Accurate detection of mosaic variants in sequencing data without matched controls

Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants (SNVs) and indels, achieving a multifold increase in specificity compared to existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90% of the mosaic SNVs and 60–80% indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease.