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Updated: Jun 12, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Combinatorial analysis of clinical and genomic data used to assess the association between SARS-CoV-2 mutations and
Saori Ishiwatari1, Kousuke Tanimoto2, Yukie Tanaka1
1Department of Molecular Microbiology and Immunology, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Bunkyo-ku, Tokyo 113-8510, Japan.
Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in late 2019 and caused the coronavirus disease 2019 pandemic, has undergone genomic evolution, yielding variants of concern which include the Alpha, Delta, and Omicron variants. Since the virus continues to mutate, we designed this study to assess the impact of SARS-CoV-2 mutations on severity; using PLINK2 software, we analyzed genomic and clinical data from 310 hospitalized patients at the Institute of Science Tokyo Hospital. The analysis identified 64 statistically significant severity-associated mutations. Although the Omicron variants are generally associated with less severe symptoms than the Delta variants, our approach identified statistically significant Omicron variant-specific mutations that were associated with severe disease, as well as additional mutations for which the odds ratios and 95% CIs indicated a consistent trend. Our retrospective analysis of SARS-CoV-2 genomic and clinical information may help clarify the biological significance of mutations.
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