Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)

The spatial signature of micro-evolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples hasn't yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity, and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.

塑造遗传变异格局的微进化过程的空间特征，或许在受选择位点的检测工作中发挥重要作用。然而，当前尚未有研究利用样本的空间位置对该效应进行量化。本研究提出一种全新的两步法空间异常位点检测方案，可针对个体水平与同类群（deme）水平开展分析，该方法依托莫兰特征向量图（Moran Eigenvector Maps, MEM）的功率谱实现。MEM功率谱可量化某一变量的变异如何在MEM空间特征向量所定义的一系列空间尺度上分布，这类变量例如单核苷酸多态性（Single Nucleotide Polymorphism, SNP）位点上等位基因的频率。第一步为莫兰光谱异常值检测（Moran Spectral Outlier Detection, MSOD），该步骤借助遗传与空间信息，通过异常的功率谱特征识别受选择的异常位点。第二步采用莫兰光谱随机化（Moran Spectral Randomization, MSR），在考量空间自相关的前提下，检验异常位点与环境预测变量之间的关联。本研究利用两篇已发表文献中的模拟数据，在不同景观格局、选择强度、扩散能力与采样设计场景下，对该两步法进行了测试。在存在空间结构的场景中，仅依靠MSOD即可在个体与同类群水平上准确识别异常位点，无需引入环境预测变量。后续结合MSR分析通常可进一步降低（本已偏低的）假阳性率，但在部分场景中会导致统计效力有所下降。该方法对样本量与采样设计的差异表现出出人意料的鲁棒性。本研究提出的方法借助此前被忽视的空间信息，为基于个体采样与种群采样的受选择潜在位点检测提供了全新工具。