Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care¹ or hospitalization^2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

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).

Host dependency factors that are required for influenza A virus infection may serve as therapeutic targets as the virus is less likely to bypass them under drug-mediated selection pressure. Previous attempts to identify host factors have produced largely divergent results, with few overlapping hits across different studies. Here, we perform a genome-wide CRISPR/Cas9 screen and devise a new approach, meta-analysis by information content (MAIC) to systematically combine our results with prior evidence for influenza host factors. MAIC out-performs other meta-analysis methods when using our CRISPR screen as validation data. We validate the host factors, WDR7, CCDC115 and TMEM199, demonstrating that these genes are essential for viral entry and regulation of V-type ATPase assembly. We also find that CMTR1, a human mRNA cap methyltransferase, is required for efficient viral cap snatching and regulation of a cell autonomous immune response, and provides synergistic protection with the influenza endonuclease inhibitor Xofluza.

Genetic markers can be used to identify and verify the origin of individuals. Motivation for the inference of ancestry ranges from conservation genetics to forensic analysis. High density assays featuring Single Nucleotide Polymorphism (SNP) markers can be exploited to create a reduced panel containing the most informative markers for these purposes. The objectives of this study were to evaluate methods of marker selection and determine the minimum number of markers from the BovineSNP50 BeadChip required to verify the origin of individuals in European cattle breeds. Delta, Wright's FST, Weir & Cockerham's FST and PCA methods for population differentiation were compared. The level of informativeness of each SNP was estimated from the breed specific allele frequencies. Individual assignment analysis was performed using the ranked informative markers. Stringency levels were applied by log-likelihood ratio to assess the confidence of the assignment test.

Improving meat quality including taste and tenderness is critical to the protection and development of markets for sheep meat. Phenotypic selection for such measures of meat quality is constrained by the fact that these parameters can only be measured post-slaughter. Carcass composition has an impact on meat quality and can be measured on live animals using advanced imaging technologies such as X-ray computed tomography (CT). Since carcass composition traits are heritable, they are potentially amenable to improvement through marker-assisted and genomic selection. We conducted a genome-wide association study (GWAS) on about 600 Scottish Blackface lambs for which detailed carcass composition phenotypes, including bone, fat and muscle components, had been captured using CT and which were genotyped for ~40,000 single nucleotide polymorphisms (SNPs) using the Illumina OvineSNP50 chip. We confirmed that the carcass composition traits were heritable with moderate to high (0.19–0.78) heritabilities. The GWAS analyses revealed multiple SNPs and quantitative trait loci (QTL) that were associated with effects on carcass composition traits and were significant at the genome-wide level. In particular, we identified a region on ovine chromosome 6 (OAR6) associated with bone weight and bone area that harboured SNPs with p values of 5.55 × 10−8 and 2.63 × 10−9, respectively. The same region had effects on fat area, fat density, fat weight and muscle density. We identified plausible positional candidate genes for these OAR6 QTL. We also detected a SNP that reached the genome-wide significance threshold with a p value of 7.28 × 10−7 and was associated with muscle density on OAR1. Using a regional heritability mapping approach, we also detected regions on OAR3 and 24 that reached genome-wide significance for bone density. We identified QTL on OAR1, 3, 24 and particularly on OAR6 that are associated with effects on muscle, fat and bone traits. Based on available evidence that indicates that these traits are genetically correlated with meat quality traits, these associated SNPs have potential applications in selective breeding for improved meat quality. Further research is required to determine whether the effects associated with the OAR6 QTL are caused by a single gene or several closely-linked genes.