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

Birth prevalence and pattern of osteochondrodysplasias in an inbred high risk population

Define the pattern and birth prevalence of the different types of osteochondrodysplasias in newborn infants in the United Arab Emirates (UAE) population, which is highly inbred and where termination of pregnancy is not accepted. METHODS: All infants with a birth weight of 500 gm and above in the three hospitals in Al Ain Medical District of the UAE were studied prospectively over a period of 5 years. For each live birth or stillbirth with suspected skeletal dysplasia, a detailed clinical and radiological examination was carried out. Pregnancy history and information regarding parental age, ethnic origin, family history, and level of consanguinity were obtained and a pedigree was constructed. RESULTS: Among the 38,048 births during the study period, 36 (9.46/10,000 births) had some type of skeletal dysplasia. Eighteen cases were attributed to autosomal recessive genes (4.7/10,000 births), 10 were due to apparent new dominant mutations (2.62/10,000), five were autosomal dominant type (1.3/10,000) and one was X-linked dominant type (0.26/10,000). In three cases, inheritance was unknown. The most common recessive type of skeletal dysplasia in our series was fibrochondrogenesis (1.05/10,000), followed by chondrodysplasia punctata (0.78/10,000). The birth prevalence rate of skeletal dysplasia doubled in the last 2 years of the 5-year observation period (6.74/10,000 in 1996 vs. 12.86/10,000 in 1999, and 13.45/10,000 in 2000). This increase involved cases caused by new dominant mutations, and occurred mainly in the first half of 1999. CONCLUSION: This prospective study has identified a high birth prevalence of skeletal dysplasia, and risk factors are postulated. These findings represent an accurate birth prevalence figure and a useful baseline for this group of birth defects in the UAE. (C) 2003 Wiley-Liss, Inc

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