{
  "abstract": "Severe COVID-19 displays substantial inter-individual variability that cannot be fully explained by common genetic variants, suggesting a contribution of rare functional variation. We performed whole-genome sequencing in 191 Italian patients (96 moderate, 95 severe) to investigate rare coding variants (minor allele frequency <1% and <5%) via complementary network-based and machine learning approaches. Classical single-variant and burden tests did not identify significant associations after multiple testing correction. However, Network-based Heterogeneity Clustering (NHC) revealed that multiple gene clusters were enriched in severe cases, particularly those involving RNA polymerase II–mediated transcription, RNA metabolism, and vesicle-mediated transport pathways. Gene-level rare variant analysis via the variable threshold method identified five genes significantly associated with severe disease (FDR < 0.05), namely, EIF4G3, GTF2F2, LY96, COX6A1, and STK11, which are involved in transcriptional and translational regulation, innate immune signaling, and mitochondrial function. Unsupervised biclustering significantly stratified patients by severity, and a decision tree model highlighted the burden of DOCK10 variants as a key discriminator between phenotypes. Convergence between the network-based and machine learning results further supported the robustness of the identified signals. Overall, our integrative framework underscores the contribution of rare variants to COVID-19 severity and highlights the coordinated perturbation of interconnected molecular pathways as a determinant of clinical outcome.",
  "abstract_shown": true,
  "affiliations": [
    "Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy",
    "Rheumatology and Autoinflammatory Diseases Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy",
    "Artificial Intelligence Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy",
    "Data Science and Computation Facility, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy",
    "Biostatistics Unit, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy",
    "Autoimmunity, Allergology and Innovative Biotechnology Unit, AUSL-IRCCS of Reggio Emilia, 42123 Reggio Emilia, Italy",
    "Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy",
    "CECAM-EPFL, 1015 Lausanne, Switzerland",
    "UOSD Research Laboratories Aggregation Area, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Francesca Rosamilia"
    },
    {
      "affs": [
        1
      ],
      "name": "Giada Recchi",
      "orcid": "0009-0009-1772-9504"
    },
    {
      "affs": [
        2
      ],
      "name": "Davide Cangelosi"
    },
    {
      "affs": [
        3
      ],
      "name": "Sergio Decherchi"
    },
    {
      "affs": [
        4
      ],
      "name": "Francesca Lantieri",
      "orcid": "0000-0002-8923-0165"
    },
    {
      "affs": [
        5
      ],
      "name": "Stefania Croci",
      "orcid": "0000-0002-8622-0439"
    },
    {
      "affs": [
        5
      ],
      "name": "Carlo Salvarani"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Andrea Cavalli"
    },
    {
      "affs": [
        0
      ],
      "name": "Marta Rusmini",
      "orcid": "0000-0002-3109-413X"
    },
    {
      "affs": [
        8
      ],
      "name": "Isabella Ceccherini"
    },
    {
      "affs": [
        0
      ],
      "name": "Paolo Uva",
      "orcid": "0000-0002-9524-8492"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9dbr0dyw0y1z1.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27209152",
  "fulltext_url": "https://europepmc.org/articles/PMC13704901",
  "id": "9dbr0dyw0y1z1",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/20/9152",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W7222358102",
  "pdf_url": "https://europepmc.org/articles/PMC13704901?pdf=render",
  "pmcid": "PMC13704901",
  "pmid": "42941921",
  "published": "2026-10-15",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/9dbr0dyw0y1z1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:57Z",
      "native_id": "10.3390/ijms27209152",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/20/9152"
    },
    {
      "first_seen": "2026-10-30T12:22:12Z",
      "native_id": "MED:42941921",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42941921"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19",
  "title_text": "Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:12Z",
  "url": "https://research.draconitic.com/paper/9dbr0dyw0y1z1",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}