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The specific mechanism by which low-risk genetic variants confer breast cancer risk is currently unclear, with contradictory evidence on the role of single nucleotide polymorphisms (SNPs) in TOX3/LOC643714 as a breast cancer susceptibility locus. Investigations of this locus using a Chinese population may indicate whether the findings initially identified in a European population are generalizable to other populations, and may provide new insight into the role of genetic variants in the etiology of breast cancer. In this case-control study, 623 Chinese female breast cancer patients and 620 cancer-free controls were recruited to investigate the role of five SNPs in TOX3/LOC643714 (rs8051542, rs12443621, rs3803662, rs4784227, and rs3112612); Linkage disequilibrium (LD) pattern analysis was performed. Additionally, we evaluated how these common SNPs influence the risk of specific types of breast cancer, as defined by estrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status. Significant associations with breast cancer risk were observed for rs4784227 and rs8051542 with odds ratios (OR) of 1.31 ((95% confidence intervals (CI), 1.10-1.57)) and 1.26 (95% CI, 1.02-1.56), respectively, per T allele. The T-rs8051542 allele was significantly associated with ER-positive and HER2-negative carriers. No significant association existed between rs12443621, rs3803662, and rs3112612 polymorphisms and risk of breast cancer. Our results support the hypothesis that the applicability of a common susceptibility locus must be confirmed among genetically different populations, which may together explain an appreciable fraction of the genetic etiology of breast cancer.
Pubmed ID: 24481062
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A web-based application designed from a genetic epidemiology point of view to analyze association studies using single nucleotide polymorphisms (SNPs). For each selected SNP, you will receive: * Allele and genotype frequencies * Test for Hardy-Weinberg equilibrium * Analysis of association with a response variable based on linear or logistic regression * Multiple inheritance models: co-dominant, dominant, recessive, over-dominant and additive * Analysis of interactions (gene-gene or gene-environment) If multiple SNPs are selected: * Linkage disequilibrium statistics * Haplotype frequency estimation * Analysis of association of haplotypes with the response * Analysis of interactions (haplotypes-covariate)
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Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.
A portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.
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