Endemic does not mean constant as SARS-CoV-2 continues to evolve.

COVID-19 has become endemic, with dynamics that reflect the waning of immunity and re-exposure, by contrast to the epidemic phase driven by exposure in immunologically naïve populations. Endemic does not, however, mean constant. Further evolution of SARS-CoV-2, as well as changes in behavior and public health policy, continue to play a major role in the endemic load of disease and mortality. In this article, we analyze evolutionary models to explore the impact that a newly arising variant can have on the short-term and longer-term endemic load, characterizing how these impacts depend on the transmission and immunological properties of the variants. We describe how evolutionary changes in the virus will increase the endemic load most for a persistently immune-escape variant, by an intermediate amount for a more transmissible variant, and least for a transiently immune-escape variant. Balancing the tendency for evolution to favor variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions that can counter these increases in the impact of disease. We end with some open questions about the future of COVID-19 as an endemic disease.

Unraveling Adaptive Evolutionary Divergence at Microgeographic Scales.

AbstractStriking examples of local adaptation at fine geographic scales are increasingly being documented in natural populations. However, the relative contributions made by natural selection, phenotype-dependent dispersal (when individuals disperse with respect to a habitat preference), and mate preference in generating and maintaining microgeographic adaptation and divergence are not well studied. Here, we develop quantitative genetics models and individual-based simulations (IBSs) to uncover the evolutionary forces that possibly drive microgeographic divergence. We also perform Bayesian estimation of the parameters in our IBS using empirical data on habitat-specific variation in bill morphology in the island scrub-jay (Aphelocoma insularis) to apply our models to a natural system. We find that natural selection and phenotype-dependent dispersal can generate the patterns of divergence we observe in the island scrub-jay. However, mate preference for a mate with similar bill morphology, even though observed in the species, does not play a significant role in driving divergence. Our modeling approach provides insights into phenotypic evolution occurring over small spatial scales relative to dispersal ranges, suggesting that adaptive divergence at microgeographic scales may be common across a wider range of taxa than previously thought. Our quantitative genetic models help to inform future theoretical and empirical work to determine how selection, habitat preference, and mate preference contribute to local adaptation and microgeographic divergence.

Density-Dependent Selection during Range Expansion Affects Expansion Load in Life History Traits.

AbstractModels of range expansion have independently explored fitness consequences of life history trait evolution and increased rates of genetic drift-or "allele surfing"-during spatial spread, but no previous model has examined the interactions between these two processes. Here, using spatially explicit simulations, we explore an ecologically complex range expansion scenario that combines density-dependent selection with allele surfing to asses the genetic and fitness consequences of density-dependent selection on the evolution of life history traits. We demonstrate that density-dependent selection on the range edge acts differently depending on the life history trait and can either diminish or enhance allele surfing. Specifically, we show that selection at the range edge is always weaker at sites affecting competitive ability (K-selected traits) than at sites affecting birth rate (r-selected traits). We then link differences in the frequency of deleterious mutations to differences in the efficacy of selection and rate of mutation accumulation across distinct life history traits. Finally, we demonstrate that the observed fitness consequences of allele surfing depend on the population density in which expansion load is measured. Our work highlights the complex relationship between ecology and expressed genetic load, which will be important to consider when interpreting both experimental and field studies of range expansion.

Seed masting as a mechanism for escape from pathogens.

Seed masting, a reproductive strategy characterized by variable and synchronous investment in reproduction among years, has attracted much attention. Masting trees incur a cost in delayed reproduction, and thus masting requires an ecological or evolutionary explanation. The two broad causal mechanisms to explain seed masting are resource availability and economies of scale (EOS); the former assumes reproductive investment simply covaries with environment, the latter suggests an adaptive advantage. Two of the most commonly proposed EOS for masting are predator satiation and pollination efficiency. Here we suggest an additional EOS: pathogen escape. We borrow from the disease ecology literature to describe alternative models of pathogen-mediated masting. By comparing and contrasting their ecological dynamics, we show how predator satiation and pathogen escape may favour masting through similar mechanisms of mass-action interactions and temporal delays. However, pathogen- and predator-mediated dynamics may also diverge as a result of host epidemiological structure and the spatial scale of the interaction. We propose that pathogen escape should be considered among the list of putative mechanisms to help explain the many diverse observations of masting across space and phylogeny.

The maintenance of genetic diversity under host-parasite coevolution in finite, structured populations.

As a corollary to the Red Queen hypothesis, host-parasite coevolution has been hypothesized to maintain genetic variation in both species. Recent theoretical work, however, suggests that reciprocal natural selection alone is insufficient to maintain variation at individual loci. As highlighted by our brief review of the theoretical literature, models of host-parasite coevolution often vary along multiple axes (e.g. inclusion of ecological feedbacks or abiotic selection mosaics), complicating a comprehensive understanding of the effects of interacting evolutionary processes on diversity. Here we develop a series of comparable models to explore the effect of interactions between spatial structures and antagonistic coevolution on genetic diversity. Using a matching alleles model in finite populations connected by migration, we find that, in contrast to panmictic populations, coevolution in a spatially structured environment can maintain genetic variation relative to neutral expectations with migration alone. These results demonstrate that geographic structure is essential for understanding the effect of coevolution on biological diversity.

Parental Population Range Expansion before Secondary Contact Promotes Heterosis.

AbstractPopulation genomic analysis of hybrid zones is instrumental to our understanding of the evolution of reproductive isolation. Many temperate hybrid zones are formed by the secondary contact between two parental populations that have undergone postglacial range expansion. Here, we show that explicitly accounting for historical parental isolation followed by range expansion prior to secondary contact is fundamental to explaining genetic and fitness patterns in these hybrid zones. Specifically, ancestral population expansion can result in allele surfing, where neutral or slightly deleterious mutations drift to high frequency at the expansion front. If these surfed deleterious alleles are recessive, they can contribute to substantial heterosis in hybrids produced at secondary contact, counteracting negative effects of Bateson-Dobzhansky-Muller incompatibilities (BDMIs) and hence weakening reproductive isolation. When BDMIs are linked to such recessive deleterious alleles, the fitness benefit of introgression at these loci can facilitate introgression at the BDMIs. The extent to which this occurs depends on the strength of selection against the linked deleterious alleles and the distribution of recombination across the chromosome. Finally, surfing of neutral loci can alter the expected pattern of population ancestry; thus, accounting for historical population expansion is necessary to develop accurate null genomic models of secondary contact hybrid zones.

Neo-sex chromosome evolution shapes sex-dependent asymmetrical introgression barrier.

Sex chromosomes play a special role in the evolution of reproductive barriers between species. Here we describe conflicting roles of nascent sex chromosomes on patterns of introgression in an experimental hybrid swarm. Drosophila nasuta and Drosophila albomicans are recently diverged, fully fertile sister species that have different sex chromosome systems. The fusion between an autosome (Muller CD) with the ancestral X and Y gave rise to neo-sex chromosomes in D. albomicans, while Muller CD remains unfused in D. nasuta. We found that a large block containing overlapping inversions on the neo-sex chromosome stood out as the strongest barrier to introgression. Intriguingly, the neo-sex chromosome introgression barrier is asymmetrical and sex-dependent. Female hybrids showed significant D. albomicans­biased introgression on Muller CD (neo-X excess), while males showed heterosis with excessive (neo-X, D. nasuta Muller CD) genotypes. We used a population genetic model to dissect the interplay of sex chromosome drive, heterospecific pairing incompatibility between the neo-sex chromosomes and unfused Muller CD, neo-Y disadvantage, and neo-X advantage in generating the observed sex chromosome genotypes in females and males. We show that moderate neo-Y disadvantage and D. albomicans specific meiotic drive are required to observe female-specific D. albomicans­biased introgression in this system, together with pairing incompatibility and neo-X advantage. In conclusion, this hybrid swarm between a young species pair sheds light onto the multifaceted roles of neo-sex chromosomes in a sex-dependent asymmetrical introgression barrier at a species boundary.

The impact of sampling bias on viral phylogeographic reconstruction.

Genomic epidemiology plays an ever-increasing role in our understanding of and response to the spread of infectious pathogens. Phylogeography, the reconstruction of the historical location and movement of pathogens from the evolutionary relationships among sampled pathogen sequences, can inform policy decisions related to viral movement among jurisdictions. However, phylogeographic reconstruction is impacted by the fact that the sampling and virus sequencing policies differ among jurisdictions, and these differences can cause bias in phylogeographic reconstructions. Here we assess the potential impacts of geographic-based sampling bias on estimated viral locations in the past, and on whether key viral movements can be detected. We quantify the effect of bias using simulated phylogenies with known geographic histories, and determine the impact of the biased sampling and of the underlying migration rate on the accuracy of estimated past viral locations. We find that overall, the accuracy of phylogeographic reconstruction is high, particularly when the migration rate is low. However, results depend on sampling, and sampling bias can have a large impact on the numbers and nature of estimated migration events. We apply these insights to the geographic spread of Ebolavirus in the 2014-2016 West Africa epidemic. This work highlights how sampling policy can both impact geographic inference and be optimized to best ensure the accuracy of specific features of geographic spread.

Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.

Birth-death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth-death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth-death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth-death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth-death processes; epidemiology; macroevolution; phylogenetics; statistical inference.].

Fundamental Identifiability Limits in Molecular Epidemiology.

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.

Feedback between coevolution and epidemiology can help or hinder the maintenance of genetic variation in host-parasite models.

Antagonistic coevolution has long been suggested to help maintain host genetic variation. Although ecological and epidemiological feedbacks are known to have important consequences on coevolutionary allele-frequency dynamics, their effects on the maintenance of genetic variation remains poorly understood. Here, we extend previous work on the maintenance of genetic variation in a classic matching alleles coevolutionary model by exploring the effects of ecological and epidemiological feedbacks, where both allele frequencies and population sizes are allowed to vary over time. We find that coevolution rarely maintains more host genetic variation than expected under neutral genetic drift alone. When and if coevolution maintains or depletes genetic variation relative to neutral drift is determined, predominantly, by two factors: the deterministic stability of the Red Queen allele-frequency cycles and the chance of allele fixation in the pathogen, as this results in directional selection and depletion of genetic variation in the host. Compared to purely coevolutionary models with constant host and pathogen population sizes, ecological and epidemiological feedbacks stabilize Red Queen cycles deterministically, but population fluctuations in the pathogen increase the rate of allele fixation in the pathogen, especially in epidemiological models. Our results illustrate the importance of considering the ecological and epidemiological context in which coevolution occurs when examining the impact of Red Queen cycles on genetic variation.

Coevolution fails to maintain genetic variation in a host-parasite model with constant finite population size.

Coevolutionary negative frequency-dependent selection has been hypothesized to maintain genetic variation in host and parasites. Despite the extensive literature pertaining to host-parasite coevolution, the temporal dynamics of genetic variation have not been examined in a matching-alleles model (MAM) with a finite population size relative to the expectation under neutral genetic drift alone. The dynamics of the MA coevolution in an infinite population, in fact, suggests that genetic variation in these coevolving populations behaves neutrally. By comparing host heterozygosity to the expectation in a single-species model of neutral genetic drift we find that while this is also largely true in finite populations two additional phenomena arise. First, reciprocal natural selection acting on stochastic perturbations in host and pathogen allele frequencies results in a slight increase or decrease in genetic variation depending on the parameter conditions. Second, following the fixation of an allele in the parasite, selection in the MAM becomes directional, which then rapidly erodes genetic variation in the host. Hence, rather than maintain it, we find that, on average, matching-alleles coevolution depletes genetic variation.

Joint coevolutionary-epidemiological models dampen Red Queen cycles and alter conditions for epidemics.

Host-parasite interactions in the form of infectious diseases are a topic of interest in both evolutionary biology and public health. Both fields have relied on mathematical models to predict and understand the dynamics and consequences of these interactions. Yet few models explicitly incorporate both epidemiological and coevolutionary dynamics, allowing for genetic variation in both hosts and parasites. By comparing a matching-alleles model of coevolution, a susceptible-infected-recovered-susceptible compartmental model from epidemiology, and a combined coevolutionary-epidemiology model we assess the effect of the coevolutionary feedback on the epidemiological dynamics and vice versa. We find that Red-Queen cycles are not robust in an epidemiological framework and that coevolutionary interactions can alter the conditions under which epidemic cycles arise. Incorporating both explicit epidemiology and genetic diversity may have important implications for the maintenance of sexual reproduction as well as disease management.

Keeping Pace with the Red Queen: Identifying the Genetic Basis of Susceptibility to Infectious Disease.

Genome-wide association studies are widely used to identify "disease genes" conferring resistance/susceptibility to infectious diseases. Using a combination of mathematical models and simulations, we demonstrate that genetic interactions between hosts and parasites [genotype-by-genotype (G × G) interactions] can drastically affect the results of these association scans and hamper our ability to detect genetic variation in susceptibility. When hosts and parasites coevolve, these G × G interactions often make genome-wide association studies unrepeatable over time or across host populations. Reanalyzing previously published data on Daphnia magna susceptibility to infection by Pasteuria ramosa, we identify genomic regions consistent with G × G interactions. We conclude by outlining possible avenues for designing more powerful and more repeatable association studies.

Trait dimensionality explains widespread variation in local adaptation.

All species are locked in a continual struggle to adapt to local ecological conditions. In cases where species fail to locally adapt, they face reduced population growth rates, or even local extinction. Traditional explanations for limited local adaptation focus on maladaptive gene flow or homogeneous environmental conditions. These classical explanations have, however, failed to explain variation in the magnitude of local adaptation observed across taxa. Here we show that variable levels of local adaptation are better explained by trait dimensionality. First, we develop and analyse mathematical models that predict levels of local adaptation will increase with the number of traits experiencing spatially variable selection. Next, we test this prediction by estimating the relationship between dimensionality and local adaptation using data from 35 published reciprocal transplant studies. This analysis reveals a strong correlation between dimensionality and degree of local adaptation, and thus provides empirical support for the predictions of our model.