Detection of prevalent SARS-CoV-2 variant lineages in wastewater and clinical sequences from cities in Québec, Canada
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Abstract
Wastewater-based epidemiology has emerged as a promising tool to monitor pathogens in a population, particularly when clinical diagnostic capacities become overwhelmed. During the ongoing COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), several jurisdictions have tracked viral concentrations in wastewater to inform public health authorities. While some studies have also sequenced SARS-CoV-2 genomes from wastewater, there have been relatively few direct comparisons between viral genetic diversity in wastewater and matched clinical samples from the same region and time period. Here we report sequencing and inference of SARS-CoV-2 mutations and variant lineages (including variants of concern) in 936 wastewater samples and thousands of matched clinical sequences collected between March 2020 and July 2021 in the cities of Montreal, Quebec City, and Laval, representing almost half the population of the Canadian province of Quebec. We benchmarked our sequencing and variant-calling methods on known viral genome sequences to establish thresholds for inferring variants in wastewater with confidence. We found that variant frequency estimates in wastewater and clinical samples are correlated over time in each city, with similar dates of first detection. Across all variant lineages, wastewater detection is more concordant with targeted outbreak sequencing than with semi-random clinical swab sampling. Most variants were first observed in clinical and outbreak data due to higher sequencing rate. However, wastewater sequencing is highly efficient, detecting more variants for a given sampling effort. This shows the potential for wastewater sequencing to provide useful public health data, especially at places or times when sufficient clinical sampling is infrequent or infeasible.
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SciScore for 10.1101/2022.02.01.22270170: (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 SNV calling and quality control: For each sample, we performed quality control of the raw reads using fastp (v.0.20.0, read length >=70, Phred Score >20 and cut_tail option (S. Chen et al. 2018). Phred Scoresuggested: (ArtificialFastqGenerator, RRID:SCR_006880)For each of these samples, we performed SNV calling from the read mapping using samtools (v.1.10) mpileup and varscan (v.2.4.1) pileup2snp (Li 2011; Koboldt et al. 2012). samtoolssuggested: (SAMTOOLS, RRID:SCR_002105)Thus, we implemented this analysis using the “ConsReg()” function from the R (R Development Core Team 2011) package … SciScore for 10.1101/2022.02.01.22270170: (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 SNV calling and quality control: For each sample, we performed quality control of the raw reads using fastp (v.0.20.0, read length >=70, Phred Score >20 and cut_tail option (S. Chen et al. 2018). Phred Scoresuggested: (ArtificialFastqGenerator, RRID:SCR_006880)For each of these samples, we performed SNV calling from the read mapping using samtools (v.1.10) mpileup and varscan (v.2.4.1) pileup2snp (Li 2011; Koboldt et al. 2012). samtoolssuggested: (SAMTOOLS, RRID:SCR_002105)Thus, we implemented this analysis using the “ConsReg()” function from the R (R Development Core Team 2011) package ConsReg (v.0.1.0), which allows us to perform linear regressions under constraints for regression coefficients. R Development Coresuggested: (R Project for Statistical Computing, RRID:SCR_001905)ConsRegsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
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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