Coalescence times for three genes provide sufficient information to distinguish population structure from population size changes.

The increasing amount of genomic data currently available is expanding the horizons of population genetics inference. A wide range of methods have been published allowing to detect and date major changes in population size during the history of species. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those generated under models of population size change. Recently, Mazet et al. (Heredity 116(4):362-371, 2016) introduced the idea that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size-change having an identical distribution of [Formula: see text] (the time of the first coalescence for a sample of size two). This implies that there is an identifiability problem between a panmictic and a structured model when we base our analysis only on [Formula: see text]. In this paper, based on an analytical study of the rate matrix of the ancestral lineage process, we obtain new theoretical results about the joint distribution of the coalescence times [Formula: see text] for a sample of three haploid genes in a n-island model with constant size. Even if, for any [Formula: see text], it is always possible to find a size-change scenario for a panmictic population such that the marginal distribution of [Formula: see text] is exactly the same as in a n-island model with constant population size, we show that the joint distribution of the coalescence times [Formula: see text] for a sample of three genes contains enough information to distinguish between a panmictic population and a n-island model of constant size.

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

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 T2 (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.

The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: insights into demographic inference and model choice.

Several inferential methods using genomic data have been proposed to quantify and date population size changes in the history of species. At the same time an increasing number of studies have shown that population structure can generate spurious signals of population size change. Recently, Mazet et al. (2016) introduced, for a sample size of two, a time-dependent parameter, which they called the IICR (inverse instantaneous coalescence rate). The IICR is equivalent to a population size in panmictic models, but not necessarily in structured models. It is characterised by a temporal trajectory that suggests population size changes, as a function of the sampling scheme, even when the total population size was constant. Here, we extend the work of Mazet et al. (2016) by (i) showing how the IICR can be computed for any demographic model of interest, under the coalescent, (ii) applying this approach to models of population structure (1D and 2D stepping stone, split models, two- and three-island asymmetric gene flow, continent-island models), (iii) stressing the importance of the sampling strategy in generating different histories, (iv) arguing that IICR plots can be seen as summaries of genomic information that can thus be used for model choice or model exclusion (v) applying this approach to the question of admixture between humans and Neanderthals. Altogether these results are potentially important given that the widely used PSMC (pairwise sequentially Markovian coalescent) method of Li and Durbin (2011) estimates the IICR of the sample, not necessarily the history of the populations.

The proto-MHC of placozoans, a region specialized in cellular stress and ubiquitination/proteasome pathways.

The MHC is a large genetic region controlling Ag processing and recognition by T lymphocytes in vertebrates. Approximately 40% of its genes are implicated in innate or adaptive immunity. A putative proto-MHC exists in the chordate amphioxus and in the fruit fly, indicating that a core MHC region predated the emergence of the adaptive immune system in vertebrates. In this study, we identify a putative proto-MHC with archetypal markers in the most basal branch of Metazoans--the placozoan Trichoplax adhaerens, indicating that the proto-MHC is much older than previously believed--and present in the common ancestor of bilaterians (contains vertebrates) and placozoans. Our evidence for a T. adhaerens proto-MHC was based on macrosynteny and phylogenetic analyses revealing approximately one third of the multiple marker sets within the human MHC-related paralogy groups have unique counterparts in T. adhaerens, consistent with two successive whole genome duplications during early vertebrate evolution. A genetic ontologic analysis of the proto-MHC markers in T. adhaerens was consistent with its involvement in defense, showing proteins implicated in antiviral immunity, stress response, and ubiquitination/proteasome pathway. Proteasome genes psma, psmb, and psmd are present, whereas the typical markers of adaptive immunity, such as MHC class I and II, are absent. Our results suggest that the proto-MHC was involved in intracellular intrinsic immunity and provide insight into the primordial architecture and functional landscape of this region that later in evolution became associated with numerous genes critical for adaptive immunity in vertebrates.

CASSIOPE: an expert system for conserved regions searches.

BACKGROUND: Understanding genome evolution provides insight into biological mechanisms. For many years comparative genomics and analysis of conserved chromosomal regions have helped to unravel the mechanisms involved in genome evolution and their implications for the study of biological systems. Detection of conserved regions (descending from a common ancestor) not only helps clarify genome evolution but also makes it possible to identify quantitative trait loci (QTLs) and investigate gene function.The identification and comparison of conserved regions on a genome scale is computationally intensive, making process automation essential. Three key requirements are necessary: consideration of phylogeny to identify orthologs between multiple species, frequent updating of the annotation and panel of compared genomes and computation of statistical tests to assess the significance of identified conserved gene clusters. RESULTS: We developed a modular system superimposed on a multi-agent framework, called CASSIOPE (Clever Agent System for Synteny Inheritance and Other Phenomena in Evolution). CASSIOPE automatically identifies statistically significant conserved regions between multiple genomes based on automated phylogenies and statistical testing. Conserved regions were searched for in 19 species and 1,561 hits were found. To our knowledge, CASSIOPE is the first system to date that integrates evolutionary biology-based concepts and fulfills all three key requirements stated above. All results are available at http://194.57.197.245/cassiopeWeb/displayCluster?clusterId=1 CONCLUSION: CASSIOPE makes it possible to study conserved regions from a chosen query genetic region and to infer conserved gene clusters based on phylogenies and statistical tests assessing the significance of these conserved regions.Source code is freely available, please contact: Pierre.pontarotti@univ-provence.fr.

Origin and evolution of the vertebrate leukocyte receptors: the lesson from tunicates.

Two selected receptor genes of the immunoglobulin superfamily (IgSF), one CTX/JAM family member, and one poliovirus receptor-like nectin that have features of adhesion molecules can be expressed by Ciona hemocytes, the effectors of immunity. They can also be expressed in the nervous system (CTX/JAM) and in the ovary (nectin). The genes encoding these receptors are located among one set of genes, spread over Ciona chromosomes 4 and 10, and containing other IgSF members homologous to those encoded by genes present in a tetrad of human (1, 3 + X, 11, 21 + 19q) or bird chromosomes (1, 4, 24, 31) that include the leukocyte receptor complex. It is proposed that this tetrad is due to the two rounds of duplication that affected a single prevertebrate ancestral region containing a primordial leukocyte receptor complex involved in immunity and other developmental regulatory functions.

On the distribution of the number of cycles in the breakpoint graph of a random signed permutation.

We use the finite Markov chain embedding technique to obtain the distribution of the number of cycles in the breakpoint graph of a random uniform signed permutation. This further gives a very good approximation of the distribution of the reversal distance between two random genomes.