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).

Hepatitis E virus (HEV) is the most common cause of acute viral hepatitis globally. HEV comprises four genotypes with different geographic distributions and host ranges. We utilize this natural case-control study for investigating the evolution of zoonotic viruses compared to single-host viruses, using 244 near-full-length HEV genomes. Genome-wide estimates of the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS ratio) located a region of overlapping reading frames, which is subject to positive selection in genotypes 3 and 4. The open reading frames (ORFs) involved have functions related to host-pathogen interaction, so genotype-specific evolution of these regions may reflect their fitness. Bayesian inference of evolutionary rates shows that genotypes 3 and 4 have significantly higher rates than genotype 1 across all ORFs. Reconstruction of the phylogenies of zoonotic genotypes demonstrates significant intermingling of isolates between hosts. We speculate that the genotype-specific differences may result from cyclical adaptation to different hosts in genotypes 3 and 4.IMPORTANCE Hepatitis E virus (HEV) is increasingly recognized as a pathogen that affects both the developing and the developed world. While most often clinically mild, HEV can be severe or fatal in certain demographics, such as expectant mothers. Like many other viral pathogens, HEV has been classified into several distinct genotypes. We show that most of the HEV genome is evolutionarily constrained. One locus of positive selection is unusual in that it encodes two distinct protein products. We are the first to detect positive selection in this overlap region. Genotype 1, which infects humans only, appears to be evolving differently from genotypes 3 and 4, which infect multiple species, possibly because genotypes 3 and 4 are unable to achieve the same fitness due to repeated host jumps. KEYWORDS evolution, evolutionary biology, genotypic identification, hepatitis E virus, positive selection H epatitis E virus (HEV) is a nonenveloped single-stranded positive-sense RNA virus that infects approximately 20 million people globally each year (1). It causes large propagated epidemics of acute hepatitis in Asia and Africa, as well as low-level, sporadic, food-associated infections in the developed world (2, 3). Its pathogenicity ranges from acute liver failure and mortality rates as high as 20% in some subpopulations (for example, in pregnant women) to apparently asymptomatic infections in others (4). Acquired via the fecal-oral route, HEV is associated with poor hygiene and living conditions. It can also be acquired by eating contaminated food, including infected artiodactyls (swine, deer, and boar) and shellfish (4-6).Mammalian HEV exists in four internationally recognized genotypes (7). Genotyping is based on nucleotide divergence in the capsid open reading frame (ORF) (8) and on whole-genome phylogenetic analysis (9). Genotypes differ at the epidemiological (distribution, hosts) and virological (pathogenicity, translation mecha...

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To examine reported prognostic associations of routine blood measurements in the intensive care unit. Materials and Methods:We searched PubMed, EMBASE through 28 th May 2020 to identify all studies in adult critical care investigating associations between parameters measured routinely in whole blood, plasma or serum, and length of stay or mortality. Registration: PROSPERO; CRD42019122058.Results: A total of 128 studies, reporting 28 different putative prognostic biomarkers, met eligibility criteria. Those most frequently examined were red cell distribution width, neutrophil-to-lymphocyte ratio, C-reactive protein, and platelet count. A higher red cell distribution width, a lower platelet count, and a higher neutrophil-to-lymphocyte ratio were consistently associated with both increased mortality and length of stay. A lower level of albumin was consistently associated with greater mortality. C-reactive protein was inconsistent. Most studies (n=110) used regression modelling with wide variation in variable selection and covariate-adjustment; none externally validated the proposed predictive models. Conclusions:Simple regression models have so far proved inadequate for the complexity of data available from routine blood sampling in critical care. Adoption of a direct causal framework may help better assess mechanistic processes, aid design of future studies, and guide clinical decision making using routine data.