Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697
Genetics. 2019 Dec;213(4):1513-1530. doi: 10.1534/genetics.119.302662. Epub 2019 Oct 25.
Predictions about the effect of natural selection on patterns of linked neutral variation are largely based on models involving the rapid fixation of unconditionally beneficial mutations. However, when phenotypes adapt to a new optimum trait value, the strength of selection on individual mutations decreases as the population adapts. Here, I use explicit forward simulations of a single trait with additive-effect mutations adapting to an "optimum shift." Detectable "hitchhiking" patterns are only apparent if (i) the optimum shifts are large with respect to equilibrium variation for the trait, (ii) mutation rates to large-effect mutations are low, and (iii) large-effect mutations rapidly increase in frequency and eventually reach fixation, which typically occurs after the population reaches the new optimum. For the parameters simulated here, partial sweeps do not appreciably affect patterns of linked variation, even when the mutations are strongly selected. The contribution of new mutations standing variation to fixation depends on the mutation rate affecting trait values. Given the fixation of a strongly selected variant, patterns of hitchhiking are similar on average for the two classes of sweeps because sweeps from standing variation involving large-effect mutations are rare when the optimum shifts. The distribution of effect sizes of new mutations has little effect on the time to reach the new optimum, but reducing the mutational variance increases the magnitude of hitchhiking patterns. In general, populations reach the new optimum prior to the completion of any sweeps, and the times to fixation are longer for this model than for standard models of directional selection. The long fixation times are due to a combination of declining selection pressures during adaptation and the possibility of interference among weakly selected sites for traits with high mutation rates.
对自然选择对连锁中性变异模式影响的预测在很大程度上基于涉及无条件有利突变快速固定的模型。然而,当表型适应新的最优性状值时,随着种群的适应,单个突变的选择强度会降低。在这里,我使用具有加性效应突变的单个性状的明确正向模拟来适应“最优转变”。只有在以下情况下,才能明显观察到可检测的“搭便车”模式:(i)相对于性状的平衡变异,最优转变很大;(ii)低效突变的突变率;(iii)大效突变迅速增加频率,最终达到固定,这通常发生在种群达到新最优之后。对于这里模拟的参数,部分清扫不会明显影响连锁变异的模式,即使突变受到强烈选择。新突变对固定的贡献取决于影响性状值的突变率。给定强烈选择的变体的固定,由于最优转变时涉及大效突变的静态变异的清扫很少,因此两种清扫类型的搭便车模式平均相似。新突变效应大小的分布对达到新最优的时间几乎没有影响,但降低突变方差会增加搭便车模式的幅度。一般来说,种群在任何清扫完成之前就达到了新的最优,并且对于这个模型来说,固定的时间比标准的定向选择模型更长。固定时间长的原因是适应过程中选择压力的下降以及高突变率性状的弱选择位点之间可能发生干扰的综合作用。