Statistical Approaches for Meta-Analysis of Genetic Mutation Prevalence

• Activity Number: 355 - Analysis of Complex Genetic Data
• Type: Contributed
• Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Margaux Hujoel* and Danielle Braun and Giovanni Parmigiani
• Companies: Harvard T.H. Chan School of Public Health / Dana-Farber Cancer Institute and Harvard T. H. Chan School of Public Health and Harvard T.H. Chan School of Public Health / Dana-Farber Cancer Institute
• Keywords: Prevalence; Rare-mutations; Meta-Analysis; Likelihood-based Estimation

Abstract

Estimating the prevalence of rare genetic mutations in the general population is of great interest as it can inform genetic counseling and risk management. Most studies which estimate prevalence of mutations are performed in high-risk populations, and each study is designed with differing inclusion-exclusion (i.e. ascertainment) criteria. Combining estimates from multiple studies through a meta-analysis is challenging due to the differing study designs and ascertainment mechanisms. We propose a general approach for conducting a meta-analysis under 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 methodology. We evaluate these approaches in simulations and show that the proposed frequentist and Bayesian methods result in unbiased estimates of the prevalence even with rare mutations (a prevalence of 0.01%). An advantage of the Bayesian approach is uncertainty in ascertainment probability values can be easily incorporated. We apply our methods in an illustrative example to estimate the prevalence of PALB2 in the general population.