Lou R Nicolas, Therkildsen Nina O, Messer Philipp W
Department of Natural Resources, Cornell University, Ithaca, New York 14853
Department of Computational Biology, Cornell University, Ithaca, New York 14853.
G3 (Bethesda). 2020 Sep 2;10(9):3213-3227. doi: 10.1534/g3.120.401287.
Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTL affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of these parameters can affect allele frequency dynamics at the QTL and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTL often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account when designing and interpreting studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.
进化与重测序（E&R）实验是在可控环境中对生物体施加人工选择，它正成为研究适应性遗传基础的一种越来越容易获得的工具。以往的工作评估了不同的实验设计参数如何影响检测此类实验中适应性反应背后的数量性状位点（QTL）的能力，但到目前为止，对于这种能力如何随进化性状的遗传结构而变化的探索还很少。在本研究中，我们使用正向模拟构建了一个更现实的E&R实验模型，其中一个数量多基因性状经历了一段短暂但强烈的截尾选择过程。我们研究了在此类实验中检测QTL的预期能力，以及这种能力如何受到性状结构不同方面的影响，包括影响该性状的QTL数量、它们的起始频率、效应大小、沿染色体的聚类、显性和上位性模式。我们表明，所有这些参数都能以复杂且往往不直观的方式影响QTL和连锁位点的等位基因频率动态，从而影响我们检测它们的能力。由此产生的一个后果是，现有的基于单个QTL独立选择扫荡模型的检测方法，其检测能力往往低于对选择前后等位基因频率差异的简单测量。我们的研究结果强调了在设计和解释具有时间数据的分子适应性研究时考虑性状结构的重要性。我们提供了一个可定制的建模框架，使研究人员能够轻松模拟具有不同性状结构和针对其特定研究系统调整参数的E&R实验，从而能够评估预期检测能力并优化实验设计。
