Correlation assessment of SARS-CoV-2 variants and their subvariants present in clinical and wastewater samples in Oregon, USA (February 7, 2021 - February 26, 2022) using the Freyja bioinformatics approach
- 1School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, United States of America
- 2Center for Quantitative Life Sciences, Oregon State University, Corvallis, Oregon, United States of America
- 3Public Health Division, Oregon Health Authority, Portland, Oregon, United States of America
Abstract
Background: Wastewater surveillance is a valuable tool for monitoring SARS-CoV-2 at the community level. As the virus diversified into many variants and subvariants that share overlapping mutations, resolving them accurately from wastewater becomes a key bioinformatic challenge. Objectives and aims: This study evaluated two distinct bioinformatic approaches, multilocus sequence typing (MLST) and Freyja, for identifying SARS-CoV-2 variants and subvariants in Oregon wastewater samples collected from February 2021 to February 2022. Methods: The MLST approach identified SARS-CoV-2 variants using unique mutations curated from clinical samples. In contrast, the Freyja approach resolved variant and subvariant abundances using genome wide mutation profiles weighted by sequencing depth. In this study, the variant and subvariants relative abundances produced by both approaches were compared against those observed in clinical surveillance data. Results: Both approaches identified SARS-CoV-2 variants at relative abundances that agreed closely with those observed in clinical surveillance data. However, only the Freyja approach identified over 200 Delta subvariants, divided into three clades (21A, 21I and 21J) and two levels (Level 1 and 2) based on Pango subvariants. Delta subvariants showed strong agreement at Level 1 subvariants (rs = 0.892–0.944), while agreement at Level 2 subvariants was inconsistent (rs = 0.324–0.903). Conclusions: The Freyja approach provided enhanced resolution of SARS-CoV-2 variants and subvariants in wastewater, at abundances that agreed with clinical surveillance. This added resolution is a critical advantage for public health surveillance as SARS-CoV-2 continues to evolve and share mutations across variants and subvariants.
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BibTeX
@article{bhatia2026correlation,
title = {{Correlation assessment of SARS-CoV-2 variants and their subvariants present in clinical and wastewater samples in Oregon, USA (February 7, 2021 - February 26, 2022) using the Freyja bioinformatics approach}},
author = {Anirudh Bhatia and Justin Elser and Steven J. Carrell and Brent Kronmiller and Melissa Sutton and Christine Kelly and Tyler S. Radniecki},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357591},
url = {https://doi.org/10.1371/journal.pone.0357591},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Correlation assessment of SARS-CoV-2 variants and their subvariants present in clinical and wastewater samples in Oregon, USA (February 7, 2021 - February 26, 2022) using the Freyja bioinformatics approach
AU - Anirudh Bhatia
AU - Justin Elser
AU - Steven J. Carrell
AU - Brent Kronmiller
AU - Melissa Sutton
AU - Christine Kelly
AU - Tyler S. Radniecki
PY - 2026
DA - 2026/09/09
JO - PLOS One
DO - 10.1371/journal.pone.0357591
UR - https://doi.org/10.1371/journal.pone.0357591
AB - Background: Wastewater surveillance is a valuable tool for monitoring SARS-CoV-2 at the community level. As the virus diversified into many variants and subvariants that share overlapping mutations, resolving them accurately from wastewater becomes a key bioinformatic challenge. Objectives and aims: This study evaluated two distinct bioinformatic approaches, multilocus sequence typing (MLST) and Freyja, for identifying SARS-CoV-2 variants and subvariants in Oregon wastewater samples collected from February 2021 to February 2022. Methods: The MLST approach identified SARS-CoV-2 variants using unique mutations curated from clinical samples. In contrast, the Freyja approach resolved variant and subvariant abundances using genome wide mutation profiles weighted by sequencing depth. In this study, the variant and subvariants relative abundances produced by both approaches were compared against those observed in clinical surveillance data. Results: Both approaches identified SARS-CoV-2 variants at relative abundances that agreed closely with those observed in clinical surveillance data. However, only the Freyja approach identified over 200 Delta subvariants, divided into three clades (21A, 21I and 21J) and two levels (Level 1 and 2) based on Pango subvariants. Delta subvariants showed strong agreement at Level 1 subvariants (rs = 0.892–0.944), while agreement at Level 2 subvariants was inconsistent (rs = 0.324–0.903). Conclusions: The Freyja approach provided enhanced resolution of SARS-CoV-2 variants and subvariants in wastewater, at abundances that agreed with clinical surveillance. This added resolution is a critical advantage for public health surveillance as SARS-CoV-2 continues to evolve and share mutations across variants and subvariants.
SN - 1932-6203
ER -