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

TL;DR

This paper introduces a statistical mixture model to accurately estimate SARS-CoV-2 variant proportions in pooled samples, aiding real-time genomic surveillance of evolving virus strains.

Contribution

The paper presents a novel mixture model that effectively estimates SARS-CoV-2 variant frequencies from pooled sequencing data, supporting both raw reads and predefined markers.

Findings

Model accurately recovers variant proportions in simulated data.

Method aligns well with epidemiological data from wastewater samples.

Supports both raw sequencing reads and VCF format markers.

Abstract

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