GWAS identifies a common breast cancer risk allele among BRCA1 carriers. Nat Genet

Peter Kraft is at the Harvard School of Public Health, Boston, Massachusetts, USA.
Nature Genetics (Impact Factor: 29.35). 10/2010; 42(10):819-20. DOI: 10.1038/ng1010-819
Source: PubMed


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.

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    • "Single nucleotide polymorphisms (SNPs) are the most common variants of human genome [1]. Genome-wide association studies (GWAS) have widely been used to detect the association models to diseases in terms of multiple SNPs [2] [3] [4] [5] [6] [7]. The SNP interaction was gradually identified in a lot of GWAS [8] [9] [10] and non-GWAS [11] [12] literature. "
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    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(4a):281263. DOI:10.1155/2015/281263 · 1.58 Impact Factor
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    • "Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome [1]. SNPs have been widely used in genome-wide association studies (GWAS) to analyze the genetic susceptibility to disease [2] [3] [4]. The combination of SNPs can identify the potential associations in which top associated SNPs with disease may influence the genetic variation. "
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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.
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    • "Genome-wide association studies (GWAS) for the analysis of gene-gene interaction are important fields for detecting the effects of cancer and disease [1] [2] [3] [4]. Such studies usually entail the collection of a vast number of samples and SNPs selected from several related genes of disease in order to identify the association amongst genes. "
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    ABSTRACT: Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.
    BioMed Research International 05/2014; 2014(6):172049. DOI:10.1155/2014/172049 · 1.58 Impact Factor
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