Code and metadata: Genetic architecture and selective sweeps after polygenic adaptation to distant trait optima

Understanding the genetic basis of phenotypic adaptation to changing environments is an essential goal of population and quantitative genetics. While technological advances now allow interrogation of genome-wide genotyping data in large panels, our theoretical understanding of the process of polygenic adaptation is still quite limited. To address this limitation, we use extensive forward-time simulation to explore the impacts of variation in demography, trait genetics, and selection on the rate and mode of adaptation and the resulting genetic architecture. We simulate a population adapting to an optimum shift, modeling sequence variation for 20 QTL for each of 12 different demographies for 100 different traits varying in the effect size distribution of new mutations, the strength of stabilizing selection, and the contribution of the genomic background. We then use random forest regression approaches to learn the relative importance of input parameters for statistics of interest such as the speed of adaptation, the relative frequency of hard sweeps and sweeps from standing variation, or the final genetic architecture of the trait. We find that selective sweeps occur even for traits under relatively weak selection and where the genetic background explains most of the variation. Though most sweeps occur from variation segregating in the ancestral population, new mutations can be important for traits under strong stabilizing selection that undergo a large optimum shift. Additionally, we find that deleterious mutations are more strongly influenced by the strength of stabilizing selection. We also show that population bottlenecks and expansion impact overall genetic variation as well as the relative importance of sweeps from standing variation and the speed with which adaptation can occur. We then use the matrix of effect sizes and allele frequencies in each population as a target for machine learning and find that demography and the effect size of new mutations have the largest influence on present day genetic architecture. Because a variety of parameter combinations can result in relatively similar genetic architectures, we conclude that it is not straightforward to infer much about the process of adaptation from the genetic architecture alone. Overall, our results underscore the complex population genetics of individual loci in even relatively simple quantitative trait models but provide a glimpse into the factors that drive this complexity.

解析表型适应多变环境的遗传基础，是群体遗传学与数量遗传学的核心研究目标之一。尽管当前技术进步已实现对大型遗传面板中全基因组分型数据的全面检测与分析，但我们对多基因适应过程的理论认知仍较为有限。为弥补这一研究局限，我们借助大规模正向时间模拟技术，探究种群人口动态、性状遗传基础以及选择作用对适应速率与模式，以及由此形成的遗传结构的影响。我们模拟了一个种群适应最优表型位移的过程：针对12种不同的种群人口动态场景，为100个不同性状各模拟20个数量性状位点（Quantitative Trait Locus, QTL）的序列变异；这些性状的差异体现在新发突变的效应大小分布、稳定选择强度，以及基因组背景的贡献度三个方面。随后，我们采用随机森林回归方法，针对若干关注的统计量学习各输入参数的相对重要性，这些统计量涵盖适应速率、硬选择扫荡与源自现存遗传变异的选择扫荡的相对频率，以及性状最终的遗传结构。研究发现，即便在选择强度相对较弱、且基因组背景可解释大部分表型变异的性状中，也会发生选择性扫荡事件。尽管多数选择扫荡源自祖先种群中已分离的遗传变异，但新发突变对于经历了大幅最优表型位移、且受强稳定选择作用的性状而言，可发挥关键作用。此外，我们发现有害突变受稳定选择强度的影响更为显著。我们还证实，种群瓶颈与种群扩张会对整体遗传变异水平，以及源自现存遗传变异的选择扫荡的相对重要性与适应发生速率产生显著影响。随后，我们将每个种群中效应大小与等位基因频率构成的矩阵作为机器学习的预测目标，结果发现种群人口动态与新发突变的效应大小，对当代种群的遗传结构影响最大。由于多种参数组合可产生较为相似的遗传结构，我们认为，仅通过遗传结构推断适应过程的相关信息并非易事。总体而言，本研究结果凸显了即便在相对简单的数量性状模型中，单个基因座的群体遗传学机制依然复杂，但同时也为解析驱动这一复杂性的核心因素提供了全新视角。