Inference on population history and model checking using DNA sequence and microsatellite data with the software...

Area of Science:

• Population Genetics
• Evolutionary Biology
• Computational Biology

Background:

• Approximate Bayesian Computation (ABC) is a powerful Monte Carlo method for inferring population evolutionary histories.
• The DIYABC software provides a user-friendly interface for complex demographic modeling.
• Previous versions primarily utilized microsatellite data for inference.

Purpose of the Study:

• To introduce significant updates to the DIYABC software, expanding its analytical capabilities.
• To enable inference using DNA sequence data alongside or independently of microsatellite data.
• To incorporate model checking for assessing the goodness-of-fit of evolutionary scenarios.

Main Methods:

• Integration of DNA sequence data (autosomal, X-linked, Y-linked, mitochondrial) for population genetic inference.
• Analysis of five locus categories accounting for varying sex ratios.
• Implementation of model checking procedures to evaluate scenario fit within the ABC framework.

Main Results:

• Simulated data demonstrated that combining microsatellite, mtDNA, and nuclear DNA sequence data improves inference accuracy.
• The new developments allow for robust comparison of competing evolutionary scenarios and parameter estimation.
• Model checking effectively identified and assessed model misfit in simulated invasive species introduction scenarios.

Conclusions:

• The updated DIYABC software facilitates inference on complex evolutionary histories using diverse molecular markers.
• It empowers non-expert users with advanced model checking capabilities within an ABC framework.
• DIYABC V1.0 offers a valuable, freely accessible tool for population genetics research.