Inference of complex population histories using whole-genome sequences from multiple populations

There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but the inference methods developed hitherto are limited in model complexity and computational scalability. Here, we present an e cient, flexible statistical method that can utilize whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. We demonstrate through an extensive simulation study that our method can accurately and e ciently infer demographic parameters in realistic biological scenarios. The algorithms described here are implemented in a new version of the software package diCal, which is available for download at https://sourceforge.net/projects/dical2. 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/026591 doi: bioRxiv preprint first posted online Sep. 16, 2015;