Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.
Understanding viral transmission is a key task for viral epidemiology. The extent to which a virus is able to transmit between hosts determines whether it is likely to cause sporadic, local outbreaks, or spread to cause a global pandemic [1,2]. In a transmission event, the transmission bottleneck, which specifies the number of viral particles founding a new infection, influences the amount of genetic diversity that is retained upon transmission, with important consequences for the evolutionary dynamics of the virus [3,4]. Recent studies have used genome sequencing approaches to study transmission bottlenecks in influenza populations. In small animal studies, the use of neutral genetic markers has shown that the transmission bottleneck is dependent upon the route of transmission, whether by contact or aerosol transmission [5,6]. In natural human influenza populations, where modification of the virus is not possible, population genetic methods have been used to analyse bottleneck sizes. Analyses of transmission have employed different approaches, exploiting the observation or non-observation of variant alleles [7] or using changes in allele frequencies to characterise the bottleneck under a model of genetic drift [8–11]. A recent publication improved this latter model, incorporating the uncertainty imposed upon allele frequencies by the process of within-host growth [12]. Two studies of within-household influenza transmission have provided strikingly different outcomes in the number of viruses involved in transmission, with estimates of 1-2 [13] and 100-200 [14] respectively, albeit that the veracity of the data used to generate the latter result has recently been challenged [15]. Another focus of research has been the role of selection during a transmission event; this is important in the context of the potential for new influenza strains to become transmissible between mammalian hosts [16,17]. Studies examining transmissibility have assessed the potential for different strains of influenza to achieve droplet transmission between ferrets under laboratory conditions [18–21]; ferrets provide a useful, if imperfect, model for transmission between humans [22,23]. The application of bioinformatic techniques to data from these experiments has identified ‘selective bottlenecks’ in the experimental evolution of these viruses [24,25], whereby some genetic variants appear to be more transmissible than others. In these studies, selection has been considered in terms of the population diversity statistic π; changes in πN/πS, the ratio between non-synonymous and synonymous diversity, have been used to evaluate patterns of selection across different viral segments. We here note the need for a greater clarity of thinking in the analysis of viral transmission events. For example, analysis of genetic variants in viral populations shows that synonymous and non-synonymous mutations both have fitness consequences for viruses [26,27]; the use of synonymous variants as a neutral reference set may not hold. More fundamentally, in an event where the effective population size is small, the influences of selection and genetic drift may be of similar magnitude [28]. However selection is assessed, this implies a need to separate stochastic changes in a population from selection, especially where a transmission bottleneck may include only a small number of viruses [5,13,29]. It is possible for the attribution of a change in diversity to the action of selection, or the attribution of allele frequency change to genetic drift to be flawed. Given the increasing availability of sequence data, more sophisticated tools for the analysis of viral transmission are required. Here we note three challenges in the analysis of data from viral transmission events. Firstly, selection can produce changes in a population equivalent to those arising through a neutral population bottleneck [30] (Fig 1A), making it necessary to distinguish between the two scenarios. A broad literature has considered the simultaneous inference of the magnitude of selection acting upon a variant along with an effective population size [31–36]. However, such approaches rely on the observation of an allele frequency at more than two time points so as to distinguish a deterministic model of selection (with an implied infinite effective population size) from a combined model of selection with genetic drift; such approaches cannot be directly applied to the analysis of viral transmission. Secondly, inferences of transmission events need to account for the haplotype structure of viral populations, whereby whole viruses, rather than sets of independent alleles, are transmitted (Fig 1B). The low rate of homologous recombination in segments of the influenza virus [37,38] implies that viral evolution proceeds at the haplotype level [39]; competition occurs between collections of linked alleles, or segments, rather than the individual alleles themselves. Under such circumstances, fitter variants do not always increase in frequency within a population [40–42]. Calculations of genetic drift, which are often derived from the evolution of independent variants [43], need to be adjusted to account for this more complex dynamics. Thirdly, noise in the measurement of a population may influence the inferred size of a transmission bottleneck (Fig 1C). A broad range of studies have examined the effect of noise in variant calling and genome sequence analysis [44–51]; more recently formulae have been proposed to measure the precision with which allele frequencies can be defined given samples from a population [52–54]. Where small changes in allele frequencies are used to assess a population bottleneck, it is important to separate the effects of noise in the measurement of populations from genuine changes in a population. We here describe a novel method for the inference of population bottlenecks in influenza which addresses the above issues. Our approach correctly evaluates changes in a population even where the data describing that change are affected by noise. It explicitly accounts for the haplotype structure of a population, utilising the data present within short sequence reads. Further, where these factors can be discriminated, our method distinguishes between the influences on the population of selection and the transmission bottleneck. Studies of viral evolution have highlighted the potential for payoffs between within-host viral growth and transmissibility [55]; given sufficient data we can evaluate how selection operates upon each of these phenotypes. Our model extends previous population genetic work on bottleneck inference to provide a more generalised model for the analysis of data spanning viral transmission events. Whilst the work presented here is modelled upon influenza viruses, the methods may be readily adapted to other viral species given minor modifications.
We applied our approach to an influenza transmission dataset obtained by Watanabe et al. [61] and subsequently analysed by Moncla et al. [25]. This dataset provides high-resolution, whole-genome sequence data describing both the within-host evolution, and airborne transmission, of a 1918-like influenza virus, that became transmissible upon introduction of three key mutations, PB2 E627K, HA E190D and G225D. This three-mutant strain was denoted ‘HA190D225D’ and successfully transmitted in one of three ferret transmission pairs. Isolation and subsequent growth in MDCK cells of viruses from the contact ferret of the successful transmission led to the generation of the ‘Mut’ strain, which transmitted in two of three instances. A previous analysis of these data using linked variants on the HA segment identified an increase in the diversity of the viral population during within-host growth, and respectively ‘loose’ and ‘stringent’ bottlenecks in the transmission of the two strains. In the transmission of the Mut strain, the fixation of sequence variants, potentially due to selection, was observed, while the observation of two out of three, rather than one out of three, successful transmissions suggested that the Mut virus may have evolved increased fitness for infection. Within and between hosts, segment-wide and localised measures of synonymous and non-synonymous sequence diversity π were used to assess the presence or absence of selection, leading to the conclusion that selection affected the system during transmission of the ‘Mut’ strain. In our study, data from serial samples from the within-host populations were used to infer a fitness landscape describing the within-host growth of the virus for each of the two experimental populations. Using a previously published approach [52] we inferred the presence of non-neutral change in the population in seven out of eight segments in the combined HA190D225D population, and in four out of eight segments in the combined Mut population. The inference of positive selection acting for multiple non-consensus viral haplotypes in the HA segment (Fig 9) explains the increase in sequence diversity previously observed in these data. Further results are shown in S9 and S10 Figs. Applying our inference framework to the data identified narrow transmission bottlenecks in each case (Fig 10). In each of our calculations a set of statistical replicate inferences was produced, corresponding to different potential reconstructions of the population qB from the sequence data (see Methods). Within the HA190D225D population, our estimated bottlenecks ranged from 3 to 6, with a median bottleneck size of 5, while for the Mut calculations, our bottlenecks ranged from 2 to 127 and 2 to 61, with medians of 6 and 2 respectively. As such, no clear evidence was found that the HA190D225D transmission involved a greater number of particles than the Mut transmissions. Given the inclusion of the inferred within-host selection SG, no evidence was found for the existence of variants making the virus more or less transmissible, with selection being inferred in only a small number of the replicate calculations (S11 Fig). Increasing the frequency cutoff at which variants were included in the calculation led to small decreases in the inferred bottleneck sizes (S12 Fig).
We have here presented an approach for jointly inferring a population bottleneck size and selection for differential transmissibility from viral sequence data describing a transmission event. While basic sampling approaches to bottleneck inference have been improved by an accounting for drift during within-host viral growth [7,12–14], our approach additionally accounts for noise in genome sequence data, exploits partial haplotype data available from short-read sequencing, and separates the influence of a finite bottleneck from that induced by selection for increased transmissibility. In multiple studies, the transmission bottleneck has been found to be narrow during natural viral spread between hosts [62]. While acknowledging previous evidence for the existence of small transition bottlenecks in viral systems, we here note that a failure to account for selection and noise in the transmission process can decrease the bottleneck that is inferred from sequence data. Our approach is suitable for the analysis of acute infectious diseases such as influenza on the basis of a small number of observed transmission events; we note that where more substantial diversity is present in a within-host viral population, or where data are available from a large number of hosts in an outbreak, phylogenetic methods of evolutionary inference become of increasing value [63–65]. Applied to the analysis of data from a recent evolutionary experiment, our approach provides a greater precision in the inference of evolutionary statistics, leading to an alternative explanation for the data observed. Where data have previously been interpreted as implying differential transmission bottlenecks between strains, our approach infers bottlenecks of similar sizes ranging from 2-6 viruses. Furthermore, where evidence has been interpreted to suggest a differing extent of transmissibility between strains, our approach attributes changes in the composition of the population to a mixture of stochastic effects and selection for increased within-host adaptation. Our result does not prove the absence of differential transmissibility among the viruses involved in this study; at the bottleneck size we inferred, selection is very hard to identify even where it does influence transmission. Rather, our claim is that under a parsimonious analysis of the data, apparent evidence for increased viral transmissibility can be explained by other evolutionary factors. Our study shows that the identification of variants conferring increased viral transmissibility is difficult when the number of transmitted viral particles is small. While improvements to our method may be achievable, this difficulty is fundamentally rooted in the nature of a transmission event; where a low number of virions are transmitted, the influence of stochastic processes becomes large, with variants fixing during transmission in a manner that cannot be distinguished from a selective sweep. The potential to infer the presence of selection increases at larger population size and given a greater number of replicate transmission events. However the amount of data required to make a statistically robust identification of a variant increasing viral transmissibility may be large. We note that, unlike more general inferences of selection from changes in viral diversity, our approach evaluates selection in terms of specific variants conveying an advantage or disadvantage for transmission. Where broad measures of diversity are calculated across segments of a genome, the background of genetic diversity across a large number of positions may be hard to separate from changes at individual positions under the action of selection. In the light of our study, we propose that the term used in some analyses of viral transmission, of a ‘selective bottleneck’ is ambiguous, failing on the one hand to distinguish changes in a population arising from selection and those occurring through stochastic change in the population, and on the other to distinguish between selection for more rapid within-host replication or for inherent viral transmissibility. While selection may act differently for these latter two phenotypes [55], their respective influences are intrinsically hard to separate when an infection is sparsely sampled. In our analysis the completeness of the collected data, covering both within-host adaptation and between-host transmission, was necessary to evaluate the cause of evolutionary change. Our study provides some insight into the potential for inferring transmissibility using small animal experiments. One approach to exploring transmissibility (in influenza virus) has been the comparison, for different viruses, of the proportion of distinct animal pairs between which transmission occurs [66]. The statistical significance achievable in these studies is limited by the number of animal pairs that can be examined [67–69]. Furthermore, the comparison between one genotype and another may be confounded by viral heterogeneity, whereby each population contains a cloud of genetic diversity [24,70]. As we have shown, data from replicate transmission events leads to an improved ability to infer selection, in particular by reducing the false positive rate of inference and by increasing the accuracy in inferred selection coefficients. We note, however, that the number of viral particles transmitted in each event is key in determining whether increased transmissibility can be identified; where a transmission bottleneck is narrow it is inherently difficult to identify selection against a background of the large changes in the population induced by stochastic effects. Where transmission bottlenecks are small, a large number of replicates might be needed to make statistically well-supported inferences of increased transmissibility. Applications of our method to simulated data could be used to gain an insight into what might be obtained from a particular experimental setup. In some situations, neutral markers or molecular barcodes may be added to a viral population [5,30]; without providing an estimate of selection, sequencing these markers before and after transmission can give a precise estimate of the population bottleneck. While our method does not require the presence of such markers, its adaptation to include marker data would likely be straightforward, including in a calculation a further probabilistic term constraining the bottleneck size. Inference of selection for transmissibility could then be conducted under this constraint; the combination of whole-genome sequence data with such information could prove powerful for the study of viral transmission. While we have here considered the transmission of influenza virus, very few steps of our approach would need to be altered for the method to be applied to another viral population. As detailed in the Methods section, it is only in accounting for genetic drift in the within-host growth of the virus that we make approximations relying on biological knowledge of the influenza virus; an alternative accounting for within-host expansion could be used. A second key assumption in the inference of selection is the existence of regions of the virus separated from each other by recombination or reassortment. This assumption would be preserved in some other viruses, as noted in observations of within-host HIV evolution [71], if not for all influenza populations [72]. Where a viral genome did not exhibit recombination, and only a single transmission event was observed, the neutral version of our method could be applied; in this context our accounting for haplotype structure and sequencing noise in transmission represents an advance over methods which ignore these factors. Viral transmission is a critical component of disease and a key factor in viral evolution. In outlining a novel framework for the interpretation of data from viral transmission events we hope to bring a greater clarity to the population genetic theory of how these events operate and a greater power in the interpretation of experimental data, so as to engender a greater understanding of this important topic of research.
We describe the viral population as a set of haplotypes, with associated frequencies, that changes in time during a transmission event. Given a number of (possibly non-consecutive) loci of interest in the viral genome, the set of haplotypes h = {hi} describes a set of sequences having specific nucleotides at these loci. Within a viral population of finite size, the number of viruses with each haplotype hi is described by the vector n = {ni}. Frequencies of each haplotype within the population are denoted by the vector q = {qi}, while observations of the population collected via sequencing are denoted by the vector x = {xi}, where xi is the number of sampled viruses with haplotype hi. The transmission event is now described according to the framework outlined in Fig 2. A population of viruses qB undergoes transmission with some bottleneck NT, creating a founder population with haplotype frequencies qF in the recipient. Selection influencing this transmission process is described by the function ST (q), which changes the frequency of haplotypes according to the relative propensity of each haplotype to transmit. For example, selection may favour the transmission of viruses containing a specific genetic variant, increasing the expected proportion of viruses with this variant in the founder population. Within the host, the viral population grows rapidly in number to create the population qA. During this growth process, genetic drift affects the population in a manner according to the effective population size NG. Observations of the system are made via genome sequencing of samples collected before and after transmission, and are denoted xB and xA respectively; the total numbers of sequence reads in each are denoted NB and NA. Given the observations xB and xA, we wish to estimate the size of the population bottleneck NT and the extent of selection for transmissibility ST. During the process of growth between qF and qA, the population may be influenced by selection for within-host growth; this acts independently of selection for transmissibility [73], and is described by the function SG (q), which changes the frequencies of haplotypes according to their relative within-host growth rates. Selection for within-host growth is challenging to separate from selection for transmissibility; we here estimate this parameter independently from the transmission event itself. Where we consider multiple replicate transmission events, we assume that each transmission has its own transmission bottleneck NT; different numbers of viruses may infect different hosts. However, we assume that selection operates consistently between hosts; a variant which makes a virus grow more efficiently in one host does the same in another. As the observations xB and xA are conditionally independent given qB, the joint probability of the system may be written as a product of individual probabilities P (x B, x A | q B, θ) = P (x B | q B) P (x A | q B, θ) (1) where θ represents the remaining variables in the system upon which only xA depends. As an approximation to this likelihood, we split the inference into two calculations, first calculating a maximum likelihood for qB given xB, then inferring the transmission event from xA given qB. Noting the potential uncertainty in the inference of qB, we introduce a variance component so that qB may be regarded as a random variable rather than a fixed quantity. The process of breaking up the inference process greatly reduces the computational time required for our approach, without considerable cost to the accuracy of the results. Splitting the likelihood in this manner, and marginalising over unknown quantities, the likelihood can be written generically as L (NT, ST|xB, xA, NG, SG) =∫P (xB|qB) P (qB) dqB︸xBcomponent×∫P (xA|qA) [ ∫P (qA|NG, SG, qF) [∫P (qF|NT, ST, qB) P (qB) dqB] dqF ]dqA︸xAcomponent (2) The first component of this likelihood, corresponding to the initial observation of the system, xB, represents a straightforward sampling of the system, drawing from a collection of viral haplotypes. Such a process can be modelled using a multinomial distribution. However, as is well known [54], next-generation sequence data are error-prone, such that less information is contained within the sample than would be contained in a multinomial sample of equivalent depth. A Dirichlet multinomial distribution may be used to capture this reduction of information [52,57], such that P (x B | q B) = Γ (N B + 1) ∏ i (x i B + 1) Γ (∑ C q i b) Γ (∑ x i B + C q i B) ∏ i Γ (x i b + C q i B) Γ (C q i B) (3) where C, which alters the variance of the distribution, characterises the extent of noise in the data. The parameter C can be estimated given independent observations of identical parameters, such as haplotype or single allele frequencies; in the application to experimental data, time-resolved variant frequencies derived from the sequence data were used for this purpose [52]. Considering the second component of the likelihood, the expression P (xA|qA) may be calculated in the same manner as in Eq 3 dependent upon the haplotype frequencies qA. The remaining parts of this component can also be described as sampling events. A sample of the population in the donor animal transmits to the recipient, generating a founder population. The founder population multiplies within the host, with offspring being sampled from the founder population qF to generate the final population qA. The xA component thus represents a compound of multiple sampling events. We will go on to describe the calculation of both components of the likelihood function. However, we first need to consider how selection is incorporated into our model. Given a mean and covariance matrix for the likelihood function, we can approximate the likelihood by the probability density function of a multivariate normal distribution. However, where the variance of this distribution is very small in one dimension, as can occur under an inference of very strong selection, the density function evaluated at a point can become arbitrarily large. For this reason a Gaussian cubature approach was used to calculate the integral of the final likelihood over the unit cube described by each observation x, once optimisation had been completed. Approximate numerical integrals were calculated using the software package cubature [81]. In the calculations above we made the implicit assumption that the observations xB and xA consist of sets of complete viral haplotypes hi. However, short-read sequencing technologies generally result in sets of individual reads which only cover a subset of the genetic loci of interest; we refer to these reads as partial haplotypes. In this framework the data represents direct observations of partial haplotypes in the set h P = { h 1 P, …, h L P }, where each of the sets h l P is a vector of haplotypes spanning a common subset of the loci spanned by the full haplotypes in h. Population-wide observations of these partial haplotypes are then defined by x P = { x 1 P, …, x L P } with x l P = { x l i P } where x l i P is the number of reads with haplotype h l i P. As a result, the total number of observations must now be defined on the basis of each set of partial haplotypes, e. g. N l B, P = ∑ i x l i P is the total number observations of partial haplotypes in the set l. As each set of partial haplotype observations is independent of the others, we may reconstruct Eq 2 in the following terms: log L (N T, S T | x B, x A, N G, S G) = ∑ l log L (N T, S T | x l B, P, x l A, P, N G, S G) (23) Within this construction, bottleneck sizes and selection are conserved between partial haplotype sets, being evaluated at the full haplotype level. Each set of partial haplotype observations x l P is considered as a sample drawn from a set of partial haplotypes with frequencies q l P, these frequencies being defined via a linear transformation of the full haplotype frequencies with matrix Tl. For example, given the full haplotypes {AG, AT, CG, CT} and a set of partial haplotypes {A-, C-}, we have q l P = T l q (24) or more explicitly, (q l 1 P q l 2 P) = (1 1 0 0 0 0 1 1) (q 1 q 2 q 3 q 4) (25) Thus, as described above, the calculation of transmission and within-host growth under selection can be performed at the level of full haplotypes, switching into partial haplotype space only to evaluate the likelihoods of the observations. Re-deriving the results of Eqs 15 and 16 for short-read sequence data, we find that the compound distribution for the xB component has mean E [ x l B, P ] = N l B, P T l μ B (26) and variance var (x l B, P) = β N l B, P M (T l μ B) + N l B, P (N l B, P - β) T l Σ B T l † (27) Similarly, for the xA component of the likelihood, we get a mean of E [ x l A, P ] = N l A, P T l S G (S T (μ B) ) (28) and variance var (xlA, P) =αNlA, PM (TlSG (ST (μB) ) ) +NlA, P (NlA, P−α) ×Tl (DSG|ST (μB) ) (γM (ST (μB) ) +δ (DST|μB) ΣB (DST|μB) †) (DSG|ST (μB) ) †Tl† (29) The mathematical framework outlined above utilises the haplotype information inherent to the data, and accounts for the effect of noise in the sequencing process (Fig 1B and 1C). However, in order to discriminate between changes in viral diversity arising from bottlenecking and selection (Fig 1A) it is necessary to consider data from different regions of the genome at which genetic diversity is nominally statistially independent. At high doses of influenza virus reassortment occurs rapidly, as has been observed both in vitro and in small animal infections [82,83]. In our analysis, distinct viral segments were therefore considered to be independent of one another in this manner, albeit sharing a common transmission bottleneck NT, each transmitted virus being assumed to contain one of each viral segment. As such the likelihood in Eq 23 becomes log L (N T, S T | x B, x A, N G, S G) = ∑ m ∑ l log L (N T, S m T | x m l B, PH, x m l A, PH, N G, S m G) (30) where the subscript m denotes information particular to a specific genomic region. Replicate data are highly valuable for evolutionary inference [84,85]. Within our calculation they provide an additional level of abstraction to the inference process. Under this framework we assumed that replicates share a common fitness landscape, ST, whilst exhibiting individual bottleneck values. As a result, the likelihood from Eq 30 becomes log L (N T, S T | x B, x A, N G, S G) = ∑ r ∑ m ∑ l log L (N r T, S m T | x r m l B, PH, x r m l A, PH, N r G, S m G) (31) where the subscript r denotes information particular to a specific replicate. For comparison of bottleneck estimates with existing methods we implemented the exact version of the beta-binomial inference scheme of Leonard et al. [12]. The likelihood function for site i was defined as L (N T) i = ∑ j = 0 N T P beta-bin (x i, minor A, SL | N i A, SL, j, N T - j) P bin (j | N T, q i, minor B, SL) (32) where NT is the bottleneck size, Pbeta-bin is the beta-binomial probability mass function, x i, minor A, SL is the number of recipient observations for the minor allele at site i, N i A, SL is the total number of recipient observations for site i, i. e. N i A, SL = x i, minor A, SL + x i, major A, SL, Pbin is the binomial probability mass function, and q i, minor B, SL is the donor frequency for the minor allele at site i. We note that the beta-binomial is undefined for j = 0 and j = NT and define j = 10−10 and j = NT−10−10 respectively in these cases. The original authors did not discuss this further. We did not make use of the cumulative version of the likelihood function as we avoided the problem of variant calling by fixing the number of required polymorphic loci when simulating data. The total likelihood for each bottleneck value was computed as L (N T) = ∑ i = 0 n sites L (N T) i (33) where nsites is the number of variant loci. Bottleneck inference was defined as the bottleneck associated with the largest likelihood value. Our method was applied to both simulated sequence data, and data from an evolutionary experiment conducted in ferrets [25]. Code and scripts related to this project can be found online at https: //bitbucket. org/casperlu/transmission_project/. Detailed descriptions of code options and user guides are available in the repository README files. Scripts and instructions relevant for generating the figures in this paper may be found online as well. The overall workflow is as follows: Time series viral sequence data are prepared in SAM format. SAMFIRE is used for filtering and splitting of data unto relevant time points, e. g. those surrounding transmission. Single- and multi-locus trajectories are computed using SAMFIRE. A list of potential haplotypes (see [52] for details) are constructed. A noise parameter C and potential within-host selection is inferred using SAMFIRE. The transmission code is used for generating simulated data based on the experimental data. Transmission bottleneck and selection is inferred for the simulated data and BIC penalty curves are determined. A final inference of bottleneck and transmissibility is carried out on the real data taking into account the BIC penalty curves. This process will be greatly simplified in the case where only a neutral estimate of the transmission bottleneck is required. Quick-start and step-by-step guides can be found in the online transmission repository.
In order to spread, pathogens must not only be able to grow within an infected host, but also transmit to found new infections. Population genetics can exploit genome sequence data to provide a great deal of insight into transmission processes. For example, the number of particles which found a new infection determine the extent to which genetic diversity is passed from host to host. The identification of genetic variants which increase the propensity of a pathogen to transmit from host to host is a valuable step in understanding how an infection might spread. Here we set out a new population genetic framework for understanding transmission events from genome sequence data collected before and after transmission. Our approach corrects for the shortcomings of existing methods for this purpose, setting out a new baseline for the statistical analysis of transmission events. We demonstrate the ability of our method to draw novel quantitative insights by application to data from simulated and real transmission events.
organismal evolution
medicine and health sciences
pathology and laboratory medicine
viral transmission and infection
pathogens
population genetics
microbiology
orthomyxoviruses
genetic mapping
simulation and modeling
viruses
rna viruses
microbial evolution
population biology
research and analysis methods
medical microbiology
microbial pathogens
viral replication
viral evolution
haplotypes
influenza viruses
natural selection
heredity
virology
viral pathogens
genetics
biology and life sciences
evolutionary biology
evolutionary processes
organisms
A novel framework for inferring parameters of transmission from viral sequence data