Computational Network Analysis of Host Genetic Risk Variants of Severe COVID-19

Abstract

Background: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. Results: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. Conclusions: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications. tions. Following SARS-CoV-2 infection, host genetic variations contribute to the development of illnesses that require critical care or hospitalization [1, 2]. Genome-wide association studies (GWAS) have been conducted to identify and validate risk tergenic, intronic, and transcript variants that are distributed in 60 genes in the 64 host genome. We found two gene clusters that have an impact on severe outcomes

Integrative Multiomics Network Analysis of Genetic Risk Factors to Infer Biomarkers and Therapeutic Targets for Rheumatoid Arthritis

“…RA also has an association with infections such as Mycoplasma, Epstein-Barr virus, Helicobacter pylori, and COVID-19 [12,13]. Such infections can trigger the activation of T cells and regulate the secretion of autoantibodies such as rheumatoid factors, which contribute to the pathogenesis of RA [13].…”

“…Further research has also linked neurological diseases such as multiple sclerosis (MS) and Alzheimer's disease (AD) with RA. In patients with MS, dysfunction of the immune system leads to antigen-specific activation and secretion of inflammatory cytokines that create a chronic systemic inflammatory state, which consequently increases the risk of developing RA [12,16]. Although RA and AD are different conditions, genetic studies have revealed a few key genes commonly associated with both RA and AD [16][17][18].…”