RT Journal Article
SR Electronic
T1 DILS : Demographic Inferences with Linked Selection by using ABC
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.15.151597
DO 10.1101/2020.06.15.151597
A1 Christelle Fraïsse
A1 Iva Popovic
A1 Clément Mazoyer
A1 Jonathan Romiguier
A1 Étienne Loire
A1 Alexis Simon
A1 Nicolas Galtier
A1 Laurent Duret
A1 Nicolas Bierne
A1 Xavier Vekemans
A1 Camille Roux
YR 2020
UL http://biorxiv.org/content/early/2020/06/15/2020.06.15.151597.abstract
AB 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 datasets and performs three types of analyses in a hierarchical manner, identifying: 1) the best demographic model to study the importance of gene flow and population size change on the genetic patterns of polymorphism and divergence, 2) the best genomic model to determine whether the effective size Ne and migration rate N.m are heterogeneously distributed along the genome and 3) 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.Competing Interest StatementThe authors have declared no competing interest.