Statistical approaches for meta-analysis of genetic mutation prevalence.

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%.