• ETRI Journal
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• v.38 no.1
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• pp.206-215
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• 2016

The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

• Genomics & Informatics
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• v.15 no.4
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• pp.170-177
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• 2017

HOTAIR is an lncRNA that has been known to have an oncogenic role in different cancers. There is limited knowledge of genetic and epigenetic elements and their interactions for the gene encoding HOTAIR. Therefore, understanding the molecular mechanism and its regulation remains to be challenging. We used different in silico analyses to find genetic and epigenetic elements of HOTAIR gene to gain insight into its regulation. We reported different regulatory elements including canonical promoters, transcription start sites, CpGIs as well as epigenetic marks that are potentially involved in the regulation of HOTAIR gene expression. We identified repeat sequences and single nucleotide polymorphisms that are located within or next to the CpGIs of HOTAIR. Our analyses may help to find potential interactions between genetic and epigenetic elements of HOTAIR gene in the human tissues and show opportunities and limitations for researches on HOTAIR gene in future studies.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.9
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• pp.1218-1228
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• 2013

Fat quality is determined by the composition of fatty acids. Genetic relationships between this composition and single nucleotide polymorphisms (SNPs) in the stearoyl-CoA desaturase1 (SCD1) gene were examined using 513 Korean native cattle. Single and epistatic effects of 7 SNP genetic variations were investigated, and the multifactor dimensionality reduction (MDR) method was used to investigate gene interactions in terms of oleic acid (C18:1), mono-unsaturated fatty acids (MUFAs) and marbling score (MS). The g.6850+77 A>G and g.14047 C>T SNP interactions were identified as the statistically optimal combination (C18:1, MUFAs and MS permutation p-values were 0.000, 0.000 and 0.001 respectively) of two-way gene interactions. The interaction effects of g.6850+77 A>G, g.10213 T>C and g.14047 C>T reflected the highest training-balanced accuracy (63.76%, 64.70% and 61.85% respectively) and was better than the individual effects for C18:1, MUFAs and MS. In addition, the superior genotype groups were AATTCC, AGTTCC, GGTCCC, AGTCCT, GGCCCT and AGCCTT. These results suggest that the selected SNP combination of the SCD1 gene and superior genotype groups can provide useful inferences for the improvement of the fatty acid composition in Korean native cattle.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Journal of Preventive Medicine and Public Health
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• v.42 no.6
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• pp.356-359
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• 2009

In the more than 100 genome wide association studies (GWAS) conducted in the past 5 years, more than 250 genetic loci contributing to more than 40 common diseases and traits have been identified. Whilst many genes have been linked to a trait, both their individual and combined effects are small and unable to explain earlier estimates of heritability. Given the rapid changes in disease incidence that cannot be accounted for by changes in diagnostic practises, there is need to have well characterized exposure information in addition to genomic data for the study of gene-environment interactions. The case-control and cohort study designs are most suited for studying associations between risk factors and occurrence of an outcome. However, the case control study design is subject to several biases and hence the preferred choice of the prospective cohort study design in investigating geneenvironment interactions. A major limitation of utilising the prospective cohort study design is the long duration of follow-up of participants to accumulate adequate outcome data. The GWAS paradigm is a timely reminder for traditional epidemiologists who often perform one- or few-at-a-time hypothesis-testing studies with the main hallmarks of GWAS being the agnostic approach and the massive dataset derived through large-scale international collaborations.

• Molecules and Cells
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• v.24 no.3
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• pp.307-315
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• 2007

Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.409-417
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• 2023

Objective : The association between boule (BOLL) and endothelin receptor type A (EDNRA) loci and intracranial aneurysm (IA) formation has been reported via genome-wide association studies. We sought to identify genome-wide interactions involving BOLL and EDNRA loci for IA in a Korean adult cohort. Methods : Genome-wide pairwise interaction analyses of BOLL and EDNRA involving 250 patients with IA and 296 controls were performed using the additive effect model after adjusting for confounding factors. Results : Among 512575 single-nucleotide polymorphisms (SNPs), 23 and 11 common SNPs suggested a genome-wide interaction threshold (p<1.25×10^-8) involving rs700651 (BOLL) and rs6841581 (EDNRA). Rather than singe SNP effect of BOLL or EDNRA on IA development, they showed a synergistic effect on IA formation via multifactorial pair-wise interactions. The rs1105980 of PTCH1 gene showed the most significant interaction with rs700651 (natural log-transformed odds ratio [lnOR], 1.53; p=6.41×10^-11). The rs74585958 of RYK gene interacted strongly with rs6841581 (lnOR, -19.91; p=1.64×10^-9). Although, there was no direct interaction between BOLL and EDNRA variants, two EDNRA-interacting gene variants of TNIK (rs11925024 and rs1231) and FTO (rs9302654), and one BOLL-interacting METTL4 gene variant (rs549315) exhibited marginal interaction with BOLL gene. Conclusion : BOLL or EDNRA may have a synergistic effect on IA formation via multifactorial pair-wise interactions.

• Journal of Life Science
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• v.6 no.3
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• pp.185-192
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• 1996

Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, is required for T7 DNA replication, recombination, and repair. T7 gene 2.5 protein has two distinctive domains, DNA binding and C-terminal domain, directly involved in protein-protein interaction. Gene 2.5 protein participates in the DNA replication of Bacteriophage T7, which makes this protein essential for the T7 growth and DNA replication. What gene 2.5 protein makes important at T7 growth and DNA replication is its binding affinity to single-stranded DNA and the protein-protein important at T7 DNA replication proteins which are essential for the T7 DNA synthesis. We have constructed pGST2.5(WT) encoding the wild-type gene 2.5 protein and pGST2.5$\Delta $21C lacking C-terminal 21 amino acid residues. The purified GST-fusion proteins, GST2.5(WT) and GST2.5(WT)$\Delta$21C, were used for whether the carboxyl-terminal domain participates in the protein-protein interactions or not. GST2.5(WT) and GST2.5$\Delta$21C showed the difference in the protein-protein interaction. GST2.5(WT) interacted with T7 DNA polymerase and gene 4 protein, but GST2.5$\Delta$21C did not interact with either protein. Secondly, GST2.5(WT) interacts with gene 4 proteins (helicase/primase) but not GST2.5$\Delta$21C. these results proved the involvement of the carboxyl-terminal domain of gene 2.5 protein in the protein-protein interaction. We clearly conclude that carboxy-terminal domain of gene 2.5 protein is firmly involved in protein-protein interactions in T7 replication proteins.

• Genomics & Informatics
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• v.15 no.4
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• pp.114-122
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• 2017

It is becoming increasingly clear that eukaryotic genomes are subjected to higher-order chromatin organization by the CCCTC-binding factor/cohesin complex. Their dynamic interactions in three dimensions within the nucleus regulate gene transcription by changing the chromatin architecture. Such spatial genomic organization is functionally important for the spatial disposition of chromosomes to control cell fate during development and differentiation. Thus, the dysregulation of proper long-range chromatin interactions may influence the development of tumorigenesis and cancer progression.