There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age‐ and sex‐specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after accounting for the mutations in the model, due to environmental or unobserved genetic risk factors. We aim to improve Mendelian risk prediction by incorporating a frailty model that contains a family‐specific frailty vector, impacting the cancer hazard function, to account for this heterogeneity. We use a discrete uniform population frailty distribution and implement a marginalized approach that averages each family's risk predictions over the family's frailty distribution. We apply the proposed approach to improve breast cancer prediction in BRCAPRO, a Mendelian model that accounts for inherited mutations in the BRCA1 and BRCA2 genes to predict breast and ovarian cancer. We evaluate the proposed model's performance in simulations and real data from the Cancer Genetics Network and show improvements in model calibration and discrimination. We also discuss alternative approaches for incorporating frailties and their strengths and limitations.

中文翻译：

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孟德尔风险预测中虚弱模型的实际实施。

有许多统计模型用于识别因遗传突变而患癌症的高风险个体。孟德尔模型通过使用家族史和估计的癌症外显率（给定突变基因型的年龄和性别特异性癌症风险）和突变流行率来预测未来的癌症风险。然而，由于环境或未观察到的遗传风险因素，即使在考虑了模型中的突变后，家庭之间也经常存在残余风险异质性。我们的目标是通过纳入一个包含特定家族衰弱向量的衰弱模型来改进孟德尔风险预测，影响癌症风险函数，以解释这种异质性。我们使用离散的均匀人口衰弱分布，并实施边缘化方法，平均每个家庭的 对家庭虚弱分布的风险预测。我们应用所提出的方法来改善 BRCAPRO 中的乳腺癌预测，这是一种孟德尔模型，可解释BRCA1和BRCA2基因可预测乳腺癌和卵巢癌。我们在来自癌症遗传学网络的模拟和真实数据中评估了所提出模型的性能，并展示了模型校准和识别方面的改进。我们还讨论了整合弱点及其优势和局限性的替代方法。