Maximum Likelihood Implementation of an Isolation-with-Migration Model for Three Species.

Area of Science:

• Population Genetics
• Genomics
• Bioinformatics

Background:

• Estimating migration rates between species is crucial for understanding evolutionary processes.
• Previous models often simplified phylogenetic relationships and migration patterns.
• Analyzing genome-scale data requires efficient computational methods.

Purpose of the Study:

• To develop a maximum likelihood (ML) method for estimating asymmetrical migration rates between species using genomic sequence data.
• To extend existing models to accommodate arbitrary loci and genome-scale datasets.
• To identify genomic regions with evidence of gene flow and assess the utility of the multispecies coalescent model.

Main Methods:

• Developed a maximum likelihood method incorporating a species tree with an out-group.
• Utilized Markov chain characterization for analytical integration of migration histories and Gaussian quadrature for numerical integration of coalescent times.
• Applied the method to analyze genome-scale data from Drosophila species.

Main Results:

• The method can analyze tens of thousands of loci, enabling genome-scale gene flow analysis.
• Inclusion of an out-group significantly improves statistical power and parameter estimation precision.
• Analysis of Drosophila data indicated no migration from D. melanogaster to D. simulans, but significant gene flow (~0.02 migrants/generation) from D. simulans to D. melanogaster.

Conclusions:

• The developed ML method provides a powerful tool for estimating interspecies migration rates from genomic data.
• The findings highlight asymmetrical gene flow between Drosophila melanogaster and Drosophila simulans.
• The multispecies coalescent model is valuable for species tree estimation, accounting for incomplete lineage sorting and migration.