Anderson Nathan W, Kirk Lloyd, Schraiber Joshua G, Ragsdale Aaron P
Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.
Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Genetics. 2025 Jan 8;229(1):1-63. doi: 10.1093/genetics/iyae182.
Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection.
许多表型性状具有多基因遗传基础，这使得了解它们的遗传结构并预测个体表型具有挑战性。解决复杂性状遗传基础的一个有前景的途径是通过进化与重测序（E&R）实验，在该实验中，实验室群体受到某种选择压力，并且通过实验过程中极端的频率变化来识别影响性状的基因座。然而，小型实验室群体将经历大量随机遗传漂变，并且很难确定选择是否在给定的等位基因频率变化（AFC）中起作用。预测漂变和选择条件下的AFC，即使对于影响简单单基因性状的等位基因，仍然是一个具有挑战性的问题。最近，人们一直在努力应用从物理学借用的路径积分方法来解决这个问题。到目前为止，这种方法仅限于基因选择，因此不足以捕捉通常研究的数量性状、高度多基因性状的复杂性。在这里，我们将这些路径积分方法之一——微扰近似，扩展到数量遗传学感兴趣的选择场景。我们推导了对受稳定选择的性状有贡献的等位基因以及对快速适应新表型最优值的性状有贡献的等位基因的转移概率（即等位基因在时间t内频率从x变为y的概率）的解析表达式。我们使用这些表达式来描述利用AFC进行选择测试的情况，并探索E&R实验的最优设计选择，以揭示选择作用下多基因性状的遗传结构。
