Abstract

Background: Multiple common variants identified by genome-wide association studies showed limited evidence of the risk of breast cancer in Taiwan. In this study, we analyzed the breast cancer risk in relation to 13 individual single-nucleotide polymorphisms (SNPs) identified by a GWAS in an Asian population. Methods: In total, 446 breast cancer patients and 514 healthy controls were recruited for this case–control study. In addition, we developed a polygenic risk score (PRS) including those variants significantly associated with breast cancer risk, and also evaluated the contribution of PRS and clinical risk factors to breast cancer using receiver operating characteristic curve (AUC). Results: Logistic regression results showed that nine individual SNPs were significantly associated with breast cancer risk after multiple testing. Among all SNPs, six variants, namely FGFR2 (rs2981582), HCN1 (rs981782), MAP3K1 (rs889312), TOX3 (rs3803662), ZNF365 (rs10822013), and RAD51B (rs3784099), were selected to create PRS model. A dose–response association was observed between breast cancer risk and the PRS. Women in the highest quartile of PRS had a significantly increased risk compared to women in the lowest quartile (odds ratio 2.26; 95% confidence interval 1.51–3.38). The AUC for a model which contained the PRS in addition to clinical risk factors was 66.52%, whereas that for a model which with established risk factors only was 63.38%. Conclusions: Our data identified a genetic risk predictor of breast cancer in Taiwanese population and suggest that risk models including PRS and clinical risk factors are useful in discriminating women at high risk of breast cancer from those at low risk.

Original languageEnglish
Pages (from-to)131-138
Number of pages8
JournalBreast Cancer Research and Treatment
Volume163
Issue number1
DOIs
Publication statusPublished - May 1 2017

Keywords

  • Breast cancer
  • Common variants
  • Polygenic risk score
  • Risk prediction

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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