Sequential Markov coalescent algorithms for population models with demographic structure

TL;DR

This paper evaluates the accuracy of sequential Markov coalescent algorithms in modeling populations with demographic structures, highlighting their strengths and limitations in different scenarios.

Contribution

It provides a detailed analysis of the sequential Markov coalescent method's performance across various demographic models, identifying conditions where it performs well or poorly.

Findings

Accurately approximates coalescent in most demographic models

Underestimates gene-history correlations with reduced gene flow

Explains the limitations of the method in specific scenarios

Abstract

We analyse sequential Markov coalescent algorithms for populations with demographic structure: for a bottleneck model, a population-divergence model, and for a two-island model with migration. The sequential Markov coalescent method is an approximation to the coalescent suggested by McVean and Cardin, and Marjoram and Wall. Within this algorithm we compute, for two individuals randomly sampled from the population, the correlation between times to the most recent common ancestor and the linkage probability corresponding to two different loci with recombination rate R between them. We find that the sequential Markov coalescent method approximates the coalescent well in general in models with demographic structure. An exception is the case where individuals are sampled from populations separated by reduced gene flow. In this situation, the gene-history correlations may be significantly underestimated. We explain why this is the case.