RESUMO
Genetic predisposition to venous thrombosis may impact COVID-19 infection and its sequelae. Participants in the ongoing prospective cohort study, Million Veteran Program (MVP), who were tested for COVID-19, with European ancestry, were evaluated for associations with polygenic venous thromboembolic risk, Factor V Leiden mutation (FVL) (rs6025) and prothrombin gene 3 -UTR mutation (F2 G20210A)(rs1799963), and their interactions. Logistic regression models assessed genetic associations with VTE diagnosis, COVID-19 (positive) testing rates and outcome severity (modified WHO criteria), and post-test conditions, adjusting for outpatient anticoagulation medication usage, age, sex, and genetic principal components. 108,437 out of 464,961 European American MVP participants were tested for COVID-19 with 9786 (9%) positive. PRS(VTE), FVL, F2 G20210A were not significantly associated with the propensity of being tested for COVID-19. PRS(VTE) was significantly associated with a positive COVID-19 test in F5 wild type (WT) individuals (OR 1.05; 95% CI [1.02-1.07]), but not in FVL carriers (0.97, [0.91-1.94]). There was no association with severe outcome for FVL, F2 G20210A or PRS(VTE). Outpatient anticoagulation usage in the two years prior to testing was associated with worse clinical outcomes. PRS(VTE) was associated with prevalent VTE diagnosis among both FVL carriers or F5 wild type individuals as well as incident VTE in the two years prior to testing. Increased genetic propensity for VTE in the MVP was associated with increased COVID-19 positive testing rates, suggesting a role of coagulation in the initial steps of COVID-19 infection. Key PointsO_LIIncreased genetic predisposition to venous thrombosis is associated with increased COVID-19 positive testing rates. C_LIO_LIPRS for VTE further risk stratifies factor V Leiden carriers regarding their VTE risk. C_LI
RESUMO
The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.
