A mixture model for determining SARS-Cov-2 variant composition in pooled\n samples

Abstract

Despite of the fast development of highly effective vaccines to control the\ncurrent COVID$-$19 pandemic, the unequal distribution and availability of these\nvaccines worldwide and the number of people infected in the world lead to the\ncontinuous emergence of SARS-CoV-2 (Severe Acute Respiratory Syndrome\ncoronavirus 2) variants of concern. It is likely that real-time genomic\nsurveillance will be continuously needed as an unceasing monitoring tool,\nnecessary to follow the spillover of the disease spread and the evolution of\nthe virus. In this context, new genomic variants of SARS-CoV-2 that may emerge\nas a response to selective pressure, including variants refractory to current\nvaccines, makes genomic surveillance programs tools of utmost importance. Here\npropose a statistical model for the estimation of the relative frequencies of\nSARS-CoV-2 variants in pooled samples. This model is built by considering a\npreviously defined selection of genomic polymorphisms that characterize\nSARS-CoV-2 variants. The methods described here support both raw sequencing\nreads for polymorphisms-based markers calling and predefined markers in the VCF\nformat. Results obtained by using simulated data show that our method is quite\neffective in recovering the correct variant proportions. Further, results\nobtained by considering longitudinal data from wastewater samples of two\nlocations in Switzerland agree well with those describing the epidemiological\nevolution of COVID-19 variants in clinical samples of these locations. Our\nresults show that the described method can be a valuable tool for tracking the\nproportions of SARS-CoV-2 variants.\n