Estimating recent migration and population-size surfaces

In many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer maps of population sizes and migration rates associated with different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates associated with different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when using a similar method that ignores haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ∼3,000 years in Europe.

在众多物种中，遗传多样性的核心特征之一是遗传相似性随地理距离增加而衰减；然而这一关系往往较为复杂，且会随空间与时间发生变化。用于揭示并可视化这类关系的方法，在分子生态学、保护遗传学、进化遗传学及人类遗传学的分析中应用广泛。尽管已有多种分析框架，但颇具前景的一种方法是推断种群迁移率随地理空间变化的分布图。原则上，可通过跨时间维度估算这类分布图，以揭示种群历史的全部复杂性。本研究在此方向上迈出了一步：我们提出一种方法，可基于每对个体间的遗传相似性矩阵，推断对应不同时间阶段的种群规模与种群迁移率分布图。具体而言，遗传相似性通过计数单倍型（haplotype）共享的长片段数量来衡量，这类片段也称为血缘同源片段（identity-by-descent tracts）。通过调整这些片段的长度，我们可得到对应不同时间阶段的参数估计值。通过模拟实验，我们证明该方法能够揭示随时间变化的种群迁移率与种群规模，包括那些在忽略单倍型结构的同类方法中无法检测到的变化。我们将该方法应用于当代欧洲个体的数据集（POPRES），并对欧洲过去约3000年以来的种群结构与增长情况开展整合分析。