Gene-Environment Interaction Effects on Behavioral Variation and Risk of Complex Disorders: The Example of Alcoholism and Other Psychiatric Disorders

There have been few replicated examples of genotype x environment interaction effects on behavioral variation or risk of psychiatric disorder. We review some of the factors that have made detection of genotype x environment interaction effects difficult, and show how genotype x shared environment interaction (GxSE) effects are commonly confounded with genetic parameters in data from twin pairs reared together. Historic data on twin pairs reared apart can in principle be used to estimate such GxSE effects, but have rarely been used for this purpose. We illustrate this using previously published data from the Swedish Adoption Twin Study of Aging (SATSA), which suggest that GxSE effects could account for as much as 25% of the total variance in risk of becoming a regular smoker. Since few separated twin pairs will be available for study in the future, we also consider methods for modifying variance components linkage analysis to allow for environmental interactions with linked loci.

Gene-environment interaction for childhood asthma and exposure to farming in Central Europe

Asthma is a disease in which both genetic and environmental factors play important roles. The farming environment has consistently been associated with protection from childhood asthma and atopy, and interactions have been reported with polymorphisms in innate immunity genes.

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

The influence of gene environment interaction on the risk of cognitive impairment: Reducing sexual risk behaviors and alcohol use in HIV-infected adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. ^ A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p^

The Influence of Gene Environment Interaction on the Risk of Cognitive Impairment: Reducing Sexual Risk Behaviors and Alcohol Use in HIV-infected Adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p

Gene by environment interaction: effects of a single-gene and social-environment on reproductive phenotypes of fire ant queens

1. The gene Pgm-3 (or a closely linked gene) influences the phenotype and reproductive success of queens in multiple-queen (polygynous) colonies but not single-queen (monogynous) colonies of the Fire Ant Solenopsis invicta. 2. We investigated the mechanisms of differential phenotypic expression of Pgm-3 in these alternate social forms. Mature winged queens with the homozygous genotype Pgm-3(a/a) averaged 26% heavier than queens with the genotypes Pgm-3(a/b) and Pgm 3(b/b) in the polygynous form. Heterozygotes were slightly heavier (2%) than Pgm-3(b/b) queens in this form, demonstrating that the allele Pgm-3(a) is not completely recessive in its effects on weight. 3. There was no significant difference in weight among queens of the three Pgm-3 genotypes in the monogynous form, with the mean weight of monogynous queens slightly greater than that of polygynous Pgm-3(a/a) queens. Differences in weight between queens of the two social forms and among queens of the three genotypes in the polygynous form are not evident at the pupal stage and thus appear to develop during sexual maturation of the adults. This suggests that some component of the social environment of polygynous colonies inhibits weight gains during queen maturation and that Pgm-(3a/a) queens are relatively less sensitive to this factor. 4. To test whether the high cumulative queen pheromone level characteristic of polygynous colonies is the factor responsible for the differential queen maturation, we compared phenotypes of winged queens reared in split colonies in which pheromone levels were manipulated by adjusting queen number. Queens produced in colony fragments made monogynous were heavier than those produced in polygynous fragments, a finding consistent with the hypothesis that pheromone level affects the reproductive development of queens. However, genotype-specific differences in weights of queens were similar between the two treatments, suggesting that pheromone level was not the key factor of the social environment responsible for the gene-environment interaction. 5. To test whether limited food availability to winged queens associated with the high brood/worker ratios in polygynous colonies is the factor responsible for this interaction, similar split-colony experiments were performed. Elevated brood/worker ratios decreased the weight of winged queens but there was no evidence that this treatment intensified differential weight gains among queens with different Pgm-3 genotypes. Manipulation of the amount of food provided to colonies had no effect on queen weight. 6. The combined data indicate that cumulative pheromone level and brood/worker ratio are two of the factors responsible for the differences in reproductive phenotypes between monogynous and polygynous winged queens but that these factors are not directly responsible for inducing the phenotypic effects of Pgm-3 in polygynous colonies.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

A unifying approach for haplotype analysis of quantitative traits in family-based association studies: Testing and estimating gene-environment interactions with complex exposure variables

We propose robust and e±cient tests and estimators for gene-environment/gene-drug interactions in family-based association studies. The methodology is designed for studies in which haplotypes, quantitative pheno- types and complex exposure/treatment variables are analyzed. Using causal inference methodology, we derive family-based association tests and estimators for the genetic main effects and the interactions. The tests and estimators are robust against population admixture and strati¯cation without requiring adjustment for confounding variables. We illustrate the practical relevance of our approach by an application to a COPD study. The data analysis suggests a gene-environment interaction between a SNP in the Serpine gene and smok- ing status/pack years of smoking that reduces the FEV1 volume by about 0.02 liter per pack year of smoking. Simulation studies show that the pro- posed methodology is su±ciently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.

From nature versus nurture, via nature and nurture, to gene x environment interaction in mental disorders

It is now generally accepted that complex mental disorders are the results of interplay between genetic and environmental factors. This holds out the prospect that by studying G x E interplay we can explain individual variation in vulnerability and resilience to environmental hazards in the development of mental disorders. Furthermore studying G x E findings may give insights in neurobiological mechanisms of psychiatric disorder and so improve individualized treatment and potentially prevention. In this paper, we provide an overview of the state of field with regard to G x E in mental disorders. Strategies for G x E research are introduced. G x E findings from selected mental disorders with onset in childhood or adolescence are reviewed [such as depressive disorders, attention-deficit/hyperactivity disorder (ADHD), obesity, schizophrenia and substance use disorders]. Early seminal studies provided evidence for G x E in the pathogenesis of depression implicating 5-HTTLPR, and conduct problems implicating MAOA. Since then G x E effects have been seen across a wide range of mental disorders (e.g., ADHD, anxiety, schizophrenia, substance abuse disorder) implicating a wide range of measured genes and measured environments (e.g., pre-, peri- and postnatal influences of both a physical and a social nature). To date few of these G x E effects have been sufficiently replicated. Indeed meta-analyses have raised doubts about the robustness of even the most well studied findings. In future we need larger, sufficiently powered studies that include a detailed and sophisticated characterization of both phenotype and the environmental risk.

A deleterious gene-by-environment interaction imposed by calcium channel blockers in Marfan syndrome.

Calcium channel blockers (CCBs) are prescribed to patients with Marfan syndrome for prophylaxis against aortic aneurysm progression, despite limited evidence for their efficacy and safety in the disorder. Unexpectedly, Marfan mice treated with CCBs show accelerated aneurysm expansion, rupture, and premature lethality. This effect is both extracellular signal-regulated kinase (ERK1/2) dependent and angiotensin-II type 1 receptor (AT1R) dependent. We have identified protein kinase C beta (PKCβ) as a critical mediator of this pathway and demonstrate that the PKCβ inhibitor enzastaurin, and the clinically available anti-hypertensive agent hydralazine, both normalize aortic growth in Marfan mice, in association with reduced PKCβ and ERK1/2 activation. Furthermore, patients with Marfan syndrome and other forms of inherited thoracic aortic aneurysm taking CCBs display increased risk of aortic dissection and need for aortic surgery, compared to patients on other antihypertensive agents.

Early adversity and 5-HTT-BDNF genes: new evidences of gene-environment interactions on depressive symptoms in a general population

Adverse childhood experiences have been described as one of the major environmental risk factors for depressive disorder. Likewise, the deleterious impact of early traumatic experiences on depression seems to be moderated by individual genetic variability. Serotonin transporter (5-HTT) and the Brain-Derived Neurotrophic Factor (BDNF) seem to modulate the effect of childhood adversity on adult depression, although inconsistencies across studies have been found. Moreover, the GxE interaction concerning the different types of childhood adversity remains poorly understood. The aim of this study is to analyse the putative interaction between the 5-HTT gene (5-HTTLPR polymorphism), BDNF gene (Val66Met polymorphism) and childhood adversity in accounting for adult depressive symptoms.

Early adversity and 5-HTT-BDNF genes: New evidences of Gene-Environment interactions on depressive symptoms in a general population

Background Adverse childhood experiences have been described as one of the major environmental risk factors for depressive disorder. Similarly, the deleterious impact of early traumatic experiences on depression seems to be moderated by individual genetic variability. Serotonin transporter (5-HTT) and brain-derived neurotrophic factor (BDNF) modulate the effect of childhood adversity on adult depression, although inconsistencies across studies have been found. Moreover, the gene×environment (G×E) interaction concerning the different types of childhood adversity remains poorly understood. The aim of this study was to analyse the putative interaction between the 5-HTT gene (5-HTTLPR polymorphism), the BDNF gene (Val66Met polymorphism) and childhood adversity in accounting for adult depressive symptoms. Method A sample of 534 healthy individuals filled in self-report questionnaires of depressive symptomatology [the Symptom Check List 90 Revised (SCL-90-R)] and different types of childhood adversities [the Childhood Trauma Questionnaire (CTQ)]. The 5-HTTLPR polymorphism (5-HTT gene) and the Val66Met polymorphism (BDNF gene) were genotyped in the whole sample. Results Total childhood adversity (β=0.27, p<0.001), childhood sexual abuse (CSA; β=0.17, p<0.001), childhood emotional abuse (β=0.27, p<0.001) and childhood emotional neglect (β=0.22, p<0.001) had an impact on adult depressive symptoms. CSA had a greater impact on depressive symptoms in Met allele carriers of the BDNF gene than in the Val/Val group (F=5.87, p<0.0001), and in S carriers of the 5-HTTLPR polymorphism (5-HTT gene) (F=5.80, p<0.0001). Conclusions Childhood adversity per se predicted higher levels of adult depressive symptoms. In addition, BDNF Val66Met and 5-HTTLPR polymorphisms seemed to moderate the effect of CSA on adult depressive symptoms.

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Gene × environment interactions in schizophrenia: evidence from genetic mouse models

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.