Research Papers:
The precision relationships between eight GWAS-identified genetic variants and breast cancer in a Chinese population
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Abstract
Yazhen Chen1,*, Fangmeng Fu1,*, Yuxiang Lin1,*, Lin Qiu1, Minjun Lu1, Jiantang Zhang1, Wei Qiu1, Peidong Yang1, Na Wu1, Meng Huang2, Chuan Wang1
1Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China
2Fujian Center for Disease Control and Prevention, Fuzhou, Fujian Province, 350001, China
*These authors have contributed equally to this work
Correspondence to:
Chuan Wang, email: [email protected]
Keywords: breast cancer, GWAS, SNP, stratified analysis, gene-environment interaction analysis
Received: May 18, 2016 Accepted: September 16, 2016 Published: September 26, 2016
ABSTRACT
Some of the new breast cancer susceptibility loci discovered in recent Genome-wide association studies (GWASs) have not been confirmed in Chinese populations. To determine whether eight novel Single-Nucleotide Polymorphisms (SNPs) have associations with breast cancer risk in women from southeast China, we conducted a case-control study of 1,156 breast cancer patients and 1,256 healthy controls. We first validated that the SNPs rs12922061, rs2290203, and rs2981578 were associated with overall breast cancer risk in southeast Chinese women, with the per-allele OR of 1.209 (95%CI: 1.064-1.372), 1.176 (95%CI: 1.048-1.320), and 0.852 (95%CI: 0.759-0.956), respectively. Rs12922061 and rs2290203 even passed the threshold for Bonferroni correction (P value: 0.00625). In stratified analysis, we found another three SNPs were significantly associated within different subgroups. However, after Bonferroni correction (P value: 0.000446), there were no statistically significant was observed. In gene-environment interaction analysis, we observed gene-environment interactions played a potential role of in the risk of breast cancer. These findings provide new insight into the associations between the genetic susceptibility and fine classifications of breast cancer. Based on these results, we encourage further large series studies and functional research to confirm these finding.
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