Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data

Area of Science:

• Evolutionary biology
• Genetics
• Bioinformatics

Background:

• Increasing availability of temporally spaced molecular sequences from pathogens and ancient sources.
• Need for robust statistical methods to infer evolutionary parameters from such data.
• Challenges in incorporating genealogical uncertainty into population genetics models.

Purpose of the Study:

• To develop a Bayesian statistical inference approach for joint estimation of mutation rate and population size.
• To incorporate uncertainty in the genealogy of temporally spaced sequences.
• To recover information about ancestral coalescent trees, population size, and mutation rates.

Main Methods:

• Bayesian statistical inference using Markov chain Monte Carlo (MCMC) integration.
• Application of the Kingman coalescent model to describe ancestral tree time structure.
• Joint estimation of mutation rate and population size from nucleotide sequences gathered at different times.

Main Results:

• Successful joint estimation of mutation rate and population size from temporally spaced sequence data.
• Demonstration of recovering information about ancestral coalescent trees and evolutionary parameters.
• Illustrative application on a human immunodeficiency virus type 1 (HIV-1) envelope gene genealogy.

Conclusions:

• The developed Bayesian approach effectively handles genealogical uncertainty in time-stamped sequence data.
• Methodological extensions allow for inference in growing populations and joint estimation of substitution models.
• Provides a powerful tool for studying pathogen evolution and ancient DNA.