The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide pandemic of coronavirus disease 2019 (COVID-19) (Coronaviridae Study Group of the International Committee on Taxonomy of Viruses, 2020; World Health Organization, 2020). As a host receptor of SARS-CoV-2, the expression level of angiotensin-converting enzyme 2 (ACE2) has been found to influence both the risk and severity of infection (Hoffmann et al., 2020; Wrapp et al., 2020; Zhou et al., 2020a; Li et al., 2003; Li et al., 2005). Moreover, a growing body of evidence from epidemiological investigations has demonstrated a substantial disparity in the susceptibility to infection (Guan et al., 2020; Hu et al., 2020; Mehra et al., 2020; Patanavanich and Glantz, 2020). For example, a multi-center study involving 8910 COVID-19 cases from 169 hospitals in Europe and North America identified an increased risk of in-hospital death among current smokers (odds ratio [OR]=1.79; 95% CI: 1.29–2.47) compared with ever-smokers or non-smokers (Mehra et al., 2020).

Consistent with findings from large-scale population-based observational studies, a laboratory-based study involving 131 RNA-sequenced human lung cancer tissues (54 samples of European ancestry individuals and 77 samples of Asian ancestry individuals) found that smokers expressed a significantly higher level of ACE2 compared to non-smokers in both populations, leading to a potentially heightened susceptibility to SARS-CoV-2 infection (Cai, 2020). Furthermore, incorporating two additional DNA microarray datasets of lung cancer, the significant smoking-ACE2 association observed in a total of 224 samples did not alter after adjusting for age, sex, race, and platforms. Nevertheless, these samples are derived from lung cancer patients, restricting its generalizability to normal lung tissues and to the general population. In a related work, Rao et al. conducted a phenome-wide Mendelian randomization (MR) study examining an extensive amount of diseases, traits, and blood proteins and identified several ‘exposures’ including diabetes, breast cancer, lung cancer, inflammatory bowel disease, and smoking to increase ACE2 expression in normal lung tissue (Rao et al., 2020). This analysis, despite its substantially augmented number of exposures (N=3948), has several limitations. First of all, disease status such as diabetes and cancers are difficult to modify, at the population level it is more important to discover and intervene with modifiable risk factors such as smoking and alcohol consumption. However, regarding smoking, only three single nucleotide polymorphisms (SNPs) were used as instrumental variables (IVs) by Rao et al., which explained negligible phenotypic variation and did not accurately predict smoking status. The hitherto largest genome-wide association study (GWAS) of tobacco use was conducted in a total of 1.2 million individuals and discovered over 400 genetic variants associated with smoking initiation and intensity (Liu et al., 2019). Last but not least, Rao et al. focused on lung tissue instead of systemically examining all human tissues. Despite lungs being the most relevant and vulnerable organ to a respiratory syndrome COVID-19, recent studies have identified the involvement of other human tissues (e.g. gastrointestinal tract) in SARS-CoV-2 infection (Zou et al., 2020).

Motivated by these findings, we aim to explore whether genetic predisposition to common human modifiable behaviours including smoking and alcohol consumption could lead to an increased ACE2 expression, which subsequently yields to an increased susceptibility and severity of COVID-19. Here, we conduct a phenome-wide MR analysis by incorporating ACE2 expressions from a broad spectrum of tissues/organs available in the GTEx database. As one of the hitherto largest databases with concomitant information on DNA genotype and RNA expression, GTEx collects a large variety of tissues (N=44) from healthy population (deceased donors) (Gamazon et al., 2018). In addition, data on COVID-19 susceptibility and severity were obtained from COVID-19 Host Genetics Initiative, a global project aims to understand the role of host genome in COVID-19 outcome (COVID-19 Host Genetics Initiative, 2020). Hundreds of genetic variants identified by a large-scale GWAS of tobacco use and alcohol consumption were used as IVs – incorporating additional loci greatly enhances the strength of genetic instruments as well as both accuracy and precision of MR estimates (Liu et al., 2019).

We extracted a total of 532 independent SNPs that achieved genome-wide significance for smoking initiation (N=378), cigarettes per day (N=55), and drinks per week (N=99) from the GSCAN consortium and used as IVs (Supplementary file 1a-c). We were able to map 80–95% of those IVs to the GTEx database and to the COVID-19 Host Genetics Initiative database – a virtually complete coverage. The flowchart of IV-selection is shown in Figure 1.

Figure 1

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We found that genetically instrumented smoking initiation was associated with a significantly increased ACE2 expression in brain putamen basal ganglia (β=1.117, p=0.006), in brain hypothalamus (β=0.848, p=0.022), and in subcutaneous adipose tissue (β=0.285, p=0.016) using the IVW approach (Table 1). Results remained directional consistent in the MR-Egger regression although a slightly increased statistical uncertainty was observed (β=1.667, p=0.334 in brain putamen basal ganglia; β=0.963, p=0.545 in brain hypothalamus; β=0.663, p=0.205 in subcutaneous adipose tissue). Additionally, increased ACE2 expression in two colon tissues was observed only through the MR-Egger regression (transverse colon [β=1.129, p=0.017] and sigmoid colon [β=1.925, p=0.042]) and the direction of estimates remained consistent in the IVW approach. For all these associations, we did not find apparent heterogeneity as indicated by Cochran’s Q statistics (all p>0.05) or horizontal pleiotropy as indicted by MR-Egger intercept (all p>0.05) and MR-PRESSO global test (all p>1.0×10⁻⁶), except that horizontal pleiotropy was observed in transverse colon by MR-Egger intercept (p=0.04). Although significant associations of smoking initiation with ACE2 expression in brain caudate basal ganglia and in cerebellar hemisphere were found using the IVW approach, the direction of estimates was opposite in the MR-Egger regression (Supplementary file 1f). These associations were therefore not considered as informative.

We further identified that genetically predicted smoking intensity as reflected by cigarettes per day was associated with a significantly elevated ACE2 expression in thyroid (β=1.468, p=1.8×10⁻⁸), in liver (β=1.216, p=0.009), in brain hypothalamus (β=1.789, p=0.014), and in ovary (β=1.545, p=0.026) using the IVW method (Table 2). Results remained directional consistent in the MR-Egger regression (β=1.739, p=0.062 in thyroid; β=1.132, p=0.226 in liver; β=0.041, p=0.983 in brain hypothalamus; β=1.658, p=0.347 in ovary). On the contrary, levels of ACE2 expression decreased with genetically instrumented smoking intensity in sigmoid colon tissue (β=−1.971, p=0.019) and in vagina tissue (β=−3.271, p=0.043) using the MR-Egger regression. For all these associations, no apparent horizontal pleiotropy and heterogeneity was found (Supplementary file 1g).

For alcohol consumption defined as drinks per week, only one suggestive association with ACE2 expression in tibial nerve was observed using the IVW approach (β=−1.462, p=0.006). However, the direction of effect from the MR-Egger regression was opposite (β=0.336, p=0.721). We therefore considered an overall null association as our main finding with alcohol consumption (Supplementary file 1h).

In the sensitivity analyses where we excluded palindromic or pleiotropic SNPs, results remained largely consistent with our primary findings (full results shown in Supplementary file 1i-1n). We sequentially excluded proxy SNPs to identify random error introduced by imperfect proxies. In the leave-one-out analyses where we iteratively removed one SNP each time and performed the IVW approach using the remaining SNPs, results were again concordant with our primary findings, indicating an absence of outlying SNPs (Appendix 1—figure 1).

Finally, complementing to findings of ACE2 expression, we tested a putative causal link between smoking status, smoking intensity, alcohol consumption, and the risk of COVID-19 related adverse outcomes. We found that smoking initiation significantly increased the risk of COVID-19 onset (IVW: OR=1.15, 95%CI: 1.07–1.23, p=8.7×10⁻⁵) even after taking into account multiple comparisons (Table 3). Results remained significant in the weighted median, and MR-PRESSO methods, however, showed larger statistical uncertainties in the MR-Egger regression. In addition, smoking initiation increased the risk of very severe respiratory confirmed COVID-19 and hospitalized COVID-19 using the IVW approach, but the results were not supported by the MR-Egger regression. Smoking intensity (cigarettes per day) only increased the risk of very severe respiratory confirmed COVID-19 as shown in the MR-Egger regression (OR=5.99, 95%CI: 1.57–22.84, p=0.012) (Supplementary file 1o). On the contrary, we did not find a causal link between alcohol consumption (drinks per week) and the risk of COVID-19 adverse outcomes (Supplementary file 1p). Findings did not alter in the sensitivity analyses (Supplementary file 1q-r and Appendix 1—figure 4).

We conducted a large-scale genetic analysis to understand the role of cigarette smoking and alcohol consumption with ACE2 expression in multiple tissues/organs, comprehending its role in the prevention of COVID-19. Strong IVs were constructed using hundreds of SNPs associated with smoking and alcohol consumption. We capitalized on the summary statistics of the largest tissue-specific eQTL conducted for ACE2 expression levels and the most up-to-date GWAS data of COVID-19 related adverse outcomes. We found a putative causal relationship between smoking-related phenotypes and an increased ACE2 expression in multiple tissues, as well as an increased susceptibility and severity of COVID-19.

Our findings are supported by previous epidemiological studies which have demonstrated a significant association between smoking and COVID-19 disease progression or death (Guan et al., 2020; Hu et al., 2020; Mehra et al., 2020). For example, a study involving 214 patients with laboratory confirmed COVID-19 from Wenzhou China found that compared to non-severe cases, patients with severe disease were more likely to be smokers (26.3% vs. 6.4%, p = 0.038) (Zheng et al., 2020). Another study recruiting 78 patients with COVID-19 in Wuhan also found a higher proportion of ever-smokers in COVID-19 progression group than in the improvement/stabilization group (OR=14.28, 95% CI:1.58–25.00, p=0.018) (Liu et al., 2020). Consistent with these findings, a meta-analysis on a total of 11,590 COVID-19 cases demonstrated a higher proportion of smokers among 2133 patients experienced disease progression, suggesting that smoking aggravated COVID-19 progression (OR=1.91, 95% CI: 1.42–2.59) (Patanavanich and Glantz, 2020). On the contrary, a few small-scale studies with sample sizes ranging from 44 to 191 conducted in Wuhan China did not report remarkable association of smoking with COVID-19 severity or progression (Huang et al., 2020; Yang et al., 2020; Zhang et al., 2020; Zhou et al., 2020b). For instance, a retrospective study including 191 patients of which 137 survived and 54 non-survived found a comparable proportion of smokers in survivors and non-survivors (9% vs. 4%, p=0.21) (Zhou et al., 2020b). A meta-analysis including five studies (four in Wuhan and one across 30 provinces in Mainland China) (Guan et al., 2020; Liu et al., 2020; Huang et al., 2020; Yang et al., 2020; Zhang et al., 2020) involving 1399 individuals with COVID-19 revealed no significant association between smoking and disease severity (OR=1.69, 95% CI: 0.41–6.92) (Lippi and Henry, 2020). Opposite to the findings from Asian population, evidence from the Veterans Affairs Birth Cohort in a US population found that current smoking was associated with a lower risk of COVID-19 susceptibility (OR=0.45, 95% CI: 0.35–0.57) (Rentsch et al., 2020). The contradictory results in those small-scale studies might be due to insufficient power, low proportion of smokers, and a limited representativeness of the study population. For example, compared with the high proportion of smokers in China (an average 26.6% prevalence in the general population), only 1.4% patients were current smokers in the study conducted by Zhang et al., 2020 and 12.6% in the study conducted by Guan et al., 2020. Given these discrepancies, additional studies are warranted to confirm the role of smoking in both the onset and progression of COVID-19.

In addition to clinical observational studies, laboratory examination has demonstrated the importance of tissues specificity. For example, Cai et al. identified that smoking was associated with an elevated expression of ACE2 in the lung, providing biological evidence of smoking with an increased susceptibility to SARS-CoV-2 infection or severity (Cai, 2020). Moreover, Rao et al. conducted a phenome-wide MR study incorporating 3948 traits, diseases, and blood proteins and identified a nominal significant association between tobacco use and ACE2 expression in the lung (IVW: β=0.918, p=0.016) (Rao et al., 2020). However, this association did not pass multiple comparisons (p-value for FDR was 0.51), which was consistent with our MR results using a greatly augmented number of IVs (for smoking status IV=378).

The biological mechanisms underlying smoking and tissue-specific ACE2 expression remain to be disclosed. ACE2 has been considered as the target receptor of SARS-CoV-2 entry into the host cells and an increased expression of ACE2 appears to raise both the susceptibility and severity of COVID-19. As a type I transmembrane metallocarboxypeptidase homologous to ACE, ACE2 is known to be expressed in a variety of tissues, including respiratory tract, cardio-renal tissues, and gastrointestinal tissues (Harmer et al., 2002). Our study found that smoking increased ACE2 expression in multiple tissues including the brain and colon tissues. While SARS-CoV-2 mainly spreads via respiratory tract, enrichment of SARS-CoV-2 in gastrointestinal tract has been confirmed by testing viral RNA in stool from 71 patients with COVID-19, suggesting the importance of gastrointestinal involvement in the infection (Xiao et al., 2020). Furthermore, nearly one-fifth COVID-19 patients remained SARS-CoV-2 RNA-positive in their stool, despite negative results in their respiratory samples. Of those 71 patients, ACE2 was abundantly expressed in the gastrointestinal epithelia, but rarely expressed in the esophageal epithelia. In addition, findings from public databases (GEO, GTEx, and HPA) have demonstrated a higher expression level of ACE2 in the gastrointestinal tract (colon, rectal, and small intestine) and liver than in the lung (Burgueño et al., 2020; Li et al., 2020; Pirola and Sookoian, 2020). Taking these pieces of evidence together, we could reasonably assume that the biological mechanisms underlying the link between ACE2 expression and COVID-19 susceptibility are complicated, involving multiple organs other than the lung. Consistent with our findings on a link between smoking and increased ACE2 expression in both transverse colon and sigmoid colon, these results collectively suggest that smoking mediates ACE2 expression in gastrointestinal tract, subsequently influence the susceptibility and severity of COVID-19.

We also found that smoking influenced ACE2 expression in the brain tissue, especially in putamen basal ganglia and hypothalamus. Smoking may promote cellular uptake of SARS-CoV-2 through nicotinic acetylcholine receptor (nAChR) signalling (Russo et al., 2020). It is worth noting that nAChR and ACE2 are known to be co-expressed on many sites such as cortex, striatum, and hypothalamus within human brain (Dani and Bertrand, 2007; Jones et al., 2009). Nicotine stimulation of the nAChR increased ACE2 expression within neural cells, indicating a likelihood that smokers are more vulnerable to COVID-19 (Olds and Kabbani, 2020).

To the best of our knowledge, we performed a large-scale phenome-wide MR study to understand a causal role of smoking and alcohol consumption in ACE2 expression, as well as in COVID-19 related outcomes, totalling 532 genetic associations from 1.2 million individuals of European ancestry and covering almost all tissues/organs of human body (N=44). In addition, we rigorously selected proxy SNPs and performed a series of sensitivity analyses to satisfy MR model assumptions, for example, we satisfied the ‘relevance’ assumption by using GWAS-identified significant SNPs as IVs; we ensured the ‘independent’ assumption and the ‘exclusion restriction’ assumption by performing several important sensitivity analyses. However, limitations need to be acknowledged. Although hundreds of SNPs were used as proxies for cigarette smoking and alcohol consumption, these GWAS-identified SNPs explained only a small fraction (1–2%) of phenotypic variance. In addition, the minimum sample size of 114 in brain substantia nigra tissue further limited the statistical power of MR analysis, albeit GTEx database is so far the largest available database with both genotype and expression data. Moreover, data on the genotypes, tissue expression, and COVID-19 related outcomes of our analyses were all based on European ancestry populations, restricting its generalizability to other ethnicities. On the other hand, our data, with exposure and outcome GWAS(s) (or eQTLs) conducted using individuals of the same underlying populations (all European ancestry), greatly reduces the population stratification as well as satisfies the MR model assumption – for a two-sample MR to be valid, the two samples have to be preferably from the same ethnicity. Finally, we have to acknowledge that few of our significant associations passed the stringent Bonferroni corrections. Our results provide a comprehensive picture for the causal relationship of smoking with ACE2 expression in various tissues and with COVID-19 susceptibility and severity, yet we stress caution for interpretation and extra analyses are needed to replicate these findings.

In conclusion, genetically instrumented smoking phenotypes reflected by both smoking initiation and smoking intensity are significantly associated with a high expression level of ACE2 in multiple tissues/organs, subsequently increasing the susceptibility and severity of COVID-19. Our results provide important clinical implications on that smokers might be more vulnerable to SARS-CoV-2 infection or severe disease. At the population level, smoking cessation is also an important actionable prevention strategy to COVID-19. Further studies are needed to confirm or refute our findings.

All data generated or analysed during this study are publicly-available. Data of the GSCAN consortium, the GTEx project, and the COVID-19 Host Genetics Initiative can be acessed at https://genome.psych.umn.edu/index.php/GSCAN, https://www.gtexportal.org, and https://www.covid19hg.org, respectively. Furthermore, 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 (copy archived at https://archive.softwareheritage.org/swh:1:rev:1a2038517d8f2c7c772e69c9c5abab7713add9bb).

1. 1. Bowden J
   2. Davey Smith G
   3. Burgess S

   (2015) Mendelian randomization with invalid instruments: effect estimation and Bias detection through egger regression

   International Journal of Epidemiology 44:512–525.

   https://doi.org/10.1093/ije/dyv080

   • PubMed
   • Google Scholar
2. 1. Bowden J
   2. Davey Smith G
   3. Haycock PC
   4. Burgess S

   (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator

   Genetic Epidemiology 40:304–314.

   https://doi.org/10.1002/gepi.21965

   • PubMed
   • Google Scholar
3. 1. Brion MJ
   2. Shakhbazov K
   3. Visscher PM

   (2013) Calculating statistical power in Mendelian randomization studies

   International Journal of Epidemiology 42:1497–1501.

   https://doi.org/10.1093/ije/dyt179

   • PubMed
   • Google Scholar
4. 1. Burgess S
   2. Scott RA
   3. Timpson NJ
   4. Davey Smith G
   5. Thompson SG
   6. EPIC- InterAct Consortium

   (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

   European Journal of Epidemiology 30:543–552.

   https://doi.org/10.1007/s10654-015-0011-z

   • PubMed
   • Google Scholar
5. 1. Burgueño JF
   2. Reich A
   3. Hazime H
   4. Quintero MA
   5. Fernandez I
   6. Fritsch J
   7. Santander AM
   8. Brito N
   9. Damas OM
   10. Deshpande A
   11. Kerman DH
   12. Zhang L
   13. Gao Z
   14. Ban Y
   15. Wang L
   16. Pignac-Kobinger J
   17. Abreu MT

   (2020) Expression of SARS-CoV-2 Entry Molecules ACE2 and TMPRSS2 in the Gut of Patients With IBD

   Inflammatory Bowel Diseases 26:797–808.

   https://doi.org/10.1093/ibd/izaa085

   • PubMed
   • Google Scholar
6. Preprint

   1. Cai G

   (2020) Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

   medRxiv.

   https://doi.org/10.1101/2020.02.05.20020107

   • Google Scholar
7. 1. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses

   (2020) The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2

   Nature Microbiology 5:536–544.

   https://doi.org/10.1038/s41564-020-0695-z

   • PubMed
   • Google Scholar
8. 1. COVID-19 Host Genetics Initiative

   (2020) The COVID-19 host genetics initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic

   European Journal of Human Genetics 28:715–718.

   https://doi.org/10.1038/s41431-020-0636-6

   • PubMed
   • Google Scholar
9. 1. Dani JA
   2. Bertrand D

   (2007) Nicotinic acetylcholine receptors and nicotinic cholinergic mechanisms of the central nervous system

   Annual Review of Pharmacology and Toxicology 47:699–729.

   https://doi.org/10.1146/annurev.pharmtox.47.120505.105214

   • PubMed
   • Google Scholar
10. 1. Gamazon ER
    2. Segrè AV
    3. van de Bunt M
    4. Wen X
    5. Xi HS
    6. Hormozdiari F
    7. Ongen H
    8. Konkashbaev A
    9. Derks EM
    10. Aguet F
    11. Quan J
    12. Nicolae DL
    13. Eskin E
    14. Kellis M
    15. Getz G
    16. McCarthy MI
    17. Dermitzakis ET
    18. Cox NJ
    19. Ardlie KG
    20. GTEx Consortium

    (2018) Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation

    Nature Genetics 50:956–967.

    https://doi.org/10.1038/s41588-018-0154-4

    • PubMed
    • Google Scholar
11. 1. Greco M FD
    2. Minelli C
    3. Sheehan NA
    4. Thompson JR

    (2015) Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome

    Statistics in Medicine 34:2926–2940.

    https://doi.org/10.1002/sim.6522

    • PubMed
    • Google Scholar
12. 1. Guan WJ
    2. Ni ZY
    3. Hu Y
    4. Liang WH
    5. Ou CQ
    6. He JX
    7. Liu L
    8. Shan H
    9. Lei CL
    10. Hui DSC
    11. Du B
    12. Li LJ
    13. Zeng G
    14. Yuen KY
    15. Chen RC
    16. Tang CL
    17. Wang T
    18. Chen PY
    19. Xiang J
    20. Li SY
    21. Wang JL
    22. Liang ZJ
    23. Peng YX
    24. Wei L
    25. Liu Y
    26. Hu YH
    27. Peng P
    28. Wang JM
    29. Liu JY
    30. Chen Z
    31. Li G
    32. Zheng ZJ
    33. Qiu SQ
    34. Luo J
    35. Ye CJ
    36. Zhu SY
    37. Zhong NS
    38. Zy N
    39. Cq O
    40. Jx H
    41. Dsc H
    42. Lj L
    43. Sy L
    44. Yh H
    45. Wang JM
    46. Liu JY
    47. Cj Y
    48. China Medical Treatment Expert Group for Covid-19

    (2020) Clinical Characteristics of Coronavirus Disease 2019 in China

    New England Journal of Medicine 382:1708–1720.

    https://doi.org/10.1056/NEJMoa2002032

    • PubMed
    • Google Scholar
13. 1. Harmer D
    2. Gilbert M
    3. Borman R
    4. Clark KL

    (2002) Quantitative mRNA expression profiling of ACE 2, a novel homologue of angiotensin converting enzyme

    FEBS Letters 532:107–110.

    https://doi.org/10.1016/S0014-5793(02)03640-2

    • PubMed
    • Google Scholar
14. 1. Hoffmann M
    2. Kleine-Weber H
    3. Schroeder S
    4. Krüger N
    5. Herrler T
    6. Erichsen S
    7. Schiergens TS
    8. Herrler G
    9. Wu NH
    10. Nitsche A
    11. Müller MA
    12. Drosten C
    13. Pöhlmann S

    (2020) SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

    Cell 181:271–280.

    https://doi.org/10.1016/j.cell.2020.02.052

    • PubMed
    • Google Scholar
15. 1. Hu L
    2. Chen S
    3. Fu Y
    4. Gao Z
    5. Long H
    6. Wang JM
    7. Ren HW
    8. Zuo Y
    9. Li H
    10. Wang J
    11. Qb X
    12. Wx Y
    13. Liu J
    14. Shao C
    15. Hao JJ
    16. Wang CZ
    17. Ma Y
    18. Wang Z
    19. Yanagihara R
    20. Deng Y

    (2020) Risk factors associated with clinical outcomes in 323 COVID-19 hospitalized patients in Wuhan, China

    Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 3:ciaa539.

    https://doi.org/10.1093/cid/ciaa539

    • Google Scholar
16. 1. Huang C
    2. Wang Y
    3. Li X
    4. Ren L
    5. Zhao J
    6. Hu Y
    7. Zhang L
    8. Fan G
    9. Xu J
    10. Gu X
    11. Cheng Z
    12. Yu T
    13. Xia J
    14. Wei Y
    15. Wu W
    16. Xie X
    17. Yin W
    18. Li H
    19. Liu M
    20. Xiao Y
    21. Gao H
    22. Guo L
    23. Xie J
    24. Wang G
    25. Jiang R
    26. Gao Z
    27. Jin Q
    28. Wang J
    29. Cao B

    (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

    The Lancet 395:497–506.

    https://doi.org/10.1016/S0140-6736(20)30183-5

    • PubMed
    • Google Scholar
17. 1. Jones AR
    2. Overly CC
    3. Sunkin SM

    (2009) The Allen Brain Atlas: 5 years and beyond

    Nature Reviews Neuroscience 10:821–828.

    https://doi.org/10.1038/nrn2722

    • PubMed
    • Google Scholar
18. 1. Li W
    2. Moore MJ
    3. Vasilieva N
    4. Sui J
    5. Wong SK
    6. Berne MA
    7. Somasundaran M
    8. Sullivan JL
    9. Luzuriaga K
    10. Greenough TC
    11. Choe H
    12. Farzan M

    (2003) Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus

    Nature 426:450–454.

    https://doi.org/10.1038/nature02145

    • PubMed
    • Google Scholar
19. 1. Li W
    2. Zhang C
    3. Sui J
    4. Kuhn JH
    5. Moore MJ
    6. Luo S
    7. Wong SK
    8. Huang IC
    9. Xu K
    10. Vasilieva N
    11. Murakami A
    12. He Y
    13. Marasco WA
    14. Guan Y
    15. Choe H
    16. Farzan M

    (2005) Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2

    The EMBO Journal 24:1634–1643.

    https://doi.org/10.1038/sj.emboj.7600640

    • PubMed
    • Google Scholar
20. 1. Li MY
    2. Li L
    3. Zhang Y
    4. Wang XS

    (2020) Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues

    Infectious Diseases of Poverty 9:45.

    https://doi.org/10.1186/s40249-020-00662-x

    • PubMed
    • Google Scholar
21. 1. Lippi G
    2. Henry BM

    (2020) Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19)

    European Journal of Internal Medicine 75:107–108.

    https://doi.org/10.1016/j.ejim.2020.03.014

    • PubMed
    • Google Scholar
22. 1. Liu M
    2. Jiang Y
    3. Wedow R
    4. Li Y
    5. Brazel DM
    6. Chen F
    7. Datta G
    8. Davila-Velderrain J
    9. McGuire D
    10. Tian C
    11. Zhan X
    12. Choquet H
    13. Docherty AR
    14. Faul JD
    15. Foerster JR
    16. Fritsche LG
    17. Gabrielsen ME
    18. Gordon SD
    19. Haessler J
    20. Hottenga JJ
    21. Huang H
    22. Jang SK
    23. Jansen PR
    24. Ling Y
    25. Mägi R
    26. Matoba N
    27. McMahon G
    28. Mulas A
    29. Orrù V
    30. Palviainen T
    31. Pandit A
    32. Reginsson GW
    33. Skogholt AH
    34. Smith JA
    35. Taylor AE
    36. Turman C
    37. Willemsen G
    38. Young H
    39. Young KA
    40. Zajac GJM
    41. Zhao W
    42. Zhou W
    43. Bjornsdottir G
    44. Boardman JD
    45. Boehnke M
    46. Boomsma DI
    47. Chen C
    48. Cucca F
    49. Davies GE
    50. Eaton CB
    51. Ehringer MA
    52. Esko T
    53. Fiorillo E
    54. Gillespie NA
    55. Gudbjartsson DF
    56. Haller T
    57. Harris KM
    58. Heath AC
    59. Hewitt JK
    60. Hickie IB
    61. Hokanson JE
    62. Hopfer CJ
    63. Hunter DJ
    64. Iacono WG
    65. Johnson EO
    66. Kamatani Y
    67. Kardia SLR
    68. Keller MC
    69. Kellis M
    70. Kooperberg C
    71. Kraft P
    72. Krauter KS
    73. Laakso M
    74. Lind PA
    75. Loukola A
    76. Lutz SM
    77. Madden PAF
    78. Martin NG
    79. McGue M
    80. McQueen MB
    81. Medland SE
    82. Metspalu A
    83. Mohlke KL
    84. Nielsen JB
    85. Okada Y
    86. Peters U
    87. Polderman TJC
    88. Posthuma D
    89. Reiner AP
    90. Rice JP
    91. Rimm E
    92. Rose RJ
    93. Runarsdottir V
    94. Stallings MC
    95. Stančáková A
    96. Stefansson H
    97. Thai KK
    98. Tindle HA
    99. Tyrfingsson T
    100. Wall TL
    101. Weir DR
    102. Weisner C
    103. Whitfield JB
    104. Winsvold BS
    105. Yin J
    106. Zuccolo L
    107. Bierut LJ
    108. Hveem K
    109. Lee JJ
    110. Munafò MR
    111. Saccone NL
    112. Willer CJ
    113. Cornelis MC
    114. David SP
    115. Hinds DA
    116. Jorgenson E
    117. Kaprio J
    118. Stitzel JA
    119. Stefansson K
    120. Thorgeirsson TE
    121. Abecasis G
    122. Liu DJ
    123. Vrieze S
    124. 23andMe Research Team
    125. HUNT All-In Psychiatry

    (2019) Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

    Nature Genetics 51:237–244.

    https://doi.org/10.1038/s41588-018-0307-5

    • PubMed
    • Google Scholar
23. 1. Liu W
    2. Tao ZW
    3. Wang L
    4. Yuan ML
    5. Liu K
    6. Zhou L
    7. Wei S
    8. Deng Y
    9. Liu J
    10. Liu HG
    11. Yang M
    12. Hu Y

    (2020) Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease

    Chinese Medical Journal 133:1032–1038.

    https://doi.org/10.1097/CM9.0000000000000775

    • PubMed
    • Google Scholar
24. 1. Mehra MR
    2. Desai SS
    3. Kuy S
    4. Henry TD
    5. Patel AN

    (2020) Cardiovascular disease, drug therapy, and mortality in Covid-19

    New England Journal of Medicine 382:e102.

    https://doi.org/10.1056/NEJMoa2007621

    • PubMed
    • Google Scholar
25. 1. Olds JL
    2. Kabbani N

    (2020) Is nicotine exposure linked to cardiopulmonary vulnerability to COVID-19 in the general population?

    The FEBS Journal 287:3651–3655.

    https://doi.org/10.1111/febs.15303

    • PubMed
    • Google Scholar
26. 1. Patanavanich R
    2. Glantz SA

    (2020) Smoking Is Associated With COVID-19 Progression: A Meta-analysis

    Nicotine & Tobacco Research 22:1653–1656.

    https://doi.org/10.1093/ntr/ntaa082

    • PubMed
    • Google Scholar
27. 1. Pierce BL
    2. Ahsan H
    3. Vanderweele TJ

    (2011) Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants

    International Journal of Epidemiology 40:740–752.

    https://doi.org/10.1093/ije/dyq151

    • PubMed
    • Google Scholar
28. 1. Pirola CJ
    2. Sookoian S

    (2020) COVID-19 and ACE2 in the Liver and Gastrointestinal Tract: Putative Biological Explanations of Sexual Dimorphism

    Gastroenterology 159:1620–1621.

    https://doi.org/10.1053/j.gastro.2020.04.050

    • PubMed
    • Google Scholar
29. 1. Rao S
    2. Lau A
    3. So HC
    4. Hc S

    (2020) Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits

    Diabetes Care 43:1416–1426.

    https://doi.org/10.2337/dc20-0643

    • PubMed
    • Google Scholar
30. Preprint

    1. Rentsch CT
    2. Kidwai-Khan F
    3. Tate JP
    4. Park LS
    5. King JT
    6. Skanderson M
    7. Hauser RG
    8. Schultze A
    9. Jarvis CI
    10. Holodniy M
    11. Vl R
    12. Akgün KM
    13. Crothers K
    14. Taddei TH
    15. Freiberg MS
    16. Justice AC

    (2020) Covid-19 testing, hospital admission, and intensive care among 2,026,227 united states veterans aged 54-75 years

    medRxiv.

    https://doi.org/10.1101/2020.04.09.20059964

    • Google Scholar
31. 1. Russo P
    2. Bonassi S
    3. Giacconi R
    4. Malavolta M
    5. Tomino C
    6. Maggi F

    (2020) COVID-19 and smoking: is nicotine the hidden link?

    European Respiratory Journal 55:2001116.

    https://doi.org/10.1183/13993003.01116-2020

    • Google Scholar
32. 1. Verbanck M
    2. Chen CY
    3. Neale B
    4. Do R

    (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

    Nature Genetics 50:693–698.

    https://doi.org/10.1038/s41588-018-0099-7

    • PubMed
    • Google Scholar
33. 1. Wang Y
    2. Wang Y
    3. Luo W
    4. Huang L
    5. Xiao J
    6. Li F
    7. Qin S
    8. Song X
    9. Wu Y
    10. Zeng Q
    11. Jin F
    12. Wang Y

    (2020) A comprehensive investigation of the mRNA and protein level of ACE2, the putative receptor of SARS-CoV-2, in human tissues and blood cells

    International Journal of Medical Sciences 17:1522–1531.

    https://doi.org/10.7150/ijms.46695

    • PubMed
    • Google Scholar
34. Report

    1. World Health Organization

    (2020) Coronavirus Disease 2019 (COVID-19) Situation Report-36

    WHO.

    https://www.who.int/publications/m/item/situation-report---36

    • Google Scholar
35. 1. Wrapp D
    2. Wang N
    3. Corbett KS
    4. Goldsmith JA
    5. Hsieh CL
    6. Abiona O
    7. Graham BS
    8. McLellan JS

    (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

    Science 367:1260–1263.

    https://doi.org/10.1126/science.abb2507

    • PubMed
    • Google Scholar
36. 1. Xiao F
    2. Tang M
    3. Zheng X
    4. Liu Y
    5. Li X
    6. Shan H

    (2020) Evidence for Gastrointestinal Infection of SARS-CoV-2

    Gastroenterology 158:1831–1833.

    https://doi.org/10.1053/j.gastro.2020.02.055

    • PubMed
    • Google Scholar
37. 1. Yang X
    2. Yu Y
    3. Xu J
    4. Shu H
    5. Xia J
    6. Liu H
    7. Wu Y
    8. Zhang L
    9. Yu Z
    10. Fang M
    11. Yu T
    12. Wang Y
    13. Pan S
    14. Zou X
    15. Yuan S
    16. Shang Y

    (2020) Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study

    The Lancet Respiratory Medicine 8:475–481.

    https://doi.org/10.1016/S2213-2600(20)30079-5

    • PubMed
    • Google Scholar
38. 1. Zhang JJ
    2. Dong X
    3. Cao YY
    4. Yuan YD
    5. Yang YB
    6. Yan YQ
    7. Akdis CA
    8. Gao YD

    (2020) Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China

    Allergy 75:1730–1741.

    https://doi.org/10.1111/all.14238

    • PubMed
    • Google Scholar
39. 1. Zheng J
    2. Baird D
    3. Borges MC
    4. Bowden J
    5. Hemani G
    6. Haycock P
    7. Evans DM
    8. Smith GD

    (2017) Recent Developments in Mendelian Randomization Studies

    Current Epidemiology Reports 4:330–345.

    https://doi.org/10.1007/s40471-017-0128-6

    • PubMed
    • Google Scholar
40. 1. Zheng KI
    2. Gao F
    3. Wang XB
    4. Sun QF
    5. Pan KH
    6. Wang TY
    7. Ma HL
    8. Chen YP
    9. Liu WY
    10. George J
    11. Zheng MH

    (2020) Letter to the editor: obesity as a risk factor for greater severity of COVID-19 in patients with metabolic associated fatty liver disease

    Metabolism 108:154244.

    https://doi.org/10.1016/j.metabol.2020.154244

    • PubMed
    • Google Scholar
41. 1. Zhou P
    2. Yang XL
    3. Wang XG
    4. Hu B
    5. Zhang L
    6. Zhang W
    7. Si HR
    8. Zhu Y
    9. Li B
    10. Huang CL
    11. Chen HD
    12. Chen J
    13. Luo Y
    14. Guo H
    15. Jiang RD
    16. Liu MQ
    17. Chen Y
    18. Shen XR
    19. Wang X
    20. Zheng XS
    21. Zhao K
    22. Chen QJ
    23. Deng F
    24. Liu LL
    25. Yan B
    26. Zhan FX
    27. Wang YY
    28. Xiao GF
    29. Shi ZL

    (2020a) A pneumonia outbreak associated with a new coronavirus of probable bat origin

    Nature 579:270–273.

    https://doi.org/10.1038/s41586-020-2012-7

    • PubMed
    • Google Scholar
42. 1. Zhou F
    2. Yu T
    3. Du R
    4. Fan G
    5. Liu Y
    6. Liu Z
    7. Xiang J
    8. Wang Y
    9. Song B
    10. Gu X
    11. Guan L
    12. Wei Y
    13. Li H
    14. Wu X
    15. Xu J
    16. Tu S
    17. Zhang Y
    18. Chen H
    19. Cao B

    (2020b) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

    The Lancet 395:1054–1062.

    https://doi.org/10.1016/S0140-6736(20)30566-3

    • PubMed
    • Google Scholar
43. 1. Zou X
    2. Chen K
    3. Zou J
    4. Han P
    5. Hao J
    6. Han Z

    (2020) Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

    Frontiers of Medicine 14:185–192.

    https://doi.org/10.1007/s11684-020-0754-0

    • PubMed
    • Google Scholar

Author details

1. Hui Liu

   Biomedical Research Center, Zhejiang Provincial Key Laboratory of Laparoscopic Technology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China

   Contribution

   Data curation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5531-3640

2. Junyi Xin

   Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China

   Contribution

   Data curation, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6677-3936

3. Sheng Cai

   Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Zhejiang University, Hangzhou, China

   Contribution

   Data curation, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

4. Xia Jiang

   Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden

   Contribution

   Conceptualization, Supervision, Writing - original draft, Writing - review and editing

   For correspondence

   xia.jiang@ki.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5878-8986

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