Estimating the prevalence of rare germline genetic mutations in the general population is of interest as it can inform genetic counseling and risk management. Most studies that estimate the prevalence of mutations are performed in high‐risk populations, and each study is designed with differing inclusion criteria, resulting in ascertained populations. Quantifying the effects of ascertainment is necessary to estimate the prevalence in the general population. This quantification is difficult as the inclusion criteria is often based on disease status and/or family history. Combining estimates from multiple studies through a meta‐analysis is challenging due to the variety of study designs and ascertainment mechanisms as well as the complexity of quantifying the effect of these mechanisms. We provide guidelines on how to quantify the ascertainment mechanism for a wide range of settings and propose a general approach for conducting a meta‐analysis in these complex settings by incorporating study‐specific ascertainment mechanisms into a joint likelihood function. We implement the proposed likelihood‐based approach using both frequentist and Bayesian methodologies. We evaluate these approaches in simulations and show that the methods are robust and produce unbiased estimates of the prevalence. An advantage of the Bayesian approach is that it can easily incorporate uncertainty in ascertainment probability values. We apply our methods to estimate the prevalence of PALB2 mutations in the United States by combining data from multiple studies and obtain a prevalence estimate of around 0.02%.

中文翻译：

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基因突变流行率荟萃分析的统计方法

估计普通人群中罕见种系基因突变的患病率很有意义，因为它可以为遗传咨询和风险管理提供信息。大多数估计突变发生率的研究都是在高危人群中进行的，每项研究都采用不同的纳入标准来设计，从而产生确定的人群。量化确定的影响对于估计一般人群的患病率是必要的。这种量化很困难，因为纳入标准通常基于疾病状况和/或家族史。由于研究设计和确定机制的多样性以及量化这些机制的影响的复杂性，通过荟萃分析结合多项研究的估计具有挑战性。我们提供了如何量化各种环境下的确定机制的指南，并提出了一种通过将研究特定的确定机制纳入联合似然函数来在这些复杂环境中进行荟萃分析的通用方法。我们使用频率论和贝叶斯方法来实现所提出的基于可能性的方法。我们在模拟中评估了这些方法，并表明这些方法是稳健的，并且可以对患病率进行公正的估计。贝叶斯方法的优点是它可以轻松地将不确定性纳入确定概率值中。我们应用我们的方法，通过结合多项研究的数据来估计 PALB2 突变在美国的患病率，并获得约 0.02% 的患病率估计值。