Biology subjects

HALKETT, F.

Publications and source records attributed to HALKETT, F..

4 recordsLinked to original sources

bioRxiv2025

Behind the shadow play: Shedding light on the population genetics of partially clonal organisms through clone age distributions

Partial clonality is widespread in natural populations; however, the distributions of clone ages under different rates of clonality and their effects on genetic diversity remain unexplored. To fill this gap, we simulated partially clonal populations over 10,000 generations across a range of clonality and mutation rates. Using a forward-in-time individual-based model, we evaluated genetic and genotypic indices alongside measures of clone age distribution to examine how different clonality rates influence clone age distributions, and how these distributions impact population structure and genetic diversity over time. Our results reveal two distinct trajectories: (i) at low to moderate rates of clonality evolution is driven by sharp and predictable patterns of rapid clone turnovers, resulting in predominantly young clones which relative abundances well align with genotypic indices ; (ii) at extreme rates of clonality, demographic stochasticity generates very variable clone age distributions which mirror the high variance of mean and variance of FIS that summarize gene reshuffling between individuals. Interestingly, a portion of the variability of these indices can be explained by differences in clone age distribution that occur by chance. Our results, therefore, complement previous theoretical studies on the population genetics of partial clonality by providing biological insights into how clonal turnover dynamics shape the temporal evolution and variability of highly clonal populations. These results have practical implications for inferring evolutionary trajectories and managing partially clonal species.

evolutionary biology↗

bioRxiv2022

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering the full trajectory compared to few time samples. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetics changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

evolutionary biology↗

bioRxiv2022

Interplay between demography and selection: a forward model illuminates the temporal genetic signatures of rapid adaptation in plant pathogens

Pathogen species are experiencing strong joint demographic and selective events, especially when they adapt to a new host, for example through overcoming plant resistance. Stochasticity in the founding event and the associated demographic variations hinder our understanding of the expected evolutionary trajectories and the genetic structure emerging at both neutral and selected loci. What would be the typical genetic signatures of such a rapid adaptation event is not elucidated. Here, we build a demogenetic model to monitor pathogen population dynamics and genetic evolution on two host compartments (susceptible and resistant). We design our model to fit two plant pathogen life cycles, with and without host alternation. Our aim is to draw a typology of eco-evolutionary dynamics. Using time-series clustering, we identify three main scenarios: 1) small variations in the pathogen population size and small changes in genetic structure, 2) a strong founder event on the resistant host that in turn leads to the emergence of genetic structure on the susceptible host, and 3) evolutionary rescue that results in a strong founder event on the resistant host, preceded by a bottleneck on the susceptible host. We pinpoint differences between life cycles with notably more evolutionary rescue with host alternation. Beyond the selective event itself, the demographic trajectory imposes specific changes in the genetic structure of the pathogen population. Most of these genetic changes are transient, with a signature of resistance overcoming that vanishes within a few years only. Considering time-series is therefore of utmost importance to accurately decipher pathogen evolution.

evolutionary biology↗

bioRxiv2021

Impact of ploidy and pathogen life cycle on resistance durability

The breeding of resistant hosts based on the gene-for-gene interaction is crucial to address epidemics of plant pathogens in agroecosystems. Resistant host deployment strategies are developed and studied worldwide to decrease the probability of resistance breakdown and increase the resistance durability in various pathosystems. A major component of deployment strategies is the proportion of resistant hosts in the landscape. However, the impact of this proportion on resistance durability remains unclear for diploid pathogens with complex life cycles. In this study, we modelled pathogen population dynamics and genetic evolution at the virulence locus to assess the impact of the ploidy (haploid or diploid) and the pathogens life cycle (with or without host alternation) on resistance durability. Ploidy has a strong impact on evolutionary trajectories, with much greater stochasticity and delayed times of resistance breakdown for diploids. This result emphasises the importance of genetic drift in this system: as the virulent allele is recessive, positive selection on resistant hosts only applies to homozygous (virulent) individuals, which may lead to population collapse at low frequencies of the virulent allele. We also observed differences in the effect of host deployment depending on the pathogens life cycle. With host alternation, the probability that the pathogen population collapses strongly increases with the proportion of resistant hosts in the landscape. Therefore, resistance breakdown events occurring at high proportions of resistant hosts frequently amount to evolutionary rescue. Last, life cycles correspond to two selection regimes: without host alternation (soft selection) the resistance breakdown is mainly driven by the migration rate. Conversely, host alternation (hard selection) resembles an all-or-nothing game, with stochastic trajectories caused by the recurrent allele redistributions on the alternate host.

evolutionary biology↗