Evaluation of Type 1 Error Rates in Duplex Sequencing for Mutagenicity Testing Using Vehicle Control Data and Simulation Analyses

Accurate mutation detection and quantification are crucial for understanding mutagenesis and its potential health implications. Traditional in vivo mutagenicity assays, such as the transgenic rodent gene mutation assay, are limited by their focus on single reporter genes and inability to efficiently generate mutation spectra. Error-corrected sequencing (ECS) technologies like Duplex Sequencing (DS) offer significant advantages, including extremely low error rates and the ability to measure mutation frequencies (MFs) across various tissues and model organisms. Before ECS approaches can be adopted for regulatory purposes, their performance characteristics, particularly the type 1 error rate, must be rigorously established. We evaluated the type 1 error rate of DS through empirical analysis of vehicle control data and complementary simulation studies. Using 138 control mouse liver samples from 28 studies analyzed with the TwinStrand Mouse Mutagenesis Panel, we performed variance component analysis and found that experiment-level variability exceeds within-experiment sample variability. To evaluate the impact of between-study heterogeneity, we simulated overdispersed binomial data informed by the observed variance components. Removing the most variable studies reduced overdispersion and improved control of the type 1 error rate. Our findings demonstrate that DS maintains appropriate type 1 error rates (~0.05) when study heterogeneity is limited and at least four samples per group are used. Under greater overdispersion, sample sizes of five or six per group may be needed to achieve comparable control of the type 1 error rate. These results underscore the importance of combining empirical and simulation-based approaches to evaluate and optimize the statistical performance of emerging genomic technologies.