Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
Genetics. 2022 Apr 4;220(4). doi: 10.1093/genetics/iyac004.
The statistical associations between mutations, collectively known as linkage disequilibrium, encode important information about the evolutionary forces acting within a population. Yet in contrast to single-site analogues like the site frequency spectrum, our theoretical understanding of linkage disequilibrium remains limited. In particular, little is currently known about how mutations with different ages and fitness costs contribute to expected patterns of linkage disequilibrium, even in simple settings where recombination and genetic drift are the major evolutionary forces. Here, I introduce a forward-time framework for predicting linkage disequilibrium between pairs of neutral and deleterious mutations as a function of their present-day frequencies. I show that the dynamics of linkage disequilibrium become much simpler in the limit that mutations are rare, where they admit a simple heuristic picture based on the trajectories of the underlying lineages. I use this approach to derive analytical expressions for a family of frequency-weighted linkage disequilibrium statistics as a function of the recombination rate, the frequency scale, and the additive and epistatic fitness costs of the mutations. I find that the frequency scale can have a dramatic impact on the shapes of the resulting linkage disequilibrium curves, reflecting the broad range of time scales over which these correlations arise. I also show that the differences between neutral and deleterious linkage disequilibrium are not purely driven by differences in their mutation frequencies and can instead display qualitative features that are reminiscent of epistasis. I conclude by discussing the implications of these results for recent linkage disequilibrium measurements in bacteria. This forward-time approach may provide a useful framework for predicting linkage disequilibrium across a range of evolutionary scenarios.
突变之间的统计关联,通常称为连锁不平衡,编码了有关在种群内起作用的进化力量的重要信息。然而,与单一位点类似物(如位点频率谱)相比,我们对连锁不平衡的理论理解仍然有限。特别是,目前对于具有不同年龄和适应成本的突变如何导致预期的连锁不平衡模式,即使在重组和遗传漂变是主要进化力量的简单情况下,也知之甚少。在这里,我引入了一种预测中性和有害突变之间连锁不平衡的正向时间框架,作为它们当前频率的函数。我表明,在突变很少的极限情况下,连锁不平衡的动力学变得简单得多,在这种情况下,它们基于潜在谱系的轨迹,采用简单的启发式描述。我使用这种方法推导出了一系列频率加权连锁不平衡统计量的解析表达式,作为重组率、频率尺度以及突变的加性和上位性适应成本的函数。我发现,频率尺度可以对产生的连锁不平衡曲线的形状产生巨大影响,反映了这些相关性出现的广泛时间尺度。我还表明,中性和有害连锁不平衡之间的差异不仅纯粹是由它们的突变频率差异驱动的,而且可以显示出与上位性相似的定性特征。最后,我讨论了这些结果对细菌中最近的连锁不平衡测量的影响。这种正向时间方法可以为在一系列进化场景中预测连锁不平衡提供有用的框架。