Understanding the origin of species with genome-scale data: modelling gene flow

Journal name:
Nature Reviews Genetics
Volume:
14,
Pages:
404–414
Year published:
DOI:
doi:10.1038/nrg3446
Published online

Abstract

As it becomes easier to sequence multiple genomes from closely related species, evolutionary biologists working on speciation are struggling to get the most out of very large population genomic data sets. Such data hold the potential to resolve long-standing questions in evolutionary biology about the role of gene exchange in species formation. In principle, the new population genomic data can be used to disentangle the conflicting roles of natural selection and gene flow during the divergence process. However, there are great challenges in taking full advantage of such data, especially with regard to including recombination in genetic models of the divergence process. Current data, models, methods and the potential pitfalls in using them will be considered here.

At a glance

Figures

  1. Alternative modes of divergence.
    Figure 1: Alternative modes of divergence.

    All models assume that an ancestral population of size NA splits into two populations at time of split (ts). The two present-day populations have effective sizes N1 and N2, respectively. Panel a shows the model in which migration rate is zero in both directions, which corresponds to an allopatric divergence scenario. Panels bd represent alternative models in which populations have been exchanging migrants. Gene flow occurs at constant rates since the split from the ancestral population (b). Migration rates are assumed to be constant through time, but gene flow can be asymmetric: that is, one migration rate for each direction. Panel c shows a scenario in which populations begin diverging in the presence of gene flow but experience a cessation of gene flow after time since isolation (ti). If the lack of current gene flow in this model is due to reproductive isolation then this represents a history in which divergence occurred to the point of speciation in the presence of gene flow. In panel d, we consider the alternative migration history in which populations were isolated and diverged for a period of time in the absence of gene flow, followed by secondary contact at time of secondary contact (tsc) and the introgression of alleles from the other population by gene flow.

  2. Disentangling ancestral polymorphism from gene flow (ABBA and BABA test).
    Figure 2: Disentangling ancestral polymorphism from gene flow (ABBA and BABA test).

    The diagram shows the divergence of two sister populations (1 and 2), a third population (potential source of introgressed genes; 3) and an outgroup population (4) over time. The black line represents the gene tree of a given site, and the star represents a mutation from the ancestral state (allele A) to the derived state (allele B). The pattern ABBA can occur owing to an ancestral polymorphism (a): that is, coalescent of lineage from population 2 with lineage from population 3 in the ancestral population (population ancestral to populations 1, 2 and 3), or gene flow from population 3 to population 2 (b). Under a model with no gene flow, we expect that the pattern ABBA is as frequent as BABA owing to the fact that there is 50% chance that either the lineage from population 1 or from population 2 coalesces with lineage from population 3 in the population ancestral to populations 1, 2 and 3.

  3. Allele frequency spectrum under alternative divergence models.
    Figure 3: Allele frequency spectrum under alternative divergence models.

    Each entry in the matrix (x,y) corresponds to the probability of observing a single-nucleotide polymorphism (SNP) with frequency of derived allele x in population 1 and y in population 2. The colours represent the log of the expected probability for each cell of the allele frequency spectrum (AFS). The white colour corresponds to –Inf: that is, to cells with an expected probability of zero. These AFSs are conditional on polymorphic SNPs, hence the cells (0,0) and (10,10) have zero probability. The likelihood for an observed AFS can be computed by comparing it with these expected AFSs. a | Isolation model. b | Isolation with migration. c | Isolation after migration. d | Secondary contact. The joint allele frequency spectrums for the different scenarios were obtained with coalescent simulations carried out with ms125. All scenarios were simulated, assuming all populations share the same effective sizes (N = 10,000), a time of split ts = 20,000 generations ago (t / 4N = 0.5), symmetrical migration rate (2N1m12 = 5, 2N2m21 = 5, for scenarios b, c and d, for scenario c, a time of isolation of ti = 2,000 generations ago (ti / 4N = 0.05) and, for scenario d, a time of secondary contact of tsc = 6,000 generations ago (tsc / 4N = 0.15).

  4. Distinguishing migration events based on linkage disequilibrium block structure.
    Figure 4: Distinguishing migration events based on linkage disequilibrium block structure.

    Schematic representation of the expected distribution of the haplotype block lengths for an old migration event (a) and a recent migration event (b). The diagram shows two diverging populations that experience migration at some time in the past after the split and a zoom-in of what happens at the population that receives immigrant haplotypes. For simplicity, we assumed that all individuals share the same haplotype in the destination population (blue haplotype in the figure): that is, this haplotype has reached fixation. When a migrant haplotype (shown in red in the figure) enters a population, as times goes by, recombination breaks it into smaller fragments. Thus, blocks are expected to be shorter following an old migration event (a) than directly after a recent migration event (b), for which blocks are expected to be larger.

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  1. Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA.

    • Vitor Sousa &
    • Jody Hey

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The authors declare no competing interests.

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  • Vitor Sousa

    Vitor Sousa conducted his Ph.D. thesis at the Gulbenkian Science Institute, Oeiras, Portugal, under the co-direction of Lounes Chikhi and Manuela Coelho and received his degree from the University of Lisbon, Portugal. He was a postdoctoral associate in the laboratory of Jody Hey at the Rutgers University, New Jersey, USA, conducting research on population genetics of diverging populations. Currently, he is pursuing his postdoctoral work with Laurent Excoffier at the University of Bern, Switzerland. In particular, he is interested in the development of statistical methods to reconstruct the recent evolutionary history of populations and its applications in speciation, molecular ecology and conservation genetics.

  • Jody Hey

    Jody Hey is a professor in the Department of Genetics of Rutgers University, New Jersey, USA. He received his Ph.D. with Walter Eanes at the State University of New York at Stony Brook, New York, USA, and did postdoctoral work with Richard C. Lewontin at Harvard University, Cambridge, Massachusetts, USA. Since moving to Rutgers in 1989, he has pursued diverse evolutionary questions, including empirical, mathematical and philosophical problems that are linked by the goal of understanding how evolutionary forces shape and cause patterns of genetic variation.

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