The IICR and the non-stationary structured coalescent: towards demographic inference with arbitrary changes in population structure.

TitleThe IICR and the non-stationary structured coalescent: towards demographic inference with arbitrary changes in population structure.
Publication TypeJournal Article
Year of Publication2018
AuthorsRodríguez, W, Mazet, O, Grusea, S, Arredondo, A, Corujo, JM, Boitard, S, Chikhi, L
JournalHeredity (Edinb)
Volume121
Issue6
Pagination663-678
Date Published2018 Dec
ISSN1365-2540
Abstract

In the last years, a wide range of methods allowing to reconstruct past population size changes from genome-wide data have been developed. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those produced under models of population size change. Recently, Mazet et al. (Heredity 116:362-371, 2016) showed that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size changes, having exactly the same distribution of T (the coalescence time for a sample of size two) as that of the structured model. They called this function IICR (Inverse Instantaneous Coalescence Rate) and showed that it does not necessarily correspond to population size changes under non-panmictic models. Besides, most of the methods used to analyse data under models of population structure tend to arbitrarily fix that structure and to minimise or neglect population size changes. Here, we extend the seminal work of Herbots (PhD thesis, University of London, 1994) on the structured coalescent and propose a new framework, the Non-Stationary Structured Coalescent (NSSC) that incorporates demographic events (changes in gene flow and/or deme sizes) to models of nearly any complexity. We show how to compute the IICR under a wide family of stationary and non-stationary models. As an example we address the question of human and Neanderthal evolution and discuss how the NSSC framework allows to interpret genomic data under this new perspective.

DOI10.1038/s41437-018-0148-0
Alternate JournalHeredity (Edinb)
PubMed ID30293985
PubMed Central IDPMC6221895