Biology subjects

Rasmussen, D. A.

Publications and source records attributed to Rasmussen, D. A..

15 recordsLinked to original sources

bioRxiv2026

Scalable phylodynamic inference of mutational and epistatic fitness effects with multi-feature birth-death models

Fitness is a complex phenotype determined by many different genomic features, their interactions with each other and the environment. For rapidly evolving microbes like viruses and bacteria, phylodynamic multi-feature birth-death (MFBD) models provide a way to infer the fitness of lineages with different genotypes from reconstructed phylogenies. However, existing phylodynamic methods assume very simple additive or multiplicative genotype-to-fitness (G-to-F) mappings that typically ignore epistatic interactions among features. We developed a gradient-based Bayesian MFBD framework for estimating the fitness effects of multiple features while allowing for both additive and epistatic G-to-F maps. The framework uses Hamiltonian Monte Carlo for posterior inference and incorporates shrinkage priors to regularize weakly supported fitness effects without cross-validation. In simulations, Bayesian inference performs well at recovering additive fitness effects, with accuracy and precision improving consistently with increasing tree size. While epistatic effects are more difficult to estimate because they depend on the co-occurrence of mutations, shrinkage improves estimation when interaction effects are weak or sparsely supported. Simulations with misspecified additive models show that ignoring moderate or strong epistasis can bias estimates of individual mutational effects. We applied the framework to a time-calibrated \textit{Escherichia coli} ST131 phylogeny with 1763 samples and estimated fitness effects for several mutations in the quinolone resistance-determining region (QRDR). These results show that Bayesian phylodynamic inference can be used to learn the genomic determinants of fitness while quantifying uncertainty in mutational and epistatic fitness effects.

evolutionary biology↗

bioRxiv2026

Molecular and ecological determinants of effective reassortment in orthohantaviruses

A central unsolved problem in RNA virus evolution is understanding why some viral reassortants establish and persist while others do not. To answer this question, we reconstructed reassortant histories across 553 viral genomes from seven orthohantavirus species between 1983 and 2024 using phylogenetic reconciliation and molecular dating. We found that the frequency of retained reassortants varied among orthohantaviruses. For example, reassortment ranged from absent in Andes virus to frequent in Dobrava-Belgrade, Sin Nombre, Seoul, Puumala, and Tula viruses, showing that effective reassortment is not a genus-wide constant. Our Bayesian hierarchical models identified local host overlap as the strongest ecological factor associated with viral reassortment, while cross-segment linkage and terminal RNA structure serve as a molecular filter. We found that the probability of reassortment establishment is highest when ecological opportunity is paired with molecular permissiveness, with their interaction term inferred as the strongest signal in our establishment models (posterior probability = 0.97). These results suggest that reassortment in orthohantaviruses is a sequentially filtered evolutionary process in which divergent lineages must first meet in a host through ecological overlap, exchange segments that are molecularly compatible, and do so within a lineage background permissive to establishment in the host population.

evolutionary biology↗

bioRxiv2025

Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes

Inferences from population genomic data provide valuable insights into the demographic history of a population. Likewise, in genomic epidemiology, pathogen genomic data provide key insights into epidemic dynamics and potential sources of transmission. Yet predicting what information will be gained from genomic data about variables of interest and how different sampling strategies will impact the quality of downstream inferences remains challenging. As a result, population genomics largely lacks theory to guide decisions on how best to sample individuals for genomic sequencing. By adopting a sequential decision making framework, we show how Markov decision processes (MDPs) can be applied to jointly model a populations dynamics along with the sampling process. Critically, these MDPs allow us to compute the expected long-term value of sampling in terms of information gained about estimated variables. This in turn allows us to very efficiently explore and identify optimal sampling strategies. To illustrate our framework, we develop MDPs for three common demographic and epidemiological inference problems: estimating population growth rates, minimizing the transmission distance between sampled individuals and estimating migration rates between subpopulations. In each case, the MDP allows us to identify optimal sampling strategies that maximize the information gained from genomic data while minimizing costs.

genomics↗

bioRxiv2024

Quantifying the genomic determinants of fitness in E. coli ST131 using phylodynamics

Antimicrobial resistant pathogens such as Escherichia coli sequence type 131 (ST131) pose a serious threat to public health globally. In the United States, ST131 acquired multiple antimicrobial resistance (AMR) genes and rapidly grew to its current high prevalence in healthcare settings. Notably, this coincided with the introduction and widespread use of antibiotics such as fluoroquinolones, suggesting AMR as the major driver of ST131s expansion. Yet, within ST131, there remains considerable diversity between strains in resistance profiles and their repertoires of virulence factors, stress factors, plasmids, and other accessory elements. Understanding which genomic features contribute to ST131s competitive advantage and their relative effects on population-level fitness therefore poses a considerable challenge. Here we use phylodynamic birth-death models to estimate the relative fitness of different ST131 lineages from bacterial phylogenies. By extending these phylodynamic methods to allow multiple genomic features to shape bacterial fitness, we further quantify the relative contribution of individual AMR genes to ST131s fitness. Our analysis indicates that while many genomic elements, including various AMR genes, virulence factors, and plasmids, have all contributed substantially to ST131s rapid growth, major increases in ST131s fitness are largely attributable to mutations in gyrase A that confer resistance to fluoroquinolones. Author summaryST131 is a pandemic lineage of E. coli that has spread globally and is now responsible for a large percentage of blood and urinary tract infections that cannot be treated with many common antibiotics. While antibiotic resistance has undoubtedly given ST131 a competitive edge, the relative importance of resistance compared with other factors shaping a pathogens growth or transmission potential (i.e. fitness) is often difficult to measure in natural settings. Here, we present a method that allows us to look at the entire spectrum of factors determining a pathogens fitness and estimate the individual contribution of each component to pathogens overall fitness. Our results suggest that resistance to fluoroquinolones, a widely used class of antibiotics, provides ST131 with a disproportionately large fitness advantage relative to many other factors with more moderate fitness effects. Understanding what determines the fitness of ST131 therefore provides insights that can be used to curb the spread of resistance and monitor for emerging lineages with high pandemic potential due to shared fitness enhancing attributes.

evolutionary biology↗

bioRxiv2023

Quantifying the strength of viral fitness tradeoffs between hosts: A meta-analysis of pleiotropic fitness effects

The range of hosts a given virus can infect is widely presumed to be limited by fitness tradeoffs between alternative hosts. These fitness tradeoffs may arise naturally due to antagonistic pleiotropy if mutations that increase fitness in one host tend to decrease fitness in alternate hosts. Yet there is also growing recognition that positive pleiotropy may be more common than previously appreciated. With positive pleiotropy, mutations have concordant fitness effects such that a beneficial mutation can simultaneously increase fitness in different hosts, providing a genetic mechanism by which selection can overcome fitness tradeoffs. How readily evolution can overcome fitness tradeoffs therefore depends on the overall distribution of mutational fitness effects between hosts, including the relative frequency of antagonistic versus positive pleiotropy. We therefore conducted a systematic meta-analysis of the pleiotropic fitness effects of viral mutations reported in different hosts. Our analysis indicates that while both antagonistic and positive pleiotropy are common, fitness effects are overall positively correlated between hosts and unconditionally beneficial mutations are not uncommon. Moreover, the relative frequency of antagonistic versus positive pleiotropy may simply reflect the underlying frequency of beneficial and deleterious mutations in individual hosts. Given a mutation is beneficial in one host, the probability that it is deleterious in another host is roughly equal to the probability that any mutation is deleterious, suggesting there is no natural tendency towards antagonistic pleiotropy. The widespread prevalence of positive pleiotropy suggests that many fitness tradeoffs may be readily overcome by evolution given the right selection pressures. Lay summary Evolutionary theory suggests that fitness tradeoffs between alternative environments constrain the potential for organisms to simultaneously adapt to multiple environments. Likewise, fitness tradeoffs between alternative hosts are widely believed to limit the ability of viruses to adapt to multiple hosts and thereby expand their host range. How strongly viruses are constrained by such tradeoffs will largely depend on the fitness effects of new mutations. Fitness tradeoffs may inevitably constrain viral evolution if mutations that increase fitness in one host tend to decrease fitness in alternative hosts. However, mutations can sometimes increase fitness in multiple hosts, allowing viruses to adapt to new hosts without paying fitness costs. Geneticists refer to these two scenarios as antagonistic and positive pleiotropy depending on whether mutations have opposite or concordant fitness effects. Because the relative frequency of antagonistic versus positive pleiotropy is centrally important to viral evolution, we conducted a systematic meta-analysis of the fitness effects of mutations reported in different hosts. Our analysis reveals that cases of positive pleiotropy where mutations have beneficial effects in more than one host may be sufficiently common for evolution to resolve many apparent fitness tradeoffs between hosts.

evolutionary biology↗

bioRxiv2023

Exploring the accuracy and limits of algorithms for localizing recombination breakpoints

Phylogenetic methods are widely used to reconstruct the evolutionary relationships among species and individuals. However, recombination can obscure ancestral relationships as individuals may inherit different regions of their genome from different ancestors. It is therefore often necessary to detect recombination events, locate recombination breakpoints and select recombination-free alignments prior to reconstructing phylogenetic trees. While many earlier studies examined the power of different methods to detect recombination, very few have examined the ability of these methods to accurately locate recombination breakpoints. In this study, we simulated genome sequences based on ancestral recombination graphs and explored the accuracy of three popular recombination detection methods: MaxChi, 3SEQ and GARD. The accuracy of inferred breakpoint locations was evaluated along with the key factors contributing to variation in accuracy across data sets. While many different genomic features contribute to the variation in performance across methods, the number of informative sites consistent with the pattern of inheritance between parent and recombinant child sequences always has the greatest contribution to accuracy. While partitioning sequence alignments based on identified recombination breakpoints can greatly decrease phylogenetic error, the quality of phylogenetic reconstructions depends very little on how breakpoints are chosen to partition the alignment. Our work sheds light on how different features of recombinant genomes affect the performance of recombination detection methods and suggests best practices for reconstructing phylogenies based on recombination-free alignments.

bioinformatics↗

bioRxiv2022

Plasmodium falciparum genetic diversity in coincident human and mosquito hosts

Population genetic diversity of P. falciparum antigenic loci is high despite large bottlenecks in population size during the parasite life cycle. The extent of this diversity in human blood-stage infections, following expansion from a small number of liver-stage schizonts, has been well described. However, little is known about parasite genetic diversity in the vector, where a similar bottleneck and expansion occurs following parasite mating and where parasite genotypes from several different human infections may accumulate. We assessed parasite genetic diversity within human and mosquito P. falciparum infections collected from the same households during a 14-month longitudinal cohort study using amplicon deep sequencing of two antigenic gene fragments (ama1 and csp). To a prior set of infected humans (n=1175/2813; 86.2% sequencing success) and mosquito abdomens (n=199/1448; 95.5% sequencing success), we added sequences from infected mosquito heads (n=134/1448; 98.5% sequencing success). Across all sample types we observed 456 ama1 and 289 csp unique haplotypes. While both hosts contained many rare haplotypes, population genetic metrics indicated that the overall and sample-level parasite populations were more diverse in mosquitoes than in humans, and infections were more likely to harbor a dominant haplotype in humans than in mosquitoes (based on relative read abundance). Finally, within a given mosquito there was little overlap in genetic composition of abdomen and head infections, suggesting that infections may be cleared from the abdomen during a mosquitos lifespan. Taken together, our observations provide evidence for the role of the mosquito vector in maintaining sequence diversity of malaria parasite populations. Significance statementConcurrent infections with multiple strains of Plasmodium falciparum, the leading causative agent of death due to malaria, are common in highly endemic regions. During transitions within and between the parasites mosquito and human hosts, population bottlenecks occur, and distinct parasite strains may have differential fitness in the various environments encountered. These bottlenecks and fitness differences may lead to differences in strain prevalence and diversity between hosts. We investigated differences in genetic diversity between P. falciparum parasites in human and mosquito hosts and found that, compared to human parasite populations and infections, mosquito populations and infections were more diverse. This suggests that the mosquito vector may play a role in in maintaining sequence diversity in malaria parasite populations.

microbiology↗

bioRxiv2022

Espalier: Efficient tree reconciliation and ARG reconstruction using maximum agreement forests

In the presence of recombination individuals may inherit different regions of their genome from different ancestors, resulting in a mosaic of phylogenetic histories across their genome. Ancestral recombination graphs (ARGs) can capture how phylogenetic relationships vary across the genome due to recombination, but reconstructing ARGs from genomic sequence data is notoriously difficult. Here we present a method for reconciling discordant phylogenetic trees and reconstructing ARGs using maximum agreement forests (MAFs). Given two discordant trees, a MAF identifies a set of topologically concordant subtrees present in both trees. We show how discordant trees can be reconciled through their MAF in a way that retains discordances strongly supported by sequence data while eliminating conflicts likely attributable to phylogenetic noise. We further show how MAFs and our reconciliation approach can be combined to select a path of local trees across the genome that maximizes the likelihood of the genomic sequence data, minimizes discordance between neighboring local trees, and identifies the recombination events necessary to explain remaining discordances to obtain a fully connected ARG. While heuristic, our ARG reconstruction approach is often as accurate as more exact methods while being much more computationally efficient. Moreover, important demographic parameters such as recombination rates can be accurately estimated from reconstructed ARGs. Finally, we apply our approach to plant infecting RNA viruses in the genus Potyvirus to demonstrate how true recombination events can be disentangled from phylogenetic noise using our ARG reconstruction methods.

evolutionary biology↗

bioRxiv2020

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behaviour influence between-host transmission potential. These challenges have been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth-death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.

evolutionary biology↗

bioRxiv2020

Evolutionary dynamics of Tomato spotted wilt virus within and between alternate plant hosts and thrips

Tomato spotted wilt virus (TSWV) is a generalist pathogen with one of the broadest known host ranges among RNA viruses. To understand how TSWV adapts to different hosts, we experimentally passaged viral populations between two alternate hosts, Emilia sochifolia and Datura stramonium, and an obligate vector in which it also replicates, western flower thrips (Frankliniella occidentalis). Deep sequencing viral populations at multiple time points allowed us to track the evolutionary dynamics of viral populations within and between hosts. High levels of viral genetic diversity were maintained in both plants and thrips between transmission events. Rapid fluctuations in the frequency of amino acid variants indicated strong host-specific selection pressures on proteins involved in viral movement (NSm) and replication (RdRp). While several genetic variants showed opposing fitness effects in different hosts, fitness effects were generally positively correlated between hosts indicating that positive rather than antagonistic pleiotropy is pervasive. These results suggest that high levels of genetic diversity together with the positive pleiotropic effects of mutations have allowed TSWV to rapidly adapt to new hosts and expand its host range.

evolutionary biology↗

bioRxiv2019

Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models

Beneficial and deleterious mutations cause fitness to vary among individuals in a population, which natural selection can then act upon to drive adaptive evolution. Non-neutral mutations can likewise cause fitness to vary among lineages in a phylogeny and thereby shape its branching structure. While standard phylogenetic models do not allow mutations to feedback and shape tree topology, birth-death models can account for this feedback by letting the fitness of lineages vary depending on their type. To date though, these multi-type birth-death models have only been applied to cases where a lineages fitness is determined by a single evolving character state and have not been extended to model sequence evolution across multiple sites. We introduce an extension of the multi-type birth-death model, the marginal fitness birth-death model, that tracks sequence evolution at multiple sites and how the fitness of a lineage depends on its genotype across all sites. This approach remains computationally tractable even for many evolving sites because it tracks the genotype of a lineage probabilistically in an approximate manner, and then marginalizes over all possible genotypes to determine the expected fitness of a lineage. Although approximate, we show that we can accurately estimate the fitness of a lineage and even site-specific mutational fitness effects from the branching pattern of a phylogeny. To demonstrate the power of this approach, we apply it to estimate the host population level fitness effects of mutations previously identified to increase the infectivity of Ebola virus in human cell lines during the 2013-16 epidemic in West Africa.

evolutionary biology↗

bioRxiv2017

MASCOT: Parameter and state inference under the marginal structured coalescent approximation

MotivationThe structured coalescent is widely applied to study demography within and migration between sub-populations from genetic sequence data. Current methods are either exact but too computationally inefficient to analyse large datasets with many states, or make strong approximations leading to severe biases in inference. We recently introduced an approximation based on weaker assumptions to the structured coalescent enabling the analysis of larger datasets with many different states. We showed that our approximation provides unbiased migration rate and population size estimates across a wide parameter range.\n\nResultsWe here extend this approach by providing a new algorithm to calculate the probability of the state of internal nodes that includes the information from the full phylogenetic tree. We show that this algorithm is able to increase the probability attributed to the true node states. Furthermore we use improved integration techniques, such that our method is now able to analyse larger datasets, including a H3N2 dataset with 433 sequences sampled from 5 different locations.\n\nAvailabilityThe here presented methods are combined into the BEAST2 package MASCOT, the Marginal Approximation of the Structured COalescenT. This package can be downloaded via the BEAUti package manager. The source code is available at https://github.com/nicfel/Mascot.git.

evolutionary biology↗

bioRxiv2017

Directly Estimating Epidemic Curves From Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

epidemiology↗

bioRxiv2016

The Structured Coalescent and its Approximations

Phylogenetics can be used to elucidate the movement of genes between populations of organisms, using phylogeographic methods. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or the gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for datasets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex datasets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. While this solution is computationally unfeasible for large datasets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.

evolutionary biology↗

bioRxiv2016

Phylodynamics on local sexual contact networks

Phylodynamic models are widely used in infectious disease epidemiology to infer the dynamics and structure of pathogen populations. However, these models generally assume that individual hosts contact one another at random, ignoring the fact that many pathogens spread through highly structured contact networks. We present a new framework for phylodynamics on local contact networks based on pairwise epidemiological models that track the status of pairs of nodes in the network rather than just individuals. Shifting our focus from individuals to pairs leads naturally to coalescent models that describe how lineages move through networks and the rate at which lineages coalesce. These pairwise coalescent models not only consider how network structure directly shapes pathogen phylogenies, but also how the relationship between phylogenies and contact networks changes depending on epidemic dynamics and the fraction of infected hosts sampled. By considering pathogen phylogenies in a probabilistic framework, these coalescent models can also be used to estimate the statistical properties of contact networks directly from phylogenies using likelihood-based inference. We use this framework to explore how much information phylogenies retain about the underlying structure of contact networks and to infer the structure of a sexual contact network underlying a large HIV-1 sub-epidemic in Switzerland.

epidemiology↗