Nagel, A. A. | bioRxiv Science

bioRxiv2025

Mutation ages and population origins inferred from genomes in structured populations

Inferring the time of origin (age) of mutations is an old question in population genetics and inferring their population of origin has become of particular interest with the sequencing of the Neanderthal genome. However, existing methods to infer mutation ages and populations of origin do not explicitly consider population structure, migration rates, and divergence times, which may bias estimates, and it is unclear how to even apply single-population estimators to structured populations. We develop a method to jointly estimate the time and population of origin of a mutation (as well as the ancestral and derived states) in a structured population using population genomic data and examine its statistical performance using simulations. Results indicate that mutation age and population of origin can be quite uncertain, even with long sequences or many samples, but this uncertainty is accurately captured using credible intervals/sets. The ancestral nucleotide state is relatively easy to infer. We apply our method to whole genome data from the 1000 Genomes Project, analyzing seven SNP mutations from six genes associated with human skin pigmentation for populations from Great Britain, China, and Kenya. Our results partially support previous conclusions, with the putative ancestral alleles from the literature matching our inferences, while the mutation age estimates only overlap in some cases. Furthermore, there are non-trivial posterior probabilities of recurrence for three of the mutations.

evolutionary biology↗

bioRxiv2022

Bayesian Phylogenetic Inference of HIV Latent Lineage Ages Using Serial Sequences

HIV evolves rapidly within individuals, allowing phylogenetic studies to infer the history of viral lineages on short time scales. Latent HIV sequences are an exception to this rapid evolution, as their transcriptional inactivity leads to negligible mutation rates in comparison to non-latent HIV lineages. Latent sequences are of keen interest as they provide insight into the formation, persistence, and decay of the latent reservoir. Different mutation rates in latent versus active HIV lineages generate potential information about the times at which sequences entered the latent reservoir. A Bayesian phylogenetic method is developed to infer integration times of latent HIV sequences. The method uses informative priors to incorporate biologically sensible bounds on inferences (such as requiring sequences to become latent before being sampled) that many existing methods lack. A new simulation method is also developed, based on widely-used epidemiological models of within-host viral dynamics, and applied to evaluate the new method, showing that point estimates and credible intervals are often more accurate by comparison with existing methods. Accurate estimates of latent integration dates are crucial in dating the formation of the latent reservoir relative to key events during HIV infection, such as the initiation of antiretroviral treatment. The method is applied to analyze publicly-available sequence data from 4 HIV patients, providing new insights regarding the temporal pattern of latent HIV integration events. Significance StatementPhylogenetic studies are increasingly being used to characterize within-host HIV evolution and the temporal dynamics of the HIV latent reservoir in particular, which is not targeted by current treatment methods and thus prevents a cure for HIV. Phylogenetic methods currently used to analyze HIV sequences suffer from conceptual and statistical problems that degrade their performance. A new Bayesian inference method to estimate the ages of latent sequences and a new simulation method based on within-host viral dynamics are developed. The new inference method outperforms existing methods, particularly in characterizing uncertainty. Understanding how the latent HIV reservoir changes overtime will allow researchers to better understand the nature of HIV infection and develop strategies for a cure.

evolutionary biology↗