scholarly journals Gene-environment dependencies lead to collider bias in models with polygenic scores

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Evelina T. Akimova ◽  
Richard Breen ◽  
David M. Brazel ◽  
Melinda C. Mills
Keyword(s):  
Complex Traits ◽  
Regression Models ◽  
Complex Interplay ◽  
Independent Variables ◽  
Gene Environment ◽  
Genes And Environment ◽  
Polygenic Scores ◽  
Endogenous Selection ◽  
Genetic And Environmental Factors ◽  
Or Gene

AbstractThe application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.

scholarly journals Heritable environments: bias due to conditioning on a collider in models with polygenic scores

2020 ◽  
Author(s):  
Evelina T. Akimova ◽  
Richard Breen ◽  
David M. Brazel ◽  
Melinda C. Mills
Keyword(s):  
Complex Traits ◽  
Regression Models ◽  
Complex Interplay ◽  
Independent Variables ◽  
Gene Environment ◽  
Genes And Environment ◽  
Polygenic Scores ◽  
Endogenous Selection ◽  
Genetic And Environmental Factors ◽  
Or Gene

AbstractThe application of polygenic scores has transformed our ability to investigate whether and how genetic and environmental factors jointly contribute to the variation of complex traits. Modelling the complex interplay between genes and environment, however, raises serious methodological challenges. Here we illustrate the largely unrecognised impact of gene-environment dependencies on the identification of the effects of genes and their variation across environments. We show that controlling for heritable covariates in regression models that include polygenic scores as independent variables introduces endogenous selection bias when one or more of these covariates depends on unmeasured factors that also affect the outcome. This results in the problem of conditioning on a collider, which in turn leads to spurious associations and effect sizes. Using graphical and simulation methods we demonstrate that the degree of bias depends on the strength of the gene-covariate correlation and of hidden heterogeneity linking covariates with outcomes, regardless of whether the main analytic focus is mediation, confounding, or gene × covariate (commonly gene × environment) interactions. We offer potential solutions, highlighting the importance of causal inference. We also urge further caution when fitting and interpreting models with polygenic scores and non-exogenous environments or phenotypes and demonstrate how spurious associations are likely to arise, advancing our understanding of such results.

A data-smoothing approach to explore and test gene-environment interaction in case-parent trios

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Ji-Hyung Shin ◽  
Claire Infante-Rivard ◽  
Brad McNeney ◽  
Jinko Graham
Keyword(s):  
Complex Traits ◽  
Environment Interaction ◽  
Data Smoothing ◽  
Joint Effects ◽  
Statistical Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genes And Environment ◽  
Test Gene

AbstractComplex traits result from an interplay between genes and environment. A better understanding of their joint effects can help refine understanding of the epidemiology of the trait. Various tests have been proposed to assess the statistical interaction between genes and the environment (

scholarly journals Statistical analysis of genetic interactions

2010 ◽  
Vol 92 (5-6) ◽  
pp. 443-459 ◽  
Author(s):  
NENGJUN YI
Keyword(s):  
Statistical Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Computer Software ◽  
Genetic Association Studies ◽  
Genetic Interactions ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genetic And Environmental Factors

SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

scholarly journals The Epistasis Boundary: Linear vs. Nonlinear Genotype-Phenotype Relationships

2018 ◽  
Author(s):  
Serge Sverdlov ◽  
Elizabeth A Thompson
Keyword(s):  
Complex Traits ◽  
Genetic Model ◽  
Association Studies ◽  
Network Models ◽  
Environment Interaction ◽  
Linear Modeling ◽  
Gene Environment ◽  
Highly Nonlinear ◽  
Biological Criteria ◽  
Or Gene

Nonlinearity in the genotype-phenotype relationship can take the form of dominance, epistasis, or gene-environment interaction. The importance of nonlinearity, especially epistasis, in real complex traits is controversial. Network models in systems biology are typically highly nonlinear, yet the predictive power of linear quantitative genetic models is rarely improved by addition of nonlinear terms, and association studies detect few strong genegene interactions. We find that complex traits satisfying certain conditions can be well represented by a linear genetic model on an appropriate scale despite underlying biological complexity. Recent mathematical results in separability theory determine these conditions, which correspond to three biological criteria (Directional Consistency, Environmental Compensability, and Pathway Redundancy) together making up an Epistatic Boundary between systems suitable and unsuitable for linear modeling. For nonlinear traits, we introduce a classification of types of nonlinearity from a systems perspective, and use this to illustrate how upstream controlling genes, potentially more important to explaining biological function, can be intrinsically harder to detect by GWAS than their downstream controlled counterparts.

Gene–environment interaction in autoimmune disease

2014 ◽  
Vol 16 ◽  
Author(s):  
Justine A Ellis ◽  
Andrew S Kemp ◽  
Anne-Louise Ponsonby
Keyword(s):  
Autoimmune Disease ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Disease Risk ◽  
Personalised Medicine ◽  
Current Evidence ◽  
Environment Interaction ◽  
Gene Environment ◽  
Genes And Environment ◽  
Genetic And Environmental Factors

Autoimmune disease manifests in numerous forms, but as a disease group is relatively common in the population. It is complex in aetiology, with genetic and environmental determinants. The involvement of gene variants in autoimmune disease is well established, and evidence for significant involvement of the environment in various disease forms is growing. These factors may act independently, or they may interact, with the effect of one factor influenced by the presence of another. Identifying combinations of genetic and environmental factors that interact in autoimmune disease has the capacity to more fully explain disease risk profile, and to uncover underlying molecular mechanisms contributing to disease pathogenesis. In turn, such knowledge is likely to contribute significantly to the development of personalised medicine, and targeted preventative approaches. In this review, we consider the current evidence for gene–environment (G–E) interaction in autoimmune disease. Large-scale G–E interaction research efforts, while well-justified, face significant practical and methodological challenges. However, it is clear from the evidence that has already been generated that knowledge on how genes and environment interact at a biological level will be crucial in fully understanding the processes that manifest as autoimmunity.

scholarly journals Estimation of parental effects using polygenic scores

2020 ◽  
Author(s):  
Jared Balbona ◽  
Yongkang Kim ◽  
Matthew C. Keller
Keyword(s):  
Behavioral Genetics ◽  
Environmental Influence ◽  
Relative Magnitude ◽  
Polygenic Score ◽  
Genetics Research ◽  
Gene Environment ◽  
Polygenic Scores ◽  
Genetic And Environmental Factors ◽  
Familial Environment ◽  
Additive Genetic Effects

AbstractOffspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents’ nontransmitted alleles, one can estimate the effect of “genetic nurture”— a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to a) account for the influence of assortative mating at both equilibrium and disequilibrium (after a single generation of assortment), and b) include measured parental phenotypes, allowing for the estimation of the total variation due to additive genetic effects and their covariance with the familial environment. By utilizing path analysis techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bruno Sauce ◽  
John Wiedenhoeft ◽  
Nicholas Judd ◽  
Torkel Klingberg
Keyword(s):  
Cognitive Development ◽  
Brain Plasticity ◽  
Training Sample ◽  
Age Related ◽  
Central Piece ◽  
Gene Environment ◽  
Related Development ◽  
Explained Variation ◽  
Genetic And Environmental Factors ◽  
Cognitive Challenges

AbstractThe interplay of genetic and environmental factors behind cognitive development has preoccupied multiple fields of science and sparked heated debates over the decades. Here we tested the hypothesis that developmental genes rely heavily on cognitive challenges—as opposed to natural maturation. Starting with a polygenic score (cogPGS) that previously explained variation in cognitive performance in adults, we estimated its effect in 344 children and adolescents (mean age of 12 years old, ranging from 6 to 25) who showed changes in working memory (WM) in two distinct samples: (1) a developmental sample showing significant WM gains after 2 years of typical, age-related development, and (2) a training sample showing significant, experimentally-induced WM gains after 25 days of an intense WM training. We found that the same genetic factor, cogPGS, significantly explained the amount of WM gain in both samples. And there was no interaction of cogPGS with sample, suggesting that those genetic factors are neutral to whether the WM gains came from development or training. These results represent evidence that cognitive challenges are a central piece in the gene-environment interplay during cognitive development. We believe our study sheds new light on previous findings of interindividual differences in education (rich-get-richer and compensation effects), brain plasticity in children, and the heritability increase of intelligence across the lifespan.

scholarly journals The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lauren L. Schmitz ◽  
Julia Goodwin ◽  
Jiacheng Miao ◽  
Qiongshi Lu ◽  
Dalton Conley
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Job Loss ◽  
Past Research ◽  
Long Term Effects ◽  
Polygenic Scores ◽  
Show Evidence ◽  
Using Data ◽  
The Impact

AbstractUnemployment shocks from the COVID-19 pandemic have reignited concerns over the long-term effects of job loss on population health. Past research has highlighted the corrosive effects of unemployment on health and health behaviors. This study examines whether the effects of job loss on changes in body mass index (BMI) are moderated by genetic predisposition using data from the U.S. Health and Retirement Study (HRS). To improve detection of gene-by-environment (G × E) interplay, we interacted layoffs from business closures—a plausibly exogenous environmental exposure—with whole-genome polygenic scores (PGSs) that capture genetic contributions to both the population mean (mPGS) and variance (vPGS) of BMI. Results show evidence of genetic moderation using a vPGS (as opposed to an mPGS) and indicate genome-wide summary measures of phenotypic plasticity may further our understanding of how environmental stimuli modify the distribution of complex traits in a population.

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