ML estimates of the strength of selection from simulations with selection with and without divergence ascertainment correction for lower 1% data.

<p>The strength of selection, α, was estimated from those loci identified as belonging to the lower 1% divergence group from the 10⁶ regions simulated from a deleterious alleles model with α = −5.0. Simulations were performed using a model closely related to Gillespie's exponential shift model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005152#pone.0005152-Gillespie2" target="_blank">[15]</a>, but rather than a distribution of selection coefficients only a single coefficient is assigned to new mutations (simulation details can be found in Kern et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005152#pone.0005152-Kern1" target="_blank">[16]</a>). This method uses a time forward population genetic simulation approach. Population size for these simulations is 10⁵, all other parameters are identical to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005152#pone-0005152-g001" target="_blank">Figure 1</a>. The left box shows MLEs using an estimation routine which does not account for divergence-based ascertainment (uncorrected), the right box shows MLEs from the same data but estimated in a fashion which accounts for ascertainment (corrected). As in the neutral setting, correction restores estimates close to their true value and evidence for negative selection once corrected for ascertainment can be thought of as conservative.