ABSTRACT
Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p=5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights. Author SummaryCOVID-19 clinical outcomes vary immensely, but a patients genetic make-up is an important determinant of how they will fare against the virus. While many genetic variants commonly found in the populations were previously found to be contributing to more severe disease by the COVID-19 Host Genetics Initiative, it isnt clear if more rare variants found in less individuals could also play a role. This is important because genetic variants with the largest impact on COVID-19 severity are expected to be rarely found in the population, and these rare variants require different technologies to be studies (usually whole-exome or whole-genome sequencing). Here, we combined sequencing results from 21 cohorts across 12 countries to perform a rare variant association study. In an analysis comprising 5,085 participants with severe COVID-19 and 571,737 controls, we found that the gene for toll-like receptor 7 (TLR7) on chromosome X was an important determinant of severe COVID-19. Importantly, despite being found on a sex chromosome, this observation was consistent across both sexes.
ABSTRACT
To distinguish correlation from causation, we performed in-silico analyses of three COVID-19 outcomes (N>1,000,000). We show genetic correlation and putative causality with depressive symptoms, metformin use, and alcohol use. COVID-19 risk loci associated with several hematologic biomarkers. Comprehensive findings inform genetic contributions to COVID-19 epidemiology, molecular mechanisms, and risk factors.
ABSTRACT
Angiotensin-converting enzyme-2 (ACE2) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed a multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their over-represented biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p<4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p<4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within {+/-} 10 kb of ACE2-network genes we characterized functional consequences (among others) using miRNA binding-site targets. MiRNAs affected by ACE2-network variants revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. With respect to variants mapped to the ACE2-network, we observed COVID-19 related associations in RORA, SLC12A6 and SLC6A19 genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/