Population divergence time estimation using individual lineage label switching.

Area of Science:

• Population Genetics
• Phylogenetics
• Computational Biology

Background:

• Estimating divergence times from multilocus genetic data is crucial in evolutionary studies.
• Existing methods may face challenges with complex demographic histories, such as gene flow.

Purpose of the Study:

• To develop and evaluate a novel Bayesian inference method for estimating divergence times.
• To assess the accuracy of this method across various demographic scenarios, including gene flow.

Main Methods:

• A new Bayesian approach treating divergence time as a random variable, integrated into the coalescence framework.
• Utilizing a hazard function of the truncated normal distribution for splitting events.
• Testing with simulated population splits and reanalyzing human population genetic data (including archaic samples).

Main Results:

• The new method demonstrates high accuracy for simple divergence models without gene flow.
• Accuracy decreases with increasing immigration rates in isolation-with-migration models.
• High gene flow can lead to inaccurate estimations of the most recent common ancestor time.

Conclusions:

• The developed Bayesian method provides a robust tool for divergence time estimation.
• Gene flow significantly impacts the accuracy of divergence time estimates, even with numerous genetic loci.
• The lineage-switching method in Migrate is comparable to other tools and scalable for large datasets.