Improved discovery of de novo mutations using TrioDNM and VRFS

Identifying de novo mutations (DNM) is an important component of both genetic research studies and clinical diagnostic workflows, but is complicated by distinguishing true mutations from sequencing errors. Likelihood-based error models are more accurate than inferring mutations from genotypes alone, but the resulting callsets still have high false positive rates.

We identify that the main source of false positive DNMs comes from the use of genotype likelihoods in an otherwise robust mutational model. To address this issue, we propose two alternative methods that build on an existing DNM calling approach, DeNovoGear, but with higher accuracy and no decrease in sensitivity.Furthermore, we developed a method that collects allele-specific frequency profiles in the sequenced cohort from across many unrelated samples and identifies sites that either demonstrate high rates of sequencing and mapping errors, or are unlikely to be clinically significant due to their high recurrence rate.