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Ho-Youl Jung

Publications and source records attributed to Ho-Youl Jung.

2 recordsLinked to original sources

Gene-based single nucleotide polymorphisms and linkage disequilibrium patterns of 29 asthma candidate genes in the chromosome 5q31-33 region in Koreans.

BACKGROUND AND METHODS: Numerous genetic studies have mapped asthma susceptibility genes to a region on chromosome 5q31-33 in several populations. This region contains a cluster of cytokines and other immune-related genes important in immune response. In the present study, to determine the genetic variations and patterns of linkage disequilibrium (LD), we resequenced all the exons and promoter regions of the 29 asthma candidate genes in the chromosome 5q31-33 region. RESULTS: We identified a total of 314 genetic variants, including 289 single nucleotide polymorphisms (SNPs), 22 insertion/deletion polymorphisms and 3 microsatellites. Standardized variance data for allele frequency revealed substantial differences in SNP allele frequencies among different ethnic groups. Interestingly, significant ethnic differences were observed mainly in intron SNPs. LD block analysis using 174 common SNPs with a frequency of >10% disclosed strong LD within most candidate genes. No significant LD was observed across genes, except for one LD block (CD14-IK block). Gene-based haplotype analyses showed that 1-5 haplotype-tagging SNPs may be used to define the six or fewer common haplotypes with a frequency of >5%, regardless of the number of SNPs. CONCLUSION: Overall, our results provide useful information for the identification of immune-mediated disease genes in the chromosome 5q31-33 region, as well as valuable evidence for gene-based haplotype analysis in disease association studies.

Alleles↗

An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis.

MOTIVATION: In this paper, we propose a fully automatic block and spot indexing algorithm for microarray image analysis. A microarray is a device which enables a parallel experiment of ten to hundreds of thousands of test genes in order to measure gene expression. Due to this huge size of experimental data, automated image analysis is gaining importance in microarray image processing systems. Currently, most of the automated microarray image processing systems require manual block indexing and, in some cases, spot indexing. If the microarray image is large and contains a lot of noise, it is very troublesome work. In this paper, we show it is possible to locate the addresses of blocks and spots by applying the Nearest Neighbors Graph Model. Also, we propose an analytic model for the feasibility of block addressing. Our analytic model is validated by a large body of experimental results. RESULTS: We demonstrate the features of automatic block detection, automatic spot addressing, and correction of the distortion and skewedness of each microarray image.

Algorithms↗