Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs
Authors: Willett Julian Daniel Sunday , Lu Tianyuan , Nakanishi Tomoko , Yoshiji Satoshi , Butler-Laporte Guillaume , Zhou Sirui , Farjoun Yossi , Richards J. Brent 
Published: June 2023

AbstractIdentifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize—which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ.We conducted MR ofcis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7.Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.Author SummaryThe genetic determinants of disease and gene expression often do not colocalize (which means they do not share a single causal signal). While some researchers have identified factors that could explain this disconnect, such as immune stimulation or tissue studied, understanding of this complex phenomenon remains incomplete. A deeper understanding could help identify additional genes that mediate disease, affording promising targets for treatment or prevention of disease. We used RNA sequencing data collected at the single cell and bulk tissue level to identify genes whose expression influenced COVID-19 outcomes. We assessed which variables influencing colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. We observed that colocalization of specific candidate genes identified by MR was highly variable and influenced by multiple factors, including cell state and cell population. These results illustrate that even after assessing multiple variables that may influence colocalization, there existed few examples of genes identified by MR that colocalized with gene expression. Future studies would benefit from larger transcriptomics study cohorts and more advanced statistical methods which better account for differences in linkage disequilibrium panels between data sources.