During an infectious disease outbreak, tracing who infected whom allows public health scientists to see how a pathogen is spreading and to establish effective control measures. Traditionally, this involves identifying the individuals an infected person comes into contact with and monitoring whether they also become unwell. However, this information is not always available and can be inaccurate.

One alternative is to track the genetic data of pathogens as they spread. Over time, pathogens accumulate mutations in their genes that can be used to distinguish them from one another. Genetically similar pathogens are more likely to have spread during the same outbreak, while genetically dissimilar pathogens may have come from different outbreaks. However, there are limitations to this approach. For example, some pathogens accumulate genetic mutations very slowly and may not change enough during an outbreak to be distinguishable from one another. Additionally, some pathogens can spread rapidly, leaving less time for mutations to occur between transmission events.

To overcome these challenges, Torres Ortiz et al. developed a more sensitive approach to pathogen genetic testing that took advantage of the multiple pathogen populations that often coexist in an infected patient. Rather than tracking only the most dominant genetic version of the pathogen, this method also looked at the less dominant ones.

Torres Ortiz et al. performed genome sequencing of SARS-CoV-2 (the virus that causes COVID-19) samples from 451 healthcare workers, patients, and patient contacts at participating London hospitals. Analysis showed that it was possible to detect multiple genetic populations of the virus within individual patients. These subpopulations were often more similar in patients that had been in contact with one another than in those that had not. Tracking the genetic data of all viral populations enabled Torres Ortiz et al. to trace transmission more accurately than if only the dominant population was used.

More accurate genetic tracing could help public health scientists better track pathogen transmission and control outbreaks. This may be especially beneficial in hospital settings where outbreaks can be smaller, and it is important to understand if transmission is occurring within the hospital or if the pathogen is imported from the community. Further research will help scientists understand how pathogen population genetics evolve during outbreaks and may improve the detection of subpopulations present at very low frequencies.

Understanding who infects whom in an infectious disease outbreak is a key component of infection prevention and control (Didelot et al., 2012). The use of whole-genome sequencing allows for detailed investigation of disease outbreaks, but the limited genetic diversity of many pathogens often hinders our understanding of transmission events (Campbell et al., 2018). As a consequence of the limited diversity, many index case and contact pairs will share identical genotypes, making it difficult to ascertain who infected whom.

Within-sample genetic diversity is common among a wide variety of pathogens (Mongkolrattanothai et al., 2011; Lieberman et al., 2016; Dinis et al., 2016; Leitner, 2019; Popa et al., 2020). This diversity may be generated de novo during infection, by a single transmission event of a diverse inoculum or by independent transmission events from multiple sources (Worby et al., 2014). The maintenance and dynamic of within-host diversity is then a product of natural selection, genetic drift, and fluctuating population size (Didelot et al., 2012). The transmission of within-host variation between individuals is also favored as a large inoculum exposure is more likely to give rise to infection (Murphy et al., 1984; Han et al., 2019; Lee et al., 2022; Sender et al., 2021; Spinelli et al., 2021; Trunfio et al., 2021). The amount of within-sample diversity transmitted from index case to contact is determined by the bottleneck size (Zwart and Elena, 2015), with stringent bottlenecks limiting the number of genotypes transmitted from the host to the recipient, and wide bottlenecks allowing for the transmission of higher levels of genetic diversity (Worby et al., 2014).

Phylogenetic analysis provides information regarding the structure of the genetic diversity among pathogen isolates. Moreover, pathogen phylogenetic trees can be used as input for many downstream analysis, including inference of transmission events, population size dynamics, or estimation of parameters of epidemiological models (Didelot et al., 2018). Most genomic and phylogenetic workflows involve either genome assembly or alignment of sequencing reads to a reference genome. In both cases, conventionally the resulting alignment exclusively represents the most common nucleotide at each position. This is often referred to as the consensus sequence. Although genome assemblers may output contigs (combined overlapping reads) representing low-frequency haplotypes, only the majority contig is kept in the final sequence. In a mapping approach, a frequency threshold for the major variant is usually pre-determined, under which a position is considered ambiguous. The lack of genetic variation between temporally proximate samples and the slow mutation rate of many pathogens results in direct transmission events sharing exact sequences between the hosts when using the consensus sequence approach. For instance, the substitution rate of SARS-CoV-2 has been inferred to be around two mutations per genome per month (Harvey et al., 2021). Given its infectious period of 6 days (Byrne et al., 2020), most consensus sequences in a small-scale outbreak will show no variation between them. This lack of resolution and poor phylogenetic signal complicate phylogenetic inference, limiting the downstream analysis and conclusions that can be extracted from the phylogenetic tree. Previous work has shown the advantages of using within-host diversity to infer transmission events compared to using consensus sequences (Wymant et al., 2018; De Maio et al., 2018). Aside from transmission inference, the use of the within-host pathogen genetic data directly within phylogenetic inference will improve any downstream analysis using a phylogenetic tree as starting point.

We hypothesize that the failure of consensus sequence approaches to capture within-sample variation arbitrarily excludes meaningful data and limits pathogen phylogenetic and transmission inference, and that including within-sample diversity in a phylogenetic model would significantly increase the evolutionary and temporal signal and thereby improve our ability to infer infectious disease phylogenies and transmission events.

We tested our hypothesis on multi-institutional SARS-CoV-2 outbreaks across London hospitals that were part of the COVID-19 Genomics UK (COG-UK) consortia (COVID-19 Genomics UK COG-UK, 2020). Technical replicates, repeated longitudinal sampling from the same patient, and epidemiological data allowed us to evaluate the presence and stability of within-sample diversity within the host and in independently determined transmission chains. We also evaluated the use of within-sample diversity in phylogenetic analysis by conducting outbreak and phylogenetic simulations of sequencing data using a phylogenetic model that accounts for the presence and transmission of within-sample variation. We show the effects on phylogenetic inference of using consensus sequences in the presence of within-sample diversity, and propose that existing phylogenetic models can leverage the additional diversity given by the within-sample variation and reconstruct the phylogenetic relationship between isolates. Lastly, we show that by taking into account within-sample diversity in a phylogenetic model, we improve the temporal signal in SARS-CoV-2 outbreak analysis. Using both phylogenetic outbreak reconstruction and simulation, we show that our approach is superior to the current gold standard whole-genome consensus sequence methods.

Detailed investigation of transmission events in an infectious disease outbreak is a prerequisite for effective prevention and control. Although whole-genome sequencing has transformed the field of pathogen genomics, insufficient pathogen genetic diversity between cases in an outbreak limits the ability to infer who infected whom. Using multi-hospital SARS-CoV-2 outbreaks and phylogenetic simulations, we show that including the genetic diversity of subpopulations within a clinical sample improves phylogenetic reconstruction of SARS-CoV-2 outbreaks and determines the direction of transmission when using a consensus sequence approach fails to do so.

The majority of samples sequenced harbored variants at low frequency. However, most variants were not consistently called in technical replicates, suggesting they were spurious or unreliable. Within-sample variation was less consistent between paired technical replicates with lower viral load (higher C_t). This is likely to be a consequence of low starting genetic material giving rise to amplification bias during library preparation and sequencing. Establishing a cut-off for high C_t values is therefore important to accurately characterize within-host variation. In our study, we excluded samples with a C_t value higher than 30 cycles based on the diagnostic PCR used at GOSH. Since C_t values are only a surrogate for viral load and are not standardized across different assays (Evans et al., 2021), appropriate thresholds would need to be determined for other primary PCR testing assays. Similarly, variant calls at very low frequency were less likely to be present in both technical replicates. These variants at low frequency are thus potentially not genuine and the result of sequencing and variant calling errors. For our work, we removed any variants with an allele frequency lower than 2%. Until sequencing and variant calling technologies improve for low-frequency variants, technical replicates will remain essential for the study of pathogen within-host diversity in order to distinguish genuine variation from sequencing noise. The effect of this noise on phylogenetic inference will depend on the signal-to-noise ratio and the amount of variation already present in the consensus sequences. Spurious low-frequency variation will likely affect only the branch length estimation in phylogenetic inference by adding potentially erroneous calls, unless there is presence of batch bias which could artificially cluster epidemiologically unrelated isolates together.

The generation, maintenance, and evolution of subpopulations within the host reflect evolutionary processes which are meaningful from phylogenetic and epidemiological perspectives. Subpopulations within a host can emerge from three mechanisms: de novo diversification in the host, transmission of a diverse inoculum, or multiple transmission events from different sources. If the subpopulations are the result of de novo mutations, nucleotide polymorphisms within the subpopulations accumulate over time and may therefore result in a phylogenetic signal useful for phylogenetic inference. In our data, longitudinal samples taken at later time points were demonstrated to accrue genomic variation. Although this pattern can be confounded by decreasing viral load as infection progresses, C_t values in our dataset were not correlated with a higher genetic distance, and clusters in our data containing both longitudinal and technical replicates also corroborate these results. Transmission of a diverse inoculum also gives rise to phylogenetically informative shared low-frequency variants, as our results show that transmission pairs are more likely to share variants at low frequency. The effect of multiple transmission events in the phylogeny depends on the relatedness of both index cases and the bottleneck size in each transmission event.

Paired samples with epidemiological links and from the same department shared a higher proportion of low-frequency variants and were located closer in the consensus tree than samples with no relationship. These patterns suggest that the distribution of low-frequency variants is linked to events of epidemiological interest. The fact that technical duplicates shared more within-host diversity than longitudinal replicates of the same sample suggests that much of the variation within hosts is transitory. Therefore, within-host diversity may be relevant on relatively short time scales, which is precisely where consensus sequences lack resolution. Combining the data derived from fixed alleles in the consensus sequences and transient within-sample minor variation enables an improved understanding of the relatedness of pathogen populations between hosts.

The effects of neglecting within-host diversity in phylogenetic inference were analyzed by using simulated sequences under a phylogenetic model that reflects the presence and evolution of within-host diversity. We compared a conventional consensus phylogenetic model and a model that leverages within-sample diversity, and evaluated their ability to infer the known phylogeny. Our proposed phylogenetic model incorporates within-sample variation by explicitly coding major and minor nucleotides as independent characters in the alignment. We demonstrated that phylogenies inferred using the conventional consensus sequence approach were unresolved and unrepresentative of the known structure of the simulated tree. However, sequences that included within-host diversity showed higher resolution that resulted in phylogenetic trees more similar to the simulated phylogeny. As other mutational models, our 16-state model assumes independence between sites in the alignment. This assumption can be violated due to the presence of genetic linkage, which can be caused by multiple biological processes, such as clonal relationships between microorganisms, recombination, or selection of co-evolving sites. To increase the amount of genetic linkage due to clonal relationships between organisms, we repeated our simulations using a coalescent model to create the starting tree, and confirmed that the 16-state model still outperformed the conventional consensus sequence in the presence of high linkage. Other sources of genetic linkage are not accounted for, and their inclusion in phylogenetic inference is out of scope of this work.

The proposed phylogenetic model used for the simulations did not include direct base transitions and transversions, but rather modeled base change as a process of minor variant acquisition and fixation. Therefore, a base change is composed of the following steps: first a minor variant is gained; then the minor variant increases in frequency and becomes the majority variant; and finally the new variant is fixed. In this way, within-host evolution is partially included in the model as a process of minor variant evolution and eventual lost or fixation. As shown in the simulations, this process of within-host evolution is also captured when the minor bases are simply incorporated as additional states in the Markov chain without explicitly limiting the possible transitions.

We complemented the phylogenetic simulations with tree inference of outbreaks simulated using TransPhylo (Didelot et al., 2017). We parameterized the simulations to reflect the transmission dynamics of SARS-CoV-2, including a generation time of 5 days and a sampling time of 7 days. Given this parameters, most simulated outbreaks lasted less than a month. We then simulated genetic sequences within the outbreak using fastsimcoal2 (Excoffier et al., 2013) as previously described by De Maio et al., 2018. Using a mutation rate of $5\times {10}^{-6}$ mutations per base per replication cycle, as previously described for SARS-CoV-2 and other betacoronaviruses (Sender et al., 2021; Amicone et al., 2022), and varying bottleneck sizes, we showed that tree inference using within-sample diversity improves as the transmission bottleneck widens, although even at low bottleneck sizes trees inferred using within-sample diversity are more accurate than those inferred using consensus sequences. Similarly, using varying mutation rates and a constant bottleneck size of 10 pathogens, we showed that tree inference was more accurate as mutation rates increased, although inference using consensus sequences improved only at a very high mutation rate of ${10}^{-3}$ mutations per base per cycle, which has mostly been observed in some HIV studies (Cuevas et al., 2015). Together, our simulations show that at the short time frame of disease transmission, phylogenetic inference using alignments that contain information regarding within-sample diversity outperform phylogenies inferred with consensus sequences, even at narrow transmission bottlenecks and very low mutation rates. Since TransPhylo simulates phylogenetic trees alongside the outbreak simulation, we could directly compare our inferred phylogenies with the known simulated trees. However, although phylogenetic trees can inform transmission inference, phylogenetic trees themselves and transmission trees are not interchangeable. Nevertheless, increasing the resolution of phylogenetic trees can improve inference of transmission chains and calculation of the likelihood of transmission events.

Previous studies have addressed the use of within-host variation to infer transmission events. Wymant et al., 2018, employed a framework based on phylogenetic inference and ancestral state reconstruction of each set of populations detected within read alignments using genomic windows. Our study extends this work by coding genome-wide diversity within the host directly in the alignment and the phylogenetic model. De Maio et al., 2018, proposed direct inference of transmission from sequencing data alongside host exposure time and sampling date within the Bayesian framework BEAST2 (Bouckaert et al., 2014). Our approach is focused on directly improving the temporal and phylogenetic signal of whole-genome sequences, and it’s especially suited for use in applications and analysis that employ a phylogenetic tree as input to infer transmission (Didelot et al., 2017).

Apart from transmission inference, phylogenetic trees can be used to infer many parameters of epidemiological interest, such as R₀ or the effective population size. In our work, we showed that the temporal signal of clusters where all isolates had the same sequences increased with the inclusion of within-sample diversity, which in turns allows better inference of phylogenetic trees. When analyzing specific outbreaks, we showed that groups of samples without genetic differences were clustered apart from other isolates of the outbreak, providing additional information on genetic relationships that could be used for transmission inference or to better understand the genetic structure of the outbreak. Even though transmission inference can be improved with epidemiological data such as collection dates even when all isolates have the same genetic sequences, such data can’t provide information regarding how samples cluster within the outbreak. Additionally, the order of collection dates not always correspond to the order of infection.

Future work will extend this model by including allele frequency data in addition to independent characters for major and minor variants. Moreover, to limit the number of character states we only allowed two variants at each position. Transmission inference of pathogens with high levels of within-host diversity, for instance as observed in HIV, could benefit from including more than two alleles. In those cases, the number of possible character state combinations would be too large, and therefore other methods such as phyloscanner (Wymant et al., 2018) could resolve transmission events more accurately. However, it’s important to note that the low frequency of a third allele could result in more sequencing and mapping errors which could in turn bias phylogenetic inference and genomic analysis. Phylogenetic models that explicitly include dynamics of within-sample variation and sequencing error may further improve phylogenetic inference or allow researchers to better estimate parameters of interest, including R0, bottleneck size, transmissibility, and the origin of outbreaks.

In line with conventional consensus sequencing approaches, we used a reference sequence for genome alignment and variant calling. Although widely used, one limitation of this approach is a potential mapping bias causing some reads to reflect the reference base at low frequencies at a position where only a variant should be present. Although we applied stringent quality filtering, we cannot rule out the persistence of some false positive minor variants. Using genome graphs to map to a reference that encompasses a wider spectrum of variation may alleviate this problem, and could be an interesting addition to pathogen population genomic analysis.

Our results demonstrate that within-sample variation can be leveraged to increase the resolution of phylogenetic trees and improve our understanding of who infected whom. Using SARS-CoV-2 hospital outbreaks and simulations, we show that variants at low frequencies are consistent within sample replicates, phylogenetically informative and are more often shared among epidemiologically related contacts. By coding within-sample variation directly in the alignment, the additional genetic information can be easily incorporated in phylogenetic inference, facilitating its application within existing epidemiology pipelines and public health infrastructure. We propose that pathogen phylogenetic models should accommodate within-host variation to improve the understanding of infectious disease transmission and aid infection control measures.

Samples sequenced as part of this study have been submitted to the European Nucleotide Archive under accession PRJEB53224.Sample metadata is included in Supplementary file 1. All custom code used in this article can be accessed at https://github.com/arturotorreso/scov2_withinHost.git (copy archived at Torres Ortiz, 2023).

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Author details

1. Arturo Torres Ortiz

   1. Department of Infectious Diseases, Imperial College London, London, United Kingdom
   2. Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   a.ortiz@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2492-0958

2. Michelle Kendall

   Department of Statistics, University of Warwick, Coventry, United Kingdom

   Contribution

   Formal analysis, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7344-7071

3. Nathaniel Storey

   Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. James Hatcher

   Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Helen Dunn

   Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Sunando Roy

   Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Rachel Williams

   UCL Genomics, University College London, London, United Kingdom

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Charlotte Williams

   UCL Genomics, University College London, London, United Kingdom

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Richard A Goldstein

   UCL Genomics, University College London, London, United Kingdom

   Contribution

   Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5148-4672

10. Xavier Didelot

    Department of Statistics, University of Warwick, Coventry, United Kingdom

    Contribution

    Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1885-500X

11. Kathryn Harris

    1. Department of Microbiology, Great Ormond Street Hospital, London, United Kingdom
    2. Department of Virology, East & South East London Pathology Partnership, Royal London Hospital, Barts Health NHS Trust, London, United Kingdom

    Contribution

    Supervision, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Judith Breuer

    Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8246-0534

13. Louis Grandjean

    Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom

    Contribution

    Resources, Data curation, Supervision, Investigation, Project administration, Writing – review and editing

    For correspondence

    l.grandjean@ucl.ac.uk

    Competing interests

    No competing interests declared

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