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 modeling Pool-seq sources of error. 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. 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, 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 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.