Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.
Genetics. 2024 Jul 8;227(3). doi: 10.1093/genetics/iyae082.
Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.
描述等位基因频率的空间模式是进化生物学的基础，因为这些模式包含了潜在过程的证据。然而，基因流动、不断变化的选择和漂变作用的空间尺度通常是未知的。这些过程中的许多过程在空间上不一致，导致非平稳模式。我们提出了一种基于小波的方法来描述等位基因频率的空间模式，有助于解决这些问题。我们展示了我们的方法如何在多个空间尺度上描述亲缘关系的空间模式，即多基因小波遗传差异。我们还开发了用于等位基因频率和数量性状基因座（QTL）的空间分化的小波检验。通过模拟，我们在不同的场景下说明了这些方法。我们还将我们的方法应用于拟南芥的自然种群，以描述种群结构并在多个尺度上识别局部适应的基因座。例如，我们发现拟南芥开花时间 QTL 在 300-1300 公里的尺度上显示出显著升高的遗传分化。等位基因频率的小波变换提供了一种灵活的方法来揭示地理模式和潜在的进化过程。
