• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2745-2749
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• 2015

Objective: To study the PLCE1 gene rs2274223 polymorphism with regard to esophageal cancer and its interaction with diet, lifestyle, psychological and environmental factors in Southwest Shandong province. Materials and Methods: A case series study (case-case) was conducted. Questionnaire data were collected and 3 ml-5ml venous blood was drawn for DNA extraction among the qualified research subjects. PLCE1 gene polymorphism was detected after PCR amplification of DNA. SPSS 13.0 software was used for statistical analysis of the data. Results: The three genotypes A/A, A/G and G/G PLCE1 gene rs2274223 was 31, 16 and 4 cases, accounting for 60.8%, 31.4%, 0.08% respectively. The difference of three genotypes (AA/GA/GG) proportion between negative and positive family history of patients was statistically significant, ${\chi}^2=6.213$, p=0.045. There was no statistically significant relationship between PLCE1 gene rs2274223 polymorphism and smoking, drinking, ${\chi}^2=0.119$, p=0.998, and ${\chi}^2=1.727$, p=0.786. There was no linkage of the three rs2274223 PLCE1 gene genotypes (AA/GA/GG) proportion with eating fried, pickled, hot, mildew, overnight, smoked, excitant food, eat speed, salt taste or not (p>0.05). or with living environment pollution and nine risk factors of occupational exposure (p>0.05). There was no statistically significant difference in TS scores between different genotype of rs2274223 PLCE1 gene. Conclusions: The PLCE1 rs2274223 polymorphism has a relationship with family history of esophageal cancer, but does not have any significant association with age, gender, smoking, alcohol drinking, food hygiene, eating habits, living around the environment and occupation in cases.

• Archives of Plastic Surgery
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• v.39 no.3
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• pp.184-189
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• 2012

Keloid disease is a fibroproliferative dermal tumor with an unknown etiology that occurs after a skin injury in genetically susceptible individuals. Increased familial aggregation, a higher prevalence in certain races, parallelism in identical twins, and alteration in gene expression all favor a remarkable genetic contribution to keloid pathology. It seems that the environment triggers the disease in genetically susceptible individuals. Several genes have been implicated in the etiology of keloid disease, but no single gene mutation has thus far been found to be responsible. Therefore, a combination of methods such as association, gene-gene interaction, epigenetics, linkage, gene expression, and protein analysis should be applied to determine keloid etiology.

• Genomics & Informatics
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• v.20 no.1
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Korean Journal of Biological Psychiatry
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• v.15 no.1
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• pp.29-34
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• 2008

Objectives : This study aimed to test potential modifying effects of education on the association between apolipoprotein E ${\varepsilon}4$ (Apo E4) and cognitive decline. Methods : A community cohort(N=683) aged 65 or over completed the Korean version of Mini-Mental State Examination(MMSE-K) at baseline and two years later(1999-2001). Apo E polymorphisms were genotyped, and classified into that with or without Apo E4. Educational levels were categorized into people with or without education. Covariates included demographic(age, gender), life style(smoking, alcohol drinking), clinical (depression, sleep disorder, vascular risk factors) characteristics. Results : The association between Apo E4 and cognitive decline was significant only in the old persons with no education. The interaction term between education and Apo E4 on cognitive decline was significant(p=0.040). Conclusion : Elders with no education might be more vulnerable to the impact of Apo E4 on cognitive decline, which suggests gene-environment interaction.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.861-869
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• 2011

One of the main objects of recent genetic studies is to understand genetic factors that induce complex diseases. If there are interactions between loci, it is difficult to find such associations through a single-locus analysis strategy. Thus we need to consider the gene-gene interactions and/or gene-environment interactions. The MDR(multifactor dimensionality reduction) method is being used frequently; however, it is not appropriate to detect interactions caused by a small fraction of the possible genotype pairs. In this study, we propose a relative risk interaction explorer that detects interactions through the calculation of the relative risks between the control and disease groups from each genetic combinations. For illustration, we apply this method to MDR open source data. We also compare the MDR and the proposed method using the simulated data eight genetic models.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.3
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• pp.141-146
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• 2010

This summary of literature during the past year reviews published studies relating to risk factors for depressive disorders in children and adolescents. Risk factors include environmental toxins, socio-environmental, and genetic factors. As depression has a complex, multifactorial causal mechanism, it is likely that the accumulation and/ or interaction among multiple risk factors lead to depression. Findings related to the result of toxin exposure have been difficult to interpret given that risk factors tend to interact and that higher mental functions are not easily measurable. However, some findings have been consistent. Clinical research data has also shown that the risk for negative outcomes may be modified both by genetic and environmental factors through a gene environment interplay mechanism.

• Journal of Society of Preventive Korean Medicine
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• v.14 no.2
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• pp.1-12
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• 2010

The individual differences in disease development and susceptibility have been researched primarily on the subject of genes, environment or the interaction between genes and the environment respectively. However, there have been limitations in explaining complex diseases, and the differences in health and diseases in monozygotic and dizygotic twins. Fortunately, thanks to active research on the relationship between genes and the environment, and epigenetics, there has been much progress in the understanding of body's reactions and changes. Epigenetics is referred to as a study of gene expression through the interactions of DNA methylation, chromatin's histone and the change of structure in tail, RNA editing without any change in DNA sequence. In this paper, we introduce the basic concepts and mechanisms of epigenetics. The result of the epigenetics is heritable ; can regulate gene expressions ; is reversible ; and has many variable forms depending on cell types. The influences of epigenetics occur throughout life, but it is mainly determined in utero during early pregnancies. Diseases occur or the risk rises if these influences continue after birth until adult life when problems occur in excess/lack of nutrition, environmental plasticity, or already inputted data. Therefore, there is a need for change and innovation, especially in interest and investment in health education for young women near pregnancies and correct treatment of epigenetic-related diseases.

• Journal of Korean Biological Nursing Science
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• v.25 no.1
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• pp.73-85
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• 2023

Purpose: This study focused on identifying the interaction effects of genetic and lifestyle-environmental factors on the development of type 2 diabetes mellitus (T2D). Methods: Study subjects were selected from the Korean Genome and Epidemiology Study (KoGES) from 2001 to 2014. Data on genetic variations, anthropometric measurements, biochemical data, and seven lifestyle factors (diet, physical activity, alcohol drinking, smoking, sleep, depression, and stress) were obtained from 4,836 Koreans aged between 40 and 59 years, including those with T2D at baseline (n = 1,209), newly developed T2D (n= 1,298) and verified controls (n = 3,538). The genetic risk score (GRS) was calculated by using 11 single-nucleotide polymorphisms (SNPs) related to T2D development and the second quartile was used as the reference category. A Cox proportional hazards regression model was used to evaluate the associations of GRS and lifestyle factors with T2D risk, controlling for covariates. Results: Multivariate regression analysis revealed that GRS was the strongest risk factor for T2D, and body mass index (BMI), smoking, drinking, and spicy food preference also increased the risk. Lifestyle/environmental factors that showed significant interactions with GRS were BMI, current smoking, current drinking, fatty food preference, and spicy food preference. Conclusions: Interactions between genetic factors and lifestyle/environmental factors were associated with an increased risk of T2D. The results will be useful to provide a new perspective on genetic profiling for the earlier detection of T2D risk and clues for personalized interventions, which might be more effective prevention strategies or therapies in individuals with a genetic predisposition to T2D.

• Korean Journal of Biological Psychiatry
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• v.15 no.1
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• pp.14-22
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• 2008

Objectives : The authors purposed to present data for explaining gene-environmental interaction causing depressive disorder by examining the effects of genetic factors related to the serotonin system and environmental factors such as stressful life events in early adulthood. Methods : The subjects were 150 young adults(mean age 25.0${\pm}$0.54), a part of 534 freshmen who had completed the previous study of genotyping of TPH1 gene. We assessed characteristics of life events, depression and anxiety scale and checked if they had a depressive disorder with DSM-IV SCID interview. Along with TPH1 A218C genotype confirmed in previous study, TPH2 -1463G/A and 5HTR2A -1438A/G genes were genotyped using the SNaPshot$^{TM}$ method. Results : In comparison with the group without C allele of TPH1 gene, the number of life events had a significant effect on the probability of depressive disorder in the group with C allele. Other alleles or genotypes did not have a significant effect on the causality of life events and depressive disorder. Conclusion : The results of this study suggest that TPH1 C allele is a significant predictor of onset of depressive disorder following environmental stress. It means that the TPH1 gene may affect the gene-environmental interaction of depressive disorder.