Evidence map›Paper›PMID 37426090›Full record

ArticleFrontiers in psychiatry2023

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

Ziqi Li, Weijia Dang, Tianqi Hao, Hualin Zhang, Ziwei Yao, Wenchao Zhou, Liufei Deng, Hongmei Yu, Yalu Wen, Long Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. MDDOmics: multi-omics resource of major depressive disorder.Database : the journal of biological databases and curation · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 2 countries.

Ziqi LiDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Weijia DangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Tianqi HaoDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Hualin ZhangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Ziwei YaoDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Wenchao ZhouDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Liufei DengDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Hongmei YuDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Yalu WenDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Long LiuDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Shanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19cross-trait meta-analysisfunctional annotationmajor depressive disorderMendelian randomization

Identifiers

PMID37426090
PMCPMC10328439
OpenAlexW4381996142

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.