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

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

• Journal of the Korean Society of Tobacco Science
• /
• v.17 no.2
• /
• pp.114-119
• /
• 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
• /
• v.14 no.2
• /
• pp.116-125
• /
• 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.

• Korean Journal of Environmental Biology
• /
• v.26 no.4
• /
• pp.377-384
• /
• 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
• /
• v.14 no.4
• /
• pp.160-165
• /
• 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
• /
• v.8 no.1
• /
• pp.113-115
• /
• 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
• /
• v.30 no.2
• /
• pp.188-194
• /
• 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.

• Journal of Life Science
• /
• v.28 no.4
• /
• pp.398-407
• /
• 2018

The Purple pericarp (Prp) trait is a trait often bred for in black rice. Generally, the Prp trait is displayed in the color variations of seeds following the 9:3:4 purple, brown, and white ratio, respectively. The Prp trait is a recessive epistasis of two gene interactions; however, it is caused by the two complementation genes Pb and Pp. Here we present a study of the genetic characteristics of the Prp trait using an $F_1$ hybrid with a Pbpb Pppp genotype. This hybrid generated four seed colors with the following numbers: 3 dark purple, 6 medium purple, 3 brown, and 4 white (or 9 purple, 3 brown, and 4 white). However, further biochemical analysis of the all progenies divided them into two groups. One group had the Pb_ Pp_ allelic constitutions and contained cyanidin 3-O-glucoside (C3G) in both the dark purple or medium purple seeds. The other group, however, was absent of C3G in both the brown and white seeds, resulting in a ratio of 9:7, respectively. This segregation revealed the extended Mendelian 9:7 ratios of the complementary gene interactions with a good fitness in ${\chi}^2$ analysis. Further analysis revealed that brown seeds with the Pb_ pppp genotype corresponded with a null C3G, indicating that the Brown pericarp trait in rice is caused by a dominant allele of the Pb gene. Therefore, we conclude that the production of C3G is a main phenotype of the black and purple colored rice in the Prp trait, and it is governed by the complementary gene interactions between Pb and Pp genes.

• Genomics & Informatics
• /
• v.10 no.4
• /
• pp.256-262
• /
• 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.