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.

Christopher Newton-Cheh and colleagues report genome-wide association analyses for QT interval, an electrocardiographic measure reflecting myocardial repolarization, in 100,000 individuals. They identify 35 loci associated with QT interval and highlight a role for calcium regulation in myocardial repolarization. The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8–10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval–associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

Janey Wiggs, Jonathan Haines and colleagues identify three new susceptibility loci for primary open-angle glaucoma. The association signals map near genes implicated in ocular development (FOXC1), neurodegeneration (ATXN2) and mitochondrial function (TXNRD2). Primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide. To identify new susceptibility loci, we performed meta-analysis on genome-wide association study (GWAS) results from eight independent studies from the United States (3,853 cases and 33,480 controls) and investigated the most significantly associated SNPs in two Australian studies (1,252 cases and 2,592 controls), three European studies (875 cases and 4,107 controls) and a Singaporean Chinese study (1,037 cases and 2,543 controls). A meta-analysis of the top SNPs identified three new associated loci: rs35934224[T] in TXNRD2 (odds ratio (OR) = 0.78, P = 4.05 × 10−11) encoding a mitochondrial protein required for redox homeostasis; rs7137828[T] in ATXN2 (OR = 1.17, P = 8.73 × 10−10); and rs2745572[A] upstream of FOXC1 (OR = 1.17, P = 1.76 × 10−10). Using RT-PCR and immunohistochemistry, we show TXNRD2 and ATXN2 expression in retinal ganglion cells and the optic nerve head. These results identify new pathways underlying POAG susceptibility and suggest new targets for preventative therapies.

Background: With the abundance of information and analysis results being collected for genetic loci, user-friendly and flexible data visualization approaches can inform and improve the analysis and dissemination of these data. A chromosomal ideogram is an idealized graphic representation of chromosomes. Ideograms can be combined with overlaid points, lines, and/or shapes, to provide summary information from studies of various kinds, such as genome-wide association studies or phenome-wide association studies, coupled with genomic location information. To facilitate visualizing varied data in multiple ways using ideograms, we have developed a flexible software tool called PhenoGram which exists as a web-based tool and also a command-line program. Results: With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail. We have now used PhenoGram to produce a variety of different plots, and provide these as examples herein. These plots include visualization of the genomic coverage of SNPs from a genotyping array, highlighting the chromosomal coverage of imputed SNPs, copy-number variation region coverage, as well as plots similar to the NHGRI GWA Catalog of genome-wide association results. Conclusions: PhenoGram is a versatile, user-friendly software tool fostering the exploration and sharing of genomic information. Through visualization of data, researchers can both explore and share complex results, facilitating a greater understanding of these data.