Quantifying the effects of computational filter criteria on the accurate identification of de novo mutations at varying levels of sequencing coverage.

The rate of spontaneous (de novo) germline mutation is a key parameter in evolutionary biology, impacting genetic diversity and contributing to the evolution of populations and species. Mutation rates themselves evolve over time but the mechanisms underlying the mutation rate variation observed across the Tree of Life remain largely to be elucidated. In recent years, whole genome sequencing has enabled the estimation of mutation rates for several organisms. However, due to a lack of community standards, many previous studies differ both empirically - most notably, in the depth of sequencing used to reliably identify de novo mutations - and computationally - utilizing different computational pipelines to detect germline mutations as well as different analysis strategies to mitigate technical artifacts - rendering comparisons between studies challenging. Using a pedigree of Western chimpanzees as an illustrative example, we here quantify the effects of commonly utilized quality metrics to reliably identify de novo mutations at different levels of sequencing coverage. We demonstrate that datasets with a mean depth of ≤ 30X are ill-suited for the detection of de novo mutations due to high false positive rates that can only be partially mitigated by computational filter criteria. In contrast, higher coverage datasets enable a comprehensive identification of de novo mutations at low false positive rates, with minimal benefits beyond a sequencing coverage of 60X, suggesting that future work should favor breadth (by sequencing additional individuals) over depth. Importantly, the simulation and analysis framework described here provides conceptual guidelines that will allow researchers to take study design and species-specific resources into account when determining computational filtering strategies for their organism of interest.