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"Gene-Environment Interaction."
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The case-only design is a powerful approach to detect interactions but should be used with caution
Background
The case-only design is a powerful approach to identify gene
×
gene and gene
×
environment interactions for complex traits. It has been demonstrated that for the case-only design to be valid the genetic and environmental factors must be independent in the population. Additionally, there is a rare disease assumption for the case-only design, but the impact of disease prevalence and other factors, e.g., size of main effects, on type I and II error rates has not been investigated.
Methods
Through theoretical and extensive simulation studies, we investigated type I error, power, and bias of interaction term for a wide variety of disease prevalences, main and interaction effect sizes, sample sizes, and variant and environmental exposure frequencies.
Results
For diseases with prevalence
<
4%, the case-only design usually has well controlled type I error rates and is substantially more powerful to detect interactions than the case–control design, but for higher disease prevalences both type I and II error rates can be inflated and the estimate of interaction term biased. However, when one or both main effects are large there can be inflated type I error rate even for low disease prevalences, e.g.,
<
1%, but if there is no or only one main effect, type I error rate is controlled regardless of the disease prevalence. Additionally, type I error rate can increase with sample size.
Conclusions
We determined the upper bound of the disease prevalence in order not to violate the rare disease assumption for the case-only design. To verify that a case-only design study does not have increased type I error rate, the bias of the interaction term should be estimated. Although the case-only design is a powerful method to detect interactions, prevalences for some complex traits are too high to implement this method without increasing type I error rates.
Journal Article
A new method for exploring gene–gene and gene–environment interactions in GWAS with tree ensemble methods and SHAP values
Background
The identification of gene–gene and gene–environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis.
Results
We propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene–gene and gene–environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates.
Conclusions
The new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data.
Journal Article
Genome‐Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes
Brain structure and function show substantial individual differences, finely controlled by genes, environments, and their interactions. Despite the increasing knowledge about genetic and environmental main effects, gene‐environment interaction effects on brain phenotypes remain elusive. This study investigates genome‐wide by environment (41 exposures) interactions on 598 brain imaging phenotypes in 7084 healthy young adults. Both univariate and multivariate analyses identify 486 significant gene‐environment interactions, scattered across the genome, exposome, and phenome. These interactions explain more variances of phenotypes than genetic and environmental main effects (100% of genetic and 96% of environmental main effects are non‐significant). Variants with interactions are enriched in intronic and intergenic regions, comprising 79 regulatory variants and 145 associated with brain gene expression. Protein‐protein interaction network analyses reveal distinct interaction networks for genes associated with air pollution (hubs: H4C6, SMARCA4, and RPS11) and urbanicity (hubs: CCND1, CALM3, and CDK2) exposures. Genes that interacted with air pollution exposures exhibit enrichment in pathways related to metal ion detoxification and homeostasis. For time‐varying exposures, 144 interactions demonstrate sensitive periods, predominantly in childhood (ages 4–7) and adolescence (ages 12–15). These findings highlight the value of genome‐wide by exposome‐wide interaction studies, which may offer crucial information for optimizing brain health outcomes. This study explores genome‐wide by lifetime environment interactions on brain imaging phenotypes. Gene‐environment interactions explain more phenotypic variance than main effects, pinpoint regulatory variants, and reveal exposure‐specific biological pathways. Critical sensitive periods are identified in childhood and adolescence, offering novel insights for optimizing brain health.
Journal Article
Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, ξ, for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
Journal Article
Mesothelioma: Scientific clues for prevention, diagnosis, and therapy
Mesothelioma affects mostly older individuals who have been occupationally exposed to asbestos. The global mesothelioma incidence and mortality rates are unknown, because data are not available from developing countries that continue to use large amounts of asbestos. The incidence rate of mesothelioma has decreased in Australia, the United States, and Western Europe, where the use of asbestos was banned or strictly regulated in the 1970s and 1980s, demonstrating the value of these preventive measures. However, in these same countries, the overall number of deaths from mesothelioma has not decreased as the size of the population and the percentage of old people have increased. Moreover, hotspots of mesothelioma may occur when carcinogenic fibers that are present in the environment are disturbed as rural areas are being developed. Novel immunohistochemical and molecular markers have improved the accuracy of diagnosis; however, about 14% (high‐resource countries) to 50% (developing countries) of mesothelioma diagnoses are incorrect, resulting in inadequate treatment and complicating epidemiological studies. The discovery that germline BRCA1‐asssociated protein 1 (BAP1) mutations cause mesothelioma and other cancers (BAP1 cancer syndrome) elucidated some of the key pathogenic mechanisms, and treatments targeting these molecular mechanisms and/or modulating the immune response are being tested. The role of surgery in pleural mesothelioma is controversial as it is difficult to predict who will benefit from aggressive management, even when local therapies are added to existing or novel systemic treatments. Treatment outcomes are improving, however, for peritoneal mesothelioma. Multidisciplinary international collaboration will be necessary to improve prevention, early detection, and treatment.
Journal Article
Amphetamine self-administration and dopamine function: assessment of gene × environment interactions in Lewis and Fischer 344 rats
Rationale
Previous research suggests both genetic and environmental influences on substance abuse vulnerability.
Objectives
The current work sought to investigate the interaction of genes and environment on the acquisition of amphetamine self-administration as well as amphetamine-stimulated dopamine (DA) release in nucleus accumbens shell using in vivo microdialysis.
Methods
Inbred Lewis (LEW) and Fischer (F344) rat strains were raised in either an enriched condition (EC), social condition (SC), or isolated condition (IC). Acquisition of amphetamine self-administration (0.1 mg/kg/infusion) was determined across an incrementing daily fixed ratio (FR) schedule. In a separate cohort of rats, extracellular DA and the metabolite 3,4-dihydroxyphenylacetic acid (DOPAC) were measured in the nucleus accumbens shell following an acute amphetamine injection (1 mg/kg).
Results
“Addiction-prone” LEW rats had greater acquisition of amphetamine self-administration on a FR1 schedule compared to “addiction-resistant” F344 rats when raised in the SC environment. These genetic differences were negated in both the EC and IC environments, with enrichment buffering against self-administration and isolation enhancing self-administration in both strains. On a FR5 schedule, the isolation-induced increase in amphetamine self-administration was greater in F344 than LEW rats. While no group differences were obtained in extracellular DA, gene × environment differences were obtained in extracellular levels of the metabolite DOPAC. In IC rats only, LEW rats showed attenuation in the amphetamine-induced decrease in DOPAC compared to F344 rats. IC LEW rats also had an attenuated DOPAC response to amphetamine compared to EC LEW rats.
Conclusions
The current results demonstrate gene × environment interactions in amphetamine self-administration and amphetamine-induced changes in extracellular DOPAC in nucleus accumbens (NAc) shell. However, the behavioral and neurochemical differences were not related directly, indicating that mechanisms independent of DA metabolism in NAc shell likely mediate the gene × environment effects in amphetamine self-administration.
Journal Article
Non-Genetic Factors in Schizophrenia
Purpose of Review
We review recent developments on risk factors in schizophrenia.
Recent Findings
The way we think about schizophrenia today is profoundly different from the way this illness was seen in the twentieth century. We now know that the etiology of schizophrenia is multifactorial and reflects an interaction between genetic vulnerability and environmental contributors. Environmental risk factors such as pregnancy and birth complications, childhood trauma, migration, social isolation, urbanicity, and substance abuse, alone and in combination, acting at a number of levels over time, influence the individual’s likelihood to develop the disorder.
Summary
Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene–environment interaction and environment–environment interaction have hugely increased our knowledge of the disorder.
Journal Article
Candidate Gene-Environment Interaction Research: Reflections and Recommendations
Studying how genetic predispositions come together with environmental factors to contribute to complex behavioral outcomes has great potential for advancing the understanding of the development of psychopathology. It represents a clear theoretical advance over studying these factors in isolation. However, research at the intersection of multiple fields creates many challenges. We review several reasons why the rapidly expanding candidate gene-environment interaction (cG×E) literature should be considered with a degree of caution. We discuss lessons learned about candidate gene main effects from the evolving genetics literature and how these inform the study of cG×E. We review the importance of the measurement of the gene and environment of interest in cG×E studies. We discuss statistical concerns with modeling cGxE that are frequently overlooked. Furthermore, we review other challenges that have likely contributed to the cG×E literature being difficult to interpret, including low power and publication bias. Many of these issues are similar to other concerns about research integrity (e.g., high false-positive rates) that have received increasing attention in the social sciences. We provide recommendations for rigorous research practices for cG×E studies that we believe will advance its potential to contribute more robustly to the understanding of complex behavioral phenotypes.
Journal Article
Impact of diet and genes on murine autoimmune pancreatitis
The impact of environmental factors, such as diet, and the genetic basis of autoimmune pancreatitis (AIP) are largely unknown. Here, we used an experimental murine AIP model to identify the contribution of diet to AIP development, as well as to fine‐map AIP‐associated genes in outbred mice prone to develop the disease. For this purpose, we fed mice of an autoimmune‐prone intercross line (AIL) three different diets (control, calorie‐reduced and western diet) for 6 months, at which point the mice were genotyped and phenotyped for AIP. Overall, 269 out of 734 mice (36.6%) developed AIP with signs of parenchymal destruction, equally affecting mice of both sexes. AIP prevalence and severity were reduced by approximately 50% in mice held under caloric restriction compared to those fed control or western diet. We identified a quantitative trait locus (QTL) on chromosome 4 to be associated with AIP, which is located within a previously reported QTL. This association does not change when considering diet or sex as an additional variable for the mapping. Using whole‐genome sequences of the AIL founder strains, we resolved this QTL to a single candidate gene, namely Map3k7. Expression of Map3k7 was largely restricted to islet cells as well as lymphocytes found in the exocrine pancreas of mice with AIP. Our studies suggest a major impact of diet on AIP. Furthermore, we identify Map3k7 as a novel susceptibility gene for experimental AIP. Both findings warrant clinical translation.
Journal Article