OptM

OptM estimates the optimal number of migration edges on population trees from genome-wide allele frequency data processed with Treemix to inform inference of gene flow in population genetic studies.

Key Features:

• Second-Order Rate of Change in Likelihood (Δm): OptM utilizes the second-order rate of change in likelihood (Δm) to infer the optimal number of migration edges, analogous to the ΔK approach used in Structure.
• Performance Validation: Simulation studies with synthetic populations demonstrate performance comparable to existing recommendations for Treemix.
• Empirical Application (domestic dogs): Application to an empirical domestic dog dataset illustrates prioritization of migration events for further investigation in complex population histories.

Scientific Applications:

• Complex population histories: Determining the number of migration edges in analyses of large and complex population histories.
• Gene flow and demographic inference: Refining estimation of migration events to inform gene flow and demographic history analyses.
• Genetic diversity and adaptation studies: Clarifying migration edges to aid interpretation of patterns of genetic diversity and adaptation.

Methodology:

Analyzes Treemix output files to calculate Δm (the second-order rate of change in likelihood) and identifies the point at which additional migration edges no longer substantially improve model fit.