Abstract
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.
Highlights
Genome-wide association studies (GWAS) can identify several highly robust and statistically significant single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility [1,2,3,4,5,6]
We analyzed the contribution of 23 SNPs from six breast cancer related genes to generate the protective SNP barcodes in a case-control study of 5000 cases and 5000 controls with genotype data simulation
The maximum difference information calculated by the IPSO algorithm can predict the relative strength of the impact of an SNP on breast cancer protection
Summary
Genome-wide association studies (GWAS) can identify several highly robust and statistically significant single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility [1,2,3,4,5,6]. The possible SNP-SNP interactions between these hormone metabolisms and signalling-related genes have hardly been addressed This is in part due to the computationally challenging nature of association studies with multiple SNP candidates. Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. We proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions This PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
