Coalescentbased

Coalescent-based approaches refer to methods in population genetics and evolutionary biology that use coalescent theory to model the ancestry of gene copies and to infer demographic parameters from genetic data. The coalescent describes how lineages traced backward in time coalesce to common ancestors, with rates determined mainly by effective population size and other demographic processes. By connecting observed genetic variation to the distribution of genealogies, coalescent-based inference provides a framework for estimating historical population dynamics.

In practice, coalescent-based methods can operate within Bayesian or frequentist frameworks, using full-likelihood or approximate likelihoods to estimate parameters such as past population sizes, growth rates, divergence times, and migration rates. They often rely on multi-locus sequence data, SNP arrays, or whole-genome data. Recombination complicates the model because different genome regions have different genealogies; several approaches address this by analyzing non-recombining segments, or by employing sequentially Markov coalescent (SMC) approximations like PSMC, MSMC, and SMC++.

Applications include reconstruction of demographic histories, species delimitation, phylogeography, and inference of ancient admixture. The methods have been implemented in software tools such as BEAST with coalescent priors, and in specialized programs for demographic inference.

Limitations include assumptions of neutral evolution and random mating within populations, sensitivity to model misspecification, and computational intensity for large genomes or complex models. Violations such as selection, migration patterns not captured, or incorrect recombination handling can bias estimates. Ongoing developments aim to scale to genomic data and to relax assumptions by incorporating recombination, structure, and selection.

See also: coalescent theory, skyline plots, PSMC, MSMC, BEAST, G-PhoCS.