SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

Area of Science:

• Virology
• Genomics
• Epidemiology

Background:

• The COVID-19 pandemic highlighted the need for tracking viral mutations and predicting disease severity.
• Emergence of SARS-CoV-2 variants increased transmissibility and severity.
• Genome-based prediction algorithms offer a potential tool for pandemic management.

Purpose of the Study:

• To evaluate a genome-based algorithm for predicting clinical severity of COVID-19.
• To assess the correlation between genome-based severity predictions and PCR-measured viral load.
• To determine if viral load, a surrogate for severity, can be inferred from genomic data.

Main Methods:

• Utilized a previously published genome-based severity predictive algorithm.
• Compared algorithm predictions with PCR-derived cycle threshold (Ct) values from 716 viral genomes.
• Analyzed Ct values for samples predicted as "severe" (>0.5 probability) and "mild" (<0.5 probability).
• Correlated predicted severity probability with Ct values and analyzed quartiles of severity probability.

Main Results:

• Samples predicted as "severe" had a lower average Ct value (18.3) than "mild" samples (20.4) (P=0.0017).
• A significant negative correlation (r=-0.199) was observed between predicted severity probability and Ct value.
• The highest severity probability quartile showed a significantly lower Ct (16.6) compared to the lowest quartile (21.4) (P=0.0045).

Conclusions:

• Genome-based severity predictions align with clinical diagnostic measures (PCR viral load).
• The findings suggest that relative disease severity can be inferred from diagnostic test values.
• This approach holds promise for real-time monitoring and prediction during pandemics.