Genetic data from two or more species provide information about the process of speciation. In their analysis of DNA from humans, chimpanzees, gorillas, orangutans and macaques (HCGOM), Patterson et al.¹ suggest that the apparently short divergence time between humans and chimpanzees on the X chromosome is explained by a massive interspecific hybridization event in the ancestry of these two species. However, Patterson et al.¹ do not statistically test their own null model of simple speciation before concluding that speciation was complex, and—even if the null model could be rejected—they do not consider other explanations of a short divergence time on the X chromosome. These include natural selection on the X chromosome in the common ancestor of humans and chimpanzees, changes in the ratio of male-to-female mutation rates over time, and less extreme versions of divergence with gene flow (see ref. 2, for example). I therefore believe that their claim of hybridization is unwarranted.

Correlations in coalescence times between two loci are derived under selectively neutral population models in which the offspring of an individual can number on the order of the population size. The correlations depend on the rates of recombination and random drift and are shown to be functions of the parameters controlling the size and frequency of these large reproduction events. Since a prediction of linkage disequilibrium can be written in terms of correlations in coalescence times, it follows that the prediction of linkage disequilibrium is a function not only of the rate of recombination but also of the reproduction parameters. Low linkage disequilibrium is predicted if the offspring of a single individual frequently replace almost the entire population. However, high linkage disequilibrium can be predicted if the offspring of a single individual replace an intermediate fraction of the population. In some cases the model reproduces the standard Wright–Fisher predictions. Contrary to common intuition, high linkage disequilibrium can be predicted despite frequent recombination, and low linkage disequilibrium under infrequent recombination. Simulations support the analytical results but show that the variance of linkage disequilibrium is very large.

Evolutionists have debated whether population-genetic parameters, such as effective population size and migration rate, differ between males and females. In humans, most analyses of this problem have focused on the Y chromosome and the mitochondrial genome, while the X chromosome has largely been omitted from the discussion. Past studies have compared F(ST) values for the Y chromosome and mitochondrion under a model with migration rates that differ between the sexes but with equal male and female population sizes. In this study we investigate rates of coalescence for X-linked and autosomal lineages in an island model with different population sizes and migration rates for males and females, obtaining the mean time to coalescence for pairs of lineages from the same deme and for pairs of lineages from different demes. We apply our results to microsatellite data from the Human Genome Diversity Panel, and we examine the male and female migration rates implied by observed F(ST) values.

We investigate the probabilities of identity-by-descent at three loci in order to find a signature which differentiates between the two types of crossing over events: recombination and gene conversion. We use a Markov chain to model coalescence, recombination, gene conversion and mutation in a sample of size two. Using numerical analysis, we calculate the total probability of identity-by-descent at the three loci, and partition these probabilities based on a partial ordering of coalescent events at the three loci. We use these results to compute the probabilities of four different patterns of conditional identity and non-identity at the three loci under recombination and gene conversion. Although recombination and gene conversion do make different predictions, the differences are not likely to be useful in distinguishing between them using three locus patterns between pairs of DNA sequences. This implies that measures of genetic identity in larger samples will be needed to distinguish between gene conversion and recombination.

We describe a forward-time haploid reproduction model with a constant population size that includes life history characteristics common to many marine organisms. We develop coalescent approximations for sample gene genealogies under this model and use these to predict patterns of genetic variation. Depending on the behavior of the underlying parameters of the model, the approximations are coalescent processes with simultaneous multiple mergers or Kingman's coalescent. Using simulations, we apply our model to data from the Pacific oyster and show that our model predicts the observed data very well. We also show that a fact which holds for Kingman's coalescent and also for general coalescent trees--that the most-frequent allele at a biallelic locus is likely to be the ancestral allele--is not true for our model. Our work suggests that the power to detect a "sweepstakes effect" in a sample of DNA sequences from marine organisms depends on the sample size.

In this article we apply some graph-theoretic results to the study of coalescence in a structured population with migration. The graph is the pattern of migration among subpopulations, or demes, and we use the theory of random walks on graphs to characterize the ease with which ancestral lineages can traverse the habitat in a series of migration events. We identify conditions under which the coalescent process in populations with restricted migration, such that individuals cannot traverse the habitat freely in a single migration event, nonetheless becomes identical to the coalescent process in the island migration model in the limit as the number of demes tends to infinity. Specifically, we first note that a sequence of symmetric graphs with Diaconis-Stroock constant bounded above has an unstructured Kingman-type coalescent in the limit for a sample of size two from two different demes. We then show that circular and toroidal models with long-range but restricted migration have an upper bound on this constant and so have an unstructured-migration coalescent in the limit. We investigate the rate of convergence to this limit using simulations.

We study the ancestral genetic process for samples from two large, subdivided populations that are connected by migration to, from, and within a small set of subpopulations, or demes. We consider convergence to an ancestral limit process as the numbers of demes in the two large, subdivided populations tend to infinity. We show that the ancestral limit process for a sample includes a recent instantaneous adjustment to the sample size and structure followed by a more ancient process that is identical to the usual structured coalescent, but with different scaled parameters. This justifies the application of a modified structured coalescent to some hierarchically structured populations.

We show that the unstructured ancestral selection graph applies to part of the history of a sample from population structured by restricted migration among subpopulations, or demes. The result holds in the limit as the number of demes tends to infinity with proportionately weak selection, and we have also made assumptions of island-type migration and that demes are equivalent in size. After an instantaneous sample-size adjustment, this structured ancestral selection graph converges to an unstructured ancestral selection graph with a mutation parameter that depends inversely on the migration rate. In contrast, the selection parameter for the population is independent of the migration rate and is identical to the selection parameter in an unstructured population. We show analytically that estimators of the migration rate, based on pairwise sequence differences, derived under the assumption of neutrality should perform equally well in the presence of weak selection. We also modify an algorithm for simulating genealogies conditional on the frequencies of two selected alleles in a sample. This permits efficient simulation of stronger selection than was previously possible. Using this new algorithm, we simulate gene genealogies under the many-demes ancestral selection graph and identify some situations in which migration has a strong effect on the time to the most recent common ancestor of the sample. We find that a similar effect also increases the sensitivity of the genealogy to selection.

Recent developments in population genetics are reviewed and placed in a historical context. Current and future challenges, both in computational methodology and in analytical theory, are to develop models and techniques to extract the most information possible from multilocus DNA datasets. As an example of the theoretical issues, five limiting forms of the island model of population subdivision with migration are presented in a unified framework. These approximations illustrate the interplay between migration and drift in structuring gene genealogies, and some of them make connections between the fairly complicated island-model genealogical process and the much simpler, unstructured neutral coalescent process which underlies most inferential techniques in population genetics.

A simple nonparameteric test for population structure was applied to temporally spaced samples of HIV-1 sequences from the gag-pol region within two chronically infected individuals. The results show that temporal structure can be detected for samples separated by about 22 months or more. The performance of the method, which was originally proposed to detect geographic structure, was tested for temporally spaced samples using neutral coalescent simulations. Simulations showed that the method is robust to variation in samples sizes and mutation rates, to the presence/absence of recombination, and that the power to detect temporal structure is high. By comparing levels of temporal structure in simulations to the levels observed in real data, we estimate the effective intra-individual population size of HIV-1 to be between 10³ and 10⁴ viruses, which is in agreement with some previous estimates. Using this estimate and a simple measure of sequence diversity, we estimate an effective neutral mutation rate of about 5 x 10^-6 per site per generation in the gag-pol region. The definition and interpretation of estimates of such ‘‘effective’’ population parameters are discussed.

A diffusion approximation is obtained for the frequency of a selected allele in a population comprised of many subpopulations or demes. The form of the diffusion is equivalent to that for an unstructured population, except that it occurs on a longer time scale when migration among demes is restricted. This many-demes diffusion limit relies on the collection of demes always being in statistical equilibrium with respect to migration and drift for a given allele frequency in the total population. Selection is assumed to be weak, in inverse proportion to the number of demes, and the results hold for any deme sizes and migration rates greater than zero. The distribution of allele frequencies among denies is also described. [copyright] 2004 Elsevier Inc. All rights reserved.

The genealogical process for a sample from a metapopulation, in which local populations are connected by migration and can undergo extinction and subsequent recolonization, is shown to have a relatively simple structure in the limit as the number of populations in the metapopulation approaches infinity. The result, which is an approximation to the ancestral behaviour of samples from a metapopulation with a large number of populations, is the same as that previously described for other metapopulation models, namely that the genealogical process is closely related to Kingman's unstructured coalescent. The present work considers a more general class of models that includes two kinds of extinction and recolonization, and the possibility that gamete production precedes extinction. In addition, following other recent work, this result for a metapopulation divided into many populations is shown to hold both for finite population sizes and in the usual diffusion limit, which assumes that population sizes are large. Examples illustrate when the usual diffusion limit is appropriate and when it is not. Some shortcomings and extensions of the model are considered, and the relevance of such models to understanding human history is discussed.