Extended Data Figure 8: Inference of turnover rates.

a–f, Schematic illustration of the algorithmic approach we use in order to infer turnover rates from the distribution of methylation patterns in clonal populations. Patterns are first split into two epiallelic groups, and then modelled as the outcome of a coalescent process in an exponentially growing population. This enables inference of gain and loss events together with the total time (in generations) in a methylated and unmethylated state for each CpG. These statistics can then be used to estimate the gain and loss rates, in particular when pooling together data from thousands of CpGs, with or without stratification for genomic and epigenomic factors of interest. We note that our implementation of the above approach uses several heuristics for enabling practical computation over millions of CpGs. For example, we use maximum-parsimony assumptions to approximate the posterior probabilities of ancestral CpG states for each locus. More details are available in Methods.