The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.
【저자키워드】 Data integration, Predictive medicine, 【초록키워드】 COVID-19, knowledge, SARS-COV-2 infection, COVID-19 pandemic, Comorbidities, COVID-19 severity, alleles, Comorbidity, SARS-CoV-2 virus, diabetes, immune system, virus, hypertension, Pathway analysis, Lung diseases, Research, Ontology, Reactome, pathway, phenotype, dataset, Mouse models, information, mouse model, Bioinformatics analysis, mechanism, Analysis, Pathways, Phenotypes, human disease, conjunction, allele, significant enrichment, datasets, enriched pathways, Phenotypic analysis, performed, subsequent, evaluated, conducted, condition, initiated, enriched pathway, the SARS-CoV-2 virus, 【제목키워드】 COVID-19, pathway, viral disease, investigation, reveal, the SARS-CoV-2,