Gail Mitchell H
Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Rm 8032, Bethesda, MD 20892-7244, USA.
J Natl Cancer Inst. 2008 Jul 16;100(14):1037-41. doi: 10.1093/jnci/djn180. Epub 2008 Jul 8.
One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.
寻找与疾病相关的常见等位基因的一个目的是利用它们来改进预测个体疾病风险的模型。两项全基因组关联研究和一项候选基因研究最近在独立样本中鉴定出七个与乳腺癌风险相关的常见单核苷酸多态性（SNP）。这七个SNP位于成纤维细胞生长因子受体2（FGFR2）、TNRC9（现称为TOX3）、丝裂原活化蛋白激酶激酶1（MAP3K1）、淋巴细胞特异性蛋白1（LSP1）、半胱天冬酶8（CASP8）、染色体区域8q和染色体区域2q35。我利用这些研究中的相对风险估计值和等位基因频率来估计这些SNP能在多大程度上提高以受试者工作特征曲线下面积（AUC）衡量的鉴别准确性。包含这七个SNP的模型（AUC = 0.574）和包含14个此类SNP的假设模型（AUC = 0.604）的鉴别准确性低于基于初潮年龄、首次生育年龄、乳腺癌家族史和乳腺活检检查史的模型——美国国立癌症研究所的乳腺癌风险评估工具（BCRAT）（AUC = 0.607）。将这七个SNP添加到BCRAT中可将鉴别准确性提高到AUC为0.632，然而，这低于添加乳腺X线密度所带来的改善。因此，这七个常见等位基因提供的鉴别准确性低于BCRAT，但有潜力适度提高BCRAT的鉴别准确性。迄今为止的经验和定量分析表明，在全基因组关联研究中需要大幅增加乳腺癌病例患者和对照受试者的数量，以找到足够多的SNP来实现高鉴别准确性。
