Addressing Issues in the Detection of Gene-Environment Interaction Through the Study of Conduct Disorder

This work addresses issues in the study of gene-environment interaction (GxE) through research of conduct disorder (CD) among adolescents and extends the recent report of significant GxE and subsequent replication studies. A sub-sample of 1,299 individual participants/649 twin pairs and their parents from the Virginia Twin Study of Adolescent and Behavioral Development was used for whom Monoamine Oxidase A (MAOA) genotype, diagnosis of CD, maternal antisocial personality symptoms, and household neglect were obtained. This dissertation (1) tested for GxE by gender using MAOA and childhood adversity using multiple approaches to CD measurement and model assessment, (2) determined whether other mechanisms would explain differences in GxE by gender and (3) identified and assessed other genes and environments related to the interaction MAOA and childhood adversity. Using a multiple regression approach, a main effect of the low/low MAOA genotype remained after controlling other risk factors in females. However, the effects of GxE were modest and were removed by transforming the environmental measures. In contrast, there was no significant effect of the low activity MAOA allele in males although significant GxE was detected and remained after transformation. The sign of the interaction for males was opposite from females, indicating genetic sensitivity to childhood adversity may differ by gender. Upon further investigation, gender differences in GxE were due to genotype-sex interaction and may involve MAOA. A Markov Chain Monte Carlo approach including a genetic Item Response Theory modeled CD as a trait with continuous liability, since false detection of GxE may result from measurement. In males and females, the inclusion of GxE while controlling for the other covariates was appropriate, but was little improvement in model fit and effect sizes of GxE were small. Other candidate genes functioning in the serotonin and dopamine neurotransmitter systems were tested for interaction with MAOA to affect risk for CD. Main genetic effects of dopamine transporter genotype and MAOA in the presence of comorbidity were detected. No epistatic effects were detected. The use of random forests systematically assessed the environment and produced several interesting environments that will require more thoughtful consideration before incorporation into a model testing GxE

Genome-wide environmental interaction analysis using multidimensional data reduction principles to identify asthma pharmacogenetic loci in relation to corticosteroid therapy

Genome-wide gene-environment (GxE) and gene-gene (GxG) interaction studies share a lot of challenges via the common genetic component they involve. GWEI studies may therefore benefit from the abundance of methodologies that are available in the context of genome-wide epistasis detection methods. One of these is Model-Based Multifactor Dimensionality Reduction (MB-MDR), which does not make any assumption about the genetic inheritance model. MB-MDR involves reducing a high-dimensional GxE space to GxE factor levels that either exhibit high or low or no evidence for their association to disease outcome. In contrast to logistic regression and random forests, MB-MDR can be used to detect GxE interactions in the absence of any main effects or when sample sizes are too small to be able to model all main and GxE interaction effects. In this ongoing study, we demonstrate the opportunities and challenges of MB-MDR for genome-wide GxE interaction analysis and analyzed the difference in prebronchodilator FEV1 following 8 weeks of inhaled corticosteroid therapy, for 565 pediatric Caucasian CAMP (ages 5-12) from the SHARE project

Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions

Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two similar methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specifically, MR-GxE requires an interaction to be explicitly identified, while MR-GENIUS does not. We critically examine the assumptions of MR-GxE and MR-GENIUS in the absence of a pre-defined covariate, and propose sensitivity analyses to evaluate their performance. Finally, we explore the effect of body mass index (BMI) upon systolic blood pressure (SBP) using data from the UK Biobank, finding evidence of a positive effect of BMI on SBP. We find both approaches share similar assumptions, though differences between the approaches lend themselves to differing research settings. Where a suitable gene-by-covariate interaction is observed MR-GxE can produce unbiased causal effect estimates. MR-GENIUS can circumvent the need to identify interactions, but as a consequence relies on either the MR-GxE assumptions holding globally, or additional information with respect to the distribution of pleiotropic effects in the absence of an explicitly defined interaction covariate

FoxO1, A2M, and TGF-beta 1 : three novel genes predicting depression in gene X environment interactions are identified using cross-species and cross-tissues transcriptomic and miRNomic analyses

To date, gene-environment (GxE) interaction studies in depression have been limited to hypothesis-based candidate genes, since genome-wide (GWAS)-based GxE interaction studies would require enormous datasets with genetics, environmental, and clinical variables. We used a novel, cross-species and cross-tissues "omics" approach to identify genes predicting depression in response to stress in GxE interactions. We integrated the transcriptome and miRNome profiles from the hippocampus of adult rats exposed to prenatal stress (PNS) with transcriptome data obtained from blood mRNA of adult humans exposed to early life trauma, using a stringent statistical analyses pathway. Network analysis of the integrated gene lists identified the Forkhead box protein O1 (FoxO1), Alpha-2-Macroglobulin (A2M), and Transforming Growth Factor Beta 1 (TGF-beta 1) as candidates to be tested for GxE interactions, in two GWAS samples of adults either with a range of childhood traumatic experiences (Grady Study Project, Atlanta, USA) or with separation from parents in childhood only (Helsinki Birth Cohort Study, Finland). After correction for multiple testing, a meta-analysis across both samples confirmed six FoxO1 SNPs showing significant GxE interactions with early life emotional stress in predicting depressive symptoms. Moreover, in vitro experiments in a human hippocampal progenitor cell line confirmed a functional role of FoxO1 in stress responsivity. In secondary analyses, A2M and TGF-beta 1 showed significant GxE interactions with emotional, physical, and sexual abuse in the Grady Study. We therefore provide a successful 'hypothesis-free' approach for the identification and prioritization of candidate genes for GxE interaction studies that can be investigated in GWAS datasets.Peer reviewe

The interaction between monoamine oxidase A and punitive discipline in the development of antisocial behavior: Mediation by maladaptive social information processing.

Previous studies demonstrate that boys' monoamine oxidase A (MAOA) genotype interacts with adverse rearing environments in early childhood, including punitive discipline, to predict later antisocial behavior. Yet the mechanisms by which MAOA and punitive parenting interact during childhood to amplify risk for antisocial behavior are not well understood. In the present study, hostile attributional bias and aggressive response generation during middle childhood, salient aspects of maladaptive social information processing, were tested as possible mediators of this relation in a sample of 187 low-income men followed prospectively from infancy into early adulthood. Given racial-ethnic variation in MAOA allele frequencies, analyses were conducted separately by race. In both African American and Caucasian men, those with the low-activity MAOA allele who experienced more punitive discipline at age 1.5 generated more aggressive responses to perceived threat at age 10 relative to men with the high-activity variant. In the African American subsample only, formal mediation analyses indicated a marginally significant indirect effect of maternal punitiveness on adult arrest records via aggressive response generation in middle childhood. The findings suggest that maladaptive social information processing may be an important mechanism underlying the association between MAOA × Parenting interactions and antisocial behavior in early adulthood. The present study extends previous work in the field by demonstrating that MAOA and harsh parenting assessed in early childhood interact to not only predict antisocial behavior in early adulthood, but also predict social information processing, a well-established social-cognitive correlate of antisocial behavior

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants

FoxO1, A2M, and TGF-beta 1: three novel genes predicting depression in gene X environment interactions are identified using cross-species and cross-tissues transcriptomic and miRNomic analyses

To date, gene-environment (GxE) interaction studies in depression have been limited to hypothesis-based candidate genes, since genome-wide (GWAS)-based GxE interaction studies would require enormous datasets with genetics, environmental, and clinical variables. We used a novel, cross-species and cross-tissues "omics" approach to identify genes predicting depression in response to stress in GxE interactions. We integrated the transcriptome and miRNome profiles from the hippocampus of adult rats exposed to prenatal stress (PNS) with transcriptome data obtained from blood mRNA of adult humans exposed to early life trauma, using a stringent statistical analyses pathway. Network analysis of the integrated gene lists identified the Forkhead box protein O1 (FoxO1), Alpha-2-Macroglobulin (A2M), and Transforming Growth Factor Beta 1 (TGF-beta 1) as candidates to be tested for GxE interactions, in two GWAS samples of adults either with a range of childhood traumatic experiences (Grady Study Project, Atlanta, USA) or with separation from parents in childhood only (Helsinki Birth Cohort Study, Finland). After correction for multiple testing, a meta-analysis across both samples confirmed six FoxO1 SNPs showing significant GxE interactions with early life emotional stress in predicting depressive symptoms. Moreover, in vitro experiments in a human hippocampal progenitor cell line confirmed a functional role of FoxO1 in stress responsivity. In secondary analyses, A2M and TGF-beta 1 showed significant GxE interactions with emotional, physical, and sexual abuse in the Grady Study. We therefore provide a successful 'hypothesis-free' approach for the identification and prioritization of candidate genes for GxE interaction studies that can be investigated in GWAS datasets