ArticleMolecular biology and evolution2017
The Structured Coalescent and Its Approximations.
Article in Molecular biology and evolution, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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70 citing papers in PubMed.
- Home is where the host is: evolutionary history of geographic spread, host switching and adaptive genomic signatures in two generalist group BMicrobial genomics · 2026Article
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- The Phylodynamic Threshold of Measurably Evolving Populations.Molecular biology and evolution · 2026Article
- Parallel algorithms for phylogenetic inference under a structured coalescent approximation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection.Molecular biology and evolution · 2026Article
- Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.Genetics · 2026Article
- Quantifying plasmid movement in drug-resistant Shigella species using phylodynamic inference.PLoS pathogens · 2025Article
- Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics.bioRxiv : the preprint server for biology · 2025Article
- When migration leaves a clean trace: Decoupling migration from coalescence in the structured serial coalescent.bioRxiv : the preprint server for biology · 2025Article
- Parallel algorithms for phylogenetic inference under a structured coalescent approximation.bioRxiv : the preprint server for biology · 2025Article
- MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.PLoS computational biology · 2025Article
- Evolution and transmission dynamics of wild poliovirus in Pakistan and Afghanistan (2012-2023).Nature communications · 2025Article
- Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.PLoS computational biology · 2025Article
- Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.Statistical methods in medical research · 2025Article
- Genomic survey of multidrug resistant Salmonella enterica serovar Minnesota clones in chicken products.npj antimicrobials and resistance · 2025Article
- Modeling the velocity of evolving lineages and predicting dispersal patterns.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses.Science (New York, N.Y.) · 2024Article
- Modeling the velocity of evolving lineages and predicting dispersal patterns.bioRxiv : the preprint server for biology · 2024Article
- Untangling lineage introductions, persistence and transmission drivers of HP-PRRSV sublineage 8.7.Nature communications · 2024Article
- A comparison of genomic diversity and demographic history of the North Atlantic and Southwest Atlantic southern right whales.Molecular ecology · 2024Article
10 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.
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