Bayesian Analysis of Evolutionary Divergence with Genomic Data under Diverse Demographic Models

Area of Science:

• Population Genetics
• Evolutionary Biology
• Bioinformatics

Background:

• Estimating demographic and phylogenetic history is crucial for understanding species evolution.
• Existing methods may face scalability challenges with large population genomic datasets.
• The Isolation-with-Migration framework is widely used but can be computationally intensive.

Purpose of the Study:

• To introduce a novel Bayesian method for inferring demographic and phylogenetic history from population genomic data.
• To enhance the study of diverse models within the Isolation-with-Migration framework.
• To provide a scalable and accurate computational tool for evolutionary analyses.

Main Methods:

• A two-step Bayesian approach involving Markov chain Monte Carlo (MCMC) and analytic integration.
• Step 1: MCMC sampling of coalescent trees in a reduced state space (without migration).
• Step 2: Calculation of joint posterior density for demographic model parameters using trees from Step 1, with migration handled analytically.

Main Results:

• The new method demonstrates accuracy and scalability with simulated data.
• Excellent MCMC mixing properties were observed, even with a large number of loci.
• The method was successfully applied to DNA sequences of two chimpanzee subspecies (Pan troglodytes troglodytes and Pan troglodytes verus).

Conclusions:

• The developed Bayesian method offers a significant advancement for demographic history inference.
• The MIST program implementation provides a scalable and efficient tool for population genomic analyses.
• This method facilitates the study of complex evolutionary models and improves our understanding of species divergence.