SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2
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Abstract
Background
Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape.
Methods
We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September–27 September 2021) and 15 (19 October–5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month.
Results
We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI 8–23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England.
Conclusions
As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.
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SciScore for 10.1101/2021.12.17.21267925: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Phylogeographic model: For all sequences from REACT-1 rounds 11 (15 April - 3 May 2021), 12 (20 May - 7 June 2021), 13 (24 June - 12 July 2021), 14 (9 September - 27 September 2021) and 15 (19 October - 5 November 2021), in which the lineage designated was Delta or a Delta sub-lineage, a maximum likelihood phylogenetic tree was constructed using a HKY model implemented in IQ-TREE [42]. IQ-TREEsuggested: (IQ-TREE, RRID:SCR_017254)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in …SciScore for 10.1101/2021.12.17.21267925: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Phylogeographic model: For all sequences from REACT-1 rounds 11 (15 April - 3 May 2021), 12 (20 May - 7 June 2021), 13 (24 June - 12 July 2021), 14 (9 September - 27 September 2021) and 15 (19 October - 5 November 2021), in which the lineage designated was Delta or a Delta sub-lineage, a maximum likelihood phylogenetic tree was constructed using a HKY model implemented in IQ-TREE [42]. IQ-TREEsuggested: (IQ-TREE, RRID:SCR_017254)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: We have presented the inferred dynamics between Delta sub-lineages in England between 9 September and 5 November 2021. Our sample’s main strength over those obtained from routine surveillance is the random nature of the testing program leading to a relatively unbiased set of positive samples. However, as the sample sizes we obtain are relatively small compared with routine national surveillance our estimates have lower precision. Lineages were only successfully determined for ∼61% of positive samples, with the ability to determine a lineage heavily influenced by a sample’s Ct value; this has potentially led to biases with lineages with lower Ct values more heavily represented in the dataset. Detecting distinct sub-lineages is a high-dimensional problem, with often many common mutations being shared between distinct lineages with only a small number of distinguishing mutations. This is exacerbated when all the lineages are highly related, as in the current nature of the pandemic in England where all samples are descendants of Delta (B.1.617.2), and can lead to incorrect designations [23]. Further, only sub-lineages that have been defined are able to be assigned to a sample. During the emergence of a new sub-lineage there is a phase of ambiguity when numbers are small and it is unclear if the mutations present warrant the declaration of a new sub-lineage. This can be seen in the detection of AY.4.2 and AY.43 ; both lineages had been circulating for months by Octobe...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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