Comparison of multiple whole-genome and Spike-only sequencing protocols for estimating variant frequencies via wastewater-based epidemiology

Sequencing of SARS-CoV-2 in wastewater provides a key opportunity to monitor the prevalence of variants spatiotemporally, potentially facilitating their detection simultaneously with, or even prior to, observation through clinical testing. However, there are multiple sequencing methodologies available. This study aimed to evaluate the performance of alternative protocols for detecting SARS-CoV-2 variants. We tested the detection of two synthetic RNA SARS-CoV-2 genomes in a wide range of ratios and at two concentrations representative of those found in wastewater using whole-genome and Spike-gene-only protocols utilising Illumina and Oxford Nanopore platforms. We developed a Bayesian hierarchical model to determine the predicted frequencies of variants and the error surrounding our predictions. We found that most of the sequencing protocols detected polymorphic nucleotide frequencies at a level that would allow accurate determination of the variants present at higher concentrations. Most methodologies, including the Spike-only approach, could also predict variant frequencies with a degree of accuracy in low-concentration samples but, as expected, with higher error around the estimates. All methods were additionally confirmed to detect the same prevalent variants in a set of wastewater samples. Our results provide the first quantitative statistical comparison of a range of alternative methods that can be used successfully in the surveillance of SARS-CoV-2 variant frequencies from wastewater. ImpactGenetic sequencing of SARS-CoV-2 in wastewater provides an ideal system for monitoring variant frequencies in the general population. The advantages over clinical data are that it is more cost efficient and has the potential to identify new variants before clinical testing. However, to date, there has been no direct comparison to determine which sequencing methodologies perform best at identifying the presence and prevalence of variants. Our study compares seven sequencing methods to determine which performs best. We also develop a Bayesian statistical methodology to estimate the confidence around variant frequency estimates. Our results will help monitor SARS-CoV-2 variants in wastewater, and the methodology could be adapted for other disease monitoring, including future pandemics.