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.

This work was carried out in collaboration between both authors. Author AOO designed the study, performed the statistical analysis, wrote the protocol and wrote the first draft of the manuscript. Both authors read and approved the final manuscript.

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The study investigated the effects of trade openness, electricity consumption, education and technology on agricultural value addition growth in Africa. It used data accessed from World Bank Data Base (1971-2011) which were subjected to econometric tests before applying the bound test for cointegration using Autoregressive Distributed Lag model. Results indicated the existence of a steady-state long-run relationship between agricultural value addition and its hypothesized determinants. Finally, technology (0.446) at p < 0.01and electricity consumption (1.695), at p<0.01 were the major long-run determinants of agricultural value addition growth. However technology (Wald stat =-0.551) with p <0.01, electricity (Wald Stat = 0.246) at p<0.01 and education (Wald F Stat =-0.417) with p < 0.01 explained the variation in agricultural value addition in Africa in the short-run. It was recommended that African nations should invest on electricity generation, technology development and skill acquisitions for developing agricultural value chain on the continent .

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