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.

在受控环境中对生物进行人工选择的进化和重排（E＆R）实验正成为研究适应性遗传基础的一种日益普及的工具。以前的工作已经评估了不同的实验设计参数如何影响检测作为此类实验中适应性反应基础的数量性状基因座（QTL）的能力，但是到目前为止，几乎没有探索这种能力如何随着进化性状的遗传结构而变化。 。在这项研究中，我们使用前向仿真来构建E＆R实验的更现实模型，在该模型中，定量多基因性状经历了短而强的截短选择。我们研究了这样一个实验中检测QTL的预期能力，以及该能力如何受性状体系结构不同方面的影响，包括影响性状的QTL数量，起始频率，效应大小，沿染色体的聚集，优势和上位性模式。我们表明，所有这些参数都可以以复杂且通常不直观的方式影响QTL和连锁基因座处的等位基因频率动态，从而影响我们检测它们的能力。其结果是，基于在单个QTL处进行独立选择性扫描的模型的现有检测方法通常比对选择前后等位基因频率差的简单测量具有较低的检测能力。我们的发现凸显了在设计和解释具有时间数据的分子适应性研究时，考虑特征性结构的重要性。我们提供了一个可定制的建模框架，使研究人员可以轻松地模拟具有不同特征结构和参数的E＆R实验，这些特征和参数已调整至他们的特定研究系统，从而可以评估预期的检测能力并优化实验设计。