• Genomics & Informatics
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• 제12권4호
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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.

• 예술인문사회 융합 멀티미디어 논문지
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• 제8권4호
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• pp.251-258
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• 2018

수학과 컴퓨터 과학 분야에서 최적화 문제란 가능한 모든 해 중에서 가장 좋은 해를 찾는 문제이다. 유전 알고리즘에서 최적화 문제의 어려운 정도는 상위의 측면에서 설명될 수 있다. 생물학에서 상위는 유전자의 표현형이 하나 혹은 그 이상의 유전자에 의해 억제되는 것을 의미하지만 진화 알고리즘에서는 유전자들 사이의 상호작용을 의미한다. 본 논문에서는 상위와 유전 알고리즘이 최적 해를 찾는 시간 사이의 상관관계를 실험적으로 확인하였다. Shannon의 정보 이론에 근거해 상위를 수치화하는 프레임워크를 사용하여 다양한 문제(One-Max, Royal Road, NK-Landscape)의 상위를 비교하였고, 그 결과 상위가 커짐에 따라 문제가 어려워져 최적 해를 찾기 어려운 경향이 있음을 확인하였다. 성능은 주어진 세대 안에 최적 해를 찾는 경우 최적 해를 찾는 데까지 걸린 세대 수로 비교하였고, 최적 해를 찾지 못하는 경우 최적 해의 적합도에 대한 주어진 세대 동안 찾은 적합도가 가장 높은 해의 적합도 비율로 비교하였다.

• 한국연초학회지
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• 제17권2호
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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.

• 한국연초학회지
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• 제14권2호
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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.

• 환경생물
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• 제26권4호
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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.

• Genomics & Informatics
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• 제14권4호
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• pp.160-165
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• 2016

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

• International Journal of Industrial Entomology and Biomaterials
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• 제8권1호
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• pp.113-115
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• 2004

Six generations (P$_1$, P$_2$, F$_1$, BC$_1$, BC$_2$ and F$_2$) of four bivoltine silkworm crosses (SKAU-R-1 ${\times}$ Yakwei, SKAU-R-6 ${\times}$ Yakwei, CSR$_2$ ${\times}$ CSR$_4$ and SH$_{6}$${\times}$ NB$_4$D$_2$) were evaluated in a completely randomized block design with 5 replications for each treatment. The generation mean 2 in respect of 3 metric traits (single cocoon weight, single shell weight, and shell ratio ％), were subjected to Cavallis joint scaling test. Additive dominance model was found to be adequate in CSR$_2$${\times}$CSR$_4$ and SH$_{6}$${\times}$ NB$_4$D$_2$ for single cocoon weight and shell weight and SKAU-R-6 ${\times}$ Yakwei for shell ratio(％). Whereas, in rest of the crosses epistasis was evident in the traits under investigation. Magnitude of additive gene effect (d) was greater than dominance(h) in SH$_{6}$${\times}$NB$_4$D$_2$ and SKUA-R-6${\times}$Yakwei for shell ratio (％) and in CSR$_2$${\times}$ CSR$_4$ for shell weight. Thus selection for these traits in early segregating generations of these crosses would be effective for obtaining considerable genetic gain. gain.

• The Plant Pathology Journal
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• 제30권2호
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• pp.188-194
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• 2014

A comprehensive study was conducted using PPSMV resistant (BSMR 736) and susceptible (ICP 8863) genotypes to develop a segregating population and understand the inheritance of PPSMV resistance. The observed segregation was comparable to 13 (susceptible): 3 (resistant). Hence, the inheritance was controlled by two genes, SV1 and SV2, with inhibitory gene interaction.

• 생명과학회지
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• 제28권4호
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• pp.398-407
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• 2018

벼 자색 종자과피(Purple pericarp, Prp) 형질은 주요 생리활성물질인 안토시아닌 C3G 생성에 관여하며 흑미를 결정하는 주요 유전형질이다. Prp 유전형질을 가진 흑미와 종자과피에 색이 없는 벼를 교배할 경우 그 후대는 검정색, 갈색, 백색이 각각 9:3:4로 분리된다. 1921년 Nagai에 의하여 제시된 바 벼 종자 색의 9:3:4 유전분리비로 인하여 벼 Prp 형질은 유전자의 열성상위(recessive epistasis) 현상으로 해석되었다. 그러나 흑미를 결정하는 Prp 형질은 두 개의 상보적 유전자들의 상호관계(complementary gene interaction)에 의한 것이기도 하다. 본 연구에서는 이러한 논란이 발생되는 이유를 설명하기 위하여 두 유전자의 조성이 완전한 이형접합인 Pbpb Pppp 유전자형을 가진 $F_1$ 잡종을 만들었다. 이들의 자손은 진한자색(검정), 중간자색, 갈색, 백색 종자이며 각각 3:6:3:4로 분리되었다. 즉, 검정색, 갈색, 백색의 종자가 각각 9:3:4의 비율로 분리된다. 그러나 생화학적인 분석결과 이들은 안토시아닌 중 cyanidin 3-O-glucoside (C3G)가 축적된 검정색 종자와 C3G가 없는 갈색 또는 백색 종자인 두 개의 집단을 분리되며 정확히 9:7의 분리 비를 갖는다. 이 경우 벼 Prp 형질을 갖는 검정쌀 또는 흑미는 전형적인 상보적 유전자의 상호관계에 의한 유전현상이다. 즉, 흑미의 자색 종피 형질 발현에는 Pb 유전자와 Pp 유전자에서 각각 한 개 이상의 우성대립인자의 발현이 필요하다. 그러나 Pb 유전자만 우성대립인자가 존재하는 Pb_ pppp 유전자형의 벼는 C3G를 생성하지 못하고 갈색 종자과피(Brown pericarp, Brp) 형질을 갖게 된다. 즉 갈색쌀는 우성 Pb 유전자의 우성대립인자에 의하여 결정된다. 그러므로 종피색을 결정하는 Prp 형질의 유전양상은 열성상위 현상으로 보이나 흑미의 결정요소인 안토시아닌 C3G의 함유 여부에 관한 유전분석을 시행하면 9:7의 비율로서 전형적인 두 개의 유전자가 모두 관여하는 상보적 유전현상이다. 유전적 정의는 유전자의 최종산물에 의한 물리적 또는 화학적 결정이다. 그러므로 결론하여 검정 쌀의 주요 생리활성물질인 안토시아닌 C3G 생성에 관한 유전현상은 Pb와 Pp 유전자의 상보적 유전자의 상호에 의한 것이다.

• Genomics & Informatics
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• 제10권4호
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• pp.256-262
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• 2012

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.