Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Species differences should be considered in the development of bile acid related therapeutics. Although the mechanism of bile acid hepatotoxicity is still not fully understood, continued mechanistic studies will deepen our understanding.

Accelerated failure time analysis of clinical factors and drug properties Spanish DILI Cohort (N = 257) LiverTox Cohort (N = 191) Development of recovery score model External validations Score = 0.227× log e (ALP) + 0.277 × log e (Bilirubin) + 0.161 × log e (Onset) -0.440 × extent of metabolism iDILIC cohort (N = 294) Highlights Bilirubin, ALP, time to onset, and drug metabolism influence DILI recovery time. Parametric time-to-event method identifies risk factors for prolonged DILI recovery. Recovery score model predicts the trajectory of DILI recovery for individual patients.