Bayesian analysis of mutational spectra

Studies that examine both the frequency of gene mutation and the pattern or spectrum of mutational changes can be used to identify chemical mutagens a nd to explore the molecular mechanisms of mutagenesis. In this article, we propose a Bayesian hierarchical modeling approach for the analysis of mutat ional spectra. We assume that the total number of independent mutations and the numbers of mutations falling into different response categories, defin ed by location within a gene and/or type of alteration, follow binomial and multinomial sampling distributions, respectively. We use prior distributio ns to summarize past information about the overall mutation frequency and t he probabilities corresponding to the different mutational categories. Thes e priors call be chosen on the basis of data from previous studies using an approach that accounts for heterogeneity among studies. Inferences about t he overall mutation frequency, the proportions of mutations in each respons e category, and the category-specific mutation frequencies can be based on posterior distributions, which incorporate past and current data on the mut ant frequency and on DNA sequence alterations. Methods are described for co mparing groups and for assessing dose-related trends. We illustrate our app roach using data from the literature.