• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.4
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• pp.251-258
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• 2018

In mathematics and computer science, an optimization problem is the problem of finding the best solution from feasible ones. In the context of genetic algorithm, the difficulty of an optimization problem can be explained in terms of problem epistasis. In biology, epistasis means that the phenotype of a gene is suppressed by one or more genes, but in an evolutionary algorithm it means the interaction between genes. In this paper, we experimentally show that problem epistasis and the performance of genetic algorithm are closely related. We compared problem epistasis (One-Max, Royal Road, and NK-Landscape) using a framework that quantifies problem epistasis based on Shannon's information theory, and could show that problem becomes more difficult as problem epistasis grows. In the case that a genetic algorithm finds the optimal solution, performance is compared through the number of generations, otherwise through the ratio of the fitness of the optimal solution to that of the best solution.

• BMB Reports
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• v.42 no.9
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• pp.617-622
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• 2009

We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-$\beta{1}$, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes.

• Genomics & Informatics
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• v.12 no.4
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• pp.216-224
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• 2014

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.21-24
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• 2016

전장유전체 연관성 연구에서 상위성 탐색은 많은 단일염기다형성 수로 인해 계산이 어렵기 때문에 네트워크에서의 탐색을 이용한 방법이 사용되고 있다. 그러나 전장유전체 연관성 연구에서 단일염기다형성들의 상위성 네트워크의 구성 역시 큰 계산 비용을 필요로 한다. 본 논문에서는 단일염기다형성과 표현형의 상호정보량을 이용한 네트워크를 구성하는데 드는 시간을 줄이는 알고리즘을 제안한다. 또한 표본 크기별로 계산 시간을 실험해 보았으며, 기존의 방법과 비교해 실행 속도가 향상됨을 보였다.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• Journal of the Korean Society of Tobacco Science
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• v.17 no.2
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• pp.114-119
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• 1995

Cultivars of Nicotiana tabacum L. normally have pink flowers, but the flue-cured tobacco mutant line, BU 8832-85, had white flower. The mutant line was crossed with five normal varieties of KF 109, NC 82, TC 499, NC 567 and Coker 176. All Fl plants showed pink flower. The progenies of F2 generations were segregated with the phenotypic ratio 9 : 3 : 4 with pink, varigated(a recombinant type) and white flower, respectively. Test-cross populations showed 1 : 1 : 2 ratios. These results showed that the white flower character was controlled by two recessive genes. The genes were designated as FFCC for pink and ffcc for white flower. The recessive gene ff was epistatic to C and c. Therefore, white flower had a recessive epistasis gene.

• Journal of the Korean Society of Tobacco Science
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• v.14 no.2
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• pp.116-125
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• 1992

This experiment was conducted to examine characteristics of agronomic characters and estimate of gene effect for several mutant characters. The genetic populations were derived from cross between 83H-5 and Hicks. There were significant difference for plant height, stlk height, leaf shape and bacterial wilt disease index except leaf number, leaf length, and what is more, F3 variance is more than Bl and B2 generation from cross 83H-5 X Hicks. Gene actions for stalk height and bacterial wilt disease were estimated by 3-parameter, and by 6- parameter model for all characters except above two characters but stalk height and bacterial wilt disease index are not significant in the additive and dominance effects. Dominant$\times$dominant epitasis for plant height, dominant and dominant$\times$dominant epistasis for leaf length, additive and additive$\times$additive and dominant$\times$dominant epistasis for leaf width, and additive and additive$\times$dominant epistasis for days to flower were appeared significant in gene action.

• Proceedings of the Korean Society of Crop Science Conference
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• 1985.10a
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• pp.16-16
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• 1985

• Korean Journal of Environmental Biology
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• v.26 no.4
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• pp.377-384
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• 2008

A cell is the product of a long period of evolution and can be represented as an optimized system (homeostasis). Stimuli from the outside environment are received by sensory apparatus on the surface of the cell and transferred through complicated pathways and eventually regulate gene expression. These signals affect cell physiology, growth, and development, and the interaction among genes in the signal transduction pathway is a critical part of the regulation. In this study, the interactions of deletion mutants and overexpression of the extracopies of the genes were used to understand their relationships to each other. Also, green fluorescent protein (GFP reporter gene) was fused to the regulatory genes to elucidate their interactions. Cooverexpression of the two genes in extracopy plasmids suggested that patS acts at the downstream of hetR in the regulatory network. The experiments using gfp fusion in different genetic background cells also indicated the epistasis relationships between the two genes. A model describing the regulatory network that controls cell development is presented.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.