Biology subjects

Boitard, S.

Publications and source records attributed to Boitard, S..

11 recordsLinked to original sources

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

New insights into past and future invasion of Drosophila suzukii using novel genomic resources and statistical methods to combine individual and pool sequencing data

Global change is accelerating biological invasions, making it crucial to understand how species adapt in new environments to improve management strategies. Genomic data provide valuable insights into adaptation through Genotype-Environment Association (GEA) studies, which identify genes and biological processes tied to invasion success, and through geometric Genomic Offset (gGO) statistics, which estimate genetic (mal)adaptation to new environments. Here, we investigate genetic adaptation in the invasive pest Drosophila suzukii using novel genomic resources and statistical methods. We use a new chromosome-level genome assembly and data from 37 populations, combining publicly available and newly generated pooled and individual sequencing data, analyzed with an enhanced version of BayPass software, tailored for such hybrid datasets. First, we identify genomic regions showing genetic differentiation between native and invasive populations. Then, using a GEA with 29 environmental covariates, we estimate the gGO between the source environments and the invaded areas, shedding light on the potential adaptive challenges D. suzukii faced during previous invasions. In addition, we estimate gGO for geographical areas not yet invaded to predict future invasion risks, and identify regions from which preadapted populations may originate. Our results reveal numerous genomic regions associated with the invasive status from genome scans. However, when considering broader patterns of adaptation to specific environmental variables through gGO analyses, we find that D. suzukii populations likely faced only limited adaptive challenges across their major invasion range, while certain uninvaded regions still remain at high risk of future invasion. Our study offers significant insights into D. suzukii adaptation and provides a practical population genomics framework to predict biological invasions, applicable to various species.

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

Predicting species invasiveness with genomic data: is Genomic Offset related to establishment probability?

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species, but have still rarely been used in this context. Here, we evaluated the relationship between GO and the estab-lishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which the-oretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public real data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the estab-lishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g. propagule pressure or admixture) will need further investigation.

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↗

bioRxiv2019

Inference of selection from genetic time series using various parametric approximations to the Wright-Fisher model

Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markoiv chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta-with-Spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluate this model for population sizes not accessible to the Wright-Fisher model and illustrate its performance on a dataset of two divergently selected chicken populations.

genetics↗

bioRxiv2019

The quail as an avian model system: its genome provides insights into social behaviour, seasonal biology and infectious disease response

The Japanese quail (Coturnix japonica) is a popular domestic poultry species and an increasingly significant model species in avian developmental, behavioural and disease research. We have produced a high-quality quail genome sequence, spanning 0.93 Gb assigned to 33 chromosomes. In terms of contiguity, assembly statistics, gene content and chromosomal organization, the quail genome shows high similarity to the chicken genome. We demonstrate the utility of this genome through three diverse applications. First, we identify selection signatures and candidate genes associated with social behaviour in the quail genome, an important agricultural and domestication trait. Second, we investigate the effects and interaction of photoperiod and temperature on the transcriptome of the quail medial basal hypothalamus, revealing key mechanisms of photoperiodism. Finally, we investigate the response of quail to H5N1 influenza infection. In quail lung, many critical immune genes and pathways were downregulated, and this may be key to the susceptibility of quail to H5N1. This genome will facilitate further research into diverse research questions using the quail as a model avian species.

genomics↗

bioRxiv2018

An ABC method for whole-genome sequence data: inferring paleolithic and neolithic human expansions

Species generally undergo a complex demographic history, consisting, in particular, of multiple changes in population size. Genome-wide sequencing data are potentially highly informative for reconstructing this demographic history. A crucial point is to extract the relevant information from these very large datasets. Here we designed an approach for inferring past demographic events from a moderate number of fully sequenced genomes. Our new approach uses Approximate Bayesian Computation (ABC), a simulation-based statistical framework that allows (i) identifying the best demographic scenario among several competing scenarios, and (ii) estimating the best-fitting parameters under the chosen scenario. ABC relies on the computation of summary statistics. Using a cross-validation approach, we showed that statistics such as the lengths of haplotypes shared between individuals, or the decay of linkage disequilibrium with distance, can be combined with classical statistics (eg heterozygosity, Tajimas D) to accurately infer complex demographic scenarios including bottlenecks and expansion periods. We also demonstrated the importance of simultaneously estimating the genotyping error rate. Applying our method on genome-wide human-sequence databases, we finally showed that a model consisting in a bottleneck followed by a Paleolithic and a Neolithic expansion was the most relevant for Eurasian populations.

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↗