Evaluation of Two Combined Analytical Approaches to Assess the Impact of Rare Variants on Severe COVID-19

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.