title: Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women.
abstract: BACKGROUND
  Recently, several genome-wide association studies (GWAS) have identified novel single nucleotide polymorphisms (SNPs) associated with breast cancer risk. However, most of the studies were conducted among Caucasians and only one from Chinese.
  
  
  METHODS
  In the current study, we first tested whether 15 SNPs identified by previous GWAS were also breast cancer marker SNPs in this Chinese population. Then, we grouped the marker SNPs, and modeled them with clinical risk factors, to see the usage of these factors in breast cancer risk assessment. Two methods (risk factors counting and odds ratio (OR) weighted risk scoring) were used to evaluate the cumulative effects of the five significant SNPs and two clinical risk factors (age at menarche and age at first live birth).
  
  
  RESULTS
  Five SNPs located at 2q35, 3p24, 6q22, 6q25 and 10q26 were consistently associated with breast cancer risk in both testing set (878 cases and 900 controls) and validation set (914 cases and 967 controls) samples. Overall, all of the five SNPs contributed to breast cancer susceptibility in a dominant genetic model (2q35, rs13387042: adjusted OR = 1.26, P = 0.006; 3q24.1, rs2307032: adjusted OR = 1.24, P = 0.005; 6q22.33, rs2180341: adjusted OR = 1.22, P = 0.006; 6q25.1, rs2046210: adjusted OR = 1.51, P = 2.40 × 10-8; 10q26.13, rs2981582: adjusted OR = 1.31, P = 1.96 × 10-4). Risk score analyses (area under the curve (AUC): 0.649, 95% confidence interval (CI): 0.631 to 0.667; sensitivity = 62.60%, specificity = 57.05%) presented better discrimination than that by risk factors counting (AUC: 0.637, 95% CI: 0.619 to 0.655; sensitivity = 62.16%, specificity = 60.03%) (P < 0.0001). Absolute risk was then calculated by the modified Gail model and an AUC of 0.658 (95% CI = 0.640 to 0.676) (sensitivity = 61.98%, specificity = 60.26%) was obtained for the combination of five marker SNPs, age at menarche and age at first live birth.
  
  
  CONCLUSIONS
  This study shows that five GWAS identified variants were also consistently validated in this Chinese population and combining these genetic variants with other risk factors can improve the risk predictive ability of breast cancer. However, more breast cancer associated risk variants should be incorporated to optimize the risk assessment.
authors.affiliation: National Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 210029, China.
authors.forename: Juncheng
authors.initials: J
authors.lastname: Dai
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Hu
authors.forename: Yue
authors.initials: Y
authors.lastname: Jiang
authors.forename: Hao
authors.initials: H
authors.lastname: Shen
authors.forename: Jing
authors.initials: J
authors.lastname: Dong
authors.forename: Hongxia
authors.initials: H
authors.lastname: Ma
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics*; D016022:Case-Control Studies; D002681:China; D005260:Female; D005787:Gene Frequency; D056726:Genetic Association Studies; D005838:Genotype; D006801:Humans; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors