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Updated: Jun 24, 2025
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
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EXACT PHYLODYNAMIC LIKELIHOOD VIA STRUCTURED MARKOV GENEALOGY PROCESSES
Aaron A King1, Qianying Lin2, Edward L Ionides3
1Department of Ecology & Evolutionary Biology, Center for the Study of Complex Systems, and Department of Mathematics, University of Michigan, Ann Arbor, MI 48109 USA and Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 USA.
Arxiv
|June 10, 2024
Summary
We developed a method to infer population structures using geneaology data from Markov population processes. This allows for efficient statistical inference in complex compartment models.
Area of Science:
- Population genetics
- Stochastic processes
- Computational biology
Background:
- Markov population processes model evolving populations with distinct states.
- Genealogies represent the ancestral relationships of sampled individuals.
- Statistical exchangeability within compartments simplifies population modeling.
Purpose of the Study:
- To construct the time-evolving genealogy process for Markov population models.
- To derive exact likelihood expressions for observed genealogies.
- To enable efficient statistical inference for compartment models using genealogy data.
Main Methods:
- Construction of the genealogy process for discrete compartment models.
- Derivation of likelihood expressions using filter equations.
- Numerical solution of filter equations via Monte Carlo integration.
Main Results:
- Statistically efficient likelihood-based inference is achieved.
- The method applies to arbitrary compartment models.
- Inference is based on observed genealogies of sampled individuals.
Conclusions:
- The developed framework provides a powerful tool for analyzing population structures.
- This approach enhances our ability to infer complex demographic histories from genetic data.
- Efficient computational methods allow for practical application in population genetics research.
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