GWAS identifies a common breast cancer risk allele among BRCA1 carriers
ABSTRACT A genome-wide association study conducted among women with deleterious BRCA1 mutations has identified a common allele associated with breast cancer risk in BRCA1 carriers and estrogen receptor-negative breast cancer in the general population. This suggests that genetic association studies focused on particular subtypes may provide further insight into complex diseases.
SourceAvailable from: Yu-Da Lin[Show abstract] [Hide abstract]
ABSTRACT: The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409–0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.BioMed Research International 01/2015; 2015:281263. · 2.71 Impact Factor
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ABSTRACT: Single nucleotide polymorphism (SNP)-SNP interaction is helpful for identifying the joint effect in disease susceptibility. However, the large amount of SNP combination makes this analysis ineffective. Here, we used double bottom map particle swarm optimization (DBM-PSO) algorithm to effectively identify the significant SNP barcodes, i.e., the combinations of SNPs with their genotypes, in a breast cancer association study. We simulated a big data set using the published genotype frequencies of 29 SNPs from seven DNA repair-related genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CHEK1, and CHEK2) of breast cancer. Most of these individual SNPs are non-significantly associated with breast cancer. After performing DBM-PSO, the 2- to 5-SNP barcodes were identified to be protective association with breast cancer susceptibility (odds ratio < 1.0; P< 0.05). The preventative effect of these SNPs of DNA repair genes on breast cancer risk is ranked as follows: SNPs rs1048108/rs2048718> SNP rs582157> SNP rs206119> SNPsrs189037/rs1799955/rs11871785. Taken together, DBM-PSO is powerful to identify the joint SNP-SNP interaction in a reversed association with breast cancer in terms of SNPs in DNA repair genes.
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ABSTRACT: Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome and are widely used to investigate the association analysis of diseases. SNP barcode is a combination of SNPs with genotypes (AA, Aa, and aa for an SNP) to find the difference between case data set and control data set for analyzing the disease association amongst SNPs. Currently, the computational time of statistical method becomes the weak to analyze the big data to find the significant SNP barcode. Here, we applied a sinusoidal particle swarm optimization (SPSO) algorithm facilitate the statistical methods to analyze the associated SNPs. We systematically evaluated the synergistic effect of 26 SNPs from eight epigenetic modifier-related genes in breast cancer. The 2-to 5-order SNP barcodes were found to determine the risk effects in breast cancer. We found that five of eight genes (BAT8, DNMT3A, EHMT1, DNMT3A, and BAT8) were statistically significant to breast cancer and play the important role in the SNP barcode. In addition, we compared the search ability between PSO and SPSO in the 2-to 5-order SNP barcodes. The results indicated that SPSO can find the better SNP barcode than PSO. In conclusion, SPSO is a precise algorithm for finding a significant model of SNP barcode.