Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Quantitative ColourDopplerSonography evaluation of cerebral venous outflow: a comparative study between patients with multiple sclerosis and controls.

BackgroundInternal Jugular Veins (IJVs) are the principle outflow pathway for intracranial blood in clinostatism condition. In the seated position, IJVs collapse, while Vertebral Veins (VVs) increase the venous outflow and partially compensate the venous drainage. Spinal Epidural Veins are an additional drainage pathway in the seated position. Colour- Doppler-Sonography (CDS) examination is able to demonstrate IJVs and VVs outflow in different postural and respiratory conditions. The purpose of this study was to evaluate CDS quantification of the cerebral venous outflow (CVF) in healthy subjects and patients with multiple sclerosis (MS).Methodology/principal findingsIn a group of 27 healthy adults (13 females and 14 males; mean age 37.8 ± 11.2 years), and 52 patients with MS (32 females and 20 males; mean age 42.6 ± 12.1 years), CVF has been measured in clinostatism and in the seated position as the sum of the flow in IJVs and VVs. The difference between CVF in clinostatism and CVF in the seated position (ΔCVF) has been correlated with patients' status (healthy or MS), and a number of clinical variables in MS patients. Statistical analysis was performed by Fisher's exact test, non-parametric Mann-Whitney U test, ANOVA Kruskal-Wallis test, and correntropy coefficient. The value of ΔCVF was negative in 59.6% of patients with MS and positive in 96.3% of healthy subjects. Negative ΔCVF values were significantly associated with MS (pConclusions/significanceNegative ΔCVF has a hemodynamic significance, since it reflects an increased venous return in the seated position. This seems to be a pathologic condition. In MS patients, a vascular dysregulation resulting from involvement of the autonomous nervous system may be supposed. ΔCVF value should be included in the quantitative CDS evaluation of the cerebral venous drainage, in order to identify cerebral venous return abnormalities

Fisher's exact test applied to ΔCSA of right and left IJV within the two study groups: patients with MS (MS) and healthy subjects (CTR).

<p>The differences were not significant.</p

Graphic corresponding to the contingency table (Tab.II).

<p>Fischer exact test resulted in highly significance (p<0.0001). The sign of the ΔCVF is associated to specific Group: negative value is related to pathologic condition.</p

Mann-Whitney test demonstrates a significant difference (p<0.0001) in the comparison of the ΔCVF values between the group of healthy subjects (CTR) and the aggregated groups (1,2,3,4) of patients (MS).

<p>Mann-Whitney test demonstrates a significant difference (p<0.0001) in the comparison of the ΔCVF values between the group of healthy subjects (CTR) and the aggregated groups (1,2,3,4) of patients (MS).</p

MS clinical summary data.

<p>MS clinical summary data.</p

Contingency table.

<p>CTR = healthy subjects; MS = patients with MS; ΔCVF = CVF in clinostatism - CVF in the seated position.</p

Box-and-Whiskers plot of ΔCVF within prevalent subgroups of MS and controls.

<p>Group 2: RRMS; Group 3: SPMS; Group 5: controls. There is a significant difference between Group 5 and the Groups 2 and 3.</p