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

Abstract

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 the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the 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 high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). 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. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, 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”, supporting their link with COVID-19 severity outcome.

Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust

“…Indeed, multiple studies have already been conducted identifying potential susceptibility loci in the human genome that may put patients at increased risk of death or other severe outcomes, including mutations in genes linked to immune response, blood clotting and mucus production [ 72 – 75 ]. In particular, a recent study using machine learning approaches such as XGBoost identified variants from whole exome sequencing associated with severe COVID-19 [ 76 ]. These data identified associations between age, gender, and 16 variants linked to immune system and inflammatory processes able to predict severe outcomes with high accuracy.…”

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

“…The role of genetics in modulating the severity and outcome of COVID-19 has been a subject of intense research and growing evidence supports the existence of individual genetic factors predisposing to a severe outcome 8 . For example, the COVID-19 Host Genetics Initiative (HGI) consortium performed large-scale meta-analyses of genome-wide data from over nine thousand critically ill cases (defined as patients who required respiratory support or died from COVID-19) and over 25 thousand hospitalized cases with moderate or severe disease, compared with up to five million controls 9 .…”

Epigenetic and transcriptional responses in circulating leukocytes are associated with future decompensation during SARS-CoV-2 infection

“… 8 Indeed, extensive work has described the delicate interplay between severity-associated genetic risk factors and immune activation in the lungs during COVID-19. 9 , 10 Additional work has focused on how host immune responses across the spectrum of disease manifest through epigenetic and transcriptional networks in circulating or resident leukocytes of various types. 11 , 12 , 13 , 14 , 15 Single-cell RNA sequencing studies of peripheral blood mononuclear cells from patients infected with SARS-CoV-2 suggest a distinct peripheral immune transcriptional signature of severe COVID-19 consisting of alteration of interferon (IFN)-stimulated genes in numerous cell types, as well as antigen presentation and proinflammatory pathways.…”