A prospective diagnostic study to measure the accuracy of detection of SARS-CoV-2 Variants Of Concern (VOC) utilising a novel RT-PCR GENotyping algorithm in an In silico Evaluation (VOC-GENIE)
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Abstract
Background
SARS-CoV-2 variants of concern (VOCs) have been associated with higher rate of transmission, and evasion of immunisation and antibody therapeutics. Variant sequencing is widely utilized in the UK. However, only 0.5% (~650k) of the 133 million cumulative positive cases worldwide were sequenced (in GISAID) on 08 April 2021 with 97% from Europe and North America and only ~0.25% (~320k) were variant sequences. This may be due to the lack of availability, high cost, infrastructure and expert staff required for sequencing.
Public health decisions based on a non-randomised sample of 0.5% of the population may be insufficiently powered, and subject to sampling bias and systematic error. In addition, sequencing is rarely available in situ in a clinically relevant timeframe and thus, is not currently compatible with diagnosis and treatment patient care pathways. Therefore, we investigated an alternative approach using polymerase chain reaction (PCR) genotyping to detect the key single nucleotide polymorphisms (SNPs) associated with increased transmission and immune evasion in SARS-CoV-2 variants.
Methods
We investigated the utility of SARS-CoV-2 SNP detection with a panel of PCR-genotyping assays in a large data set of 640,482 SARS-CoV-2 high quality, full length sequences using a prospective in silico trial design and explored the potential impact of rapid in situ variant testing on the COVID-19 diagnosis and treatment patient pathway.
Results
Five SNPs were selected by screening the published literature for a reported association with increased transmission and / or immune evasion. 344881 sequences contained one or more of the five SNPs. This algorithm of SNPs was found to be able to identify the four variants of concern (VOCs) and sequences containing the E484K and L452R escape mutations.
Interpretation
The in silico analysis suggest that the key mutations and variants of SARS-CoV-2 may be reliably detected using a focused algorithm of biologically relevant SNPs. This highlights the potential for rapid in situ PCR genotyping to compliment or replace sequencing or to be utilized instead of sequences in settings where sequencing is not feasible, accessible or affordable. Rapid detection of variants with in situ PCR genotyping may facilitate a more effective COVID-19 diagnosis and treatment patient pathway.
Funding
The study was funded by Primer Design (UK), with kind contributions from all academic partners.
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SciScore for 10.1101/2021.05.05.21256396: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Subsequently, a further alignment using a multiple sequence alignment program (MAFFT) with a NJ / UPGMA phylogeny was performed. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study …SciScore for 10.1101/2021.05.05.21256396: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Subsequently, a further alignment using a multiple sequence alignment program (MAFFT) with a NJ / UPGMA phylogeny was performed. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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