Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks
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eLife assessment
This valuable study presents a novel and theoretically interesting model to account for viral diversity within hosts in evolutionary and genomic analyses of pathogens. The simulation results presented are solid, although there are some aspects of the methodology that require further investigation in order to establish their validity. The application to SARS-CoV-2 shows promise, but would benefit from further evaluation.
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Abstract
Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
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Author Response
Reviewer #1 (Public Review):
This is an interesting manuscript that proposes a new approach to for accounting for viral diversity within hosts in phylogenetic analyses of pathogens. Concretely, the authors consider sites for which a minor allele exist as an additional base in the substitution model. For example, if at a particular site 60% of reads have an C and 40% have a G, then this site is assigned Cg, as opposed to an C which is typical of analysing consensus sequences. Because we typically model sequence evolution as a Markovian process, as is the case here, the data become naturally more informative, given that there are more states in the Markov chain when adding these bases. As a result, phylogenetic trees estimated using these data are better resolved than those from consensus sequences. The branches of the …
Author Response
Reviewer #1 (Public Review):
This is an interesting manuscript that proposes a new approach to for accounting for viral diversity within hosts in phylogenetic analyses of pathogens. Concretely, the authors consider sites for which a minor allele exist as an additional base in the substitution model. For example, if at a particular site 60% of reads have an C and 40% have a G, then this site is assigned Cg, as opposed to an C which is typical of analysing consensus sequences. Because we typically model sequence evolution as a Markovian process, as is the case here, the data become naturally more informative, given that there are more states in the Markov chain when adding these bases. As a result, phylogenetic trees estimated using these data are better resolved than those from consensus sequences. The branches of the trees are probably also longer, which is why temporal signal becomes more apparent.
I commend the authors on their rigorous simulation study and careful empirical data analyses. However, I strongly suggest they consider whether treating minor alleles as an additional base is biologically realistic and whether this may have implication for other analyses, particularly when there is very high within-host diversity and the number of states in becomes very large.
We thank the reviewer for the helpful and thorough review. We have included a paragraph in the Discussion regarding the biological interpretation of the 16-state model (Line 344-351), as well as the consequences when there’s high within-host diversity (Line 398).
Reviewer #2 (Public Review):
I agree that minor genetic variation could potentially be used to more accurately infer who-infected- whom in an outbreak scenario. Indeed, the use of minor genetic variation has proven very useful in reconstructing transmission chains for chronic infections such as HIV (e.g., see applications using Phyloscanner). To me, it seems that considering the full spectrum of viral genetic diversity within infected hosts would necessarily do the same if not better than considering only consensus-level viral sequence data. This is because there is a necessarily a loss of data and potentially a loss of information when going from considering the genetic composition of viral populations within a host to only considering the consensus sequences of those viral populations. As such, Ortiz et al.'s hypothesis stated on lines 66-70 is a reasonable one, and I was looking forward to seeing this hypothesis evaluated in detail in this manuscript.
R2.1 There are several parts of this manuscript I really like. In particular, encoding within-sample diversity as character states and using that alternative representation of sequence data for phylogenetic inference (as shown in Figure 3) is a very interesting idea, I think. There are some limitations that are not explicitly mentioned, however. For example, when using this 16-character state representation for phylogenetic inference, they assume independence between nucleotide sites. This is a major assumption that can be violated when considering longitudinal intrahost data and transmission dynamics in an outbreak setting, given genetic linkage between sites.
We have generated another set of simulations where the starting tree was a coalescent tree rather than a random phylogeny. This is described in the Results section, Line 228, and Figure 4—figure supplement 2. By using a coalescent tree, we increase the genetic linkage between sites. For all metrics used, the 16-state model performed better than the consensus sequence model. It is also important to note, as the reviewer points out, that longitudinal isolates should be removed from transmission inference, as we do in Figure 7 and Figure 7—figure supplement 2.. This point is now reflected in the Results (Line 286) and Methods (Line 534).
I have several major concerns about the work as it stands, particularly in the context of the SARS-CoV-2 application.
Concerns not related to the SARS-CoV-2 application:
R2.2 Figure 4 shows that a model using within-sample diversity can more accurately reconstruct evolutionary histories than a model that uses only consensus-level genetic data. This is really interesting. The Materials and Methods section (particularly lines 351-354) indicates that the sequence data were generated using certain specified substitution rates. The rates specified seem to be chosen in such a way to facilitate finding an improvement when using within-sample diversity. I don't know whether the relative rates of these 'substitutions' at all mirror "real-life". It would be very useful to have a broader set of analyses here to examine the effect of these 'substitution' rates on the utility of incorporating within-sample diversity into phylogenetic inference. (Also, 1, 100, 200 (line 353) inconsistent with 1, 20, 200 in Supp Table 3)
We have now corrected Supp Table 3 to reflect the rates described in the Methods section.
We defined our model with three rates: rate of minor variant acquisition, rate of minor-major variant switch, and rate of minor variant loss. We chose the rates for the simulations (1, 100, 200) to reflect a low rate of minor variant acquisition (1) and high rates of minor-major variant switch (200) and minor variant loss (100). These rates will result in pure bases (A,C,G and T) 100 times more likely to be present than low frequency variants, as seen in the base frequencies in Supp Table 1 and 3, which would in turn minimize the effect of including minor variations. We chose these rates to reflect the high turnover of minor variation often observed in real data and the frequencies of minor alleles in the SARS-CoV-2 dataset, but we agree with the reviewer that this may not always be the case. We also agree with the reviewer that changing the parameters in the simulations also affects the effect of including low frequency variation in the model. As such, we have now included simulations using different sets of rates (Figure 4—figure supplement ):
With a high rate of variant switch and loss compared to acquisition (1, 10, 100), reducing the frequency of minor variation.
With a lower rate of switch and loss (1, 10, 10), promoting a stable landscape of low frequency variation.
With no low frequency variation (Jukes-cantor model)
R2.3 Figure 5 is very interesting, particularly the results at bottleneck sizes of 1-10. What are the 'substitution' rates that are inferred here from using this simulated dataset? The Material and Methods section also does not mention the within-host viral generation time anywhere, as far as I can see (~line 384 states the mutation rate per base per generation cycle but not the length of the generation cycle anywhere).
Fastsimcoal2 is a coalescent simulator of population histories over several generations, given a population size and a mutation rate. For our purposes, transmissions are simulated as bottlenecks of constant size, and a generation is represented by each time step in the outbreak simulation, which corresponds to 1 day. This is further clarified in the Methods section (Line 475).
Concerns related to the SARS-CoV-2 application:
R2.4 I am very concerned about the testing of this hypothesis on the SARS-CoV-2 data presented. First, 1% is a very low variant calling threshold. Second, analysis of the 17 samples that were resequenced (out of 454) indicated that on average, 39% of iSNVS (intrahost single nucleotide variants) called between duplicate runs were only observed in one of the two runs (line 117). Their analysis in Figure 1 indicates that these discrepant (and seemingly spurious) variants occur at higher levels in high Ct samples (which makes sense; Figure 1b). They therefore decide to limit their analyses to samples with Ct values <= 30. This results in 249 samples. However, if we look at Figure 1b, only ~10% of iSNVs called across duplicate runs with Ct = 30 are shared! That means that 90% of iSNVs in the set appear to be spurious. If we assume that each duplicate run of a sample has approximately the same number of spurious iSNVs, then approximately 82% of iSNVs called in a sample with a Ct of 30 would be spurious. This fraction decreases with samples that have lower Ct values, but even at a Ct of 27, only ~60% of iSNVs called across duplicate runs are shared. All the downstream SARS-CoV-2 analyses based on within-host sample diversity therefore are based on samples where the large majority of considered sample diversity is not real. This leads to me necessarily discounting all of those downstream SARS-CoV-2 results.
We agree with the reviewer that, as the results show, datasets that incorporate within-sample low frequency variation are expected to have considerably more noise than using exclusively consensus sequences, and perhaps this wasn’t properly discussed in the manuscript. We have incorporated some notes about this in the Discussion section (Line 408-413).
The 1% variant frequency threshold was used to generate the analysis of Fig. 1 and Supp. Fig. 1-4. Looking at these results, we decided to establish the Ct cutt-off of 30 as mentioned by the reviewer, as well as a variant frequency threshold of 2% (as shown in the x-axis of Fig. 2). We overlooked this second variant frequency threshold in the manuscript, which has been added. As shown in Supp. Fig 4, this variant frequency threshold will increase the concordance between technical replicates, although some level of noise persists.
R2.5 Lines 153-167: I can't figure out how to square the quantitative results given in this paragraph with what is shown in Figure 2. To me, Figure 2 shows only that Technical Replicates have higher probabilities of sharing a variant than with 'No' relationship. What would also be helpful here so that the reader can get a better feel for the data would be to see the iSNV frequencies plotted over time for the longitudinal replicate samples in the supplement and, for the 'epidemiological' samples to show 'TV plots' in the supplement (as in Fig 3c in McCrone et al. eLife)
Figure 2 shows that technical replicates, longitudinal replicates, epidemiological samples and, in some instances, from the same department have a higher probability of sharing low frequency variants than those with no relationship (also shown in Supp Figure 5). However, also shown in Figure 2 is that the 95% CI is very wide, and therefore in many instances low frequency variants won’t be shared between epidemiological samples or samples from the same department.
We have also added Figure 2—figure supplement showing the low frequency variants plotted over time for longitudinal replicates. Unlike McCrone et al, we don’t have proven transmission between pairs of samples, although we believe our analysis also shows a pattern of shared low frequency variants among potential epidemiological links.
R2.6 Figure 6 and associated text: (a) root-to-tip distance: what units is this distance in? (b) That the authors find a temporal signal in these transmission clusters (where all consensus sequences within a cluster are the same) is interesting but also a bit baffling to me. Given the inference of very small transmission bottlenecks in previous studies (e.g., Martin & Koelle - reanalysis of Popa et al.; Lythgoe et al.; Braun et al.), I don't understand where the temporal signal comes in. Do the samples become more genetically diverse over the outbreak (this seems to be indicated in lines 260-262 but never shown and unlikely given bottleneck sizes)? Additional analyses to help the reader understand WHY within-sample diversity allows for the identification of temporal signal is important. This could involve plotting genetic diversity of the samples by collection date or some other, similar analyses.
a) The units of the y-axis (root-to-tip distance) are measured in substitutions per genome. This is now reflected in the legend of the figure.
b) As shown in Figure 5, even at small bottleneck sizes we are able to pick some of the diversity that evolves during the course of an outbreak. As hinted by the reviewer, the smaller the bottleneck the less diversity we can leverage for phylogenetic inference, and in fact for some epidemiological samples all the diversity will be lost during transmission, which is why many of the within-sample variants are not shared between the epidemiologically related samples. Figure 6 is indeed showing that the genetic distance (measured as number of substitutions per genome) increases per collection date. We have also added a Figure 6—figure supplement showing the increase in low frequency variants within outbreaks as the outbreaks progress in time (explained in Line 261 of the Results section), which explain in part the increasing temporal signal in clusters.
R2.7 Paragraph consisting of lines 229-238 and Figure 7: This analysis stops abruptly. What are the conclusions here? Figure 7a (right) seems inconsistent to me with Figure 7b and 7C results. Also, the main hypothesis put forward in this paper is that within-sample sequence data can better resolve who-infected-whom in an outbreak setting. Figure 7b and 7c however are never compared against analogous panels that use just consensus sequences. (Even though the consensus sequences are the same, according to Figure 7a, the inferences shown in Figures 7b and 7c could use additional data such as collection times, etc. that would provide information even when using exclusively consensus-level data). Also, do the analyses in Figures 7b and 7c use the 16-character state model at all? I think Supp Figure 9 is relevant here but not sure how?)
We have extended this section of the results to make it more coherent and clear (Line 284-293) and in the Discussion (Line 385-395). As added into the Discussion, we agree with the reviewer that even with equal sequences some inferences about transmission can be made with epidemiological data, specially collection dates. However, such data can’t be used to infer the genetic structure of the cluster, which complicates any analysis that can use a phylogenetic as input.
Additional concerns:
R2.8 Some of the stated conclusions, particularly in the Discussion section and in the Abstract, do not seem to be supported by the presented results. For example, line 27: 'within-sample diversity is stable among repeated serial samples from the same host': Figure 2 does not show this conclusively. Line 28: 'within-sample diversity... is transmitted between those cases with known epidemiological links': Figure 2 also does not show this conclusively. Line 29: 'within-sample diversity... improves phylogenetic inference and our understanding of who infected whom': Figure 7b/c results using within-sample diversity is never compared against results that use only consensus, so improvement not demonstrated. Line 272-273: 'samples with shorter distance in the consensus phylogeny were more likely to share low frequency variants'. Line 287: 'We demonstrated that phylogenies... were heavily biased'.
Line 27 and Line 28: We agree with the reviewer that the genomic analysis of SARS-CoV-2 sequences show only partial congruence within technical replicates and epidemiological links. We have appropriately addressed this in the Abstract.
Line 29 and Fig 7: Transmission inference using the consensus sequence in Figure 7b/c couldn’t be performed because the lack of any genetic difference between the consensus sequence meant that all sequences had the same transmission likelihood. This is now better explained in the Discussion section, lines 385-395.
Line 272-273: We have removed this section as we did not perform this analysis, as pointed out by the reviewer.
Line 287: The conclusion expressed in line 287 (now line 340) has been changed.
R2.9 The manuscript at times does not cite previous work that is highly relevant and thus overstates the novelty of the current work. For example: lines 21-23: '..conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences...' Phyloscanner uses within-sample diversity, for example, as does SCOTTI. These are finally cited in the discussion section (~line 310), but because this previous work is not acknowledged earlier in the manuscript, the novelty of the work presented here is somewhat overstated.
We have included background information in the introduction regarding the use of within-sample diversity for transmission inference (Line 69-73), as well as emphasizing that the novelty of our work lies more in the use of within-sample diversity in phylogenetic inference rather than exclusively transmission inference (Line 74, and other instance along the manuscript).
In sum, I think that the 16 character-state model is a very interesting model. More analyses on simulated data would be helpful to expand on when below-the-consensus level genetic data would truly be informative of phylogenetic relationships and who-infected-whom in outbreak settings. The SARS-CoV-2 analyses are very worrisome to me, given the inclusion of samples where the majority of considered within-sample genetic diversity is very likely not real. Some of the stated conclusions appear to either be at odds with the results presented or not directly evaluated.
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eLife assessment
This valuable study presents a novel and theoretically interesting model to account for viral diversity within hosts in evolutionary and genomic analyses of pathogens. The simulation results presented are solid, although there are some aspects of the methodology that require further investigation in order to establish their validity. The application to SARS-CoV-2 shows promise, but would benefit from further evaluation.
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Reviewer #1 (Public Review):
This is an interesting manuscript that proposes a new approach to for accounting for viral diversity within hosts in phylogenetic analyses of pathogens. Concretely, the authors consider sites for which a minor allele exist as an additional base in the substitution model. For example, if at a particular site 60% of reads have an C and 40% have a G, then this site is assigned Cg, as opposed to an C which is typical of analysing consensus sequences. Because we typically model sequence evolution as a Markovian process, as is the case here, the data become naturally more informative, given that there are more states in the Markov chain when adding these bases. As a result, phylogenetic trees estimated using these data are better resolved than those from consensus sequences. The branches of the trees are probably …
Reviewer #1 (Public Review):
This is an interesting manuscript that proposes a new approach to for accounting for viral diversity within hosts in phylogenetic analyses of pathogens. Concretely, the authors consider sites for which a minor allele exist as an additional base in the substitution model. For example, if at a particular site 60% of reads have an C and 40% have a G, then this site is assigned Cg, as opposed to an C which is typical of analysing consensus sequences. Because we typically model sequence evolution as a Markovian process, as is the case here, the data become naturally more informative, given that there are more states in the Markov chain when adding these bases. As a result, phylogenetic trees estimated using these data are better resolved than those from consensus sequences. The branches of the trees are probably also longer, which is why temporal signal becomes more apparent.
I commend the authors on their rigorous simulation study and careful empirical data analyses. However, I strongly suggest they consider whether treating minor alleles as an additional base is biologically realistic and whether this may have implication for other analyses, particularly when there is very high within-host diversity and the number of states in becomes very large.
-
Reviewer #2 (Public Review):
I agree that minor genetic variation could potentially be used to more accurately infer who-infected- whom in an outbreak scenario. Indeed, the use of minor genetic variation has proven very useful in reconstructing transmission chains for chronic infections such as HIV (e.g., see applications using Phyloscanner). To me, it seems that considering the full spectrum of viral genetic diversity within infected hosts would necessarily do the same if not better than considering only consensus-level viral sequence data. This is because there is a necessarily a loss of data and potentially a loss of information when going from considering the genetic composition of viral populations within a host to only considering the consensus sequences of those viral populations. As such, Ortiz et al.'s hypothesis stated on …
Reviewer #2 (Public Review):
I agree that minor genetic variation could potentially be used to more accurately infer who-infected- whom in an outbreak scenario. Indeed, the use of minor genetic variation has proven very useful in reconstructing transmission chains for chronic infections such as HIV (e.g., see applications using Phyloscanner). To me, it seems that considering the full spectrum of viral genetic diversity within infected hosts would necessarily do the same if not better than considering only consensus-level viral sequence data. This is because there is a necessarily a loss of data and potentially a loss of information when going from considering the genetic composition of viral populations within a host to only considering the consensus sequences of those viral populations. As such, Ortiz et al.'s hypothesis stated on lines 66-70 is a reasonable one, and I was looking forward to seeing this hypothesis evaluated in detail in this manuscript.
There are several parts of this manuscript I really like. In particular, encoding within-sample diversity as character states and using that alternative representation of sequence data for phylogenetic inference (as shown in Figure 3) is a very interesting idea, I think. There are some limitations that are not explicitly mentioned, however. For example, when using this 16-character state representation for phylogenetic inference, they assume independence between nucleotide sites. This is a major assumption that can be violated when considering longitudinal intrahost data and transmission dynamics in an outbreak setting, given genetic linkage between sites.I have several major concerns about the work as it stands, particularly in the context of the SARS-CoV-2 application.
Concerns not related to the SARS-CoV-2 application:
Concern #1: Figure 4 shows that a model using within-sample diversity can more accurately reconstruct evolutionary histories than a model that uses only consensus-level genetic data. This is really interesting. The Materials and Methods section (particularly lines 351-354) indicates that the sequence data were generated using certain specified substitution rates. The rates specified seem to be chosen in such a way to facilitate finding an improvement when using within-sample diversity. I don't know whether the relative rates of these 'substitutions' at all mirror "real-life". It would be very useful to have a broader set of analyses here to examine the effect of these 'substitution' rates on the utility of incorporating within-sample diversity into phylogenetic inference. (Also, 1, 100, 200 (line 353) inconsistent with 1, 20, 200 in Supp Table 3)Concern #2: Figure 5 is very interesting, particularly the results at bottleneck sizes of 1-10. What are the 'substitution' rates that are inferred here from using this simulated dataset? The Material and Methods section also does not mention the within-host viral generation time anywhere, as far as I can see (~line 384 states the mutation rate per base per generation cycle but not the length of the generation cycle anywhere).
Concerns related to the SARS-CoV-2 application:
Concern #3: I am very concerned about the testing of this hypothesis on the SARS-CoV-2 data presented. First, 1% is a very low variant calling threshold. Second, analysis of the 17 samples that were resequenced (out of 454) indicated that on average, 39% of iSNVS (intrahost single nucleotide variants) called between duplicate runs were only observed in one of the two runs (line 117). Their analysis in Figure 1 indicates that these discrepant (and seemingly spurious) variants occur at higher levels in high Ct samples (which makes sense; Figure 1b). They therefore decide to limit their analyses to samples with Ct values <= 30. This results in 249 samples. However, if we look at Figure 1b, only ~10% of iSNVs called across duplicate runs with Ct = 30 are shared! That means that 90% of iSNVs in the set appear to be spurious. If we assume that each duplicate run of a sample has approximately the same number of spurious iSNVs, then approximately 82% of iSNVs called in a sample with a Ct of 30 would be spurious. This fraction decreases with samples that have lower Ct values, but even at a Ct of 27, only ~60% of iSNVs called across duplicate runs are shared. All the downstream SARS-CoV-2 analyses based on within-host sample diversity therefore are based on samples where the large majority of considered sample diversity is not real. This leads to me necessarily discounting all of those downstream SARS-CoV-2 results.Concern #4: Lines 153-167: I can't figure out how to square the quantitative results given in this paragraph with what is shown in Figure 2. To me, Figure 2 shows only that Technical Replicates have higher probabilities of sharing a variant than with 'No' relationship. What would also be helpful here so that the reader can get a better feel for the data would be to see the iSNV frequencies plotted over time for the longitudinal replicate samples in the supplement and, for the 'epidemiological' samples to show 'TV plots' in the supplement (as in Fig 3c in McCrone et al. eLife)
Concern #5: Figure 6 and associated text: (a) root-to-tip distance: what units is this distance in? (b) That the authors find a temporal signal in these transmission clusters (where all consensus sequences within a cluster are the same) is interesting but also a bit baffling to me. Given the inference of very small transmission bottlenecks in previous studies (e.g., Martin & Koelle - reanalysis of Popa et al.; Lythgoe et al.; Braun et al.), I don't understand where the temporal signal comes in. Do the samples become more genetically diverse over the outbreak (this seems to be indicated in lines 260-262 but never shown and unlikely given bottleneck sizes)? Additional analyses to help the reader understand WHY within-sample diversity allows for the identification of temporal signal is important. This could involve plotting genetic diversity of the samples by collection date or some other, similar analyses.
Concern #6: Paragraph consisting of lines 229-238 and Figure 7: This analysis stops abruptly. What are the conclusions here? Figure 7a (right) seems inconsistent to me with Figure 7b and 7C results. Also, the main hypothesis put forward in this paper is that within-sample sequence data can better resolve who-infected-whom in an outbreak setting. Figure 7b and 7c however are never compared against analogous panels that use just consensus sequences. (Even though the consensus sequences are the same, according to Figure 7a, the inferences shown in Figures 7b and 7c could use additional data such as collection times, etc. that would provide information even when using exclusively consensus-level data). Also, do the analyses in Figures 7b and 7c use the 16-character state model at all? I think Supp Figure 9 is relevant here but not sure how?)
Additional concerns:
Concern #7: Some of the stated conclusions, particularly in the Discussion section and in the Abstract, do not seem to be supported by the presented results. For example, line 27: 'within-sample diversity is stable among repeated serial samples from the same host': Figure 2 does not show this conclusively. Line 28: 'within-sample diversity... is transmitted between those cases with known epidemiological links': Figure 2 also does not show this conclusively. Line 29: 'within-sample diversity... improves phylogenetic inference and our understanding of who infected whom': Figure 7b/c results using within-sample diversity is never compared against results that use only consensus, so improvement not demonstrated. Line 272-273: 'samples with shorter distance in the consensus phylogeny were more likely to share low frequency variants'. Line 287: 'We demonstrated that phylogenies... were heavily biased'.Concern #8: The manuscript at times does not cite previous work that is highly relevant and thus overstates the novelty of the current work. For example: lines 21-23: '..conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences...' Phyloscanner uses within-sample diversity, for example, as does SCOTTI. These are finally cited in the discussion section (~line 310), but because this previous work is not acknowledged earlier in the manuscript, the novelty of the work presented here is somewhat overstated.
In sum, I think that the 16 character-state model is a very interesting model. More analyses on simulated data would be helpful to expand on when below-the-consensus level genetic data would truly be informative of phylogenetic relationships and who-infected-whom in outbreak settings. The SARS-CoV-2 analyses are very worrisome to me, given the inclusion of samples where the majority of considered within-sample genetic diversity is very likely not real. Some of the stated conclusions appear to either be at odds with the results presented or not directly evaluated.
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