Integrative Multi-Omics Framework for Causal Gene Discovery in Long COVID

BackgroundLong COVID, or Post-Acute Sequelae of COVID-19 (PASC), involves persistent, multisystemic symptoms in about 10-20% of COVID-19 patients. Although age, sex, ethnicity, and comorbidities are recognized as risk factors, identifying genetic contributors is essential for developing targeted therapies. MethodsWe developed a multi-omics framework using Transcriptome-Wide Mendelian Randomization (TWMR) and Control Theory (CT). This approach integrates Expression Quantitative Trait Loci (eQTL), Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and Protein-Protein Interaction (PPI) networks to detect causal genes and regulatory nodes that drive critical expression changes in Long COVID. ResultsWe identified 32 causal genes (19 previously reported and 13 novel), which act as regulatory drivers influencing disease risk, progression, and stability. Enrichment analyses highlighted pathways linked to the SARS-CoV-2 response, viral carcinogenesis, cell cycle regulation, and immune function. Analysis of other pathophysiological conditions revealed shared genetic factors across syndromic, metabolic, autoimmune, and connective tissue disorders. Using these genes, we identified three distinct symptom-based subtypes of Long COVID, offering insights for more precise diagnosis and potential therapeutic interventions. Additionally, we provided an open-source Shiny application to enable further data exploration. ConclusionIntegrating TWMR and CT revealed genetic mechanisms and therapeutic targets for Long COVID, with novel genes informing pathogenesis and precision medicine strategies.