biostudies-literatureAdams CJNational Institute of Diabetes and Digestive and Kidney DiseasesNIDDK NIH HHSe1010807https://www.ebi.ac.uk/biostudies/studies/S-EPMC10355397biostudies-literatureUnknown19(7)Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures thPLoS geneticsRegularized sequence-context mutational trees capture variation in mutation rates across the human genome.PMC10355397R56 DK101478DK126194R01 DK101478DK101478UM1 DK126194Auerbach BJJensen STAdams CJConery MMathieson IVoight BFfalseRegularized sequence-context mutational trees capture variation in mutation rates across the human genome.Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures th2023-01-01T00:00:00Z2023 Jul2025-04-05T14:52:21.364Z2025-04-05T14:52:21.364ZS-EPMC103553973741848910.1371/journal.pgen.1010807