Genetree

Genetree infers genealogies and demographic parameters from DNA sequence data in subdivided populations to reconstruct ancestral relationships and population history.

Key Features:

• Coalescent-based modeling: Implements the coalescent process model to represent genealogical relationships among sampled sequences in subdivided populations.
• Infinitely-many-sites mutation model: Assumes the infinitely-many-sites mutation model and constructs perfect phylogenies reflecting observed mutation patterns.
• Probability distribution recursion: Computes the probability distribution of gene trees for subdivided populations using a recursion method.
• Maximum likelihood estimation: Performs maximum likelihood estimation of migration and mutation rates from sequence data.
• Population growth detection: Uses likelihood techniques to detect signals of population growth.
• TMRCA and mutation-age inference: Determines the distribution of the time to the most recent common ancestor (TMRCA) for a sample and assesses ages of mutations on the gene tree.
• Subpopulation ancestor localization: Identifies the subpopulation of the most recent common ancestor and determines locations and times of subpopulation ancestors.
• Mutation localization: Infers in which subpopulations specific mutations occurred to map geographic and temporal distributions of variation.
• Markov chain simulation: Implements the Griffiths and Tavaré Markov chain simulation technique to simulate gene trees conditional on topology implied by mutation patterns.

Scientific Applications:

• Ancestral inference in subdivided populations: Reconstructs genealogical relationships and ancestral locations within structured populations.
• Estimation of migration and mutation rates: Provides parameter estimates for migration and mutation processes shaping genetic variation.
• Demographic history inference: Detects population growth and informs on historical population size changes.
• Phylogenetic dating and mutation mapping: Estimates TMRCA and mutation ages to time lineage divergence and map mutation occurrences across subpopulations.
• Reconstruction of historical population structure: Supports tracing the evolutionary history of species or genetic traits across subdivided demographic scenarios.

Methodology:

Uses the coalescent process model with the infinitely-many-sites mutation model to construct perfect phylogenies; applies a recursion to compute gene-tree probability distributions; employs maximum likelihood and likelihood-based tests for parameter estimation and growth detection; computes distributions of TMRCA and mutation ages; identifies subpopulation ancestors and mutation locations; and performs Markov chain simulation following Griffiths and Tavaré conditional on tree topology implied by mutation patterns.