Biology subjects

Volz, E. M.

Publications and source records attributed to Volz, E. M..

4 recordsLinked to original sources

bioRxiv2025

Quantifying the predictability of evolution by analysis of coalescent rate variation

We investigate the predictability of evolution in terms of the phylogenetic placement of new lineages. This leads us to develop a class of coalescent models that relax neutrality by allowing the rate of coalescence to vary as a continuous heritable trait. In this setting, each lineage has a relative propensity to coalesce, with coalescent odds ratios defined from the product of pairwise propensities. Estimated coalescent odds provide a statistic that captures variation in lineage growth and are informative about the strength of natural selection acting on individual lineages. A number of practical statistical methods are then developed: techniques to adjust for biased and non-uniform sampling; procedures to automatically calibrate hyperparameters governing the evolution of coalescent propensity; and, methods for clustering phylogenies into sets that delineate clades according to coalescent propensity. Simulations show sensitivity of these methods to detecting small selective effects acting on rare variants, and strong robustness to imbalanced sampling. We demonstrate these methods using two datasets reflecting microbial populations evolving under strong selection. First, we examine a large set of Neisseria gonorrhoeae genomes, and show that lineages with high coalescent odds feature a unique antibiotic resistance pattern which presaged its subsequent expansion. We then re-analyse SARS-CoV-2 data in combination with independent estimates of reproduction numbers corresponding to major variants of concern 2020-2023. This indicates that coalescent odds can function as an excellent tree-based proxy for relative fitness of major SARS-CoV-2 lineages.

evolutionary biology↗

bioRxiv2025

Untangling the effects of within-host and between-host natural selection in viruses with applications to evolution of HIV-1 co-receptor usage

The fitness of a pathogen can be characterised at multiple levels corresponding to different stages of its life cycle, including replication within target cells (replicative capacity), immune evasion, and the capacity to infect new hosts (transmissibility). Pathogens evolve towards greater fitness within hosts, which can potentially compromise transmissibility. Typically, epidemiological studies for measuring pathogen fitness compute a single selection coefficient for pathogen variants that encompasses all stages of the pathogen life cycle. To address this problem, we develop a population-genetic model and software for the inference of multi-level selection coefficients which represent relative within-host fitness and transmissibility. Our approach builds on population genetic models previously applied to macro-evolution, whereby speciation and extinction rates are state-dependent. We relate the state-dependence in speciation and extinction rates to competing within- and between-host selection. Evolution of beneficial phenotypes within hosts may occur rapidly (higher speciation), but reduce rates of dispersal between hosts (higher extinction). This new coalescent-based method also accounts for highly nonlinear-population dynamics and variable sampling through time making it suitable for epidemiological studies. We derive the equilibrium proportion of a variant with multi-level selection under mutation-selection balance and show how this can be used in combination with the coalescent to greatly increase accuracy and precision of estimated selection coefficients. We characterise the accuracy of this method using a variety of birth-death and coalescent simulations before analysing a real dataset of HIV-1 genetic sequences from a large population-based sample of 4056 people living with HIV in Zambia. We quantified the multi-level relative fitness underlying HIV-1 co-receptor switching. In accordance with previous observations, we estimate that the relative transmissibility of X4-tropic virus is 9%, while within-host switching to X4-tropism is approximately seven-fold higher than switching to R5-tropism, indicating a strong within-host and between-host tradeoff of co-receptor usage. Our methods are available in an open-source R package musseco, providing a general-purpose tool for inferring multi-level selection coefficients for epidemiological sequence data. Author SummaryThe fitness of a pathogen can be characterised at each stage of its life cycle. However, epidemiological studies measuring pathogen fitness typically estimate a single value encompassing the entire life cycle. Pathogens evolve towards greater fitness within hosts, but this can compromise the capacity to infect new hosts. Our new method quantifies relative fitness at both levels. This method accounts for rapidly changing pathogen populations and variable sampling through time making it suitable for epidemiological studies. We tested our method using simulations and demonstrate its application by analysing real HIV-1 genetic sequences from people living with HIV in Zambia. This method sheds new light on long-standing questions about the relative fitness of HIV-1 variants which utilise different host-cell co-receptors. Using our new method, we estimate that the capacity of X4 variants to infect new people is just 9% of that of R5 variants, while within-host switching from R5 to X4 usage is seven times greater than the reverse switch. This indicates a strong within-host and between-host trade-off in co-receptor usage.

evolutionary biology↗

bioRxiv2020

Emerging phylogenetic structure of the SARS-CoV-2 pandemic

Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity, and whether it reveals epidemiological insights, are fundamental questions for understanding the evolutionary trajectory of this virus. Here we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Mutations that characterize Lineage C in particular are non-synonymous and occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.

genetics↗

bioRxiv2017

Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance

AO_SCPLOWBSTRACTC_SCPLOWNon-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of {beta}-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.

evolutionary biology↗