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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.

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.

Behavioral genetics studies and new empirical evidence suggest that depression cannot simply be explained by the influence of single genes but that gene–gene–environment interactions are important to better understanding the etiology of depression. The present study investigated the main and interactive effects of COMT gene Val158Met polymorphism, DAT1 gene rs27072 polymorphism, and peer relationships (i.e., peer acceptance and rejection) on adolescent depressive symptoms. In a sample of 1045 Chinese Han adolescents (Mage = 12.34 ± 0.47 years, 50.1% girls), saliva samples, self-reported depressive symptoms and within-classroom peer nominations were collected. After controlling for gender, age, and SES, the three-way interaction of COMT, DAT1, and peer acceptance significantly concurrently predicted adolescent depressive symptoms. Adolescents with ValVal genotype of COMT and CC genotype of DAT1 were more sensitive to acceptance, compared to their counterparts carrying other combined genotypes. However, a similar three-way interaction was not significant in the case of peer rejection. Additionally, the split-half validation generally replicated these findings. More importantly, this study underscores complex polygenic underpinnings of depression and lends support for the gene–gene–environment interactions implicated in the etiology of depressive symptoms.

Summary Inconsistent results of association studies investigated the role of glutathione S–transferase genes in idiopathic male infertility may be explained by ethnical differences in gene–gene and gene–environment interactions. In this study, we investigated a joint contribution of GSTM1, GSTT1 and GSTP1 gene polymorphisms and cigarette smoking to the risk of idiopathic infertility in Russian men. DNA samples from 203 infertile and 227 fertile men were genotyped by a multiplex polymerase chain reaction (GSTM1 and GSTT1 deletions) and PCR‐restriction fragment length polymorphism (GSTP1 I105V) methods. The GSTP1 genotype 105IV was associated with increased risk of male infertility (OR = 1.50 95% CI 1.02–2.20 P = 0.04). Genotype combinations GSTP1 105II/GSTT1 del (G1), GSTM1 del/GSTT1 del (G2) and GSTM1 + /GSTT1 del (G3) were associated with decreased risk of male infertility (P ≤ 0.003), whereas a genotype combination GSTP1 105IV/GSTT1 + (G4) was associated with increased disease risk (P = 0.001). The genotype combinations G3 and G4 showed a significant association with infertility in smokers; however, nonsmokers carriers did show the disease risk. In conclusion, GSTM1, GSTT1 and GSTP1 genes are collectively involved in the development of idiopathic male infertility and their phenotypic effects on the disease risk are potentiated by cigarette smoking.

(1) Background: Numerous studies suggest strong associations between childhood maltreatment and nonsuicidal self-injury (NSSI); this is also true for the roles of dopaminergic genes in the etiology of some psychopathologies related to NSSI. Investigating the interactions of environments and genes is important in order to better understand the etiology of NSSI. (2) Methods: Within a sample of 269 Chinese male adolescents (Mage = 14.72, SD = 0.92), childhood maltreatment and NSSI were evaluated, and saliva samples were collected for MAOA T941G and COMT Val158Met polymorphism analyses. (3) Results: The results revealed no primary effects attributable to MAOA T941G and COMT Val158Met polymorphism on NSSI. However, there was a significant three-way interaction between MAOA, COMT, and child abuse (β = −0.34, p < 0.01) in adolescent NSSI. Except for carriers of the T allele of MAOA and the Met allele of COMT, all studied male adolescents displayed higher NSSI scores when exposed to a higher level of child abuse. A similar three-way interaction was not observed in the case of child neglect. (4) Conclusions: The results indicate that the MAOA gene and COMT gene play moderating roles in the association between child abuse and NSSI of male adolescents and suggest the polygenic underpinnings of NSSI.

Depressive disorders are influenced by a complex interplay between genetic and environmental factors. Multiple studies support a role of serotonergic pathways in the pathophysiology of depressive disorders. As a rate-limiting enzyme of serotonin synthesis in the brain, tryptophan hydroxylase 2 (TPH2) represents a plausible candidate gene. This also applies to the serotonin reuptake transporter (5-HTTLPR) regulating the availability of serotonin in the synaptic gap. We hypothesize that functional polymorphisms (TPH2: rs7305115, 5-HTTLPR and rs25531) within both genes contribute to the risk of depressive disorders after childhood abuse in adult life. To confirm our results, we investigated two independent samples of Caucasian subjects from the study of health in Pomerania (SHIP–LEGEND: n  = 2,029 and SHIP–TREND-0: n  = 2,475). Depression severity was assessed by the Beck depression inventory (BDI-II) for LEGEND and the patient health questionnaire (PHQ-9) for TREND-0. Childhood abuse was assessed by the childhood trauma questionnaire. Rs7305115 (TPH2) revealed significant effects in SNP × abuse and SNP × SNP as well as in the three-way interaction. This three-way interaction among abuse, TPH2 and 5-HTTLPR showed a significant effect on depression score ( p  = 0.023). The SS genotype of 5-HTTLPR was associated with increased depression scores after childhood abuse only in carriers of the low-expression TPH2 GG genotype, whereas the TPH2 AA genotype reversed this effect. Our results support the role of interaction effects of genetic variants within serotonergic pathways. Genetic variants that may decrease the presynaptic serotonin concentration were associated with increased adult depressive symptoms in subjects with childhood abuse.

Background: Allergic rhinitis (AR) is a nasal inflammatory disease resulting from a complex interplay between genetic and environmental factors. The association between Toll-like receptor (TLR) signaling pathway and environmental factors in AR pathogenesis remains to be explored. This study aims to assess the genetic association of AR with single nucleotide polymorphisms (SNPs) in TLR signaling pathway, and investigate the roles of gene-gene and gene-environment interactions in AR. Methods: A total of 452 AR patients and 495 healthy controls from eastern China were enrolled in this hospital-based case-control study. We evaluated putatively functional genetic polymorphisms in TLR2, TLR4 and CD14 genes for their association with susceptibility to AR and related clinical phenotypes. Interactions between environmental factors (such as traffic pollution, residence, pet keeping) and polymorphisms with AR were examined using logistic regression. Models were stratified by genotype and interaction terms, and tested for the significance of gene-gene and gene-environment interactions. Results: In the single-locus analysis, two SNPs in CD14, rs2563298 (A/C) and rs2569191 (CAT) were associated with a significantly decreased risk of AR. Compared with the GG genotype, the GT and GT/TT genotypes of TLR2 rs7656411 (GAT) were associated with a significantly increased risk of AR. Gene-gene interactions (eg, TLR2 rs7656411, TLR4 rsl927914, and CD14 rs2563298) was associated with AR. Gene-environment interactions (eg, TLR4 or CD14 polymorphisms and certain environmental exposures) were found in AR cases, but they were not significant after Bonferroni correction. Conclusion: The genetic polymorphisms of TLR2 and CD14 and gene-gene interactions in TLR signaling pathway were associated with susceptibility to AR in this Han Chinese population. However, the present results were limited to support the association between gene-environment interactions and AR. Keywords: allergic rhinitis, toll-like receptors, CD14, single nucleotide polymorphism, gene-gene interaction, gene-environment interaction

The detection of gene-gene and gene-environment interactions associated with complex human disease or pharmacogenomic endpoints is a difficult challenge for human geneticists. Unlike rare, Mendelian diseases that are associated with a single gene, most common diseases are caused by the non-linear interaction of numerous genetic and environmental variables. The dimensionality involved in the evaluation of combinations of many such variables quickly diminishes the usefulness of traditional, parametric statistical methods. Multifactor dimensionality reduction (MDR) is a novel and powerful statistical tool for detecting and modelling epistasis. MDR is a non-parametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies. MDR has detected interactions in diseases such as sporadic breast cancer, multiple sclerosis and essential hypertension. As this method is more frequently applied, and was gained acceptance in the study of human disease and pharmacogenomics, it is becoming increasingly important that the implementation of the MDR approach is properly understood. As with all statistical methods, MDR is only powerful and useful when implemented correctly. Concerns regarding dataset structure, configuration parameters and the proper execution of permutation testing in reference to a particular dataset and configuration are essential to the method's effectiveness. The detection, characterisation and interpretation of gene-gene and gene-environment interactions are expected to improve the diagnosis, prevention and treatment of common human diseases. MDR can be a powerful tool in reaching these goals when used appropriately.

The importance of gene-–gene and gene-–environment interactions in the underlying genetic architecture of common, complex phenotypes is gaining wide recognition in the field of pharmacogenomics. In epidemiological approaches to mapping genetic variants that predict drug response, it is important that researchers investigate potential epistatic interactions. In the current review, we discuss data-mining tools available in genetic epidemiology to detect such interactions and appropriate applications. We survey several classes of novel methods available and present an organized collection of successful applications in the literature. Finally, we provide guidance as to how to incorporate these novel methods into a genetic analysis. The overall goal of this paper is to aid researchers in developing an analysis plan that accounts for gene-–gene and gene-–environment in their own work.

We develop an information-theoretic method for gene-gene (GGI) and gene-environmental interactions (GEI) analysis of syndromes, defined as a phenotype vector comprising multiple quantitative traits (QTs). The K-way interaction information (KWII), an information-theoretic metric, was derived for multivariate normal distributed phenotype vectors. The utility of the method was challenged with three simulated data sets, the Genetic Association Workshop-15 (GAW15) rheumatoid arthritis data set, a high-density lipoprotein (HDL) and atherosclerosis data set from a mouse QT locus study, and the 1000 Genomes data. The dependence of the KWII on effect size, minor allele frequency, linkage disequilibrium, population stratification/admixture, as well as the power and computational time requirements of the novel method was systematically assessed in simulation studies. In these studies, phenotype vectors containing two and three constituent multivariate normally distributed QTs were used and the KWII was found to be effective at detecting GEI associated with the phenotype. High KWII values were observed for variables and variable combinations associated with the syndrome phenotype compared with uninformative variables not associated with the phenotype. The KWII values for the phenotype-associated combinations increased monotonically with increasing effect size values. The KWII also exhibited utility in simulations with non-linear dependence between the constituent QTs. Analysis of the HDL and atherosclerosis data set indicated that the simultaneous analysis of both phenotypes identified interactions not detected in the analysis of the individual traits. The information-theoretic approach may be useful for non-parametric analysis of GGI and GEI of complex syndromes.