Cheng-Hong Yang

Combinational polymorphisms of four DNA repair genes XRCC1, XRCC2, XRCC3, and XRCC4 and their association with oral cancer in Taiwan.

BACKGROUND Many single nucleotide polymorphisms (SNPs) have been found to be associated with oral cancer but the biological interactions through SNPs are seldom addressed. In this study, we focused on the joint effect for SNP combinations of four DNA repair genes, X-ray repair cross-complementing groups (XRCCs) 1-4, involved in major cancer-related pathways. METHODS Single nucleotide polymorphism genotyping was determined using by polymerase chain reaction-restriction fragment length polymorphism in this study (case = 103, control = 98). Different numbers of combinational SNPs with genotypes called the pseudo-haplotypes from these chromosome-wide genes were used to evaluate their joint effect on oral cancer risk. RESULTS Except for XRCC2 rs2040639-AG, none of these SNPs was found to individually contribute to oral cancer risk. However, for two combined SNPs, the proportion of subjects with oral cancer was significantly higher in the pseudo-haplotype with AG-CC genotypes in rs2040639-rs861539 (XRCC2-XRCC3) compared with those with non-AG-CC genotypes. Similarly, the pseudo-haplotype of rs2040639-rs861539-rs2075685 (XRCC2-XRCC3-XRCC4) and rs2040639-rs861539-rs2075685-rs1799782 (XRCCs 1-4) with specific genotype pattern (AG-CC-TG and CT-AG-CC-TG) among three and four combinational SNPs were significantly associated with oral cancer. After controlling for age, gender, smoking, drinking, and betel nut chewing, the estimated odds ratio of oral cancer were 2.45, 5.03, and 10.10 for two, three and four specific SNP combinations, respectively, comparing these specific pseudo-haplotypes to their corresponding non-pseudo-haplotypes. CONCLUSION We have identified the potential combined XRCCs 1-4 SNPs with genotypes that were associated with oral cancer risk and may have an impact on identification of a high-risk population.

Chaotic maps based on binary particle swarm optimization for feature selection

Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective is to remove irrelevant features in the feature space and to correctly identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully to solving feature selection problems. In this paper, two kinds of chaotic maps-so-called logistic maps and tent maps-are embedded in BPSO. The purpose of chaotic maps is to determine the inertia weight of the BPSO. We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. The proposed feature selection method shows promising results with respect to the number of feature subsets. The classification accuracy is superior to other methods from the literature.

Combinational Polymorphisms of Seven CXCL12-Related Genes Are Protective against Breast Cancer in Taiwan

Many single nucleotide polymorphisms (SNPs) have been found to be associated with breast cancer, but their SNP interactions are seldom addressed. In this study, we focused on the joint effect for SNP combinations of seven CXCL12-related genes involved in major cancer-related pathways. SNP genotyping was determined by PCR-restriction fragment length polymorphism (RFLP) in this study (case = 220, control = 334). Different numbers of combinational SNPs with genotypes called the SNP barcodes from different chromosomes were used to evaluate their joint effect on breast cancer risk. Except for vascular endothelial growth factor (VEGF) rs3025039-CT, none of these SNPs were found to individually contribute to breast cancer risk. However, for two combined SNPs, the proportion of subjects with breast cancer was significantly low in the SNP barcode with CC-GG genotypes in rs2228014-1801157 (CXCR4-CXCL12) compared to those with non-CC-GG genotypes. Similarly, the SNP barcode of rs12812942-rs2228014-rs3025039 (CD4-CXCR4-VEGF) and rs12812942-rs3136685-rs2228014-rs1801157 (CD4- CCR7-CXCR4-CXCL12) with specific genotype patterns (AT-CC-CC and AT-AG-CC-GG) among three and four combinational SNPs were significantly low in breast cancer occurrence. More SNP combinations larger than five SNPs were also addressed, and these showed similar effects. After controlling for age, and comparing their corresponding non-SNP barcodes, the estimated odds ratios for breast cancer ranged between 0.20 and 0.71 for specific SNP barcodes with two to seven SNPs. In conclusion, we have associated the potential combined CXCL12-related SNPs with genotypes that were protective against breast cancer, and that may contribute to identification of a low-risk population for the development of breast cancer.

A hybrid feature selection method for DNA microarray data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.

Computational Analysis of Simulated SNP Interactions Between 26 Growth Factor-Related Genes in a Breast Cancer Association Study

Many association studies analyze the genotype frequencies of case and control data to predict susceptibility to diseases and cancers. Without providing the raw data for genotypes, many association studies cannot be interpreted fully. Often, the interactions of the single nucleotide polymorphisms (SNPs) are not addressed and this limits the potential of such studies. To solve these problems, we propose a novel computational method with source codes to generate a stimulated genotype dataset based on published SNP genotype frequencies. In this study we evaluate the combined effect of 26 SNP combinations related to eight published growth factor-related genes involved in carcinogenesis pathways of breast cancer. The genetic algorithm (GA) was chosen to provide simultaneous analysis of multiple independent SNPs. The GA can perform feature selection from different SNP combinations via their corresponding genotype (called the SNP barcode), and the approach is able to provide a specific SNP barcode with an optimized fitness value effectively. The best SNP barcode with the maximal occurrence difference between groups for the control and breast cancer, together with an odds ratio analysis, is used to evaluate breast cancer susceptibility. When they are compared to their corresponding non-SNP barcodes, the estimated odds ratios for breast cancer are less than 1 (about 0.85 and 0.87; confidence interval: 0.7473∼0.9585, p < 0.01) for specific SNP barcodes with two to five SNPs. Therefore, we were able to identify potential combined growth factor-related genes together with their SNP barcodes that were protective against breast cancer by in silico analysis.

An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer

Background Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions. Methodology/Principal Findings In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification. Conclusions/Significance Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.

Single nucleotide polymorphism barcoding to evaluate oral cancer risk using odds ratio-based genetic algorithms

Cancers often involve the synergistic effects of gene–gene interactions, but identifying these interactions remains challenging. Here, we present an odds ratio‐based genetic algorithm (OR‐GA) that is able to solve the problems associated with the simultaneous analysis of multiple independent single nucleotide polymorphisms (SNPs) that are associated with oral cancer. The SNP interactions between four SNPs—namely rs1799782, rs2040639, rs861539, rs2075685, and belonging to four genes (XRCC1, XRCC2, XRCC3, and XRCC4)—were tested in this study, respectively. The GA decomposes the SNPs sets into different SNP combinations with their corresponding genotypes (called SNP barcodes). The GA can effectively identify a specific SNP barcode that has an optimized fitness value and uses this to calculate the difference between the case and control groups. The SNP barcodes with a low fitness value are naturally removed from the population. Using two to four SNPs, the best SNP barcodes with maximum differences in occurrence between the case and control groups were generated by GA algorithm. Subsequently, the OR provides a quantitative measure of the multiple SNP synergies between the oral cancer and control groups by calculating the risk related to the best SNP barcodes and others. When these were compared to their corresponding non‐SNP barcodes, the estimated ORs for oral cancer were found to be great than 1 [approx. 1.72–2.23; confidence intervals (CIs): 0.94–5.30, p < 0.03–0.07] for various specific SNP barcodes with two to four SNPs. In conclusion, the proposed OR‐GA method successfully generates SNP barcodes, which allow oral cancer risk to be evaluated and in the process the OR‐GA method identifies possible SNP–SNP interactions.

SNP-RFLPing 2: an updated and integrated PCR-RFLP tool for SNP genotyping.

BackgroundPCR-restriction fragment length polymorphism (RFLP) assay is a cost-effective method for SNP genotyping and mutation detection, but the manual mining for restriction enzyme sites is challenging and cumbersome. Three years after we constructed SNP-RFLPing, a freely accessible database and analysis tool for restriction enzyme mining of SNPs, significant improvements over the 2006 version have been made and incorporated into the latest version, SNP-RFLPing 2.ResultsThe primary aim of SNP-RFLPing 2 is to provide comprehensive PCR-RFLP information with multiple functionality about SNPs, such as SNP retrieval to multiple species, different polymorphism types (bi-allelic, tri-allelic, tetra-allelic or indels), gene-centric searching, HapMap tagSNPs, gene ontology-based searching, miRNAs, and SNP500Cancer. The RFLP restriction enzymes and the corresponding PCR primers for the natural and mutagenic types of each SNP are simultaneously analyzed. All the RFLP restriction enzyme prices are also provided to aid selection. Furthermore, the previously encountered updating problems for most SNP related databases are resolved by an on-line retrieval system.ConclusionsThe user interfaces for functional SNP analyses have been substantially improved and integrated. SNP-RFLPing 2 offers a new and user-friendly interface for RFLP genotyping that can be used in association studies and is freely available at http://bio.kuas.edu.tw/snp-rflping2.

Sequence-based polymorphisms in the mitochondrial D-loop and potential SNP predictors for chronic dialysis.

Background The mitochondrial (mt) displacement loop (D-loop) is known to accumulate structural alterations and mutations. The aim of this study was to investigate the prevalence of single nucleotide polymorphisms (SNPs) within the D-loop among chronic dialysis patients and healthy controls. Methodology and Principal Findings We enrolled 193 chronic dialysis patients and 704 healthy controls. SNPs were identified by large scale D-loop sequencing and bioinformatic analysis. Chronic dialysis patients had lower body mass index, blood thiols, and cholesterol levels than controls. A total of 77 SNPs matched with the positions in reference of the Revised Cambridge Reference Sequence (CRS) were found in the study population. Chronic dialysis patients had a significantly higher incidence of 9 SNPs compared to controls. These include SNP5 (16108Y), SNP17 (16172Y), SNP21 (16223Y), SNP34 (16274R), SNP35 (16278Y), SNP55 (16463R), SNP56 (16519Y), SNP64 (185R), and SNP65 (189R) in D-loop of CRS. Among these SNPs with genotypes, SNP55-G, SNP56-C, and SNP64-A were 4.78, 1.47, and 5.15 times more frequent in dialysis patients compared to controls (P<0.05), respectively. When adjusting the covariates of demographics and comorbidities, SNP64-A was 5.13 times more frequent in dialysis patients compared to controls (P<0.01). Furthermore, SNP64-A was found to be 35.80, 3.48, 4.69, 5,55, and 4.67 times higher in female patients and in patients without diabetes, coronary artery disease, smoking, and hypertension in an independent significance manner (P<0.05), respectively. In patients older than 50 years or with hypertension, SNP34-A and SNP17-C were found to be 7.97 and 3.71 times more frequent (P<0.05) compared to patients younger than 50 years or those without hypertension, respectively. Conclusions and Significance The results of large-scale sequencing suggest that specific SNPs in the mtDNA D-loop are significantly associated with chronic dialysis. These SNPs can be considered as potential predictors for chronic dialysis.

A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data

Microarray analysis promises to detect variations in gene expressions, and changes in the transcription rates of an entire genome in vivo. Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the genes. The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. A classification process is often employed which decreases the dimensionality of the microarray data. In order to correctly analyze microarray data, the goal is to find an optimal subset of features (genes) which adequately represents the original set of features. A hybrid method of binary particle swarm optimization (BPSO) and a combat genetic algorithm (CGA) is to perform the microarray data selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The proposed BPSO-CGA approach is compared to ten microarray data sets from the literature. The experimental results indicate that the proposed method not only effectively reduce the number of genes expression level, but also achieves a low classification error rate.