UnMuted: Defining SARS-CoV-2 Lineages According to Temporally Consistent Mutation Clusters in Wastewater Samples

TL;DR

This paper introduces UnMuted, a method to define SARS-CoV-2 lineages from wastewater samples by analyzing mutation clusters over time, without relying on clinical data, and demonstrates its effectiveness in matching known lineages.

Contribution

UnMuted is a novel approach that infers SARS-CoV-2 lineages from wastewater mutation data using temporal clustering, independent of clinical sequence information.

Findings

Estimated lineage definitions match known lineages with consistent temporal trends.

The models effectively capture lineage dynamics over time.

Mutation clusters can define lineages without clinical sequence data.

Abstract

SARS-CoV-2 lineages are defined according to placement in a phylogenetic tree, but approximated by a list of mutations based on sequences collected from clinical sampling. Wastewater lineage abundance is generally found under the assumption that the mutation frequency is approximately equal to the sum of the abundances of the lineages to which it belongs. By leveraging numerous samples collected over time, I am able to estimate the temporal trends of the abundance of lineages as well as the definitions of those lineages. This is accomplished by assuming that collections of mutations that appear together over time constitute lineages, then estimating the proportions as before. Three main models are considered: Two that incorporate an explicit temporal trend with different constraints on the abundances, and one that does not estimate a temporal component. It is found that estimated lineage definitions correspond to known lineage definitions with matching temporal trends for the lineage abundances, despite having no information from clinical samples. I refer to this set of methods as "UnMuted" since the mutations are allowed to speak for themselves.