Comparing pool‐seq, rapture, and GBS genotyping for inferring weak population structure: the American lobster (Homarus americanus) as a case study

Unraveling genetic population structure is challenging in species potentially characterized by large population size and high dispersal rates, often resulting in weak genetic differentiation. Genotyping a large number of samples can improve the detection of subtle genetic structure, but this may substantially increase sequencing cost and downstream bioinformatics computational time. To overcome this challenge, alternative, cost‐effective sequencing approaches, namely Pool‐seq and Rapture, have been developed. We empirically measured the power of resolution and congruence of these two methods in documenting weak population structure in nonmodel species with high gene flow comparatively to a conventional genotyping‐by‐sequencing (GBS) approach. For this, we used the American lobster (Homarus americanus) as a case study. First, we found that GBS, Rapture, and Pool‐seq approaches gave similar allele frequency estimates (i.e., correlation coefficient over 0.90) and all three revealed the same weak pattern of population structure. Yet, Pool‐seq data showed FST estimates three to five times higher than GBS and Rapture, while the latter two methods returned similar FST estimates, indicating that individual‐based approaches provided more congruent results than Pool‐seq. We conclude that despite higher costs, GBS and Rapture are more convenient approaches to use in the case of species exhibiting very weak differentiation. While both GBS and Rapture approaches provided similar results with regard to estimates of population genetic parameters, GBS remains more cost‐effective in project involving a relatively small numbers of genotyped individuals (e.g., <1,000). Overall, this study illustrates the complexity of estimating genetic differentiation and other summary statistics in complex biological systems characterized by large population size and migration rates.

对于种群规模庞大、扩散率极高的物种而言，解析其遗传种群结构颇具挑战，这类物种往往表现出微弱的遗传分化。对大量样本进行基因分型虽可提升对微弱遗传结构的检测效能，但会大幅提高测序成本与下游生物信息学计算耗时。为解决这一难题，科研人员已开发出Pool-seq与Rapture两种替代性高性价比测序方法。本研究以美洲螯龙虾（Homarus americanus）为案例研究物种，对比评估了这两种方法与常规测序分型（genotyping-by-sequencing, GBS）在高基因流非模式物种中检测微弱种群结构的分辨效力与结果一致性。首先，研究结果显示，GBS、Rapture与Pool-seq三种方法得到的等位基因频率估计值高度相似（相关系数均超过0.90），且三者均揭示了一致的微弱种群结构模式。但Pool-seq得到的遗传分化系数（FST）估计值是GBS与Rapture的3至5倍，而后两种基于个体的分型方法得到的FST估计值更为相近，表明基于个体的分型方案比Pool-seq的结果更具一致性。本研究结论认为，尽管成本更高，但对于遗传分化极微弱的物种，GBS与Rapture是更为便捷的分型方案。尽管GBS与Rapture在种群遗传参数估计上结果相近，但当分型个体数量相对较少（如少于1000个）时，GBS的成本效益更为突出。总体而言，本研究阐明了在种群规模庞大、迁移率较高的复杂生物系统中，估算遗传分化及其他汇总统计量的复杂性。