Robust Estimation of Recent Effective Population Size from Number of Independent Origins in Soft Sweeps

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

The population genetics of partial diapause, with applications to the aestivating malaria mosquito Anopheles coluzzii

Diapause, a form of dormancy to delay or halt the reproductive development during unfavourable seasons, has evolved in many insect species. One example is aestivation, an adult-stage diapause enhancing malaria vectors' survival during the dry season (DS) and their re-establishment in the next rainy season (RS). This work develops a novel genetic approach to estimate the number or proportion of individuals undergoing diapause, as well as the breeding sizes of the two seasons, using signals from temporal allele frequency dynamics. Our modelling shows the magnitude of drift is dampened at early RS when previously aestivating individuals reappear. Aestivation severely biases the temporal effective population size (N e $$ {N}_e $$ ), leading to overestimation of the DS breeding size by1 / 1 - α 2 $$ 1/{\left(1-\alpha \right)}^2 $$ across 1 year, whereα $$ \alpha $$ is the aestivating proportion. We find sampling breeding individuals in three consecutive seasons starting from an RS is sufficient for parameter estimation, and perform extensive simulations to verify our derivations. This method does not require sampling individuals in the dormant state, the biggest challenge in most studies. We illustrate the method by applying it to a published data set for Anopheles coluzzii mosquitoes from Thierola, Mali. Our method and the expected evolutionary implications are applicable to any species in which a fraction of the population diapauses for more than one generation, and are difficult or impossible to sample during that stage.

A theory of resistance to multiplexed gene drive demonstrates the significant role of weakly deleterious natural genetic variation

Evolution of resistance is a major barrier to successful deployment of gene-drive systems to suppress natural populations, which could greatly reduce the burden of many vector-borne diseases. Multiplexed guide RNAs (gRNAs) that require resistance mutations in all target cut sites are a promising antiresistance strategy since, in principle, resistance would only arise in unrealistically large populations. Using stochastic simulations that accurately model evolution at very large population sizes, we explore the probability of resistance due to three important mechanisms: 1) nonhomologous end-joining mutations, 2) single-nucleotide mutants arising de novo, or 3) single-nucleotide polymorphisms preexisting as standing variation. Our results explore the relative importance of these mechanisms and highlight a complexity of the mutation-selection-drift balance between haplotypes with complete resistance and those with an incomplete number of resistant alleles. We find that this leads to a phenomenon where weakly deleterious naturally occurring variants greatly amplify the probability of multisite resistance compared to de novo mutation. This key result provides design criterion for antiresistance multiplexed systems, which, in general, will need a larger number of gRNAs compared to de novo expectations. This theory may have wider application to the evolution of resistance or evolutionary rescue when multiple changes are required before selection can act.

Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.

Contemporary Ne estimation using temporally spaced data with linked loci

The contemporary effective population size Ne is important in many disciplines including population genetics, conservation science and pest management. One of the most popular methods of estimating this quantity uses temporal changes in allele frequency due to genetic drift. A significant assumption of the existing methods is the independence among loci while constructing confidence intervals (CI), which restricts the types of species or genetic data applicable to the methods. Although genetic linkage does not bias point Ne estimates, applying these methods to linked loci can yield unreliable CI that are far too narrow. We extend the current methods to enable the use of many linked loci to produce precise contemporary Ne estimates, while preserving the targeted CI width and coverage. This is achieved by deriving the covariance of changes in allele frequency at linked loci in the face of recombination and sampling errors, such that the extra sampling variance due to between‐locus correlation is properly handled. Extensive simulations are used to verify the new method. We apply the method to two temporally spaced genomic data sets of Anopheles mosquitoes collected from a cluster of villages in Burkina Faso between 2012 and 2014. With over 33,000 linked loci considered, the Ne estimate for Anopheles coluzzii is 9,242 (95% CI 5,702–24,282), and for Anopheles gambiae it is 4,826 (95% CI 3,602–7,353).

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.

A large effective population size for established within-host influenza virus infection

Strains of the influenza virus form coherent global populations, yet exist at the level of single infections in individual hosts. The relationship between these scales is a critical topic for understanding viral evolution. Here we investigate the within-host relationship between selection and the stochastic effects of genetic drift, estimating an effective population size of infection Ne for influenza infection. Examining whole-genome sequence data describing a chronic case of influenza B in a severely immunocompromised child we infer an Ne of 2.5 × 107 (95% confidence range 1.0 × 107 to 9.0 × 107) suggesting that genetic drift is of minimal importance during an established influenza infection. Our result, supported by data from influenza A infection, suggests that positive selection during within-host infection is primarily limited by the typically short period of infection. Atypically long infections may have a disproportionate influence upon global patterns of viral evolution.

The genome of pest Rhynchophorus ferrugineus reveals gene families important at the plant-beetle interface

The red palm weevil, Rhynchophorus ferrugineus, infests palm plantations, leading to large financial losses and soil erosion. Pest-host interactions are poorly understood in R. ferrugineus, but the analysis of genetic diversity and pest origins will help advance efforts to eradicate this pest. We sequenced the genome of R. ferrugineus using a combination of paired-end Illumina sequencing (150 bp), Oxford Nanopore long reads, 10X Genomics and synteny analysis to produce an assembly with a scaffold N50 of ~60 Mb. Structural variations showed duplication of detoxifying and insecticide resistance genes (e.g., glutathione S-transferase, P450, Rdl). Furthermore, the evolution of gene families identified those under positive selection including one glycosyl hydrolase (GH16) gene family, which appears to result from horizontal gene transfer. This genome will be a valuable resource to understand insect evolution and behavior and to allow the genetic modification of key genes that will help control this pest.

Deep-Time Demographic Inference Suggests Ecological Release as Driver of Neoavian Adaptive Radiation

Assessing the applicability of theory to major adaptive radiations in deep time represents an extremely difficult problem in evolutionary biology. Neoaves, which includes 95% of living birds, is believed to have undergone a period of rapid diversification roughly coincident with the Cretaceous–Paleogene (K-Pg) boundary. We investigate whether basal neoavian lineages experienced an ecological release in response to ecological opportunity, as evidenced by density compensation. We estimated effective population sizes (Ne) of basal neoavian lineages by combining coalescent branch lengths (CBLs) and the numbers of generations between successive divergences. We used a modified version of Accurate Species TRee Algorithm (ASTRAL) to estimate CBLs directly from insertion–deletion (indel) data, as well as from gene trees using DNA sequence and/or indel data. We found that some divergences near the K-Pg boundary involved unexpectedly high gene tree discordance relative to the estimated number of generations between speciation events. The simplest explanation for this result is an increase in Ne, despite the caveats discussed herein. It appears that at least some early neoavian lineages, similar to the ancestor of the clade comprising doves, mesites, and sandgrouse, experienced ecological release near the time of the K-Pg mass extinction.