ArticlePNAS nexus2026
Combinatorial analysis of clinical and genomic data used to assess the association between SARS-CoV-2 mutations and disease severity.
Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
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