Today I provide links to scripts and tutorials that should help people interested in incorporating heterogeneous migration rates in their isolation-with-migration models.
It is increasingly acknowledged that gene flow between two populations is heterogeneous across the genome (“semi-permeable genome”), since effective migration rates are reduced around genes under positive selection or involved in reproductive isolation. Besides, speciation and differentiation often involves episodes of secondary contact or progressive interruption of gene flow. Incorporating these aspects improves model fitting to observed data, and can improve detection of genomic region that are selected or introgressed (see for example Christe et al. 2016).
Below you will find scripts for dadi and to perform ABC inference (using either ms, msms, or msnsam). I also provide the links in the tables.
A zip file containing scripts and tutorials to compare various models of divergence with heterogeneous gene flow, kindly provided by Christelle Fraïsse (Merci Christelle !):
tutoriel_dadi and a modified version of dadi that is needed for the annealing step dadi-1.7.0_modif
You can find more scripts in this dryad folder, that correspond to analyses performed in Christe et al. 2016.
And a website that also provides details about how to implement heterogeneous gene flow in dadi:
This link leads to the github repository for the recent paper by Camille Roux et al. (2016), that used ABC to compare 14 different models of divergence using sequence data. The key script here is priorgen.py, that generates priors for the 14 models and can be piped through ms/msms/msnsam. The script popPhyl2ABC_v2.py converts a phased fasta file into input files required by priorgen.py. Look at the example folder to check the way the fasta file is organized. If I get it right, sequence header names should follow this convention: >Gene_name|Species|Individual|Allele_number
Thanks to Christelle Fraïsse , Camille Roux and P-A Gagnaire for putting their scripts online.
More posts and updates to come, with a new version of fastsimcoal that can incorporate information about inbreeding, tests for ancient balancing selection, inferring population structure from RAD-seq and methods for inferring pedigrees using population genomics data…
Christe, C., Stolting, K. N., Paris, M., Frayisse, C., Bierne, N., & Lexer, C. (2016). Adaptive evolution and segregating load contribute to the genomic landscape of divergence in two tree species connected by episodic gene flow. Molecular Ecology. https://doi.org/10.1111/mec.13765
Roux, C., Fraïsse, C., Romiguier, J., Anciaux, Y., Galtier, N., & Bierne, N. (2016). Shedding Light on the Grey Zone of Speciation along a Continuum of Genomic Divergence. PLOS Biology. https://doi.org/10.1371/JOURNAL.PBIO.2000234