%0 Journal Article %1 dewitt2021nonparametric %A DeWitt, William S. %A Harris, Kameron Decker %A Ragsdale, Aaron P. %A Harris, Kelley %D 2021 %J Proceedings of the National Academy of Sciences %K demographic_history inverse_problems mutation_spectrum %N 21 %P e2013798118 %R 10.1073/pnas.2013798118 %T Nonparametric coalescent inference of mutation spectrum history and demography %U https://www.pnas.org/doi/abs/10.1073/pnas.2013798118 %V 118 %X Population histories are encoded by genomic variation among modern individuals. Population genetic inference methods, all theoretically rooted in probabilistic population models, can recover complex demographic histories from genomic variation data. However, the mutation process is treated very simply in these models—usually as a single constant. Recent empirical findings show that the mutation process is complex and dynamic over a range of evolutionary timescales and thus, deserving of richer descriptions in population genetic models. Here, we show that complex mutation spectrum histories can be accommodated by extending classical theoretical tools. We develop mathematical optimization methods and software to infer both demographic history and mutation spectrum history, revealing human mutation signatures varying through time and global divergence of mutational processes. As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

@article{dewitt2021nonparametric, abstract = { Population histories are encoded by genomic variation among modern individuals. Population genetic inference methods, all theoretically rooted in probabilistic population models, can recover complex demographic histories from genomic variation data. However, the mutation process is treated very simply in these models—usually as a single constant. Recent empirical findings show that the mutation process is complex and dynamic over a range of evolutionary timescales and thus, deserving of richer descriptions in population genetic models. Here, we show that complex mutation spectrum histories can be accommodated by extending classical theoretical tools. We develop mathematical optimization methods and software to infer both demographic history and mutation spectrum history, revealing human mutation signatures varying through time and global divergence of mutational processes. As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history. }, added-at = {2022-03-09T13:15:56.000+0100}, author = {DeWitt, William S. and Harris, Kameron Decker and Ragsdale, Aaron P. and Harris, Kelley}, biburl = {https://www.bibsonomy.org/bibtex/2f24daaa33862904e229d426fd1f83598/peter.ralph}, doi = {10.1073/pnas.2013798118}, eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2013798118}, interhash = {789bac9257d969ab884ad18c0f7ed6ae}, intrahash = {f24daaa33862904e229d426fd1f83598}, journal = {Proceedings of the National Academy of Sciences}, keywords = {demographic_history inverse_problems mutation_spectrum}, number = 21, pages = {e2013798118}, timestamp = {2022-03-09T13:15:56.000+0100}, title = {Nonparametric coalescent inference of mutation spectrum history and demography}, url = {https://www.pnas.org/doi/abs/10.1073/pnas.2013798118}, volume = 118, year = 2021 }