Katki Hormuzd A, Blackford Amanda, Chen Sining, Parmigiani Giovanni
Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville, MD 20852-4910, USA.
Stat Med. 2008 Sep 30;27(22):4532-48. doi: 10.1002/sim.3302.
Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 families from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO's concordance index from 0.758 to 0.762 (p=0.046), improves its positive predictive value from 35 to 39 per cent (p<10(-6)) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability<10 per cent.
孟德尔模型可以根据家族病史预测谁携带已知疾病基因的遗传性有害突变。例如，BRCAPRO模型通常用于根据家族性乳腺癌和卵巢癌来识别携带BRCA1和BRCA2突变的家族。这些模型纳入了亲属疾病的诊断年龄以及当前年龄或死亡年龄。我们为处理带有删失值的多种疾病建立了一个严谨的基础。我们证明，任何与突变无关的疾病都可以从模型中排除，除非它足够常见且依赖于与突变相关的疾病时间。此外，如果家庭成员在突变携带者中患某种疾病的概率密度更高，但模型未考虑到这一点，那么携带者概率就会被低估。然而，即使一个家族只有模型所考虑的疾病，但如果模型排除了一种与突变相关的疾病，那么携带者概率就会被高估。鉴于这些结果，我们扩展了BRCAPRO，将存活于所有非乳腺癌/卵巢癌作为一个单一结果来考虑。这种扩展还使BRCAPRO能够从男性亲属中提取更多有用信息。使用癌症遗传网络的1500个家族数据，考虑存活于其他癌症的情况后，BRCAPRO的一致性指数从0.758提高到0.762（p = 0.046），阳性预测值从35%提高到39%（p < 10⁻⁶），且不影响其阴性预测值，并改善了其整体校准情况，不过对于携带者概率<10%的人群，校准情况略有恶化。
