MultiWaver

MultiWaver infers complex population admixture histories by modeling both discrete and continuous gene flow from the length distribution of ancestral chromosome tracks.

Key Features:

• Flexible Framework: Supports a range of admixture models, including discrete and continuous frameworks, and infers complex histories from ancestral chromosome track length distributions.
• Model Selection and Parameter Estimation: Uses a likelihood ratio test (LRT) for discrete model determination, an expectation-maximization (EM) algorithm for parameter estimation, and compares discrete versus continuous models with the Bayesian Information Criterion (BIC).
• Bootstrapping Technique: Applies bootstrapping to provide support levels for chosen models and confidence intervals (CIs) for admixture time estimates.
• Validation through Simulation Studies: Validated using simulation studies to assess reliability and performance.
• Application to Real Datasets: Demonstrated on admixed population datasets including African Americans, Uyghurs, and Hazaras.

Scientific Applications:

• Reconstruction of complex admixture histories: Reconstructs multi-wave and complex admixture events and timing in population genetics studies.
• Estimation of admixture parameters: Estimates admixture times and related parameters with associated confidence intervals and model support.
• Analysis of human admixed populations: Applied to study historical intermixing and genetic makeup in populations such as African Americans, Uyghurs, and Hazaras.

Methodology:

Analyzes length distributions of ancestral chromosome tracks; performs likelihood ratio tests for discrete model selection; uses an expectation-maximization algorithm for parameter estimation; compares models using BIC; applies bootstrapping to obtain model support and confidence intervals; validated via simulation studies.