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. 2014 Aug 26:5:295.
doi: 10.3389/fgene.2014.00295. eCollection 2014.

Assessing when chromosomal rearrangements affect the dynamics of speciation: implications from computer simulations

Jeffrey L Feder  1 , Patrik Nosil  2 , Samuel M Flaxman  3
Affiliations

Assessing when chromosomal rearrangements affect the dynamics of speciation: implications from computer simulations

Jeffrey L Feder et al. Front Genet. .

Abstract

Many hypotheses have been put forth to explain the origin and spread of inversions, and their significance for speciation. Several recent genic models have proposed that inversions promote speciation with gene flow due to the adaptive significance of the genes contained within them and because of the effects inversions have on suppressing recombination. However, the consequences of inversions for the dynamics of genome wide divergence across the speciation continuum remain unclear, an issue we examine here. We review a framework for the genomics of speciation involving the congealing of the genome into alternate adaptive states representing species ("genome wide congealing"). We then place inversions in this context as examples of how genetic hitchhiking can potentially hasten genome wide congealing. Specifically, we use simulation models to (i) examine the conditions under which inversions may speed genome congealing and (ii) quantify predicted magnitudes of these effects. Effects of inversions on promoting speciation were most common and pronounced when inversions were initially fixed between populations before secondary contact and adaptation involved many genes with small fitness effects. Further work is required on the role of underdominance and epistasis between a few loci of major effect within inversions. The results highlight five important aspects of the roles of inversions in speciation: (i) the geographic context of the origins and spread of inversions, (ii) the conditions under which inversions can facilitate divergence, (iii) the magnitude of that facilitation, (iv) the extent to which the buildup of divergence is likely to be biased within vs. outside of inversions, and (v) the dynamics of the appearance and disappearance of exceptional divergence within inversions. We conclude by discussing the empirical challenges in showing that inversions play a central role in facilitating speciation with gene flow.

Keywords: ecological speciation; genetic hitchhiking; genomic architecture; genomic islands of divergence; inversions; linkage disequilibrium; models; speciation-with-gene-flow.

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Figures

Figure 1
Figure 1
Genome wide congealing can cause dramatic, nonlinear shifts in (A) local adaptation, (B) effective migration, (C) linkage disequilibrium and FST, and (D) the rate of accumulation of divergently selected mutations. (A) Average fitness of residents (orange) and immigrants (purple) over time relative to a randomly assembled genotype (yellow), depicting the rapid transition from genic to genomic phases of population divergence as GWC occurs. Gray dots are a random subsample of individual fitness values (200 individuals per generation sampled). (B) The effective migration rate, me, as a measure of reproductive isolation arising from divergent local adaptation, during the transition into GWC. (C) The genome-wide average of linkage disequilibrium (LD) for pairs of loci on different chromosomes (single, red line) and FST values (blue lines) over time for a random subsample of loci, depicting dramatic rises in these metrics of divergence accompanying GWC. (D) Jump in the rate of accumulation of divergently selected alleles as GWC occurs. Plots were produced from one example simulation run of the BU2S model (Methods; see also Flaxman et al., 2014) with N = 20,000 individuals, m = 0.1, s = 0.01, and no inverted regions in the genome.
Figure 2
Figure 2
Mean FST values for loci within and outside inverted regions for 984 simulation runs. Circles show runs in which divergence reached a designated barrier strength, m/me≥500; “x” symbols show runs that did not reach this barrier strength within the allotted time (1,200,000 mutations and generations). Periods of allopatry were varied in steps of 1000 generations. As colors change from cyan to red, the period of allopatry changes, respectively, from zero to as long as 50,000 generations (when red symbols are not visible, e.g., in (C), it is because the barrier was reached even prior to the end of the allopatric period). The 1:1 line is the null expectation that mean FST for loci within inverted regions would be the same as those outside inversions. Each point shown is from a different simulation run. Combinations of the gross migration rate, m, and the average per locus strength of divergent selection, s, are given above each panel: (A) m = 0.05, s = 0.005; (B) m = 0.1, s = 0.005, (C) m = 0.05, s = 0.01, (D) m = 0.1, s = 0.01.
Figure 3
Figure 3
Proportions of divergent loci found within inverted regions for the same set of simulation runs as in Figure 2. The x-axis shows the proportion of the genome with segregating inversions; the y-axis shows the proportion of divergent loci located within inversions. Both were measured at the end of each of 984 simulation runs. Interpretation of symbol shapes and colors is the same as in Figure 2. The black, dashed line is the null expectation (1:1) if inversions have no effect. Solid, red lines are fits from power law regression (equations in legend within each panel) through the subset of points from those runs that reached the designiated barrier strength (i.e., the points represented by open circles), forced through the origin. For numbers of polymorphic loci (rather than proportions), see Figure S3. Parameter combinations are the same as in Figure 2: (A) m = 0.05, s = 0.005; (B) m = 0.1, s = 0.005, (C) m = 0.05, s = 0.01, (D) m = 0.1, s = 0.01.
Figure 4
Figure 4
Waiting times to reach the designated barrier strength, m/me≥500. Data are from the same set of simulation runs as Figures 2, 3, and the interpretation of the symbols and the solid, red lines are the same. The “waiting time” was calculated as the number of generations that elapsed between the end of the period of allopatry and the time when the barrier strength was reached. Note that y-axis scaling differs across panels in order to maximize visual resolution. Parameter combinations are the same as in Figure 2: (A) m = 0.05, s = 0.005; (B) m = 0.1, s = 0.005, (C) m = 0.05, s = 0.01, (D) m = 0.1, s = 0.01.
Figure 5
Figure 5
Time series of average divergence at sites inside inversions relative to outside inversions. Data are from the same set of simulation runs as Figures 2–4, with parameter combinations noted above each panel. Whereas those figures show results at the ends of runs, here the results are time series from the beginning to end of each run. Each run is a separate line. At each point in time, the value of the line is the ratio of mean FST for polymorphic sites inside inversions divided by mean FST of polymorphic sites outside inversions. Note that the y-axes are Log10-transformed, and the x-axes vary in scaling from panel to panel in order to provide maximum visual resolution. Parameter combinations are the same as in Figure 2: (A) m = 0.05, s = 0.005; (B) m = 0.1, s = 0.005, (C) m = 0.05, s = 0.01, (D) m = 0.1, s = 0.01.
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