Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Division of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Genet Epidemiol. 2021 Mar;45(2):154-170. doi: 10.1002/gepi.22364. Epub 2020 Sep 30.
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%.
评估常见基因突变的罕见种系在一般人群中的流行率是很有意义的，因为它可以为遗传咨询和风险管理提供信息。大多数评估突变流行率的研究都是在高危人群中进行的，而且每项研究的设计都有不同的纳入标准，导致确定了研究人群。为了估计一般人群中的流行率，量化确定法的效果是必要的。由于纳入标准通常基于疾病状态和/或家族史，因此很难进行这种量化。通过荟萃分析综合多个研究的估计值是具有挑战性的，因为研究设计和确定机制多种多样，以及量化这些机制的影响的复杂性。我们提供了针对广泛情况量化确定机制的指南，并提出了一种在这些复杂情况下进行荟萃分析的一般方法，即将研究特定的确定机制纳入联合似然函数中。我们使用频率主义和贝叶斯方法实现了所提出的基于似然的方法。我们在模拟中评估这些方法，表明这些方法是稳健的，并产生了流行率的无偏估计。贝叶斯方法的一个优点是它可以轻松地将确定概率值的不确定性纳入其中。我们通过合并多个研究的数据来估计美国 PALB2 突变的流行率，并获得了约 0.02%的流行率估计值。
