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).
SummaryA balanced translocation affecting DISC1 cosegregates with several psychiatric disorders, including schizophrenia, in a Scottish family. DISC1 is a hub protein of a network of protein-protein interactions involved in multiple developmental pathways within the brain. Gene set-based analysis has been proposed as an alternative to individual analysis of single nucleotide polymorphisms (SNPs) to get information from genome-wide association studies. In this work, we tested for an overrepresentation of the DISC1 interacting proteins within the top results of our ranked list of genes based on our previous genome-wide association study of missense SNPs in schizophrenia. Our data set consisted of 5100 common missense SNPs genotyped in 476 schizophrenic patients and 447 control subjects from Galicia, NW Spain. We used a modification of the Gene Set Enrichment Analysis adapted for SNPs, as implemented in the GenGen software. The analysis detected an overrepresentation of the DISC1 interacting proteins (permuted P-value = 0.0158), indicative of the role of this gene set in schizophrenia risk. We identified seven leading-edge genes, MACF1, UTRN, DST, DISC1, KIF3A, SYNE1, and AKAP9, responsible for the overrepresentation. These genes are involved in neuronal cytoskeleton organization and intracellular transport through the microtubule cytoskeleton, suggesting that these processes may be impaired in schizophrenia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.