Combinatorial analysis of clinical and genomic data used to assess the association between SARS-CoV-2 mutations and disease severity

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ID: 315512
2026
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Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in late 2019 and caused the COVID-19 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% confidence intervals 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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openalex_W7163173712 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors 石渡さおり, Kousuke Tanimoto, Yukië Tanaka, Chihiro Tani‐Sassa, Yuta Takahashi, Kazunari Sonobe, Shuji Tohda, Sayaka Sukegawa, Akinori Kimura, Yoshiaki Gu, Hiroaki Takeuchi
Journal PNAS nexus
Year 2026
DOI
10.1093/pnasnexus/pgag191
URL
https://doi.org/10.1093/pnasnexus/pgag191
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