Why study gene-environment interactions?

PURPOSE OF REVIEW: We examine the reasons for investigating gene-environment interactions and address recent reports evaluating interactions between genes and environmental modulators in relation to cardiovascular disease and its common risk factors. RECENT FINDINGS: Studies focusing on smoking, physical activity, and alcohol and coffee consumption are observational and include relatively large sample sizes. They tend to examine single genes, however, and fail to address interactions with other genes and other correlated environmental factors. Studies examining gene-diet interactions include both observational and interventional designs. These studies are smaller, especially those including dietary interventions. Among the reported gene-diet interactions, it is important to highlight the strengthened position of APOA5 as a major gene that is involved in triglyceride metabolism and modulated by dietary factors, and the identification of APOA2 as a modulator of food intake and obesity risk. SUMMARY: The study of gene-environment interactions is an active and much needed area of research. Although technical barriers of genetic studies are rapidly being overcome, inclusion of comprehensive and reliable environmental information represents a significant shortcoming of genetics studies. Progress in this area requires inclusion of larger populations but also more comprehensive, standardized, and precise approaches to capturing environmental information.     

Pink1, Parkin, DJ-1 and mitochondrial dysfunction in Parkinson's disease

Mutations in PARKIN, PTEN-induced kinase 1 (PINK1) and DJ-1 are found in autosomal recessive forms and some sporadic cases of Parkinson's disease. Recent work on these genes underscores the central importance of mitochondrial dysfunction and oxidative stress in Parkinson's disease. In particular, pink1 and parkin loss-of-function mutants in Drosophila show similar phenotypes, and pink1 acts upstream of parkin in a common genetic pathway to regulate mitochondrial function. DJ-1 has a role in oxidative stress protection, but a direct role of DJ-1 in mitochondrial function has not been fully established. Importantly, defects in mitochondrial function have also been identified in patients who carry both PINK1 and PARKIN mutations, and in those who have sporadic Parkinson's disease. Future studies of the biochemical interactions between Pink1 and Parkin, and identification of other components in this pathway, are likely to provide insight into Parkinson's disease pathogenesis, and might identify new therapeutic targets.     

HPA-axis activity in alcoholism: examples for a gene-environment interaction.

Genetic and environmental influences are both known to be causal factors in the development and maintenance of substance abuse disorders. This review aims to focus on the contributions of genetic and environmental research to the understanding of alcoholism and how gene-environment interactions result in a variety of addiction phenotypes. Gene-environment interactions have been reviewed by focusing on one of the most relevant environmental risk factors for alcoholism, stress. This is examined in more detail by reviewing the functioning of the hypothalamic-pituitary-adrenal (HPA) axis and its genetic and molecular components in this disorder. Recent evidence from animal and human studies have shown that the effects of stress on alcohol drinking are mediated by core HPA axis genes and are associated with genetic variations in those genes. The findings of the studies discussed here suggest that the collaborations of neuroscience, psychobiology and molecular genetics provide a promising framework to elucidate the exact mechanisms of gene-environment interactions as seen to convene upon the HPA axis and effect phenotypes of addiction.     

Parkinson Disease from Mendelian Forms to Genetic Susceptibility: New Molecular Insights into the Neurodegeneration Process

Parkinson disease (PD) is known as a common progressive neurodegenerative disease which is clinically diagnosed by the manifestation of numerous motor and nonmotor symptoms. PD is a genetically heterogeneous disorder with both familial and sporadic forms. To date, researches in the field of Parkinsonism have identified 23 genes or loci linked to rare monogenic familial forms of PD with Mendelian inheritance. Biochemical studies revealed that the products of these genes usually play key roles in the proper protein and mitochondrial quality control processes, as well as synaptic transmission and vesicular recycling pathways within neurons. Despite this, large number of patients affected with PD typically tends to show sporadic forms of disease with lack of a clear family history. Recent genome-wide association studies (GWAS) meta-analyses on the large sporadic PD case–control samples from European populations have identified over 12 genetic risk factors. However, the genetic etiology that underlies pathogenesis of PD is also discussed, since it remains unidentified in 40% of all PD-affected cases. Nowadays, with the emergence of new genetic techniques, international PD genomics consortiums and public online resources such as PDGene, there are many hopes that future large-scale genetics projects provide further insights into the genetic etiology of PD and improve diagnostic accuracy and therapeutic clinical trial designs.     

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.     

Genetic Insights into Sporadic Parkinson's Disease Pathogenesis

Intensive research over the last 15 years has led to the identification of several autosomal recessive and dominant genes that cause familial Parkinson’s disease (PD). Importantly, the functional characterization of these genes has shed considerable insights into the molecular mechanisms underlying the etiology and pathogenesis of PD. Collectively; these studies implicate aberrant protein and mitochondrial homeostasis as key contributors to the development of PD, with oxidative stress likely acting as an important nexus between the two pathogenic events. Interestingly, recent genome-wide association studies (GWAS) have revealed variations in at least two of the identified familial PD genes (i.e. α-synuclein and LRRK2) as significant risk factors for the development of sporadic PD. At the same time, the studies also uncovered variability in novel alleles that is associated with increased risk for the disease. Additionally, in-silico meta-analyses of GWAS data have allowed major steps into the investigation of the roles of gene-gene and gene-environment interactions in sporadic PD. The emergent picture from the progress made thus far is that the etiology of sporadic PD is multi-factorial and presumably involves a complex interplay between a multitude of gene networks and the environment. Nonetheless, the biochemical pathways underlying familial and sporadic forms of PD are likely to be shared.     

Molecular genetics of Parkinson's disease

The current views on the role of genetic factors in the pathogenesis of Parkinson's disease are considered. The review is focused on monogenic forms of the disease, for which 11 loci are mapped and seven genes whose mutations cause the disease are identified. In addition, a number of candidate genes for sporadic Parkinson's disease are described. The further development of studying genetic bases of Parkinson's disease will follow two main directions: in-depth analysis of genes related to the monogenic form of the disease and more large-scale associative investigation of candidate genes for the sporadic form of Parkinson's disease.     

Genes, environment and the value of prospective cohort studies

Case-control studies have many advantages for identifying disease-related genes, but are limited in their ability to detect gene-environment interactions. The prospective cohort design provides a valuable complement to case-control studies. Although it has disadvantages in duration and cost, it has important strengths in characterizing exposures and risk factors before disease onset, which reduces important biases that are common in case-control studies. This and other strengths of prospective cohort studies make them invaluable for understanding gene-environment interactions in complex human disease.     

Parkinson's disease: one biochemical pathway to fit all genes?

Although originally discounted, hereditary factors have emerged as the focus of research in Parkinson's disease (PD). Genetic studies have identified mutations in alpha-synuclein and ubiquitin C-terminal hydrolase as rare causes of autosomal dominant PD and mutations in parkin as a cause of autosomal recessive PD. Functional characterization of the identified disease genes implicates the ubiquitin-mediated protein degradation pathway in these hereditary forms of PD and also in the more common sporadic forms of PD. Subsequent identification of further loci in familial PD and diverse genetic factors modulating the risk for sporadic PD point to substantial genetic heterogeneity in the disease. Thus, new candidate genes are expected to encode proteins either involved in ubiquitin-mediated protein degradation or sequestrated in intracytoplasmic protein aggregations. Future identification of disease genes is required to confirm this hypothesis, thereby unifying the clinical and genetic heterogeneity of PD, including the common sporadic form of the disease, by one biochemical pathway.     

Identification of SNP interactions using logic regression

Interactions of single nucleotide polymorphisms (SNPs) are assumed to be responsible for complex diseases such as sporadic breast cancer. Important goals of studies concerned with such genetic data are thus to identify combinations of SNPs that lead to a higher risk of developing a disease and to measure the importance of these interactions. There are many approaches based on classification methods such as CART and random forests that allow measuring the importance of single variables. But none of these methods enable the importance of combinations of variables to be quantified directly. In this paper, we show how logic regression can be employed to identify SNP interactions explanatory for the disease status in a case-control study and propose 2 measures for quantifying the importance of these interactions for classification. These approaches are then applied on the one hand to simulated data sets and on the other hand to the SNP data of the GENICA study, a study dedicated to the identification of genetic and gene-environment interactions associated with sporadic breast cancer.     

Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions

MOTIVATION: The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined. RESULTS: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.     

Genetic contributions to behavioural diversity at the gene-environment interface

Recent work on behavioural variation within and between species has furthered our understanding of the genetic architecture of behavioural traits, the identities of relevant genes and the ways in which genetic variants affect neuronal circuits to modify behaviour. Here we review our understanding of the genetics of natural behavioural variation in non-human animals and highlight the implications of these findings for human genetics. We suggest that gene-environment interactions are central to natural genetic variation in behaviour and that genes affecting neuromodulatory pathways and sensory processing are preferred sites of naturally occurring mutations.     

Amyotrophic lateral sclerosis as a complex genetic disease

In complex diseases like ALS, there are multiple genetic and environmental factors all contributing to disease liability. The genetic factors causing susceptibility to developing ALS can be considered a spectrum from single genes with large effect sizes causing classical Mendelian ALS, to genes of smaller effect, producing apparently sporadic disease. We examine the statistical genetic principles that underpin this model and review what is known about ALS as a disease with complex genetics.     

Pathogenesis of familial Parkinson's disease: new insights based on monogenic forms of Parkinson's disease

Parkinson's disease (PD) is one of the most common movement disorders caused by the loss of dopaminergic neuronal cells. The molecular mechanisms underlying neuronal degeneration in PD remain unknown; however, it is now clear that genetic factors contribute to the pathogenesis of this disease. Approximately, 5% of patients with clinical features of PD have clear familial etiology, which show a classical recessive or dominant Mendelian mode of inheritance. Over the decade, more than 15 loci and 11 causative genes have been identified so far and many studies shed light on their implication in not only monogenic but also sporadic form of PD. Recent studies revealed that PD-associated genes play important roles in cellular functions, such as mitochondrial functions, ubiquitin-proteasomal system, autophagy-lysosomal pathway and membrane trafficking. Furthermore, the proteins encoded by PD-associated genes can interact with each other and such gene products may share a common pathway that leads to nigral degeneration. However, their precise roles in the disease and their normal functions remain poorly understood. In this study, we review recent progress in knowledge about the genes associated with familial PD.     

Genetic variants predisposing to cardiovascular disease

PURPOSE OF REVIEW: The goal of this review is to provide an update on the most recent and relevant findings in the area of genotype-phenotype associations as well as the relationships between genetic factors and cardiovascular disease risk markers and events. In addition, emphasis will be placed on the methodological problems associated with studying the genetics of complex disorders, specifically cardiovascular diseases. RECENT FINDINGS: Genes associated with cardiovascular disease predisposition have been examined, including traditional cardiovascular disease candidate genes, such as ACE, AGT, eNOS, PON and MTHFR, new loci that have recently been added to the growing list of cardiovascular disease candidate genes (i.e. MEF2A, ALOX5, LTA, APOM, PDE4D), and genes that have been shown to be at the intersection of several age-related disorders through interaction with one another or with environmental factors (i.e. APOA5, APOE, PPARgamma, LPL and LIPC). SUMMARY: During the last year, tremendous effort has been made in elucidating new genes associated with cardiovascular disease predisposition. For the most part, however, major breakthroughs have not been made, primarily due to the poor replication of results among studies, as a consequence of poor experimental design. Nevertheless, we have increased our understanding of the complexity of cardiovascular disease and the relevance of gene-environment interactions as the ultimate drivers of the individual predisposition to the disease. It is essential, therefore, that present and future genetic studies in this area take into consideration the inclusion of high-quality environmental data in the analytical process to test the clinical usefulness of a genetic marker as a risk predictor.     

Identifying interactions between genes and early environment in the mouse

Interactions between genetic and early environmental factors are recognized to play a critical role in modulating susceptibility to disease, particularly mental illness. In order to better understand such mechanisms at the molecular level, we have developed a screening paradigm in mice that allows us to test the ability of targeted mutations in candidate genes to modify susceptibility to the long-term effects of different maternal environment. Offspring of genetically identical F1 hybrid dams produced by reciprocal breeding of C57BL/6 and BALB/c parents show alterations in anxiety-related behavior as a consequence of their different maternal environment. Introduction of targeted mutations into these offspring via the father allows for the identification of candidate genes that alter these maternal effects. Our strategy offers several advantages over other methods to study maternal effects, including the use of genetically identical parents, the ability to identify both prenatal and postnatal effects, the straightforward introduction of mutations and its adaptability to high-throughput screening. In order to test the utility of this paradigm to screen candidate genes, we tested for gene-environment interactions involving loss-of-function mutations in the serotonin 1A receptor gene. Our studies demonstrate that early gene-environment interactions can be successfully tested in the mouse. When combined with conditional gene targeting and other molecular genetic techniques available in the mouse, this approach has the potential to identify the molecular mechanisms underlying early gene-environment effects.     

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

ABSTRACT: BACKGROUND: The analysis of complex diseases is an important problem in human genetics. Because multifactoriality isexpected to play a pivotal role, many studies are currently focused on collecting information on the geneticand environmental factors that potentially influence these diseases. However, there is still a lack of efficientand thoroughly tested statistical models that can be used to identify implicated features and theirinteractions. Simulations using large biologically realistic data sets with known gene-gene andgene-environment interactions that influence the risk of a complex disease are a convenient and useful wayto assess the performance of statistical methods. RESULTS: The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and oneenvironmental factor and also allows for epistatic interactions. GENS2 is based on data with realisticpatterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to besimulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive,the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Pythonlanguage and takes advantage of operators and modules provided by the simuPOP simulation environment.It can be used through a graphical or a command-line interface and is freely available fromhttp://sourceforge.net/projects/gensim. The software is released under the GNU General Public Licenseversion 3.0. CONCLUSIONS: Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for theidentification of gene-gene and gene-environment interactions.     

Population genetic approaches to neurological disease: Parkinson's disease as an example

Parkinson's disease (PD) is a common, progressive, incurable disabling condition. The cause is unknown but over the past few years tremendous progress in our understanding of the genetic bases of this condition has been made. To date, this has almost exclusively come from the study of relatively rare Mendelian forms of the disease and there are no currently, widely accepted common variants known to increase susceptibility.The role that the "Mendelian" genes play in common sporadic forms of PD is unknown. Moreover, most studies in PD can really be described as candidate polymorphism studies rather than true and complete assessments of the genes themselves. We provide a model of how one might tackle some of these issues using Parkinson's disease as an illustration. One of the emerging hypotheses of gene environment interaction in Parkinson's disease is based on drug metabolizing (or xenobiotic) enzymes and their interaction with putative environmental toxins. This motivated us to describe a tagging approach for an extensive but not exhaustive list of 55 drug metabolizing enzyme genes. We use these data to illustrate the power, and some of the limitations of a haplotype tagging approach. We show that haplotype tagging is extremely efficient and works well with only a modest increase in effort through different populations. The tagging approach works much less well if the minor allele frequency is below 5%. However, it will now be possible using these tags to evaluate these genes comprehensively in PD and other neurodegenerative conditions.     

Genes and environment in arthritis: can RA be prevented?

Understanding of how interactions between genes and environment contribute to the development of arthritis is a central issue in understanding the etiology of rheumatoid arthritis (RA), as well as for eventual subsequent efforts to prevent the disease. In this paper, we review current published data on genes and environment in RA as well as in certain induced animal models of disease, mainly those in which adjuvants only or adjuvants plus organ-specific autoantigens are used to induce arthritis. We refer to some new data on environmental and genetic factors of importance for RA generated from a large case-control study in Sweden (1200 patients, 1200 matched controls). We found an increased risk of seropositive but not of seronegative RA in smokers, and there are indications that this effect may be due to a gene-environment interaction involving MHC class II genes. We also found an increased risk of RA in individuals heavily exposed to mineral oils. This was of particular interest because mineral oils are strong inducers of arthritis in certain rodent strains and because polymorphisms in human genetic regions syntenic with genes predisposing for oil-induced arthritis in rats have now been shown to associate with RA in humans. Taken together, our data support the notion that concepts and data on gene-environment interactions in arthritis can now be taken from induced animal models of arthritis to generate new etiological hypotheses for RA.     

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.