Table 2.

Association of XRCC4 tSNPs with breast cancer risk and age at diagnosis (cases only)

SNP (n cases/n controls)Breast cancer risk
Age at diagnosis
OR (95% CI)PMeans statisticP
X1: rs1478485 (457/576)
    DOM*0.98 (0.75-1.27)0.8920.730.470
    REC1.08 (0.79-1.48)0.5963.010.003
    HET vs. WT§0.95 (0.71-1.27)0.735−0.240.818
    HOM vs. WT1.05 (0.74-1.47)0.7672.440.021
    ANOVA4.290.017
X2: rs13180316 (458/576)
    DOM1.23 (0.97-1.56)0.081−2.560.010
    REC1.05 (0.64-1.74)0.8370.660.503
    HET vs. WT1.25 (0.98-1.59)0.079−2.880.004
    HOM vs. WT1.15 (0.69-1.90)0.590−0.030.97
    ANOVA4.190.013
X3: rs963248 (455/574)
    DOM1.01 (0.78-1.30)1.00−0.780.441
    REC0.86 (0.43-1.72)0.6490.920.353
    HET vs. WT1.02 (0.79-1.33)0.903−1.090.283
    HOM vs. WT0.87 (0.43-1.74)0.6640.810.405
    ANOVA1.220.291
X4: rs1056503 (459/576)
    DOM1.08 (0.81-1.45)0.5830.520.611
    REC0.94 (0.23-3.92)0.9510.640.558
    HET vs. WT1.09 (0.81-1.47)0.5500.410.690
    HOM vs. WT0.96 (0.22-4.11)0.9710.670.539
    ANOVA0.220.806
  • NOTE: Interesting results are shown in bold.

  • * Dominant model (carriage of rare allele versus noncarriage).

  • Recessive model (homozygous for the rare allele versus other genotypes).

  • Heterozygous genotype.

  • § Homozygous for common allele.

  • Homozygous for rare allele.

  • ANOVA comparison includes three groups, one for each genotype (homozygous, heterozygous, and WT).