Single Nucleotide Polymorphism Barcodes Research Articles

Preventive SNP–SNP interactions in the mitochondrial displacement loop (D-loop) from chronic dialysis patients

Chronic dialysis association study involving individual single nucleotide polymorphisms (SNPs) in the mitochondrial displacement loop (D-loop) has previously been reported. However, possible SNP–SNP interactions for SNPs in the D-loop which could be associated with a reduced risk for chronic dialysis were not investigated. The purpose of this study was to propose an effective algorithm to identify protective SNP–SNP interactions in the D-loop from chronic dialysis patients. We introduce ISGA that uses an initialization strategy for genetic algorithms (GA) to improve the computational analysis for protective SNP–SNP interactions. ISGA generates genotype patterns with combined SNPs (SNP barcodes) for chronic dialysis. Using our previously reported 77 SNPs in the D-loop, the algorithm-generated protective SNP barcodes for chronic dialysis were evaluated. ISGA provides the SNP barcodes with the maximum frequency differences of occurrence between the cases and controls. The identified SNP barcodes with the lowest odds ratio (OR) values were regarded as the best preventive SNP barcodes against chronic dialysis. The best ISGA-generated SNP barcodes (two to nine SNPs) are more closely associated with the prevention of chronic dialysis when more SNPs are chosen (OR=0.64 to 0.32; 95% confidence interval=0.882 to 0.198). The cumulative effects of SNP–SNP interactions were more dominant in ISGA rather than in GA without the initialization strategy. We provide a fast identification of chronic dialysis-associated protective SNP barcodes and demonstrate that the SNP–SNP interactions may have a cumulative effect on prediction for chronic dialysis.

SNP barcodes generated using particle swarm optimization to detect susceptibility to breast cancer

Considerable research has been devoted to investigating variations in disease susceptibility using SNPs associated with the individual cooccurrence of single nucleotide polymorphisms (SNPs) in genetic and phenotypic variability. Without the raw genotype data, these association studies are difficult to conduct and often omit SNP interactions, thus limiting their reliability and potential applicability. In this study, we apply a particle swarm optimization (PSO) algorithm to detect and identify the best protective SNP barcodes (i.e., SNP combinations and genotypes with a maximum difference between cases and controls) associated with chronic dialysis patients. SNP barcodes containing different numbers of SNPs were computed. We evaluated the combined effects of 27 SNPs related to nine published epigenetic modifier-related genes on breast cancer. Eleven different SNP combinations were found to be protective associated with the risk of breast cancer (odds ratio, OR p-value in silico analysis.

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

BackgroundPossible 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 FindingsIn 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/SignificanceOverall, 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.

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.

Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization

Background: High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging. Methods: In this study, we propose an odds ratio-based binary particle swarm optimization (OR-BPSO) method to evaluate the risk of breast cancer. Results: BPSO provides the combinational SNPs with their corresponding genotype, called SNP barcodes, with the maximal difference of occurrence between the control and breast cancer groups. A specific SNP barcode with an optimized fitness value was identified among seven SNP combinations within the space of one minute. The identified SNP barcodes with the best performance between control and breast cancer groups were found to be control-dominant, suggesting that these SNP barcodes may prove protective against breast cancer. After statistical analysis, these control-dominant SNP barcodes were processed for odds ratio analysis for quantitative measurement with regard to the risk of breast cancer. Conclusion: This study proposes an effective high-speed method to analyze the SNP–SNP interactions for breast cancer association study.

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.

Generating SNP barcode to evaluate SNP–SNP interaction of disease by particle swarm optimization

Genome-wide association analysis involved many single-nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. Hence, we propose the odds ratio-based discrete binary particle swarm optimization (OR-DBPSO) method that uses the OR as a new quantitative measure of disease risk among many SNP combinations with genotypes called “SNP barcode”. DBPSO are applied to generate SNP barcode, which computes the maximal difference of occurrence between the case and control groups, to predict disease susceptibility such as osteoporosis. Different SNP barcode patterns may occur several times in either low or high bone mineral density (BMD) groups. Our results showed that a DBPSO can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed to OR analysis to determine the maximum difference between the low and high BMD groups in statistics manner. Therefore, this paper introduces a powerful procedure to analyze disease-associated SNP–SNP interaction in genome-wide genes.

Odds Ratio-Based Genetic Algorithms for Generating SNP Barcodes of Genotypes to Predict Disease Susceptibility

Genome-wide association analysis involving many single nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. It is time consuming to classify the combination of multilocus genotypes into high- and low-risk groups without false positive and negative errors. Hence, we propose the odds ratio-based genetic algorithms (OR-GA) method that uses the odds ratio as a new quantitative measure of disease risk among many SNP combinations. Genetic algorithms (GA) are applied to generate SNP "barcodes" of genotypes, which propose the maximal difference of occurrence between the case and control groups, to predict disease susceptibility (e.g., osteoporosis). When individuals are grouped into a low and high bone mass density (BMD) range, different SNP barcode patterns may occur several times in each of these two groups. Our results showed that a GA can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed by odds ratio analysis to determine the maximum difference between the low and high BMD groups in a statistical manner. Therefore, this paper introduces a powerful procedure for analysis of disease-associated SNP barcode in genome-wide genes.