Integrating Pool-seq uncertainties into demographic inference

Abstract

Next-generation sequencing of pooled samples (Pool-seq) is a popular method to assess genome-wide diversity patterns in natural and experimental populations. However, Pool-seq is associated with specific sources of noise, such as unequal individual contributions. Consequently, using Pool-seq for the reconstruction of evolutionary history has remained underexplored. Here we describe a method to simulate Pool-seq data, implemented in an Approximate Bayesian Computation (ABC) framework to infer demographic history under models with two and four populations. By jointly modeling Pool-seq data, demographic history and the effects of selection due to barrier loci, we obtain estimates of demographic history parameters accounting for technical errors associated with Pool-seq. Rather than simulating genome-wide data, we perform ABC on subsets of loci, using relative summary statistics and parameter estimates. Our simulation study results indicate Pool-seq data allows distinction between general scenarios of ecotype formation (single versus parallel origin), and to infer relevant demographic parameters (e.g., effective sizes, split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod \textit{Littorina saxatilis}, sampled on a narrow geographical scale at two Swedish locations where two ecotypes (wave and crab) are found. Our model choice and parameter estimates show that ecotypes formed before colonization of the two locations (i.e., single origin) and are maintained despite gene flow. These results indicate that demographic modeling and inference can be successful based on pool-sequencing using ABC, contributing to the development of suitable null models that allow for a better understanding of the genetic basis of divergent adaptation.

poolHelper: an R package to help in designing Pool-Seq studies

“…Larger Pool-seq errors lead to larger variance, resulting in more unequal contributions from individuals and pools. Although the selection of an appropriate Pool-seq error might be potentially hard, given its unknown nature, we previously estimated values ranging from 24 to 236, with posterior means for two different models of 182 and 102 (Carvalho et al, 2022). Thus, the Pool-seq errors used here are within the reasonable ranges for this parameter (see Figure 1 for an example of how different Pool-seq errors impact individual contribution).…”

“…The steps required to simulate Pool-seq allele frequencies at biallelic SNPs are detailed in Carvalho, Morales, Faria, Butlin, and Sousa (2022). Briefly, we model the depth of coverage at each SNP (i.e., number of reads per site) with a negative binomial distribution (Sampson, Jacobs, Yeager, Chanock, & Chatterjee, 2011), which is defined based on the mean and variance of the depth of coverage across all sites.…”

poolHelper: An R package to help in designing Pool‐Seq studies

“…We follow a series of steps (Figure 1) to model and simulate allele frequencies obtained with Pool‐seq for biallelic SNPs, as described in Carvalho et al (2023). The variation in depth of coverage across SNPs is assumed to follow a negative binomial distribution (, following e.g.…”

“…The variance of contribution depends on the experimental error as and . Although the selection of an appropriate pooling error might be potentially hard, given its unknown nature, we previously estimated values ranging from 24 to 236 (Carvalho et al, 2023). Furthermore, previous studies have also considered values ranging from 0 to 250 (Gautier et al, 2013).…”