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.

[1]  Alison M Dunning,et al.  Association between Common Variation in 120 Candidate Genes and Breast Cancer Risk , 2007, PLoS genetics.

[2]  Peter Kraft,et al.  GWAS identifies a common breast cancer risk allele among BRCA1 carriers , 2010, Nature Genetics.

[3]  V. Bazan,et al.  Breast cancer genome-wide association studies: there is strength in numbers , 2012, Oncogene.

[4]  Li-Yeh Chuang,et al.  SNP combinations in chromosome-wide genes are associated with bone mineral density in Taiwanese women. , 2008, The Chinese journal of physiology.

[5]  C Sonnenschein,et al.  The two faces of janus: sex steroids as mediators of both cell proliferation and cell death. , 2001, Journal of the National Cancer Institute.

[6]  José Rueff,et al.  Association of common variants in mismatch repair genes and breast cancer susceptibility: a multigene study , 2009, BMC Cancer.

[7]  R. Frairia,et al.  Sex Hormone-Binding Globulin (SHBG), estradiol and breast cancer , 2010, Molecular and Cellular Endocrinology.

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Shyh-Huei Chen,et al.  A support vector machine approach for detecting gene‐gene interaction , 2008, Genetic epidemiology.

[10]  N. Ben-Jonathan,et al.  Novel roles of prolactin and estrogens in breast cancer: resistance to chemotherapy. , 2010, Endocrine-related cancer.

[11]  Wan Li,et al.  Disease Gene Interaction Pathways: A Potential Framework for How Disease Genes Associate by Disease-Risk Modules , 2011, PloS one.

[12]  Dong Liang,et al.  Genetic Variants in TGF-β Pathway Are Associated with Ovarian Cancer Risk , 2011, PloS one.

[13]  Carlos Caldas,et al.  Common germline polymorphisms in COMT, CYP19A1, ESR1, PGR, SULT1E1 and STS and survival after a diagnosis of breast cancer , 2009, International journal of cancer.

[14]  Anbupalam Thalamuthu,et al.  A Genome-wide Association Scan on Estrogen Receptor-negative Breast Cancer , 2022 .

[15]  Taesung Park,et al.  Odds ratio based multifactor-dimensionality reduction method for detecting gene – gene interactions , 2006 .

[16]  Alfons Meindl,et al.  Identification of Novel Susceptibility Genes for Breast Cancer – Genome-Wide Association Studies or Evaluation of Candidate Genes? , 2009, Breast Care.

[17]  Giu-Cheng Hsu,et al.  Genetic variation in the genome-wide predicted estrogen response element-related sequences is associated with breast cancer development , 2011, Breast Cancer Research.

[18]  S. Andò,et al.  Breast cancer: from estrogen to androgen receptor , 2002, Molecular and Cellular Endocrinology.

[19]  Alison M. Dunning,et al.  Phytoestrogen Exposure, Polymorphisms in COMT, CYP19, ESR1, and SHBG Genes, and Their Associations With Prostate Cancer Risk , 2006, Nutrition and cancer.

[20]  Hsueh-Wei Chang,et al.  Computational analysis of simulated SNP interactions between 26 growth factor-related genes in a breast cancer association study. , 2011, Omics : a journal of integrative biology.

[21]  Shyun-Yeu Liu,et al.  Combinational polymorphisms of four DNA repair genes XRCC1, XRCC2, XRCC3, and XRCC4 and their association with oral cancer in Taiwan. , 2007, Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology.

[22]  Li-Yeh Chuang,et al.  Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization. , 2009, Cancer epidemiology.

[23]  P. Vineis,et al.  ERCC1 haplotypes modify bladder cancer risk: a case-control study. , 2010, DNA repair.

[24]  Pär Stattin,et al.  Cumulative association of five genetic variants with prostate cancer. , 2008, The New England journal of medicine.

[25]  Stephen J. Chanock,et al.  Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions , 2008, BMC Bioinformatics.

[26]  Chih-Jen Huang,et al.  Combinational polymorphisms of seven CXCL12-related genes are protective against breast cancer in Taiwan. , 2009, Omics : a journal of integrative biology.

[27]  Wonshik Han,et al.  SNP–SNP interactions between DNA repair genes were associated with breast cancer risk in a Korean population , 2012, Cancer.

[28]  G. Castoria,et al.  Targeting rapid action of sex-steroid receptors in breast and prostate cancers. , 2012, Frontiers in bioscience.

[29]  M. Permutt,et al.  Post Genome-Wide Association Studies of Novel Genes Associated with Type 2 Diabetes Show Gene-Gene Interaction and High Predictive Value , 2008, PloS one.

[30]  W. Willett,et al.  A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1) , 2009, Nature Genetics.

[31]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[32]  G. Castoria,et al.  Sex-steroid hormones and EGF signalling in breast and prostate cancer cells: Targeting the association of Src with steroid receptors , 2008, Steroids.