Showing papers by "Roger K. Butlin published in 2023"
TL;DR: In this article , an approximate Bayesian Computation (ABC) method was proposed to infer demographic history from pool-sequencing data, including demographic history and the effects of selection due to barrier loci.
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 novel Approximate Bayesian Computation (ABC) method to infer demographic history, explicitly modelling Pool-seq sources of error. By jointly modelling 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. Our ABC approach is computationally efficient as it relies on simulating subsets of loci (rather than the whole-genome) and on using relative summary statistics and relative model parameters. 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 and split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod 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 modelling 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.
1 citations
TL;DR: In this paper , the authors introduce an R package, poolHelper, enabling users to simulate Pool-seq data under different combinations of average depth of coverage, pool sizes and number of pools, modelled by parameters that users can modify.
Abstract: Next-generation sequencing of pooled samples (Pool-seq) is an important tool in population genomics and molecular ecology. In Pool-seq, the relative number of reads with an allele reflects the allele frequencies in the sample. However, unequal individual contributions to the pool and sequencing errors can lead to inaccurate allele frequency estimates. When designing Pool-seq studies, researchers need to decide the pool size (number of individuals) and average depth of coverage (sequencing effort). An efficient sampling design should maximize the accuracy of allele frequency estimates while minimizing the sequencing effort. We introduce an R package, poolHelper, enabling users to simulate Pool-seq data under different combinations of average depth of coverage, pool sizes and number of pools, accounting for unequal individual contribution and sequencing errors, modelled by parameters that users can modify. poolHelper can be used to assess how different combinations of those parameters influence the error of sample allele frequencies and expected heterozygosity. The mean absolute error is computed by comparing the sample allele frequencies obtained based on individual genotypes with the frequency estimates obtained with Pool-seq. Using simulations under a single population model, we illustrate that increasing the depth of coverage does not necessarily lead to more accurate estimates, reinforcing that finding the best Pool-seq study design is not straightforward. Moreover, we show that simulations can be used to identify different combinations of parameters with similarly low mean absolute errors. The poolHelper package provides tools for performing simulations with different combinations of parameters before sampling and generating data, allowing users to define sampling schemes that minimize the sequencing effort.
TL;DR: A recent transition from egg-laying to live-bearing in Littorina snails provides the opportunity to study the architecture of an innovation that has evolved repeatedly in animals as discussed by the authors .
Abstract: Key innovations are fundamental to biological diversification, but their genetic architecture is poorly understood. A recent transition from egg-laying to live-bearing in Littorina snails provides the opportunity to study the architecture of an innovation that has evolved repeatedly in animals. Samples do not cluster by reproductive mode in a genome-wide phylogeny, but local genealogical analysis revealed numerous genomic regions where all live-bearers carry the same core haplotype. Associated regions show evidence for live-bearer-specific positive selection, and are enriched for genes that are differentially expressed between egg-laying and live-bearing reproductive systems. Ages of selective sweeps suggest live-bearing alleles accumulated gradually, involving selection at different times in the past. Our results suggest that innovation can have a polygenic basis, and that novel functions can evolve gradually, rather than in a single step.