Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

中文翻译：

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通过进化和重测序研究优化识别复杂性状遗传基础的能力。

进化和重测序 (E&R) 研究经常用于剖析数量性状的遗传基础。通过对群体进行几代的截断选择，并使用下一代测序（NGS）估计所选群体和非所选群体之间的等位基因频率差异，可以鉴定对所选性状有贡献的基因座。先前的工作已经研究了不同参数的作用，例如群体大小或重复群体的数量。然而，选择机制的影响，即实验过程中截断选择的强度，仍然很少被探讨。使用全基因组、基于个体的 E&R 研究正向模拟，我们发现通过在实验过程中逐渐增加截断选择的强度可以最大化识别致病等位基因的能力。值得注意的是，这种最佳选择机制在测序工作和实验时间方面没有或几乎没有额外成本。有趣的是，我们还发现优化识别致病位点能力的选择机制不一定与最大化表型反应的机制相同。最后，我们的模拟表明，采用优化选择机制的 E&R 研究可能比全基因组关联研究具有更高的能力来识别数量性状的遗传基础，这凸显了 E&R 是寻找复杂性状背后基因座的强大方法。