DILS: Demographic inferences with linked selection by using ABC. (3rd February 2021)

• Record Type: Journal Article
• Authors: Fraïsse, Christelle; Popovic, Iva; Mazoyer, Clément; Spataro, Bruno; Delmotte, Stéphane; Romiguier, Jonathan; Loire, Étienne; Simon, Alexis; Galtier, Nicolas; Duret, Laurent; Bierne, Nicolas; Vekemans, Xavier; Roux, Camille
• Other Names: Fountain‐Jones Nicholas M. guestEditor.; Smith Megan L. guestEditor.; Austerlitz Frédéric guestEditor.
• Is Part Of: Molecular ecology resources. Volume 21:Number 8(2021)
• Page Start: 2629
• Page End: 2644
• Publication Date: 2021-02-03
• Subjects: Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13323
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: We present DILS, a deployable statistical analysis platform for conducting demographic inferences with linked selection from population genomic data using an Approximate Bayesian Computation framework. DILS takes as input single‐population or two‐population data sets (multilocus fasta sequences) and performs three types of analyses in a hierarchical manner, identifying: (a) the best demographic model to study the importance of gene flow and population size change on the genetic patterns of polymorphism and divergence, (b) the best genomic model to determine whether the effective size Ne and migration rate N, m are heterogeneously distributed along the genome (implying linked selection) and (c) loci in genomic regions most associated with barriers to gene flow. Also available via a Web interface, an objective of DILS is to facilitate collaborative research in speciation genomics. Here, we show the performance and limitations of DILS by using simulations and finally apply the method to published data on a divergence continuum composed by 28 pairs of Mytilus mussel populations/species.