Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis.

Introduction: The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection). Methods: In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis. Results: We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction. Discussion: Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.

The Evolution of Publication Hotspots in Electronic Health Records from 1957 to 2016 and Differences Among Six Countries.

This study aims to reveal the evolution of publication hotspots in the field of electronic health records (EHRs) and differences among countries. We applied keyword frequency analysis, keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and visualization technology to compare the high-frequency Medical Subject Heading (MeSH) terms in six countries during the periods 1957-2008 and 2009-2016. After 2009, the number of MeSH terms reflecting information exchange and information mining increased, and various types of evaluations based on EHRs and cohort studies significantly increased. The top 20 MeSH terms between 2009 and 2016 constitute five relatively larger knowledge groups. Thus, we conclude that publication hotspots in EHR field have shifted from issues related to the adoption of EHRs to the utilization of EHRs, and the knowledge structure has become systematic. The publication's focus was different in the six countries, which may relate to their national characteristics.