonding and neuroticism.MethodAtthe 25-year follow-up, genomic DNA was obtained from 127 cohort members (mean age 48 years) to determine the genotype of the serotonin transporter gene-linked promoter region (5-HTTLPR). Associations were investigated between the 5-HTTLPR genotype, positive and adverse life events and the gene × environment interaction, and also between the 5-HTTLPR genotype and risk factors for depression.ResultsNo relationship was found between 5-HTTLPR genotype and either risk factors for depression or positive life events. Adverse life events had a significantly greater impact on the onset of depression for individuals with the s/s genotype.ConclusionsThe 5-HTTLPR genotype is a significant predictor of onset of major depression following multiple adverse events. This is one of the more robust findings concerning specific biological risk factors for depression.

Sign in / Sign up

Export Citation Format

Share Document