Mendelian randomization analysis provides causality of smoking on the expression of ACE2, a putative SARS-CoV-2 receptor
Abstract
Background: To understand a causal role of modifiable lifestyle factors in ACE2 expression (a putative SARS-CoV-2 receptor) across 44 human tissues/organs, and in COVID-19 susceptibility and severity, we conducted a phenome-wide two-sample Mendelian randomization (MR) study.
Methods: More than 500 genetic variants were used as instrumental variables to predict smoking and alcohol consumption. Inverse-variance weighted approach was adopted as the primary method to estimate a causal association, while MR-Egger regression, weighted median and MR-PRESSO were performed to identify potential horizontal pleiotropy.
Results: We found that genetically predicted smoking intensity significantly increased ACE2 expression in thyroid (β=1.468, p=1.8 10-8); and increased ACE2 expression in adipose, brain, colon and liver with nominal significance. Additionally, genetically predicted smoking initiation significantly increased the risk of COVID-19 onset (odds ratio=1.14, p=8.7 10-5). No statistically significant result was observed for alcohol consumption.
Conclusions: Our work demonstrates an important role of smoking, measured by both status and intensity, in the susceptibility to COVID-19.
Funding: Dr. Jiang is supported by research grants from the Swedish Research Council (VR-2018-02247) and Swedish Research Council for Health, Working Life and Welfare (FORTE-2020-00884).
Data availability
Data and main programming codes with annotations have been uploaded to GitHub and made publicly available at https://github.com/hye-hz/MR_Smoke_COVID19.git.
-
Genetic effects on gene expression across human tissuesdoi: 10.1038/nature24277.
-
COVID-19 Host Genetics Initiativedoi: 10.1038/s41431-020-0636-6.
Article and author information
Author details
Funding
Swedish Research Council (VR-2018-02247)
- Xia Jiang
Swedish Research Council for Health, Working Life and Welfare (FORTE-2020-00884)
- Xia Jiang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- M Dawn Teare, Newcastle University, United Kingdom
Version history
- Received: October 20, 2020
- Accepted: June 19, 2021
- Accepted Manuscript published: July 6, 2021 (version 1)
- Version of Record published: July 15, 2021 (version 2)
Copyright
© 2021, Liu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,146
- views
-
- 119
- downloads
-
- 12
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
-
- Ecology
- Epidemiology and Global Health
Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.