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. 2014 Nov 10;9(11):e110579.
doi: 10.1371/journal.pone.0110579. eCollection 2014.

On the prospect of identifying adaptive loci in recently bottlenecked populations

Yu-Ping Poh  1 , Vera S Domingues  2 , Hopi E Hoekstra  2 , Jeffrey D Jensen  3
Affiliations

On the prospect of identifying adaptive loci in recently bottlenecked populations

Yu-Ping Poh et al. PLoS One. .

Abstract

Identifying adaptively important loci in recently bottlenecked populations - be it natural selection acting on a population following the colonization of novel habitats in the wild, or artificial selection during the domestication of a breed - remains a major challenge. Here we report the results of a simulation study examining the performance of available population-genetic tools for identifying genomic regions under selection. To illustrate our findings, we examined the interplay between selection and demography in two species of Peromyscus mice, for which we have independent evidence of selection acting on phenotype as well as functional evidence identifying the underlying genotype. With this unusual information, we tested whether population-genetic-based approaches could have been utilized to identify the adaptive locus. Contrary to published claims, we conclude that the use of the background site frequency spectrum as a null model is largely ineffective in bottlenecked populations. Results are quantified both for site frequency spectrum and linkage disequilibrium-based predictions, and are found to hold true across a large parameter space that encompasses many species and populations currently under study. These results suggest that the genomic footprint left by selection on both new and standing variation in strongly bottlenecked populations will be difficult, if not impossible, to find using current approaches.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The inferred demographic history of the Florida and Nebraska populations.
Geographic location and photos of the derived light and ancestral dark mouse populations from (A) Florida (photos by J. Miller and S. Carey) and (B) Nebraska (photos by C. Linnen) (top panel). Cartoon representation of the inferred demographic model of the two species (middle panel). Both models include selection acting on the bottlenecked population (with effective population size reduced to fN e, where N e is the ancestral population size) immediately after the divergence from the ancestral population at time d, and the selected allele becomes fixed at time τ. Likelihood ratio (LR) profile of Sweepfinder in both populations of light-colored mice (bottom panel), where the horizontal line indicates the significance cutoff. Stars indicate the approximate location of causal mutations conferring light pigmentation. Because there are multiple Agouti alleles, we here polarize (into “light” or “dark” class) based on the SNP mostly strongly associated with pigment variation (as described in [26]).
Figure 2
Figure 2. The fraction of simulated replicates rejecting the neutral model by Sweepfinder and ωmax, with varying population size.
The simulations with demographic models mimic the history of (A) Florida beach mice (N e = 2500) and (B) Nebraska Sand Hills mice (N e = 50,000). The time of the bottleneck (t r = 0.1) and time since fixation (τ = 0.1) are fixed, but selection strength varies from s = 0.001 to 0.1. Ideal performance would be indicated by all replicates showing a significant signal at very small window sizes, suggesting an ability to localize the target.
Figure 3
Figure 3. The fraction of simulated replicates rejecting neutrality by Sweepfinder and ω max, with varying bottleneck severity.
Simulations with ancestral population size equal to (A) 104, (B) 105 and (C) 106. Selection strength (s = 0.01), time since fixation (τ = 0.1), and time since bottleneck (t r = 0.1) are fixed, but bottleneck severity (f) varied from 0.01 to 0.5.
Figure 4
Figure 4. The fraction of simulated replicates rejecting neutrality by Sweepfinder and ωmax, varying the time since the beneficial fixation.
Simulations with ancestral population size equal to (A) 104, (B) 105 and (C) 106. Selection strength (s = 0.01) and time since bottleneck (t r = 0.01) are fixed, but the time of selected allele fixation (τ) varied from 0.01 to 0.3.
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