Population divergence time estimation using individual lineage label switching.

Divergence time estimation from multilocus genetic data has become common in population genetics and phylogenetics. We present a new Bayesian inference method that treats the divergence time as a random variable. The divergence time is calculated from an assembly of splitting events on individual lineages in a genealogy. The time for such a splitting event is drawn from a hazard function of the truncated normal distribution. This allows easy integration into the standard coalescence framework used in programs such as Migrate. We explore the accuracy of the new inference method with simulated population splittings over a wide range of divergence time values and with a reanalysis of a dataset of 5 populations consisting of 3 present-day populations (Africans, Europeans, Asian) and 2 archaic samples (Altai and Ust'Isthim). Evaluations of simple divergence models without subsequent geneflow show high accuracy, whereas the accuracy of the results of isolation with migration models depends on the magnitude of the immigration rate. High immigration rates lead to a time of the most recent common ancestor of the sample that, looking backward in time, predates the divergence time. Even with many independent loci, accurate estimation of the divergence time with high immigration rates becomes problematic. Our comparison to other software tools reveals that our lineage-switching method, implemented in Migrate, is comparable to IMa2p. The software Migrate can run large numbers of sequence loci (>1,000) on computer clusters in parallel.

Population Genetic Inference With MIGRATE.

Many evolutionary biologists collect genetic data from natural populations and then need to investigate the relationship among these populations to compare different biogeographic hypotheses. MIGRATE, a useful tool for exploring relationships between populations and comparing hypotheses, has existed since 1998. Throughout the years, it has steadily improved in both the quality of algorithms used and in the efficiency of carrying out those calculations, thus allowing for a larger number of loci to be evaluated. This efficiency has been enhanced, as MIGRATE has been developed to perform many of its calculations concurrently when running on a computer cluster. The program is based on the coalescence theory and uses Bayesian inference to estimate posterior probability densities of all the parameters of a user-specified population model. Complex models, which include migration and colonization parameters, can be specified. These models can be evaluated using marginal likelihoods, thus allowing a user to compare the merits of different hypotheses. The three presented protocols will help novice users to develop sophisticated analysis techniques useful for their research projects. © 2019 The Authors. Basic Protocol 1: First steps with MIGRATE Basic Protocol 2: Population model specification Basic Protocol 3: Prior distribution specification Basic Protocol 4: Model selection Support Protocol 1: Installing the program MIGRATE Support Protocol 2: Installation of parallel MIGRATE.

Fractional coalescent.

An approach to the coalescent, the fractional coalescent (f-coalescent), is introduced. The derivation is based on the discrete-time Cannings population model in which the variance of the number of offspring depends on the parameter α. This additional parameter α affects the variability of the patterns of the waiting times; values of [Formula: see text] lead to an increase of short time intervals, but occasionally allow for very long time intervals. When [Formula: see text], the f-coalescent and the Kingman's n-coalescent are equivalent. The distribution of the time to the most recent common ancestor and the probability that n genes descend from m ancestral genes in a time interval of length T for the f-coalescent are derived. The f-coalescent has been implemented in the population genetic model inference software Migrate Simulation studies suggest that it is possible to accurately estimate α values from data that were generated with known α values and that the f-coalescent can detect potential environmental heterogeneity within a population. Bayes factor comparisons of simulated data with [Formula: see text] and real data (H1N1 influenza and malaria parasites) showed an improved model fit of the f-coalescent over the n-coalescent. The development of the f-coalescent and its inclusion into the inference program Migrate facilitates testing for deviations from the n-coalescent.