Multiple genetic control of anti-COVID-19 vaccine response by HLA locus (preprint)

Since the beginning of the anti-COVID-19 vaccination campaign, it has become evident that vaccinated subjects exhibit considerable inter-individual variability in the response to the vaccine that could be partly explained by host genetic factors. A recent study reported that the immune response elicited by the Oxford-AstraZeneca vaccine in individuals from the United Kingdom was influenced by a specific allele of the human leukocyte antigen gene HLA-DQB1. We performed a genome-wide association study to investigate the genetic determinants of the antibody response to the Pfizer-BioNTech vaccine in an Italian cohort of 1,351 subjects. We confirmed the involvement of the HLA locus and observed significant associations with variants in HLA-A gene. In particular, the HLA-A*03:01 was the most significantly associated with serum levels of anti-SARS-CoV-2 antibodies. These results support the hypothesis that HLA genes modulate the response to anti-COVID-19 vaccines and highlight the need for genetic studies in diverse populations.

An explainable model of host genetic interactions linked to COVID-19 severity (preprint)

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k -fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from tree-based models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.

COVID-19 Mortality in Italy Varies by Patient Age, Sex and Pandemic Wave (preprint)

SARS-CoV-2 has caused a worldwide epidemic of enormous proportions, which resulted in different mortality rates in different countries for unknown reasons. We analyzed factors associated with mortality using data from the Italian national database of SARS-CoV-2-positive cases, including more than 4 million cases, >415 thousand hospitalized for coronavirus disease-19 (COVID-19) and >127 thousand deceased. For patients for whom age, sex and date of infection detection were available, we determined the impact of these variables on mortality 30 days after the date of diagnosis or hospitalization. Multivariable Cox analysis showed that each of the analyzed variables independently affected COVID-19 mortality. Specifically, in the overall series, age was the main risk factor for mortality, with HR >100 in the age groups older than 65 years compared with a reference group of 15-44 years. Male sex presented a two-fold higher risk of death than females. Patients infected after the first pandemic wave, defined up to 30 June 2020, had about 3-fold lower risk of death than those infected during the first wave. Thus, in a series of all confirmed SARS-CoV-2-infected cases in an entire European nation, elderly age was by far the most significant risk factor for COVID-19 mortality, confirming that protecting the elderly should be a priority in pandemic management. Male sex and being infected during the first wave were additional risk factors associated with COVID-19 mortality.

COVID-19 mortality in Italy varies by patient age, sex and pandemic wave (preprint)

BackgroundSARS-CoV-2 has caused a worldwide epidemic of enormous proportions, which resulted in different mortality rates in different countries for unknown reasons. AimWe aimed to evaluate which independent parameters are associated with risk of mortality from COVID-19 in a series that includes all Italian cases, ie, more than 4 million individuals infected with the SARS-CoV-2 coronavirus. MethodsWe analyzed factors associated with mortality using data from the Italian national database of SARS-CoV-2-positive cases, including more than 4 million cases, >415 thousand hospitalized for coronavirus disease-19 (COVID-19) and >127 thousand deceased. For patients for whom age, sex and date of infection detection were available, we determined the impact of these variables on mortality 30 days after the date of diagnosis or hospitalization. ResultsMultivariable Cox analysis showed that each of the analyzed variables independently affected COVID-19 mortality. Specifically, in the overall series, age was the main risk factor for mortality, with HR >100 in the age groups older than 65 years compared with a reference group of 15-44 years. Male sex presented an excess risk of death (HR = 2.1; 95% CI, 2.0-2.1). Patients infected in the first pandemic wave (before 30 June 2020) had a greater risk of death than those infected later (HR = 2.7; 95% CI, 2.7-2.8). ConclusionsIn a series of all confirmed SARS-CoV-2-infected cases in an entire European nation, elderly age was by far the most significant risk factor for COVID-19 mortality, confirming that protecting the elderly should be a priority in pandemic management. Male sex and being infected during the first wave were additional risk factors associated with COVID-19 mortality.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. (preprint)

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 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, 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. Noteworthly, 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.

Lung expression of genes encoding SARS-CoV-2 cell entry molecules and antiviral restriction factors: interindividual differences are associated with age and germline variants (preprint)

Germline variants in genes involved in SARS-CoV-2 cell entry (i.e. ACE2 and TMPRSS2) may influence the susceptibility to infection, as may polymorphisms in genes involved in the innate host response to viruses (e.g. APOBEC3 family). We searched for polymorphisms acting, in lung tissue, as expression quantitative trait loci (eQTLs) for 15 candidate COVID-19 susceptibility genes, selected for their roles in virus cell entry and host antiviral responses. No significant eQTLs were identified for ACE2 and TMPRSS2 genes, whose expression levels did not associate with either sex or age of the 408 patients whose non-diseased lung tissue was analyzed. Instead, we identified seven cis-eQTLs (FDR<0.05) for APOBEC3D and APOBEC3G (rs139296, rs9611092, rs139331, rs8177832, rs17537581, rs61362448, and rs738469). The genetic control of the expression of APOBEC3 genes, which encode enzymes that interfere with virus replication, may explain interindividual differences in risk or severity of viral infections. Future studies should investigate the role of host genetics in COVID-19 patients using a genome-wide approach, to identify other genes whose expression levels are associated with susceptibility to SARS-CoV-2 infection or COVID-19 severity. Author summaryIdentification of expression quantitative trait loci (eQTLs) has become commonplace in functional studies on the role of individual genetic variants in susceptibility to diseases. In COVID-19, it has been proposed that individual variants in SARS-CoV-2 cell entry and innate host response genes may influence the susceptibility to infection. We searched for polymorphisms acting, in non-diseased lung tissue of 408 patients, as eQTLs for 15 candidate COVID-19 susceptibility genes, selected for their roles in virus cell entry and host antiviral responses. Seven cis-eQTLs were detected for APOBEC3D and APOBEC3G genes, which encode enzymes that interfere with virus replication. No significant eQTLs were identified for ACE2 and TMPRSS2 genes. Therefore, the identified eQTLs may represent candidate loci modulating interindividual differences in risk or severity of SARS-CoV-2 virus infection.

The effectiveness of non-pharmaceutical interventions in containing epidemics: a rapid review of the literature and quantitative assessment (preprint)

The number of confirmed COVID-19 cases has rapidly increased since discovery of the disease in December 2019. In the absence of medical countermeasures to stop the spread of the disease (i.e. vaccines), countries have responded by implementing a suite of non-pharmaceutical interventions (NPIS) to contain and mitigate COVID-19. Individual NPIs range in intensity (e.g. from lockdown to public health campaigns on personal hygiene), as does their impact on reducing disease transmission. This study uses a rapid review approach and investigates evidence from previous epidemic outbreaks to provide a quantitative assessment of the effectiveness of key NPIs used by countries to combat the COVID-19 pandemic. Results from the study are designed to help countries enhance their policy response as well as inform transition strategies by identifying which policies should be relaxed and which should not.