• Journal of Environmental Health Sciences
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• v.36 no.1
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• pp.1-13
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• 2010

Evidences supporting gene-environment interaction are accumulating in terms of environmental exposure including lifestyle factors and related genetic variants. One form of defense mechanism against cancer development involves a series of genes whose role is to metabolize (activation/detoxification) and excrete potentially toxic compounds and to repair subtle mistakes in DNA. The purpose of this article is to provide a brief review of the notion of gene-environment interaction, environmental/occupational carcinogens and related cancers, and previous studies of gene-environment interaction on cancers caused by exposure to carcinogenesis. With a number of studies on the interaction between lifestyle factors (e.g., smoking and diet) and genetic polymorphisms in genes involved in xenobiotic metabolism and DNA repair excluded, only several studies have been conducted on the interactive effects between polymorphisms of CYPs, GSTs, ERCCs, XRCCs and environmental/occupational carcinogens such as vinyl chloride, benzo[a]pyrene, and chloroform on carcinogenesis or genotoxicity. Future studies may need to be conducted with sufficient number of subjects and based on occupational cohorts to provide useful information in terms of advanced risk assessment and regulation of exposure level.

• Korean Journal of Biological Psychiatry
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• v.17 no.2
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• pp.65-69
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• 2010

Gene-environment interactions are important in pathogenesis of suicide or suicidal behavior. Twin and adoption studies and family studies show that genetic factors play a critical role in suicide or suicidal behavior. Given the strong association between serotonergic neurotransmission and suicide, recent molecular genetic studies have focused on polymorphisms of serotonin genes, especially on serotonin transporter and tryptophan hydroxylase genes. Some studies have revealed a significant interaction between s allele of the serotonin transporter gene and the risk of suicide attempt associated with childhood trauma. In addition, the polymorphism of brain-derived neurotrophic factor gene also may influence the effect of childhood trauma in relation to the risk of attempting suicide. Future studies should explore genetic and environmental factors in suicide or suicidal behavior and examine for gene and environment interaction.

• Animal cells and systems
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• v.8
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• pp.51-51
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• 2004

• Genomics & Informatics
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• v.7 no.1
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• pp.20-25
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• 2009

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that is characterized by inflammation of the synovial tissue and deterioration of the joint and bone. A recent study reported a potential gene-environment interaction between HLA-DR and smoking. The present study investigated whether a specific gene was related to the association between smoking and the severity of RA (rheumatoid factor levels > 20 IU/ml). We used the resources of the NARAC family collection of GAW 15 databases, and 1139 subjects with RF>20 IU/ml were included in the current analysis. The linkage panel contained 5858 SNP markers, and 5744 SNPs passed quality control criteria. Linear regression analyses, using PLINK software and generalized estimating equation regression models, were used to test for associations between the SNPs and the severity of RA according to smoking groups. Two major findings were established. First, the severity of RA in smokers was associated with rs703618 (p=$6{\times}10^{-5}$), which lies in the intronic region of the stabilin 2 (STAB2) gene on chromosome 12. Second, there were significant differences in the levels of RF between 'ever smokers' and 'never smokers' according to the rs703618 genotype (G/G, A/G, A/A). We investigated whether a specific gene acts as a mediator between smoking and the severity of RA and found that the STAB2 gene could affect this relationship. Our finding indicates that smoking may mediate RA severity by affecting the expression level of a specific gene.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.108-113
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• 2007

When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.

• Journal of Preventive Medicine and Public Health
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• v.49 no.5
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• pp.253-259
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• 2016

Objectives: Central obesity plays a major role in the development of many chronic diseases, including cardiovascular disease and cancer. Chronic stress may be involved in the pathophysiology of central obesity. Although several large-scale genome-wide association studies have reported susceptibility genes for central adiposity, the effects of interactions between genes and psychosocial stress on central adiposity have rarely been examined. A recent study focusing on Caucasians discovered the novel gene early B-cell factor 1 (EBF1), which was associated with central obesity-related traits via interactions with stress levels. We aimed to evaluate EBF1 gene-by-stress interaction effects on central adiposity traits, including visceral adipose tissue (VAT), in Korean adults. Methods: A total of 1467 Korean adults were included in this study. We selected 22 single-nucleotide polymorphisms (SNPs) in the EBF1 gene and analyzed their interactions with stress on central adiposity using additive, dominant, and recessive genetic modeling. Results: The four SNPs that had strong linkage disequilibrium relationships (rs10061900, rs10070743, rs4704967, and rs10056564) demonstrated significant interactions with the waist-hip ratio in the dominant model ($p_{int}$<0.007). In addition, two other SNPs (rs6556377 and rs13180086) were associated with VAT by interactions with stress levels, especially in the recessive genetic model ($p_{int}$<0.007). As stress levels increased, the mean values of central adiposity traits according to SNP genotypes exhibited gradual but significant changes (p<0.05). Conclusions: These results suggest that the common genetic variants for EBF1 are associated with central adiposity through interactions with stress levels, emphasizing the importance of managing stress in the prevention of central obesity.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2031-2037
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• 2012

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

• Korean Journal of Biological Psychiatry
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• v.20 no.4
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• pp.129-135
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• 2013

Aggression can be defined as 'behavior intended to harm another' which can be seen both from humans and animals. However, trying to understand aggression in a simplistic view may make it difficult to develop an integrated approach. So, we tried to explain aggression in a multidisciplinary approach, affected by various factors such as neuroanatomical structures, neurotransmitter, genes, and sex hormone. Parallel with animal models, human aggression can be understood with two phenomena, offensive aggression and defensive aggression. Neurobiological model of aggression give a chance to explain aggression with an imbalance between prefrontal regulatory influences and hyper-reactivity of the subcortical areas involved in affective evaluation, finally in an aspect of brain organization. Serotonin and GABA usually inhibit aggression and norepinephrine while glutamate and dopamine precipitate aggressive behavior. As there is no one gene which has been identified as a cause of aggression, functions between gene to gene interaction and gene to environment interaction are being magnified. Contributions of sex hormone to aggression, especially molecular biologic interaction of testosterone and regulation of estrogen receptor have been emphasized during the research on aggression. This multidisciplinary approach on aggression with types, neurochemical bases, and animal models can bring integrated interpretation on aggression.

• Environmental Analysis Health and Toxicology
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• v.25 no.4
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• pp.259-262
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• 2010

In this review, we give an overview of psychiatric perspectives on environmental disease. The concept of genetic heritability and its meaning with regard to environmental risk factors will be discussed. Recent ideas of gene-environment interaction and neurodevelopmental disorder in psychiatry will also be introduced. This article discusses the environmental risk factors for attention deficit hyperactivity disorder (ADHD) and autism, the two major environmental diseases and neurodevelopmental disorders in psychiatry. Given that both ADHD and autism are complex conditions, the etiology is likely to involve multiple genes of moderate effect interacting with environmental factors. We will introduce recent environmental issues related to these two disorders.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.31-36
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• 2005

An identification and characterization of susceptibility genes for common complex multifactorial diseases is a challengeable task, in which the effect of single genetic variation will be likely dependent on other genetic variations(gene-gene interaction) and environmental factors (gene-environment interaction). To address is issue, the multifactor dimensionality reduction (MDR) has been proposed and implemented by Ritchie et al. (2001), Moore et al. (2002), Hahn et al.(2003) and Ritchie et al. (2003). With MDR, multilocus genotypes effectively reduce the dimension of genotype predictors from n to one, which improves the identification of polymorphism combinations associated with disease risk. However, MDR cannot handle missing observations appropriately, in which missing observation is treated as an additional genotype category. This approach may suffer from a sparseness problem since when high-order interactions are considered, an additional missing category would make the contingency table cells more sparse. We propose a new MDR approach with minimum loss of sample sizes by considering missing data over all possible multifactor classes. We evaluate the proposed MDR by using the prediction errors and cross validation consistency.