Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19
9 affiliations
- 1Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
- 2Rheumatology and Autoinflammatory Diseases Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
- 3Artificial Intelligence Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
- 4Data Science and Computation Facility, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy
- 5Biostatistics Unit, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy
- 6Autoimmunity, Allergology and Innovative Biotechnology Unit, AUSL-IRCCS of Reggio Emilia, 42123 Reggio Emilia, Italy
- 7Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy
- 8CECAM-EPFL, 1015 Lausanne, Switzerland
- 9UOSD Research Laboratories Aggregation Area, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
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.
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BibTeX
@article{rosamilia2026evaluation,
title = {{Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19}},
author = {Francesca Rosamilia and Giada Recchi and Davide Cangelosi and Sergio Decherchi and Francesca Lantieri and Stefania Croci and Carlo Salvarani and Andrea Cavalli and Marta Rusmini and Isabella Ceccherini and Paolo Uva},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27209152},
url = {https://www.mdpi.com/1422-0067/27/20/9152},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19
AU - Francesca Rosamilia
AU - Giada Recchi
AU - Davide Cangelosi
AU - Sergio Decherchi
AU - Francesca Lantieri
AU - Stefania Croci
AU - Carlo Salvarani
AU - Andrea Cavalli
AU - Marta Rusmini
AU - Isabella Ceccherini
AU - Paolo Uva
PY - 2026
DA - 2026/10/15
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27209152
UR - https://www.mdpi.com/1422-0067/27/20/9152
AB - 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.
SN - 1422-0067
ER -