Biology subjects

Mazet, O.

Publications and source records attributed to Mazet, O..

9 recordsLinked to original sources

bioRxiv2026

Modelling and Inferring Large-scale Demographic Fluctuations in Structured Populations Through Simulations and PSMC-based Methods.

The climatic oscillations of the Quaternary have likely affected the demographic history of many species, and PSMC (Pairwise Sequentially Markovian Coalescent) has been widely used to investigate these histories. However, it is increasingly acknowledged that PSMC trajectories are difficult to interpret. First, they are influenced by connectivity changes, even without population size changes. Second, most PSMC curves exhibit a few humps when tens of cycles occurred during the Pleistocene. Finally, responses to ancient habitat change have been shown to be species-specific. To address these issues, we simulated structured populations where connectivity (or population size and connectivity) varied according to successive interglacial and glacial periods during the last 2.6 million years. We computed the IICR (Inverse Instantaneous Coalescence Rate), the function that PSMC estimates, and ran PSMC. We further varied the generation length and assumed that some species were positively or negatively affected by glacials. We found that the IICR carries information regarding the demographic oscillations, but that PSMC fails to recover it for times older than 300 ky. For the last 200 ky, PSMC was often able to reproduce qualitatively the demographic oscillations. We also tested SNIF (Structured Non-stationary Inferential Framework), which produced good results using the IICR curve as an input but not when using the PSMC curve. Altogether, our study suggests that the humps older than 300 ky in PSMC histories are unlikely to represent trends of population size or connectivity. However, improving the estimation of the IICR could potentially help reconstruct some of these past demographic changes.

genomics↗

bioRxiv2025

Structural mutations set an equilibrium non-coding genome fraction

Non-coding genome size evolution is poorly understood. While some fraction of non-coding DNA has arguably a regulatory function, a large part does not seem to have a detectable impact on any phenotypic trait. The abundance of non-functional DNA in genomes, observed across the Tree of Life, challenges a purely adaptationist explanation. Several non-adaptive theories have been proposed to explain its presence and identify its determinants, emphasizing either the mutational processes or the mutational hazard entailed by non-coding and non-functional DNA. However, those theories have not yet been integrated into a single framework, and the exact nature of the mutational hazard is not yet fully understood. In this work, we propose a simple mathematical model of genome size evolution. The model shows how the non-coding fraction of the genome is shaped by two factors: unavoidable biases in the neutrality of the different mutation types (adding base pairs is more likely to be neutral than removing some), and the robustness selection imposed by the mere existence of structural mutations (larger genomes are more prone to double-strand breaks that can initiate structural mutations, imposing a second-order selection on robustness). Together, these two factors ensure the existence of an equilibrium non-coding fraction. We show that this equilibrium depends solely on mutation biases and the product of population size and mutation rate.

evolutionary biology↗

bioRxiv2024

Extending the IICR to complex non-stationary structured models

Population genetic studies use genetic data to understand aspects of the evolutionary history of species, particularly to infer their demography. Here, we extend the non-stationary structured coalescent framework to complex models where the parameters of the model, and thus the state space, change between stationary periods. To do this, we introduce glue matrices that allow us to map one state space to another, hence enabling the computation of the Inverse Instantaneous Coalescence Rate (IICR) using Q-matrices of different dimensions. This approach allows us to study models where the number of demes changes due to extinction or foundation. Our analysis confirms that interpreting IICR curves as indicators of population size changes can be misleading. However, we also show that there are cases where new deme foundations can generate an increase in the IICR (forward in time). We explore the impact of changes in migration rates, timing of deme creation or deletion, and sampling strategies on the IICR, identifying several counter-intuitive results. For instance, we find that the IICR starts to decrease several generations before any change in the number of demes, as if the population "knew" something was about to happen. It is particularly unexpected when a deme is founded and the population is only increasing. Our results underscore the need for careful interpretation of PSMC and similar curves, which are often used and interpreted as population size changes. By presenting examples of IICRs for various transitions and sampling scenarios, we emphasize the importance of nuanced approaches in population genetic studies.

evolutionary biology↗

bioRxiv2024

Extending the IICR to multiple genomes and identification of limitations of some demographic inferential methods

Reconstructing the demographic history of populations and species is one of the greatest challenges facing population geneticists. [50] introduced, for a sample of size k = 2 haploid genomes, a time- and sample-dependent parameter which they called the IICR (inverse instantaneous coalescence rate). Here we extend their work to larger sample sizes and focus on Tk, the time to the first coalescence event in a haploid sample of size k where k [≥] 2. We define the IICRk as the Inverse Instantaneous Coalescence Rate among k lineages. We show that (i) under a panmictic population [Formula] is equivalent to Ne, (ii) the IICRk can be obtained by either simulating Tk values or by using the Q-matrix approach of [61] and we provide the corresponding Python and R scripts. We then study the properties of the [Formula] under a limited set of n-island and stepping-stone models. We show that (iii) in structured models the [Formula] is dependent on the sample size and on the sampling scheme, even when the genomes are sampled in the same deme. For instance, we find that [Formula] plots for individuals sampled in the same deme will be shifted towards recent times with a lower plateau as k increases. We thus show that (iv) the [Formula] cannot be used to represent "the demographic history" in a general sense, (v) the [Formula] can be estimated from real or simulated genomic data using the PSMC/MSMC methods [44, 65] (vi) the MSMC2 method produces smoother curves that infer something that is not the [Formula], but are close to the [Formula] in the recent past when all samples are obtained from the same deme. Altogether we argue that the PSMC, MSMC and MSMC2 plots are not expected to be identical even when the genomes are sampled from the same deme, that none can be said to represent the "demographic history of populations" and that they should be interpreted with care. We suggest that the PSMC, MSMC and MSMC2 could be used together with the [Formula] to identify the signature of population structure, and to develop new strategies for model choice.

evolutionary biology↗

bioRxiv2024

On the demographic history of chimpanzees and some consequences of integrating population structure in chimpanzees and other great apes

Reconstructing the evolutionary history of great apes is of particular importance for our understanding of the demographic history of humans. The reason for this is that modern humans and their hominin ancestors evolved in Africa and thus shared the continent with the ancestors of chimpanzees and gorillas. Common chimpanzees (Pan troglodytes) are our closest relatives with bonobos (Pan paniscus) and most of what we know about their evolutionary history comes from genetic and genomic studies. Most evolutionary studies of common chimpanzees have assumed that the four currently recognised subspecies can be modelled using simple tree models where each subspecies is panmictic and represented by one branch of the evolutionary tree. However, several studies have identified the existence of significant population structure, both within and between subspecies, with evidence of isolation-by-distance (IBD) patterns. This suggests that demographic models integrating population structure may be necessary to improve our understanding of their evolutionary history. Here we propose to use n-island models within each subspecies to infer a demographic history integrating population structure and changes in connectivity (i.e. gene flow). For each subspecies, we use SNIF (structured non-stationary inference framework), a method developed to infer a piecewise stationary n-island model using PSMC (pairwise sequentially Markovian coalescent) curves as summary statistics. We then propose a general model integrating the four subspecies metapopulations within a phylogenetic tree. We find that this model correctly predicts estimates of within subspecies genetic diversity and differentiation, but overestimates genetic differentiation between subspecies as a consequence of the tree structure. We argue that spatial models integrating gene flow between subspecies should improve the prediction of between subspecies differentiation and IBD patterns. We also use a simple spatially structured model for bonobos and chimpanzees (without admixture) and find that it explains signals of admixture between the two species that have been reported and could thus be spurious. This may have implications for our understanding of the evolutionary history of the Homo genus.

evolutionary biology↗

bioRxiv2023

Exact calculation of the expected SFS in structured populations

The Site Frequency Spectrum (SFS), summary statistic of the distribution of derived allele frequencies in a sample of DNA sequences, provides information about genetic variation and can be used to make population inferences. The exact calculation of the expected SFS in panmictic population has been derived in the Markovian coalescent theory for decades, but its generalization to the structured coalescent is hampered by the almost exponential growth of the states space. We propose here a complete algorithmic procedure, from how to build a suitable state space and sort it, to how to take advantage of the sparsity of the rate matrix and to solve numerically the linear system by an iterative method. The simplest case of the symmetrical n-island is then processed to arrive at a ready-to-use demographic parameters inference framework.

genetics↗

bioRxiv2021

Heterogeneity in effective size across the genome: effects on the Inverse Instantaneous Coalescence Rate (IICR) and implications for demographic inference under linked selection.

The relative contribution of selection and neutrality in shaping species genetic diversity is one of the most central and controversial questions in evolutionary theory. Genomic data provide growing evidence that linked selection, i.e. the modification of genetic diversity at neutral sites through linkage with selected sites, might be pervasive over the genome. Several studies proposed that linked selection could be modelled as first approximation by a local reduction (e.g. purifying selection, selective sweeps) or increase (e.g. balancing selection) of effective population size (Ne). At the genome-wide scale, this leads to variations of Ne from one region to another, reflecting the heterogeneity of selective constraints and recombination rates between regions. We investigate here the consequences of such genomic variations of Ne on the genome-wide distribution of coalescence times. The underlying motivation concerns the impact of linked selection on demographic inference, because the distribution of coalescence times is at the heart of several important demographic inference approaches. Using the concept of Inverse Instantaneous Coalescence Rate, we demonstrate that in a panmictic population, linked selection always results in a spurious apparent decrease of Ne along time. Balancing selection has a particularly large effect, even when it concerns a very small part of the genome. We also study more general models including genuine population size changes, population structure or transient selection and find that the effect of linked selection can be significantly reduced by that of population structure. The models and conclusions presented here are also relevant to the study of other biological processes generating apparent variations of Ne along the genome.

genetics↗

bioRxiv2020

Inferring number of populations and changes in connectivity under the n-island model

Inferring the demographic history of species is one of the greatest challenges in populations genetics. This history is often represented as a history of size changes, thus ignoring population structure. Alternatively, structure is defined a priori as a population tree and not inferred. Here we propose a framework based on the IICR (Inverse Instantaneous Coalescence Rate), which can be estimated using the PSMC method of Li and Durbin (2011) for a single diploid individual. For an isolated population, the IICR matches the population size history, which is how the PSMC outputs are generally interpreted. However, it is increasingly acknowledged that the IICR is a function of the demographic model and sampling scheme. Our automated method fits observed IICR curves of diploid individuals with IICR curves obtained under piecewise-stationary symmetrical island models, in which we assume a fixed number of time periods during which gene flow is constant. We infer the number of islands, their sizes, the periods at which connectivity changes and the corresponding rates of connectivity. Validation with simulated data showed that the method can accurately recover most of the scenario parameters. Our application to a set of five human PSMCs yielded demographic histories that are in agreement with previous studies using similar methods and with recent research suggesting ancient human structure. They are in contrast with the widely accepted view of human evolution consisting of one ancestral population branching into three large continental and panmictic populations with varying degrees of connectivity and no population structure within each continent.

evolutionary biology↗

bioRxiv2018

The IICR and the Non-Stationary Structured Coalescent: 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. (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) to 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 (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.\n\nAuthor summaryGenomic data are becoming available for a rapidly increasing number of species, and contain information about their recent evolutionary history. If we wish to understand how they expanded, contracted or admixed as a consequence of recent and ancient environmental changes, we need to develop general inferential methods. Currently, demographic inference is either done assuming that a species is a single panmictic population or using arbitrary structured models. We use the concept of IICR (Inverse of the Instantaneous Coalescence Rate) together with Markov chains theory to develop a general inferential framework which we call the Non-Stationary Structured Coalescent and apply it to explain human and Neanderthal genomic data in a single structured model.

evolutionary biology↗