TRAILS: Tree reconstruction of ancestry using incomplete lineage sorting

Area of Science:

• Evolutionary biology
• Population genetics
• Genomics

Background:

• Genome-wide genealogies offer insights into species' demographic and selection histories.
• Incomplete lineage sorting (ILS) fragments contain valuable evolutionary information.
• Accurate inference of ancestral population parameters is crucial for evolutionary studies.

Purpose of the Study:

• To introduce TRAILS, a novel hidden Markov model for inferring time-resolved population genetics parameters.
• To leverage ILS fragments for detailed reconstruction of ancestral demographic histories.
• To enable genome-wide scans for detecting deviations from evolutionary neutrality.

Main Methods:

• Developed TRAILS, a hidden Markov model utilizing multi-species alignments (three species + outgroup).
• Modeled genome-wide genealogies as rooted three-leaved trees with coalescent events in discretized time intervals.
• Employed posterior decoding of the hidden Markov model to infer ancestral recombination graphs and demographic changes.
• Performed base-pair level analysis for high-resolution genomic scans.

Main Results:

• Accurately inferred time-resolved population genetics parameters, including ancestral effective population sizes and speciation times.
• Successfully modeled genealogies and coalescent events within phylogenetic branches.
• Recovered speciation parameters and detailed information on topology and coalescent times for human-chimp-gorilla-orangutan alignment.
• Demonstrated the capability of genome-wide scans for detecting deviations from neutrality.

Conclusions:

• TRAILS provides a powerful framework for inferring detailed ancestral demographic histories from genomic data.
• The model effectively utilizes information from incomplete lineage sorting across multiple species.
• TRAILS facilitates high-resolution analysis of evolutionary processes and neutrality testing at the genomic level.