Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

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.

The clinical presentation of COVID-19 is extremely heterogeneous, ranging from asymptomatic to severely ill patients. Thus, host genetic factors may be involved in determining disease presentation and progression. Given that carriers of single cystic fibrosis (CF)-causing variants of the CFTR gene—CF-carriers—are more susceptible to respiratory tract infections, our aim was to determine their likelihood of undergoing severe COVID-19. We implemented a cohort study of 874 individuals diagnosed with COVID-19, during the first pandemic wave in Italy. Whole exome sequencing was performed and validated CF-causing variants were identified. Forty subjects (16 females and 24 males) were found to be CF-carriers. Among mechanically ventilated patients, CF-carriers were more represented (8.7%) and they were significantly (p < 0.05) younger (mean age 51 years) compared to noncarriers (mean age 61.42 years). Furthermore, in the whole cohort, the age of male CF-carriers was lower, compared to noncarriers (p < 0.05). CF-carriers had a relative risk of presenting an abnormal inflammatory response (CRP ≥ 20 mg/dL) of 1.69 (p < 0.05) and their hazard ratio of death at day 14 was 3.10 (p < 0.05) in a multivariate regression model, adjusted for age, sex and comorbidities. In conclusion, CF-carriers are more susceptible to the severe form of COVID-19, showing also higher risk of 14-day death.

Host genetics is an emerging theme in COVID-19 and few common polymorphisms and some rare variants have been identified, either by GWAS or candidate gene approach, respectively. However, an organic model is still missing. Here, we propose a new model that takes into account common and rare germline variants applied in a cohort of 1,300 Italian SARS-CoV-2 positive individuals. Ordered logistic regression of clinical WHO grading on sex and age was used to obtain a binary phenotypic classification. Genetic variability from WES was synthesized in several boolean representations differentiated according to allele frequencies and genotype effect. LASSO logistic regression was used for extracting relevant genes. We defined about 100 common driver polymorphisms corresponding to classical “threshold model”. Extracted genes were demonstrated to be gender specific. Stochastic rare more penetrant events on about additional 100 extracted genes, when occurred in a medium or severe background (common within the family), simulate Mendelian inheritance in 14% of subjects (having only 1 mutation) or oligogenic inheritance (in 10% having 2 mutations, in 11% having 3 mutations, etc).The combined effect of common and rare results can be described as an integrated polygenic score computed as: (nseverity − nmildness) + F (mseverity − mmildness)where n is the number of common driver genes, m is the number of driver rare variants and F is a factor for appropriately weighing the more powerful rare variants. We called the model “post-Mendelian”. The model well describes the cohort, and patients are clustered in severe or mild by the integrated polygenic scores, the F factor being calibrated around 2, with a prediction capacity of 65% in males and 70% in females. In conclusion, this is the first comprehensive model interpreting host genetics in a holistic post-Mendelian manner. Further validations are needed in order to consolidate and refine the model which however holds true in thousands of SARS-CoV-2 Italian subjects.

Thromboembolism is a frequent cause of severity and mortality in COVID-19. However, the etiology of this phenomenon is not well understood. A cohort of 1,186 subjects, from the GEN-COVID consortium, infected by SARS-CoV-2 with different severity were stratified by sex and adjusted by age. Then, common coding variants from whole exome sequencing were mined by LASSO logistic regression. The homozygosity of the cell adhesion molecule P-selectin gene (SELP) rs6127 (c.1807G>A; p.Asp603Asn) which increases platelet activation is found to be associated with severity in the male subcohort of 513 subjects (Odds Ratio= 2.27, 95% Confidence Interval 1.54-3.36). As the SELP gene is downregulated by testosterone, the odd ratio is increased in males older than 50 (OR 2.42, 95% CI 1.53-3.82). Asn/Asn homozygotes have increased D-dimers values especially when associated with poly Q≥23 in the androgen receptor (AR) gene (OR 3.26, 95% CI 1.41-7.52). These results provide a rationale for the repurposing of antibodies against P-selectin as adjuvant therapy in rs6127 male homozygotes especially if older than 50 or with impaired AR gene.