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The Effects of Background and Interference Selection on Patterns of Genetic Variation in Subdivided Populations

机译:背景和干扰选择对细分人群遗传变异模式的影响

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摘要

It is well known that most new mutations that affect fitness exert deleterious effects and that natural populations are often composed of subpopulations (demes) connected by gene flow. To gain a better understanding of the joint effects of purifying selection and population structure, we focus on a scenario where an ancestral population splits into multiple demes and study neutral diversity patterns in regions linked to selected sites. In the background selection regime of strong selection, we first derive analytic equations for pairwise coalescent times and FST as a function of time after the ancestral population splits into two demes and then construct a flexible coalescent simulator that can generate samples under complex models such as those involving multiple demes or nonconservative migration. We have carried out extensive forward simulations to show that the new methods can accurately predict diversity patterns both in the nonequilibrium phase following the split of the ancestral population and in the equilibrium between mutation, migration, drift, and selection. In the interference selection regime of many tightly linked selected sites, forward simulations provide evidence that neutral diversity patterns obtained from both the nonequilibrium and equilibrium phases may be virtually indistinguishable for models that have identical variance in fitness, but are nonetheless different with respect to the number of selected sites and the strength of purifying selection. This equivalence in neutral diversity patterns suggests that data collected from subdivided populations may have limited power for differentiating among the selective pressures to which closely linked selected sites are subject.
机译:众所周知,大多数影响适应性的新突变都具有有害作用,并且自然种群通常由通过基因流连接的亚种群(种群)组成。为了更好地了解净化选择和人口结构的共同影响,我们将重点放在一个祖先的人口分裂成多个特征的场景,并研究与选定地点相关的区域的中性多样性模式。在强选择的背景选择机制中,我们首先推导成对的合并时间和FST作为祖先种群分裂成两个demes后的时间的函数的解析方程,然后构造一个灵活的合并模拟器,该模拟器可以在复杂模型(例如那些模型)下生成样本涉及多个领域或非保守迁移。我们进行了广泛的前向仿真,以表明新方法可以准确预测祖先种群分裂后的非平衡阶段以及突变,迁移,漂移和选择之间的平衡中的多样性模式。在许多紧密链接的选定位置的干扰选择机制中,前向仿真提供了证据,从非平衡和平衡阶段获得的中性分集模式对于适应度相同但模型数量不同的模型实际上可能是无法区分的。选择的站点以及净化选择的力量。中立多样性模式的这种等效性表明,从细分人群中收集的数据在区分紧密联系的选定地点所受到的选择压力方面可能具有有限的能力。

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