• Clinical and Experimental Pediatrics
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• v.62 no.9
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• pp.325-333
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• 2019

Allergic diseases, including allergic rhinitis, asthma, and atopic dermatitis, are common heterogeneous diseases that encompass diverse phenotypes and different pathogeneses. Phenotype studies of allergic diseases can facilitate the identification of risk factors and their underlying pathophysiology, resulting in the application of more effective treatment, selection of better treatment responses, and prediction of prognosis for each phenotype. In the early phase of phenotype studies in allergic diseases, artificial classifications were usually performed based on clinical features, such as triggering factors or the presence of atopy, which can result in the biased classification of phenotypes and limit the characterization of heterogeneous allergic diseases. Subsequent phenotype studies have suggested more diverse phenotypes for each allergic disease using relatively unbiased statistical methods, such as cluster analysis or latent class analysis. The classifications of phenotypes in allergic diseases may overlap or be unstable over time due to their complex interactions with genetic and encountered environmental factors during the illness, which may affect the disease course and pathophysiology. In this review, diverse phenotype classifications of allergic diseases, including atopic dermatitis, asthma, and wheezing in children, allergic rhinitis, and atopy, are described. The review also discusses the applications of the results obtained from phenotype studies performed in other countries to Korean children. Consideration of changes in the characteristics of each phenotype over time in an individual's lifespan is needed in future studies.

• BMB Reports
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• v.43 no.2
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• pp.79-90
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• 2010

Mouse models are crucial for the functional annotation of human genome. Gene modification techniques including gene targeting and gene trap in mouse have provided powerful tools in the form of genetically engineered mice (GEM) for understanding the molecular pathogenesis of human diseases. Several international consortium and programs are under way to deliver mutations in every gene in mouse genome. The information from studying these GEM can be shared through international collaboration. However, there are many limitations in utility because not all human genes are knocked out in mouse and they are not yet phenotypically characterized by standardized ways which is required for sharing and evaluating data from GEM. The recent improvement in mouse genetics has now moved the bottleneck in mouse functional genomics from the production of GEM to the systematic mouse phenotype analysis of GEM. Enhanced, reproducible and comprehensive mouse phenotype analysis has thus emerged as a prerequisite for effectively engaging the phenotyping bottleneck. In this review, current information on systematic mouse phenotype analysis and an issue-oriented perspective will be provided.

• Horticultural Science & Technology
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• v.33 no.4
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• pp.559-565
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• 2015

The rind phenotype of watermelon fruits is an important agronomic characteristic in the watermelon market. Inheritance and linkage analyses were performed for three rind-related traits that together determine the rind phenotype: foreground stripe pattern, rind background color, and depth of rind color. The inheritance of the foreground stripe pattern was analyzed using three different $F_2$ populations, showing that the striped pattern is dominant over the non-striped pattern. The inheritance analysis of the rind background color was performed using $F_2$ populations of the '10909' and '109905', and the depth of rind color was analyzed using $F_2$ populations of the '90509' and '109905'. Yellow color was found to be dominant over green color, and a deep color was dominant over the standard color. Linkage analysis of the three traits was conducted using three $F_2$ populations in which two traits were segregating. Each pair of traits was inherited independently, which demonstrated that the three traits are not linked. Therefore, we propose a three-locus model for the determination of rind phenotype, providing novel insight that rind phenotype is determined by the combination of three genetically independent loci.

• Translational and Clinical Pharmacology
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• v.25 no.3
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• pp.147-152
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• 2017

This study focused on the role of cytochrome P450 2D6 (CYP2D6) genotypes to predict phenotypes in the metabolism of dextromethorphan. CYP2D6 genotypes and metabolic ratios (MRs) of dextromethorphan were determined in 201 Koreans. Unsupervised clustering algorithms, hierarchical and k-means clustering analysis, and color visualizations of CYP2D6 activity were performed on a subset of 130 subjects. A total of 23 different genotypes were identified, five of which were observed in one subject. Phenotype classifications were based on the means, medians, and standard deviations of the log MR values for each genotype. Color visualization was used to display the mean and median of each genotype as different color intensities. Cutoff values were determined using receiver operating characteristic curves from the k-means analysis, and the data were validated in the remaining subset of 71 subjects. Using the two highest silhouette values, the selected numbers of clusters were three (the best) and four. The findings from the two clustering algorithms were similar to those of other studies, classifying $^*5/^*5$ as a lowest activity group and genotypes containing duplicated alleles (i.e., $CYP2D6^*1/^*2N$) as a highest activity group. The validation of the k-means clustering results with data from the 71 subjects revealed relatively high concordance rates: 92.8% and 73.9% in three and four clusters, respectively. Additionally, color visualization allowed for rapid interpretation of results. Although the clustering approach to predict CYP2D6 phenotype from CYP2D6 genotype is not fully complete, it provides general information about the genotype to phenotype relationship, including rare genotypes with only one subject.

• Genomics & Informatics
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• v.12 no.4
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• pp.289-292
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• 2014

Next-generation sequencing (NGS) is widely used to identify the causative mutations underlying diverse human diseases, including cancers, which can be useful for discovering the diagnostic and therapeutic targets. Currently, a number of single-nucleotide variant (SNV)-calling algorithms are available; however, there is no tool for visualizing the recurrent and phenotype-specific mutations for general researchers. In this study, in order to support defining the recurrent mutations or phenotype-specific mutations from NGS data of a group of cancers with diverse phenotypes, we aimed to develop a user-friendly tool, named mutation arranger for defining phenotype-related SNV (MAP). MAP is a user-friendly program with multiple functions that supports the determination of recurrent or phenotype-specific mutations and provides graphic illustration images to the users. Its operation environment, the Microsoft Windows environment, enables more researchers who cannot operate Linux to define clinically meaningful mutations with NGS data from cancer cohorts.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.83-90
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• 2020

In this paper, a web-based seed phenotype visualization support system is proposed to extract and visualize data such as the surface color, length, area, perimeter and compactness of seed, which is phenotype information from the image of soybean/rice seeds. This system systematically stores data extracted from seeds in databases, and provides a web-based user interface that facilitates the analysis of data by researchers using data tables and charts. Conventional seed characteristic studies have been manually measured by humans, but the system developed in this paper allows researchers to simply upload seed images for analysis and obtain seed's numerical data after image processing. It is expected that the proposed system will be able to obtain time efficiency and remove spatial restriction, if it is used in seed characterization research, and it will be easy to analyze through systematic management of research results and visualization of the phenotype characteristics.

• Nutrition Research and Practice
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• v.6 no.2
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• pp.120-125
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• 2012

The aim of this study was to investigate the influencing factors that characterize low density lipoprotein (LDL) phenotype and the levels of LDL particle size in healthy Korean women. In 57 healthy Korean women (mean age, $57.4{\pm}13.1$ yrs), anthropometric and biochemical parameters such as lipid profiles and LDL particle size were measured. Dietary intake was estimated by a developed semi-quantitative food frequency questionnaire. The study subjects were divided into two groups: LDL phenotype A (mean size: $269.7{\AA}$, n = 44) and LDL phenotype B (mean size: $248.2{\AA}$, n = 13). Basic characteristics were not significantly different between the two groups. The phenotype B group had a higher body mass index, higher serum levels of triglyceride, total-cholesterol, LDL-cholesterol, apolipoprotein (apo)B, and apoCIII but lower levels of high density lipoprotein (HDL)-cholesterol and LDL particle size than those of the phenotype A group. LDL particle size was negatively correlated with serum levels of triglyceride (r = -0.732, $P$ < 0.001), total-cholesterol, apoB, and apoCIII, as well as carbohydrate intake (%En) and positively correlated with serum levels of HDL-cholesterol and ApoA1 and fat intake (%En). A stepwise multiple linear regression analysis revealed that carbohydrate intake (%En) and serum triglyceride levels were the primary factors influencing LDL particle size ($P$ < 0.001, $R^2$ = 0.577). This result confirmed that LDL particle size was closely correlated with circulating triglycerides and demonstrated that particle size is significantly associated with dietary carbohydrate in Korean women.

• Genomics & Informatics
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• v.16 no.4
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• pp.38.1-38.3
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• 2018

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.

• Genomics & Informatics
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• v.16 no.4
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• pp.33.1-33.9
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• 2018

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.60-60
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• 2003

GSH-dependent glutaredoxinl (Grxl) was characterized in Dictyostelium discoideum. After starvation, the mRNA levels of grx1 gene increased during aggregation, thereafter decreased up to tip formation and increased again during culmination. To investigate the function of Grxl, the protein was overexpressed in D. discoideum using actinl5 promoter, The phenotype analysis on Grxl-overexpressed cells showed the maintenance of slug stage for a long period and delayed culmination under dark condition. To corroborate these phenotype by the enzyme, the two mutant forms of Grxl (C21S and C24S) were overexpressed in D. discoideum. The phenotype of two mutant cells represented no slug formation and the early culmination on dark condition. These results indicate that Grxl might regulate the transition from slug to culminant in darkness.