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1.  Variation in the ICAM1–ICAM4–ICAM5 locus is associated with systemic lupus erythematosus susceptibility in multiple ancestries 
Annals of the rheumatic diseases  2012;71(11):1809-1814.
Systemic lupus erythematosus (SLE; OMIM 152700) is a chronic autoimmune disease for which the aetiology includes genetic and environmental factors. ITGAM, integrin αΜ (complement component 3 receptor 3 subunit) encoding a ligand for intracellular adhesion molecule (ICAM) proteins, is an established SLE susceptibility locus. This study aimed to evaluate the independent and joint effects of genetic variations in the genes that encode ITGAM and ICAM.
The authors examined several markers in the ICAM1–ICAM4–ICAM5 locus on chromosome 19p13 and the single ITGAM polymorphism (rs1143679) using a large-scale case–control study of 17 481 unrelated participants from four ancestry populations. The single marker association and gene–gene interaction were analysed for each ancestry, and a meta-analysis across the four ancestries was performed.
The A-allele of ICAM1–ICAM4–ICAM5 rs3093030, associated with elevated plasma levels of soluble ICAM1, and the A-allele of ITGAM rs1143679 showed the strongest association with increased SLE susceptibility in each of the ancestry populations and the trans-ancestry meta-analysis (ORmeta=1.16, 95% CI 1.11 to 1.22; p=4.88×10−10 and ORmeta=1.67, 95% CI 1.55 to 1.79; p=3.32×10−46, respectively). The effect of the ICAM single-nucleotide polymorphisms (SNPs) was independent of the effect of the ITGAM SNP rs1143679, and carriers of both ICAM rs3093030-AA and ITGAM rs1143679-AA had an OR of 4.08 compared with those with no risk allele in either SNP (95% CI 2.09 to 7.98; p=3.91×10−5).
These findings are the first to suggest that an ICAM–integrin-mediated pathway contributes to susceptibility to SLE.
PMCID: PMC3466387  PMID: 22523428
2.  Analysis of an extended chromosome locus 2p14–21 for replication of the 2p16.3 association with glaucoma susceptibility 
Molecular Vision  2011;17:1136-1143.
Susceptibility to primary open-angle glaucoma (POAG) has recently associated with three intergenic single-nucleotide polymorphisms (SNPs) on human chromosome 2p16.3, just outside of the POAG-linkage locus GLC1H (2p15–16.2), in an Afro-Caribbean population. Especially, association of one SNP (rs12994401) was very strong (odds ratio 35) and later replicated in Afro-Americans but not in Ghanaians or Japanese. An extended region was examined in this study to look for SNPs of cross-population association.
The three reported SNPs and all 63 SNPs considerably correlating with rs12994401 (r2≥0.3) in the African-descendent Yoruba were examined for POAG susceptibility association in a Korean population of 1,159 unrelated participants including 226 cases with glaucoma. As these 66 SNPs were spread from 2p14 to 2p21, all SNPs in this extended region were imputed for susceptibility association tests.
No susceptibility association was detected with rs12994401 in comparisons between 933 controls and 188 POAG (or 175 high-tension glaucoma) cases (statistical power of 100%), as well as with all 19 other typed SNPs, using logistic regression with adjustment for age and gender. The other 46 SNPs were deemed non-polymorphic in Koreans. Among 21,201 SNPs located in 2p14–21, only 4,260 were imputed to be non-monomorphic, but none of them passed a significance level of multiple testing. No association was observed when the samples were stratified by age or gender.
No typed or imputed SNPs within 2p14–21 showed association with susceptibility to POAG, suggesting that the population inconsistency in 2p16.3 association was unlikely due to linkage disequilibrium differences.
PMCID: PMC3087448  PMID: 21552472
3.  Mutation spectrum of CYP1B1 and MYOC genes in Korean patients with primary congenital glaucoma 
Molecular Vision  2011;17:2093-2101.
To elucidate the incidence of cytochrome P450 1B1 (CYP1B1) and myocillin (MYOC) mutations in Korean patients with primary congenital glaucoma (PCG).
Genomic DNA was collected from peripheral blood of 85 unrelated Korean patients who were diagnosed as having PCG by standard ophthalmological examinations and screened for mutations in the CYP1B1 and MYOC genes by using bi-directional sequencing.
Among 85 patients with PCG, 22 patients (22/85; 25.9%) had either one (n=11) or two (n=11) mutant alleles of the CYP1B1 gene. Among 11 different CYP1B1 mutations identified, a frameshift mutation (c.970_971dupAT; p.T325SfsX104) was the most frequent mutant allele (6/33; 18.2%) while p.G329S and p.V419Gfs11X were novel. In the MYOC gene, two variants of unknown significance (p.L228S and p.E240G) were identified in two PCG patients (2/85; 2.4%), respectively. No patient had mutations in both genes.
Although CYP1B1 mutations are major causes of PCG in Korea, ~70% of PCG patients have neither CYP1B1 nor MYOC mutations suggesting a high degree of genetic heterogeneity. Furthermore, the fact that 11 out of 22 patients had only one mutant allele in the CYP1B1 gene necessitates further investigation for other genetic backgrounds underlying PCG.
PMCID: PMC3156779  PMID: 21850185
4.  Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast 
BMC Genomics  2009;10:440.
Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction.
Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes.
A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.
PMCID: PMC2753555  PMID: 19761608
5.  A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data 
BMC Bioinformatics  2008;9:56.
The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a number of different ways to identify cell cycle dependent genes. In this study, we pose the hypothesis that cell cycle dependent genes are considered as oscillating systems with a rhythm, i.e. systems producing response signals with period and frequency. Therefore, we are motivated to apply the theory of multivariate phase synchronization for clustering cell cycle specific genome-wide expression data.
We propose the strategy to find groups of genes according to the specific biological process by analyzing cell cycle specific gene expression data. To evaluate the propose method, we use the modified Kuramoto model, which is a phase governing equation that provides the long-term dynamics of globally coupled oscillators. With this equation, we simulate two groups of expression signals, and the simulated signals from each group shares their own common rhythm. Then, the simulated expression data are mixed with randomly generated expression data to be used as input data set to the algorithm. Using these simulated expression data, it is shown that the algorithm is able to identify expression signals that are involved in the same oscillating process. We also evaluate the method with yeast cell cycle expression data. It is shown that the output clusters by the proposed algorithm include genes, which are closely associated with each other by sharing significant Gene Ontology terms of biological process and/or having relatively many known biological interactions. Therefore, the evaluation analysis indicates that the method is able to identify expression signals according to the specific biological process. Our evaluation analysis also indicates that some portion of output by the proposed algorithm is not obtainable by the traditional clustering algorithm with Euclidean distance or linear correlation.
Based on the evaluation experiments, we draw the conclusion as follows: 1) Based on the theory of multivariate phase synchronization, it is feasible to find groups of genes, which have relevant biological interactions and/or significantly shared GO slim terms of biological process, using cell cycle specific gene expression signals. 2) Among all the output clusters by the proposed algorithm, the cluster with relatively large size has a tendency to include more known interactions than the one with relatively small size. 3) It is feasible to understand the cell cycle specific gene expression patterns as the phenomenon of collective synchronization. 4) The proposed algorithm is able to find prominent groups of genes, which are not obtainable by traditional clustering algorithm.
PMCID: PMC2335309  PMID: 18221564
6.  Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks 
BMC Bioinformatics  2007;8:251.
A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.
This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|Hi), which is used as confidence level. The unit network with higher P(D|Hi) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|Hi), which is a unique property of the proposed algorithm.
The algorithm is evaluated with synthetic and Saccharomyces cerevisiae expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.
The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and Saccharomyces cerevisiae expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.
From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.
PMCID: PMC1959566  PMID: 17626641

Results 1-6 (6)