Gene-Environment Interactions in Schizophrenia: A Literature Review.
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ABSTRACT:
SUBMITTER: Wahbeh MH
PROVIDER: S-EPMC8702084 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The pathogenesis of schizophrenia is considered to be multi-factorial, with likely gene-environment interactions (GEI). Genetic and environmental risk factors are being identified with increasing frequency, yet their very number vastly increases the scope of possible GEI, making it difficult to identify them with certainty. Accumulating evidence suggests a dysregulated complement pathway among the pathogenic processes of schizophrenia. The complement pathway mediates innate and acquired immunity, and its activation drives the removal of damaged cells, autoantigens and environmentally derived antigens. Abnormalities in complement functions occur in many infectious and autoimmune disorders that have been linked to schizophrenia. Many older reports indicate altered serum complement activity in schizophrenia, though the data are inconclusive. Compellingly, recent genome-wide association studies suggest repeat polymorphisms incorporating the complement 4A (C4A) and 4B (C4B) genes as risk factors for schizophrenia. The C4A/C4B genetic associations have re-ignited interest not only in inflammation-related models for schizophrenia pathogenesis, but also in neurodevelopmental theories, because rodent models indicate a role for complement proteins in synaptic pruning and neurodevelopment. Thus, the complement system could be used as one of the 'staging posts' for a variety of focused studies of schizophrenia pathogenesis. They include GEI studies of the C4A/C4B repeat polymorphisms in relation to inflammation-related or infectious processes, animal model studies and tests of hypotheses linked to autoimmune diseases that can co-segregate with schizophrenia. If they can be replicated, such studies would vastly improve our understanding of pathogenic processes in schizophrenia through GEI analyses and open new avenues for therapy.
| S-EPMC5656502 | biostudies-literature
Project description:INTRODUCTION:Schizophrenia (SZ) and Bipolar disorder (BD) are considered as severe multifactorial diseases, stemming from genetic and environmental influences. Growing evidence supports gene x environment (GxE) interactions in these disorders and neuroimaging studies can help us to understand how those factors mechanistically interact. No reviews synthesized the existing data of neuroimaging studies in these issues. METHODS:We conduct a systematic review on the neuroimaging studies exploring GxE interactions relative to SZ or BD in PubMed. RESULTS:First results of the influence of genetic and environmental risks on brain structures came from monozygotic twin pairs concordant and discordant for SZ or BD. Few structural magnetic resonance imaging (sMRI) studies have explored the GxE interactions. No other imaging methods were found. Two main GxE interactions on brain volumes have arisen. First, an interaction between genetic liability to SZ and obstetric complications on gray matter, cerebrospinal fluid, and hippocampal volumes. Second, cannabis use and genetic liability interaction effects on cortical thickness and white matter volumes. CONCLUSION:Combining GxE interactions and neuroimaging domains is a promising approach. Genetic risk and environmental exposures such as cannabis or obstetrical complications seem to interact leading to specific neuroimaging cerebral alterations in SZ. They are suggestive of GxE interactions that confer phenotypic abnormalities in SZ and possibly BD. We need further, larger neuroimaging studies of GxE interactions for which we may propose a framework focusing on GxE interactions data already known to have a clinical effect such as infections, early stress, urbanicity, and substance abuse.
| S-EPMC3796286 | biostudies-literature
Project description:Large-scale transcriptome and methylome data analyses obtained by high-throughput technologies have been enabling the identification of novel imprinted genes. We investigated genome-wide DNA methylation patterns in multiple human tissues, using a high-resolution microarray to uncover hemimethylated CpGs located in promoters overlapping CpG islands, aiming to identify novel candidate imprinted genes. Using our approach, we recovered ~30% of the known human imprinted genes, further 168 candidates were identified, 61 of which with at least three hemimethylated CpGs shared by more than two tissue types. Thirty-four of these candidate genes are members of the protocadherins cluster on 5q31.3; in mice, protocadherin genes have non-imprinted monoallelic randomic expression, which might be the case in humans. Among the remaining 27 genes, ZNF331 was recently validated as an imprinted gene, and six of them have been reported as candidates, supporting our prediction. Five candidates (CCDC166, ARC, PLEC, TONSL and VPS28) map to 8q24.3, and might constitute a novel imprinted cluster. Additionally, we performed a comprehensive compilation of known human and mice imprinted genes from literature and databases, and a comparison among high-throughput imprinting studies in humans. The screening for hemimethylated CpGs shared by multiple human tissues, together with the extensive review, appears as a useful approach to reveal candidate imprinted genes.
2017-09-26 | GSE103413 | GEO
Project description:BACKGROUND:Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature. METHODS:To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed. RESULTS:A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest. CONCLUSION:Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings.
| S-EPMC4911236 | biostudies-literature
Project description:Schizophrenia is among the leading causes of disability worldwide. Prior studies have conclusively demonstrated that the etiology of schizophrenia contains a strong genetic component. However, the understanding of environmental contributions and gene-environment interactions have remained less well understood. Here, we estimated the genetic and environmental contributions to schizophrenia risk using a unique combination of data sources and mathematical models. We used the administrative health records of 481,657 U.S. individuals organized into 128,989 families. In addition, we employed rich geographically specific measures of air, water, and land quality across the United States. Using models of progressively increasing complexity, we examined both linear and non-linear contributions of genetic variation and environmental exposures to schizophrenia risk. Our results demonstrate that heritability estimates differ significantly when gene-environment interactions are included in the models, dropping from 79% for the simplest model, to 46% in the best-fit model which included the full set of linear and non-linear parameters. Taken together, these findings suggest that environmental factors are an important source of explanatory variance underlying schizophrenia risk. Future studies are warranted to further explore linear and non-linear environmental contributions to schizophrenia risk and investigate the causality of these associations.
| S-EPMC9261082 | biostudies-literature
Project description:The underlying pathogenesis of asthma, one of the most common chronic diseases of childhood, is not fully understood. There is a well-documented heritable component to this disease and environmental factors associated with a Westernised lifestyle have also been implicated; recent studies suggest gene-environment interactions are important in the development of this disease. In the absence of a previous review in children, the present report presents the accumulating evidence for gene-environment interactions in asthma pathogenesis. Studies of these interactions in different populations have yielded both expected and unexpected results. This is a new and rapidly developing field where there are currently many more questions than answers.
| S-EPMC2083615 | biostudies-literature
Project description:Schizophrenia is a multi-factorial disease characterized by a high heritability and environmental risk factors. In recent years, an increasing number of researchers worldwide have started investigating the "two-hit hypothesis" of schizophrenia predicting that genetic and environmental risk factors (GxE) interactively cause the development of the disorder. This work is starting to produce valuable new animal models and reveal novel insights into the pathophysiology of schizophrenia. This mini review will focus on recent advancements in the field made by challenging mutant and transgenic rodent models for the schizophrenia candidate gene neuregulin 1 (NRG1) with particular environmental factors. It will outline results obtained from mouse and rat models for various Nrg1 isoforms/isoform types (e.g., transmembrane domain Nrg1, Type II Nrg1), which have been exposed to different forms of stress (acute versus chronic, restraint versus social) and housing conditions (standard laboratory versus minimally enriched housing). These studies suggest Nrg1 as a prime candidate for GxE interactions in schizophrenia rodent models and that the use of rodent models will enable a better understanding of GxE interactions and the underlying mechanisms.
| S-EPMC3744031 | biostudies-other
Project description:It has been suggested that children with larger brains tend to perform better on IQ tests or cognitive function tests. Prenatal head growth and head growth in infancy are two crucial periods for subsequent intelligence. Studies have shown that environmental exposure to air pollutants during pregnancy is associated with fetal growth reduction, developmental delay, and reduced IQ. Meanwhile, genetic polymorphisms may modify the effect of environment on head growth. However, studies on gene-environment or gene-gene interactions on growth trajectories have been quite limited partly due to the difficulty to quantitatively measure interactions on growth trajectories. Moreover, it is known that assessing the significance of gene-environment or gene-gene interactions on cross-sectional outcomes empirically using the permutation procedures may bring substantial errors in the tests. We proposed a score that quantitatively measures interactions on growth trajectories and developed an algorithm with a parametric bootstrap procedure to empirically assess the significance of the interactions on growth trajectories under the likelihood framework. We also derived a Wald statistic to test for interactions on growth trajectories and compared it to the proposed parametric bootstrap procedure. Through extensive simulation studies, we demonstrated the feasibility and power of the proposed testing procedures. We applied our method to a real dataset with head circumference measures from birth to age 7 on a cohort currently being conducted by the Columbia Center for Children's Environmental Health (CCCEH) in Krakow, Poland, and identified several significant gene-environment interactions on head circumference growth trajectories.
| S-EPMC3380164 | biostudies-literature
Project description:Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation.
| S-EPMC2947989 | biostudies-literature
Project description:BackgroundThe analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods.ResultsThe Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool 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 Python language 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 from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0.ConclusionsData produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.
| S-EPMC3538511 | biostudies-literature