We present a model to predict the chance of each possible de novo mutation in the human genome informed by recent insights into determinants of mutagenesis. Predictions were applied to refine demographic models, identify constrained genes, and uncover mutagenic effects of polymerase III transcription and transcription factor binding in testis.
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References
Seplyarskiy, V. B. & Soldatov, R. A. et al. Population sequencing data reveal a compendium of mutational processes in the human germ line. Science 373, 1030–1035 (2021). This paper uses unsupervised machine learning to investigate variation in the rate and spectra of mutations along the human genome.
Carlson, J. et al. Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans. Nat. Commun. 9, 3753 (2018). This paper presents an earlier, sophisticated human mutation rate model that we attempted to improve upon.
Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature. 586, 757–762 (2020). This paper presents DeNovoWest, the current state-of-the-art method for identifying genes associated with rare developmental disorders.
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This is a summary of: Seplyarskiy, V. et al. A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription. Nat. Genet. https://doi.org/10.1038/s41588-023-01562-0 (2023).
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A biology-aware mutation rate model for human germline. Nat Genet 55, 2033–2034 (2023). https://doi.org/10.1038/s41588-023-01564-y
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DOI: https://doi.org/10.1038/s41588-023-01564-y
