Kyubum Wee

Association of Four-locus Gene Interaction with Aspirin-intolerant Asthma in Korean Asthmatics

IntroductionAspirin-intolerant asthma (AIA), a major clinical presentation of aspirin hypersensitivity, affects 10% of adult asthmatics. The genetic risk factors involved in the susceptibility to AIA have recently been investigated, but multilocus single-nucleotide polymorphisms (SNPs) associated with this susceptibility has not been evaluated.MethodsWe examined 246 asthmatic patients: 94 having aspirin intolerance and 152 having aspirin tolerance. We selected 23 SNPs of 13 candidate genes and genotyped each SNP using a primer extension method. Multilocus genetic interactions were examined using multifactor dimensionality reduction (MDR) to test all multilocus SNP combinations to identify a useful SNP set for predicting the AIA phenotype.ResultsWe identified the best model using the MDR method, which consisted of a four-locus gene–gene interaction with 65.16% balanced accuracy and a cross-validation consistency of 70% in predicting AIA disease risk among asthmatic patients. This model included four SNPs such as B2ADR 46A>G, CCR3–520T>G, CysLTR1–634C>T, and FCER1B–109T>C.DiscussionThese results suggest that a multilocus SNP acts in combination to influence the susceptibility to aspirin intolerance in asthmatics and could be a useful genetic marker for the diagnosis of AIA.

Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure

There are many algorithms for detecting epistatic interactions in GWAS. However, most of these algorithms are applicable only for detecting two-locus interactions. Some algorithms are designed to detect only two-locus interactions from the beginning. Others do not have limits to the order of interactions, but in practice take very long time to detect higher order interactions in real data of GWAS. Even the better ones take days to detect higher order interactions in WTCCC data. We propose a fast algorithm for detection of high order epistatic interactions in GWAS. It runs k-means clustering algorithm on the set of all SNPs. Then candidates are selected from each cluster. These candidates are examined to find the causative SNPs of k-locus interactions. We use mutual information from information theory as the measure of association between genotypes and phenotypes. We tested the power and speed of our method on extensive sets of simulated data. The results show that our method has more or equal power, and runs much faster than previously reported methods. We also applied our algorithm on each of seven diseases in WTCCC data to analyze up to 5-locus interactions. It takes only a few hours to analyze 5-locus interactions in one dataset. From the results we make some interesting and meaningful observations on each disease in WTCCC data. In this study, a simple yet powerful two-step approach is proposed for fast detection of high order epistatic interaction. Our algorithm makes it possible to detect high order epistatic interactions in GWAS in a matter of hours on a PC.

Automatic Generation of Finite State Automata for Detecting Intrusions Using System Call Sequences

Analysis of system call sequences generated by privileged programs has been proven to be an effective way of detecting intrusions. There are many approaches of analyzing system call sequences including N-grams, rule induction, finite automata, and Hidden Markov Models. Among these techniques use of finite automata has the advantage of analyzing whole sequences without imposing heavy load to the system. There have been various studies on how to construct finite automata modeling normal behavior of privileged programs. However, previous studies had disadvantages of either constructing finite automata manually or requiring system information other than system calls. In this paper we present fully automatized algorithms to construct finite automata recognizing sequences of normal behaviors and rejecting those of abnormal behaviors without requiring system information other than system calls. We implemented our algorithms and experimented with well-known data sets of system call sequences. The results of the experiments show the efficiency and effectiveness of our system.

Optimal Scheduling Algorithms in WDM Optical Passive Star Networks

All-to-all broadcast scheduling problems are considered in WDM optical passive star networks where k wavelengths are available in the network. It is assumed that each node has exactly one tunable transmitter and one fixed tuned receiver. All transmitters can tune to k different wavelengths, and have the same tuning delay δ to tune from one wavelength to another. In this paper, we take δ to be a nonnegative integer which can be expressed in units of packet durations. When all-to-all broadcasts are scheduled periodically in the network, the lower bounds are established on the minimum cycle length depending on whether each node sends packets to itself or not. And then, we present optimal scheduling algorithms in both cases for arbitrary number of wavelengths and for arbitrary value of the tuning delay.

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer

BackgroundRecent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.MethodsWe propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.ResultsWe demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.ConclusionsWe have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

Construction of hypercycles in typogenetics with evolutionary algorithms

The concept of hypercycles was proposed by M. Eigen and P. Schuster to study the origin-of-life problem. A hypercycle is a simple self-reproducing system modeling molecular evolution in the abiotic period. Typogenetics is a formal system of strings originally devised by D. Hofstadter to explain the connection between computation and molecular genetics. It was later established by H. Morris as a formal system to study artificial life. Evolutionary algorithms were used by Kvasnicka et al. to find a small hypercycle in typogenetics. We improve upon their algorithm and construct many hypercycles of large sizes. We also experimented with enzymes of different lengths and various mappings between enzymes and their functions.

Extensions and variations on construction of autoreplicators in typogenetics

Typogenetics was originally devised as a formal system with operations on DNA strands. It was recently demonstrated to be an effective model on which to study the emergence of self-replication by Kvasnicka et al.’s work. We make several extensions and variations on their work. The way of measuring difference and similarity between strands are improved. Many different mappings between doublet codes and their enzyme functions are tried. Triplet codes are also introduced. Through various experiments we observe frequent emergence of autoreplicators. We also find that emergence of self-replicators are robust phenomenon under various environments in typogenetics.

Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population

BackgroundAspirin Exacerbated Respiratory Disease (AERD) is a chronic medical condition that encompasses asthma, nasal polyposis, and hypersensitivity to aspirin and other non-steroidal anti-inflammatory drugs. Several previous studies have shown that part of the genetic effects of the disease may be induced by the interaction of multiple genetic variants. However, heavy computational cost as well as the complexity of the underlying biological mechanism has prevented a thorough investigation of epistatic interactions and thus most previous studies have typically considered only a small number of genetic variants at a time.MethodsIn this study, we propose a gene network based analysis framework to identify genetic risk factors from a genome-wide association study dataset. We first derive multiple single nucleotide polymorphisms (SNP)-based epistasis networks that consider marginal and epistatic effects by using different information theoretic measures. Each SNP epistasis network is converted into a gene-gene interaction network, and the resulting gene networks are combined as one for downstream analysis. The integrated network is validated on existing knowledgebase of DisGeNET for known gene-disease associations and GeneMANIA for biological function prediction.ResultsWe demonstrated our proposed method on a Korean GWAS dataset, which has genotype information of 440,094 SNPs for 188 cases and 247 controls. The topological properties of the generated networks are examined for scale-freeness, and we further performed various statistical analyses in the Allergy and Asthma Portal (AAP) using the selected genes from our integrated network.ConclusionsOur result reveals that there are several gene modules in the network that are of biological significance and have evidence for controlling susceptibility and being related to the treatment of AERD.

A comment on two-locus epistatic interaction models for genome-wide association studies

Detection of epistatic interactions in genome-wide association studies is a computationally hard problem. Many detection algorithms have been proposed and will continue to be. Most of those algorithms measure their predictive power by running on simulated data many times under various disease models. However, we find that there have been subtle differences in interpreting the meaning of existing disease models among the previous studies on detection of epistatic interactions. We elucidate those differences and suggest that future studies on epistatic interactions in GWAS state explicitly which versions/interpretations are employed. We also provide a way to facilitate setting parameters of disease models.

Cluster Analysis of SNPs with Entropy Distance and Prediction of Asthma Type Using SVM

Single nucleotide polymorphisms (SNPs) are a very important tool for the study of human genome structure. Cluster analysis of the large amount of gene expression data is useful for identifying biologically relevant groups of genes and for generating networks of gene-gene interactions. In this paper we compared the clusters of SNPs within asthma group and normal control group obtained by using hierarchical cluster analysis method with entropy distance. It appears that the 5-cluster collections of the two groups are significantly different. We searched the best set of SNPs that are useful for diagnosing the two types of asthma using representative SNPs of the clusters of the asthma group. Here support vector machines are used to evaluate the prediction accuracy of the selected combinations. The best combination model turns out to be the five-locus SNPs including one on the gene ALOX12 and their accuracy in predicting aspirin tolerant asthma disease risk among asthmatic patients is 66.41%.