miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes

The interaction between genetic factors and environmental factors has critical roles in determining the phenotype of an organism. In recent years, a number of studies have reported that the dysfunctions on microRNA (miRNAs), environmental factors and their interactions have strong effects on phenotypes and even may result in abnormal phenotypes and diseases, whereas there has been no a database linking miRNAs, environmental factors and phenotypes. Such a resource platform is believed to be of great value in the understanding of miRNAs, environmental factors, especially drugs and diseases. In this study, we constructed the miREnvironment database, which contains a comprehensive collection and curation of experimentally supported interactions among miRNAs, environmental factors and phenotypes. The names of miRNAs, phenotypes, environmental factors, conditions of environmental factors, samples, species, evidence and references were further annotated. miREnvironment represents a biomedical resource for researches on miRNAs, environmental factors and diseases. AVAILABILITY: http://cmbi.bjmu.edu.cn/miren. CONTACT: cuiqinghua@hsc.pku.edu.cn.     

Genetics of life span in mice

Thymic involution that occurs earlier in some individuals than others may be the result of complex interactions between genetic factors and the environment. Such interactions may produce defects of thymus-dependent immune regulation associated with susceptibility to developing autoimmune diseases, malignancy, and an increased number of infections associated with aging.The major histocompatibility complex may be important in determining profiles of cause of death and length of life in mice. Genetic influences on life span involve interactions between loci and allelic interactions during life which may change following viral infections or exposure to other environmental factors. We have used different experimental protocols to study the influence of H-2 on life span and found that interactions between genetic regions, are inconsistent, particularly when comparing mice infected or not infected with Sendai virus.Genes important for life span need to be studied against many genetic backgrounds and under differing environmental conditions because of the complexity of the genetics of life span. Several genetic models were used to demonstrate that the MHC is a marker of life span in backcross and intercross male mice of the H-2^d and H-2^b genotypes in B10 congenic mice. Females lived longer than males in backcross and intercross mice, while males lived longer than females in B10 congenics. H-2^d was at a disadvantage for life span in backcross mice of the dilute brown and brown males exposed to Sendai infection, but intercross mice not exposed to Sendai virus of the same genotype were not at a disadvantage. H-2^d mice were not disadvantaged when compared to H-2^b in B10 congenics that had not been exposed to Sendai virus infection but the reverse was true when they were exposed. Overall, all our studies suggest that genetic influences in life span may involve interactions between loci and many allelic interactions in growing animals or humans. These genetic influences on life span may vary after they are exposed to infections or other environmental conditions. This paper emphasizes the need to use several genetic models, especially animals that have been monitored for infections, to study the genetics of life span.     

Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions

MOTIVATION: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. RESULTS: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. AVAILABILITY: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. SUPPLEMENTARY INFORMATION: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.     

Molecular genetics of atherosclerosis

Atherosclerosis is a complex multifocal arterial disease involving interactions of multiple genetic and environmental factors. Advances in techniques of molecular genetics have revealed that genetic polymorphisms significantly influence susceptibility to atherosclerotic vascular diseases. A large number of candidate genes, genetic polymorphisms and susceptibility loci associated with atherosclerotic diseases have been identified in recent years and their number is rapidly increasing. In this review we focus on some of the major candidate genes and genetic polymorphisms associated with human atherosclerotic vascular diseases.     

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.     

Genetic Diversity and Virulence Potential of Shiga Toxin-Producing Escherichia coli O113:H21 Strains Isolated from Clinical,Environmental, and Food Sources

Shiga toxin-producing Escherichia coli strains of serotype O113:H21 have caused severe human diseases, but they are unusual in that they do not produce adherence factors coded by the locus of enterocyte effacement. Here, a PCR microarray was used to characterize 65 O113:H21 strains isolated from the environment, food, and clinical infections from various countries. In comparison to the pathogenic strains that were implicated in hemolytic-uremic syndrome in Australia, there were no clear differences between the pathogens and the environmental strains with respect to the 41 genetic markers tested. Furthermore, all of the strains carried only Shiga toxin subtypes associated with human infections, suggesting that the environmental strains have the potential to cause disease. Most of the O113:H21 strains were closely related and belonged in the same clonal group (ST-223), but CRISPR analysis showed a great degree of genetic diversity among the O113:H21 strains.     

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.     

Genetic factors affecting HDL levels, structure, metabolism and function

PURPOSE OF REVIEW: HDL is a recognized negative risk factor for the cardiovascular diseases. Establishing the genetic determinants of HDL concentration and functions would add to the prediction of cardiovascular risk and point to the biochemical mechanisms underlying this risk. The present review focuses on various approaches to establish genetic determinants of the HDL concentration, structure and function. RECENT FINDINGS: While many genes contribute to the HDL concentration and collectively account for half of the variability, polymorphism of individual candidate genes contributes little. There are strong interactions between environmental and genetic influences. Recent findings have confirmed that APOA1 and ABCA1 exert the strongest influence on HDL concentrations and risk of atherosclerosis. CETP and lipases also affect the HDL concentration and functionality, but their connection to the atherosclerosis risk is conditional on the interaction between environmental and genetic factors. SUMMARY: Analysis of genetic determinants of HDL-cholesterol in patients with specific disease states or in response to the environmental condition may be a more accurate way to assess variations in HDL concentration. This may result in defining the rules of interaction between genetic and environmental factors and lead to understanding the mechanisms responsible for the variations in HDL concentration and functionality.     

Gene-gene and gene-environment interactions involving HLA-DRB1, PTPN22, and smoking in two subsets of rheumatoid arthritis

Gene-gene and gene-environment interactions are key features in the development of rheumatoid arthritis (RA) and other complex diseases. The aim of this study was to use and compare three different definitions of interaction between the two major genetic risk factors of RA--the HLA-DRB1 shared epitope (SE) alleles and the PTPN22 R620W allele--in three large case-control studies: the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) study, the North American RA Consortium (NARAC) study, and the Dutch Leiden Early Arthritis Clinic study (in total, 1,977 cases and 2,405 controls). The EIRA study was also used to analyze interactions between smoking and the two genes. "Interaction" was defined either as a departure from additivity, as interaction in a multiplicative model, or in terms of linkage disequilibrium--for example, deviation from independence of penetrance of two unlinked loci. Consistent interaction, defined as departure from additivity, between HLA-DRB1 SE alleles and the A allele of PTPN22 R620W was seen in all three studies regarding anti-CCP-positive RA. Testing for multiplicative interactions demonstrated an interaction between the two genes only when the three studies were pooled. The linkage disequilibrium approach indicated a gene-gene interaction in EIRA and NARAC, as well as in the pooled analysis. No interaction was seen between smoking and PTPN22 R620W. A new pattern of interactions is described between the two major known genetic risk factors and the major environmental risk factor concerning the risk of developing anti-CCP-positive RA. The data extend the basis for a pathogenetic hypothesis for RA involving genetic and environmental factors. The study also raises and illustrates principal questions concerning ways to define interactions in complex diseases.     

The genetics of complex diseases

Genetic factors influence virtually every human disorder, determining disease susceptibility or resistance and interactions with environmental factors. Our recent successes in the genetic mapping and identification of the molecular basis of mendelian traits have been remarkable. Now, attention is rapidly shifting to more-complex, and more-prevalent, genetic disorders and traits that involve multiple genes and environmental effects, such as cardiovascular disease, diabetes, rheumatoid arthritis and schizophrenia. Rather than being due to specific and relatively rare mutations, complex diseases and traits result principally from genetic variation that is relatively common in the general population. Unfortunately, despite extensive efforts by many groups, only a few genetic regions and genes involved in complex diseases have been identified. Completion of the human genome sequence will be a seminal accomplishment, but it will not provide an immediate solution to the genetics of complex traits.     

Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological mechanisms and may also strengthen the basis for risk assessment and prevention.     

The genetics of complex diseases

Genetic factors influence virtually every human disorder, determining disease susceptibility or resistance and interactions with environmental factors. Our recent successes in the genetic mapping and identification of the molecular basis of mendelian traits have been remarkable. Now, attention is rapidly shifting to more-complex, and more-prevalent, genetic disorders and traits that involve multiple genes and environmental effects, such as cardiovascular disease, diabetes, rheumatoid arthritis and schizophrenia. Rather than being due to specific and relatively rare mutations, complex diseases and traits result principally from genetic variation that is relatively common in the general population. Unfortunately, despite extensive efforts by many groups, only a few genetic regions and genes involved in complex diseases have been identified. Completion of the human genome sequence will be a seminal accomplishment, but it will not provide an immediate solution to the genetics of complex traits.     

The genetics of complex diseases

Genetic factors influence virtually every human disorder, determining disease susceptibility or resistance and interactions with environmental factors. Our recent successes in the genetic mapping and identification of the molecular basis of mendelian traits have been remarkable. Now, attention is rapidly shifting to more-complex, and more-prevalent, genetic disorders and traits that involve multiple genes and environmental effects, such as cardiovascular disease, diabetes, rheumatoid arthritis and schizophrenia. Rather than being due to specific and relatively rare mutations, complex diseases and traits result principally from genetic variation that is relatively common in the general population. Unfortunately, despite extensive efforts by many groups, only a few genetic regions and genes involved in complex diseases have been identified. Completion of the human genome sequence will be a seminal accomplishment, but it will not provide an immediate solution to the genetics of complex traits.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

Gene-environment interaction effects on the development of immune responses in the 1st year of life

Asthma is a common disease that results from both genetic and environmental risk factors. Children attending day care in the 1st year of life have lower risks for developing asthma, although the mechanism for this "day care" effect is largely unknown. We investigated the interactions between day care exposure in the 1st 6 mo of life and genotypes for 72 polymorphisms at 45 candidate loci and their effects on cytokine response profiles and on the development of atopic phenotypes in the 1st year of life in the Childhood Onset of Asthma (COAST) cohort of children. Six interactions (at four polymorphisms in three loci) with "day care" that had an effect on early-life immune phenotypes were significant at P<.001. The estimated false-discovery rate was 33%, indicating that an estimated four P values correspond to true associations. Moreover, the "day care" effect at some loci was accounted for by the increased number of viral infections among COAST children attending day care, whereas interactions at other loci were independent of the number of viral infections, indicating the presence of additional risk factors associated with day care environment. This study identified significant gene-environment interactions influencing the early patterning of the immune system and the subsequent development of asthma and highlights the importance of considering environmental risk factors in genetic analyses.     

The role of infectious agents in sudden infant death syndrome

Abstract Epidemiological factors associated with susceptibility to respiratory infections are similar to those associated with Sudden Infant Death Syndrome. Here we review the evidence that respiratory pathogens might be involved in some cases of Sudden Infact Death Syndrome in the context of factors identified in epidemiological studies of cot deaths: the age range affected; mother's smoking; respiratory viral infections; immunisation status. Both laboratory and epidimiological evidence suggests that vulnerability of infants to infectious agents depends on interactions between genetic, developmental and environmental factors that contribute to colonisation by microorganisms, the inflammatory and specific immune responses and the infants' physiological responses to inflammatory mediators. A model is proposed to explain how microorganisms might trigger a series of events resulting in some of these unexpected deaths and discusses how the present recommendations regarding child care practices might help reduce the numbers of Sudden Infant Death Syndrome cases associated with infectious agents.     

Coeliac disease: dissecting a complex inflammatory disorder

The disease mechanisms of complex inflammatory disorders are difficult to define because of extensive interactions between genetic and environmental factors. Coeliac disease is a typical complex inflammatory disorder, but this disease is unusual in that crucial genetic and environmental factors have been identified. This knowledge has allowed functional studies of the predisposing HLA molecules, the identification of antigenic epitopes and detailed studies of disease-relevant T cells in coeliac disease. This dissection of the pathogenic mechanisms of coeliac disease has uncovered principles that are relevant to other chronic inflammatory diseases.     

Varying coefficient model for gene-environment interaction: a non-linear look

MOTIVATION: The genetic basis of complex traits often involves the function of multiple genetic factors, their interactions and the interaction between the genetic and environmental factors. Gene-environment (G×E) interaction is considered pivotal in determining trait variations and susceptibility of many genetic disorders such as neurodegenerative diseases or mental disorders. Regression-based methods assuming a linear relationship between a disease response and the genetic and environmental factors as well as their interaction is the commonly used approach in detecting G×E interaction. The linearity assumption, however, could be easily violated due to non-linear genetic penetrance which induces non-linear G×E interaction. RESULTS: In this work, we propose to relax the linear G×E assumption and allow for non-linear G×E interaction under a varying coefficient model framework. We propose to estimate the varying coefficients with regression spline technique. The model allows one to assess the non-linear penetrance of a genetic variant under different environmental stimuli, therefore help us to gain novel insights into the etiology of a complex disease. Several statistical tests are proposed for a complete dissection of G×E interaction. A wild bootstrap method is adopted to assess the statistical significance. Both simulation and real data analysis demonstrate the power and utility of the proposed method. Our method provides a powerful and testable framework for assessing non-linear G×E interaction.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.