RESUMEN
The ongoing COVID-19 pandemic, caused by SARS-CoV-2, constitutes a tremendous global health issue. Continuous monitoring of the virus has become a cornerstone to make rational decisions on implementing societal and sanitary measures to curtail the virus spread. Additionally, emerging SARS-CoV-2 variants have increased the need for genomic surveillance to detect particular strains because of their potentially increased transmissibility, pathogenicity and immune escape. Targeted SARS-CoV-2 sequencing of wastewater has been explored as an epidemiological surveillance method for the competent authorities. Few quality criteria are however available when sequencing wastewater samples, and those available typically only pertain to constructing the consensus genome sequence. Multiple variants circulating in the population can however be simultaneously present in wastewater samples. The performance, including detection and quantification of low-abundant variants, of whole genome sequencing (WGS) of SARS-CoV-2 in wastewater samples remains largely unknown. Here, we evaluated the detection and quantification of mutations present at low abundances using the SARS-CoV-2 lineage B.1.1.7 (alpha variant) defining mutations as a case study. Real sequencing data were in silico modified by introducing mutations of interest into raw wild-type sequencing data, or by mixing wild-type and mutant raw sequencing data, to mimic wastewater samples subjected to WGS using a tiling amplicon-based targeted metagenomics approach and Illumina sequencing. As anticipated, higher variation, lower sensitivity and more false negatives, were observed at lower coverages and allelic frequencies. We found that detection of all low-frequency variants at an abundance of 10%, 5%, 3% and 1%, requires at least a sequencing coverage of 250X, 500X, 1500X and 10,000X, respectively. Although increasing variability of estimated allelic frequencies at decreasing coverages and lower allelic frequencies was observed, its impact on reliable quantification was limited. This study provides a highly sensitive low-frequency variant detection approach, which is publicly available at https://galaxy.sciensano.be, and specific recommendations for minimum sequencing coverages to detect clade-defining mutations at specific allelic frequencies.
