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This study presents a new computational method for inferring population size changes over time using the bounded coalescent model, which assumes genealogies have a known maximum time depth. The researchers developed efficient algorithms for both simulating genetic genealogies and estimating population size trajectories using Bayesian methods, treating the problem as a point process estimation task. They validated the approach through simulations and applied it to SARS-CoV-2 genomic data from Washington State.
Why it matters
This method enables more accurate reconstruction of population dynamics in scenarios with known time boundaries, such as tracking infectious disease outbreaks from known introduction dates or analyzing cell lineages in biological experiments. The computational efficiency gains make it practical to analyze larger genomic datasets under this more realistic model.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: The coalescent is a central framework in population genetics for modelling the ancestral relationships among sampled individuals through a genealogy, represented as a rooted and ranked binary tree. In this model, lineages coalesce at a rate inversely proportional to the effective population size, a time-varying quantity of primary interest. The bounded coalescent conditions genealogies on the time to the most recent common ancestor being bounded above by a fixed time. This model is useful in various contexts, such as phylodynamics of infectious diseases with known introduction times and single-cell lineage tracing in synthetic barcoding experiments. To our knowledge, there is no existing tool that infers variable effective population size trajectories under the bounded coalescent. We view estimation under the bounded coalescent as equivalent to estimation of the intensity function of an inhomogeneous point process. We provide an efficient algorithm for coalescent simulation under the bounded coalescent using point process methods, retaining the exactness of naive rejection sampling while substantially reducing computational cost and avoiding repeated numerical inversion of the bounded cumulative hazard. We then develop a Markov chain Monte Carlo procedure for posterior inference of effective population size trajectories that avoids discretization of the likelihood integrals. In simulations, conditioning on the bound reduces the median sum of squared errors in two of three settings, with less favourable results in the most rapidly varying setting. We illustrate the method using severe acute respiratory syndrome coronavirus 2 sequence data from Washington State.
Source: Phylodynamic inference with the bounded coalescent: a point process perspective