Gene-environment interactions in sporadic Parkinson's disease

Much has been learned in recent years about the genetics of familial Parkinson's disease. However, far less is known about those malfunctioning genes which contribute to the emergence and/or progression of the vast majority of cases, the 'sporadic Parkinson's disease', which is the focus of our current review. Drastic differences in the reported prevalence of Parkinson's disease in different continents and countries suggest ethnic and/or environmental-associated multigenic contributions to this disease. Numerous association studies showing variable involvement of multiple tested genes in these distinct locations support this notion. Also, variable increases in the risk of Parkinson's disease due to exposure to agricultural insecticides indicate complex gene-environment interactions, especially when genes involved in protection from oxidative stress are explored. Further consideration of the brain regions damaged in Parkinson's disease points at the age-vulnerable cholinergic-dopaminergic balance as being involved in the emergence of sporadic Parkinson's disease in general and in the exposure-induced risks in particular. More specifically, the chromosome 7 ACHE/PON1 locus emerges as a key region controlling this sensitive balance, and animal model experiments are compatible with this concept. Future progress in the understanding of the genetics of sporadic Parkinson's disease depends on globally coordinated, multileveled studies of gene-environment interactions.     

Gene-environment interactions in human disease: nuisance or opportunity?

Many environmental risk factors for common, complex human diseases have been revealed by epidemiologic studies, but how genotypes at specific loci modulate individual responses to environmental risk factors is largely unknown. Gene-environment interactions will be missed in genome-wide association studies and could account for some of the 'missing heritability' for these diseases. In this review, we focus on asthma as a model disease for studying gene-environment interactions because of relatively large numbers of candidate gene-environment interactions with asthma risk in the literature. Identifying these interactions using genome-wide approaches poses formidable methodological problems, and elucidating molecular mechanisms for these interactions has been challenging. We suggest that studying gene-environment interactions in animal models, although more tractable, might not be sufficient to shed light on the genetic architecture of human diseases. Lastly, we propose avenues for future studies to find gene-environment interactions.     

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.     

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Genome-wide association studies have identified hundreds of common genetic variants associated with the risk of multifactorial diseases. However, their impact on discrimination and risk prediction is limited. It has been suggested that the identification of gene-gene (G-G) and gene-environment (G-E) interactions would improve disease prediction and facilitate prevention. We conducted a simulation study to explore the potential improvement in discrimination if G-G and G-E interactions exist and are known. We used three diseases (breast cancer, type 2 diabetes, and rheumatoid arthritis) as motivating examples. We show that the inclusion of G-G and G-E interaction effects in risk-prediction models is unlikely to dramatically improve the discrimination ability of these models.     

Gene-environment interactions in psychiatry: joining forces with neuroscience

Gene-environment interaction research in psychiatry is new, and is a natural ally of neuroscience. Mental disorders have known environmental causes, but there is heterogeneity in the response to each causal factor, which gene-environment findings attribute to genetic differences at the DNA sequence level. Such findings come from epidemiology, an ideal branch of science for showing that a gene-environment interactions exist in nature and affect a significant fraction of disease cases. The complementary discipline of epidemiology, experimental neuroscience, fuels gene-environment hypotheses and investigates underlying neural mechanisms. This article discusses opportunities and challenges in the collaboration between psychiatry, epidemiology and neuroscience in studying gene-environment interactions.     

Gene-Environment Dependence Creates Spurious Gene-Environment Interaction

Gene-environment interactions have the potential to shed light on biological processes leading to disease and to improve the accuracy of epidemiological risk models. However, relatively few such interactions have yet been confirmed. In part this is because genetic markers such as tag SNPs are usually studied, rather than the causal variants themselves. Previous work has shown that this leads to substantial loss of power and increased sample size when gene and environment are independent. However, dependence between gene and environment can arise in several ways including mediation, pleiotropy, and confounding, and several examples of gene-environment interaction under gene-environment dependence have recently been published. Here we show that under gene-environment dependence, a statistical interaction can be present between a marker and environment even if there is no interaction between the causal variant and the environment. We give simple conditions under which there is no marker-environment interaction and note that they do not hold in general when there is gene-environment dependence. Furthermore, the gene-environment dependence applies to the causal variant and cannot be assessed from marker data. Gene-gene interactions are susceptible to the same problem if two causal variants are in linkage disequilibrium. In addition to existing concerns about mechanistic interpretations, we suggest further caution in reporting interactions for genetic markers.     

Fifty years of co‐evolution and beyond: integrating co‐evolution from molecules to species

Fifty years after Ehrlich and Raven's seminal paper, the idea of co‐evolution continues to grow as a key concept in our understanding of organic evolution. This concept has not only provided a compelling synthesis between evolutionary biology and community ecology, but has also inspired research that extends beyond its original scope. In this article, we identify unresolved questions about the co‐evolutionary process and advocate for the integration of co‐evolutionary research from molecular to interspecific interactions. We address two basic questions: (i) What is co‐evolution and how common is it? (ii) What is the unit of co‐evolution? Both questions aim to explore the heart of the co‐evolutionary process. Despite the claim that co‐evolution is ubiquitous, we argue that there is in fact little evidence to support the view that reciprocal natural selection and coadaptation are common in nature. We also challenge the traditional view that co‐evolution only occurs between traits of interacting species. Co‐evolution has the potential to explain evolutionary processes and patterns that result from intra‐ and intermolecular biochemical interactions within cells, intergenomic interactions (e.g. nuclear‐cytoplasmic) within species, as well as intergenomic interactions mediated by phenotypic traits between species. Research that bridges across these levels of organization will help to advance our understanding of the importance of the co‐evolutionary processes in shaping the diversity of life on Earth.     

Variant TREM2 Signaling in Alzheimer's Disease

Alzheimer's disease is the most common form of dementia, accounting for as much as three-quarters of cases globally with individuals in low- and middle-income countries being worst affected. Numerous risk factors for the disease have been identified and our understanding of gene-environment interactions have shed light on several gene variants that contribute to the most common, sporadic form of Alzheimer’s disease. Triggering Receptor Expressed on Myeloid cells 2 (TREM2) is an important receptor that is crucial to the functioning of microglial cells, and variants of this protein have been found to be associated with a significantly increased risk of Alzheimer's disease. Several studies have elucidated the signaling processes involved in the normal functioning of the TREM2 receptor. However, current knowledge of the idiosyncrasies of the signaling processes triggered by stimulation of the variants of this receptor is limited.In this review, we examine the existing literature and highlight the effects that various receptor variants have on downstream signaling processes and discuss how these perturbations may affect physiologic processes in Alzheimer's disease. Despite the fact that this is a territory yet to be fully explored, the studies that currently exist report mostly quantitative effects on signaling. More mechanistic studies with the aim of providing qualitative results in terms of downstream signaling among these receptor variants are warranted. Such studies will provide better opportunities of identifying therapeutic targets that may be exploited in designing new drugs for the management of Alzheimer's disease.     

The molecular architecture of protein-protein binding sites

The formation of specific protein interactions plays a crucial role in most, if not all, biological processes, including signal transduction, cell regulation, the immune response and others. Recent advances in our understanding of the molecular architecture of protein-protein binding sites, which facilitates such diversity in binding affinity and specificity, are enabling us to address key questions. What is the amino acid composition of binding sites? What are interface hotspots? How are binding sites organized? What are the differences between tight and weak interacting complexes? How does water contribute to binding? Can the knowledge gained be translated into protein design? And does a universal code for binding exist, or is it the architecture and chemistry of the interface that enable diverse but specific binding solutions?     

The genetics of multiple sclerosis: SNPs to pathways to pathogenesis

Multiple sclerosis (MS) is an autoimmune demyelinating disease and a common cause of neurological disability in young adults. The modest heritability of MS reflects complex genetic effects and multifaceted gene-environment interactions. The human leukocyte antigen (HLA) region is the strongest susceptibility locus for MS, but a genome-wide association study recently identified new susceptibility genes. Progress in high-throughput genotyping and sequencing technologies and a better understanding of the structural organization of the human genome, together with powerful brain-imaging techniques that refine the phenotype, suggest that the tools could finally exist to identify the full set of genes influencing the pathogenesis of MS.     

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.     

Enriched environments, experience-dependent plasticity and disorders of the nervous system

Behavioural, cellular and molecular studies have revealed significant effects of enriched environments on rodents and other species, and provided new insights into mechanisms of experience-dependent plasticity, including adult neurogenesis and synaptic plasticity. The demonstration that the onset and progression of Huntington's disease in transgenic mice is delayed by environmental enrichment has emphasized the importance of understanding both genetic and environmental factors in nervous system disorders, including those with Mendelian inheritance patterns. A range of rodent models of other brain disorders, including Alzheimer's disease and Parkinson's disease, fragile X and Down syndrome, as well as various forms of brain injury, have now been compared under enriched and standard housing conditions. Here, we review these findings on the environmental modulators of pathogenesis and gene-environment interactions in CNS disorders, and discuss their therapeutic implications.     

The effects of phenological mismatches on demography

Climate change is altering the phenology of species across the world, but what are the consequences of these phenological changes for the demography and population dynamics of species? Time-sensitive relationships, such as migration, breeding and predation, may be disrupted or altered, which may in turn alter the rates of reproduction and survival, leading some populations to decline and others to increase in abundance. However, finding evidence for disrupted relationships, or lack thereof, and their demographic effects, is difficult because the necessary detailed observational data are rare. Moreover, we do not know how sensitive species will generally be to phenological mismatches when they occur. Existing long-term studies provide preliminary data for analysing the phenology and demography of species in several locations. In many instances, though, observational protocols may need to be optimized to characterize timing-based multi-trophic interactions. As a basis for future research, we outline some of the key questions and approaches to improving our understanding of the relationships among phenology, demography and climate in a multi-trophic context. There are many challenges associated with this line of research, not the least of which is the need for detailed, long-term data on many organisms in a single system. However, we identify key questions that can be addressed with data that already exist and propose approaches that could guide future research.     

Genes, diet and serum lipid concentrations: lessons from ethnically diverse populations and their relevance to coronary heart disease in Asia

PURPOSE OF REVIEW: The burden of coronary heart disease (CHD) in Asia has risen in tandem with socio-economic development and urbanization. Although all ethnic groups have been affected, some appear to be at particularly high risk. The basis of these ethnic differences remains poorly understood. RECENT FINDINGS: Differing levels of risk factors for CHD have been observed between ethnic groups. Previous studies, however, may be confounded by a large ethnic variation in socio-economic status and place of residence. Few studies have taken dietary factors into account. Recent studies involving Chinese, Malays and Asian Indians living in Singapore suggest that neither dietary nor genetic factors, taken in isolation, sufficiently explain ethnic differences in serum lipid profiles. Several genetic variants in key candidate genes (apolipoprotein E, APOE, cholesteryl ester transfer protein, CETP and hepatic lipase, LIPC) have recently been found to modulate the association between dietary factors and serum lipid concentrations in these ethnic groups and in other populations. SUMMARY: To fully evaluate the differences in CHD risk between ethnic groups, environmental exposures, including dietary factors need to be carefully evaluated, and gene-environment interactions that may give rise to these differences need to be taken into account. These are critical steps in the development of targeted strategies to contain the epidemic of coronary heart disease in Asia. An understanding of the basis of these differences may also provide insights into the pathogenesis of disease that one cannot get through the examination of more homogenous populations.     

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.     

Cholecystokinin and panic disorder--three unsettled questions

The serendipitously discovered panicogenic effect of the cholecystokinin fragment, the C-terminal tetrapeptide amide (CCK-4), has suggested that the widespread network of CCK neurons and corresponding CCK-B receptors in the brain are in some way involved in pathogenesis panic disorders in man. Two decades of research have now established that exogenous CCK-4 in a reproducible, dose-dependent and sensitive manner indeed evokes panic attacks in both healthy subjects and at even lower doses in anxiety patients. But several questions about the molecular mechanisms by which endogenous CCK peptides may precipitate panic attacks remain to be answered. This review focuses on three immediate questions. (1) Does endogenous CCK-4 exist? (2) Is the panicogenic effect mediated only through CCK-B receptors? (3) Are measurements of CCK peptides in cerebrospinal fluid of use in elucidating the pathogenesis and/or diagnosis? This review concludes that the answers to these questions may further the understanding of panic disorder substantially, and hence contribute to improved diagnosis and therapy of the disease.     

Using genomics for birth defects epidemiology: can epigenetics cut the GxE Gordian knot?

Most birth defects are etiologically complex disorders caused by combinations of genetic and environmental factors, but most studies of birth defect etiology have examined only genetic factors or only environmental factors and have not considered interactions among them. Genome-wide epigenetic studies, which use the same genomic technologies that have revolutionized our ability to identify genetic causes of disease, provide an attractive way to study gene-environment interactions. However, finding an association between epigenetic variation and an etiologically complex birth defect without knowledge of the genetic variation and environmental exposures affecting the individuals who were studied usually provides little or no information regarding the cause of the disorder. In order for genome-wide studies of epigenetic variation to contribute to our understanding of the causes of birth defects, these studies must be combined with studies of environmental exposures and studies of genetic variation in the same subjects. Under such circumstances, epigenetic studies may help to establish the molecular basis for gene-environment interactions.