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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
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Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination
Fangfang Guo1, Ignazio Carbone1,2, David A Rasmussen1,3
1Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, North Carolina, United States of America.
Plos Computational Biology
|August 19, 2022
Summary
Recombination creates complex genetic histories, but the new Structured Coalescent with Ancestral Recombination (SCAR) model can infer migration patterns across genomes. This advances phylogeographic studies by analyzing genomic mosaicism.
Area of Science:
- Population genetics
- Genomics
- Evolutionary biology
Background:
- Gene flow between populations shapes genetic variation.
- The structured coalescent models population structure and gene flow.
- Genomic recombination creates mosaic ancestry, complicating traditional phylogeographic inference.
Purpose of the Study:
- To develop a novel model that incorporates recombination into structured coalescent theory.
- To infer genome-wide migration histories from complex ancestral recombination graphs (ARGs).
- To improve estimates of population genetic parameters like migration and recombination rates.
Main Methods:
- Introduction of the Structured Coalescent with Ancestral Recombination (SCAR) model.
- Utilizing Ancestral Recombination Graphs (ARGs) to represent mosaic ancestry.
- Application of the SCAR model to fungal (Aspergillus flavus) population data.
Main Results:
- The SCAR model successfully integrates recombination into population history inference.
- Inferred migration patterns varied across the genome in Aspergillus flavus.
- SCAR model improved estimation of population size, recombination, and migration rates.
Conclusions:
- The SCAR model provides a powerful framework for phylogeography in recombining organisms.
- Genomic mosaicism can be leveraged to gain deeper insights into population connectivity.
- Understanding recombination and migration is crucial for accurate evolutionary inference.
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