Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes.

Area of Science:

• Population Genetics
• Computational Biology
• Statistical Genetics

Background:

• Demographic events significantly influence a population's genetic diversity.
• The coalescent-with-recombination model links population history with genetic data through unobserved genomic genealogies.
• Inferring demographic history from genetic data is computationally challenging due to the complexity of genomic genealogies.

Observation:

• The coalescent-with-recombination model was formulated as a continuous-time and -space Markov jump process.
• A novel particle filter was developed for this process, utilizing waypoints to simplify computations.
• The Auxiliary Particle Filter was generalized for discrete-time models, and Variational Bayes was employed to handle parameter uncertainty in rare events.

Findings:

• The developed method accurately infers past population sizes over extended historical periods.
• This approach overcomes limitations of previous methods in analyzing demographic history.
• The study demonstrates improved inference accuracy using real and simulated genomic data.

Implications:

• Enables more accurate reconstruction of population demographic histories.
• Facilitates joint analysis of multiple genomes under complex demographic models.
• Advances the field of population genetics by providing a robust computational tool for evolutionary inference.