A coalescent-based method for population tree inference with haplotypes

Area of Science:

• Population Genetics
• Evolutionary Biology
• Computational Biology

Background:

• Population trees are crucial for understanding past population divergence and evolution.
• Large-scale genetic projects present computational challenges for ancestral population inference.
• Current methods often use unlinked genetic variants, potentially losing information present in haplotypes.

Purpose of the Study:

• To introduce STELLSH, a novel method for population tree inference.
• To leverage haplotype data across multiple SNPs within non-recombining regions for improved accuracy.
• To address computational challenges in inferring population divergence histories from large datasets.

Main Methods:

• Developed STELLSH, a coalescent likelihood-based population tree inference method.
• Utilized haplotype data over multiple SNPs in non-recombining regions.
• Employed an approximated likelihood model, avoiding Monte Carlo methods for efficiency.

Main Results:

• STELLSH accurately infers population trees using haplotype data.
• The method demonstrates reasonable efficiency and scalability for current and whole-genome data.
• Validation performed using simulation data and the 1000 Genomes Project dataset.

Conclusions:

• STELLSH provides accurate and efficient population tree inference using haplotype information.
• The method is suitable for analyzing large-scale population genetic data.
• STELLSH advances the study of population evolution and divergence history.