Enrichment analysis of polymorphic loci associated with SARS-CoV-2 infection and its outcomes

doi:10.21203/rs.3.rs-5234460/v1

Background: There is a relatively long list of polymorphic genes associated with susceptibility to, hospitalization for, severity of, and death from COVID-19.

Methods: Enrichment analysis was performed using polymorphic loci retrieved from the Genome-wide association studies (GWAS) database associated with susceptibility to, hospitalization for, severity of, and death from COVID-19. Only protein-coding genes were included in this study.

Results: A total of 177, 91, 67, 67, and 22 loci were identified whose genetic polymorphisms were associated with susceptibility to, hospitalization for, severity of, and death from COVID-19, respectively. No significant results were found in the enrichment analysis. The common genes among the different aspects of COVID-19 were low. Susceptibility to, hospitalization for, severity of, and death from COVID-19 shared 39, 22, 19, and 9 loci with smoking initiation trait, respectively. Statistical analysis indicated that the levels of sharing were highly significant. The low number of shared genes among the four sets of susceptibility, hospitalization, disease severity, and death due to COVID-19 suggests that these aspects of COVID-19 are distinct multifactorial traits.

Conclusion: It was concluded that factors such as chronic cardiovascular disease and diabetes mellitus are not independent risk factors for COVID-19 and its outcomes, and that smoking is the most important and influential risk factor. Further analysis using Mendelian randomization on the available information is needed to confirm or reject this conclusion.

Enrichment analysis

Susceptibility

Hospitalization

Severity

Death

COVID-19

There was little time between the report of a cluster of fatal pneumonia cases in Wuhan, China (December 2019) and the day the World Health Organization (WHO) declared a new pandemic (March 11, 2020). After three and a half years, the WHO has declared that the emergency is over. The cause of the disease (coronavirus disease-2019, COVID-19) was quickly identified, so the new disease was named COVID-19, which occurred after infection with a novel member of the coronavirus family called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. In many cases, complications led to death. During the pandemic, hundreds of millions of people in countries around the world became ill and several million died, according to official statistics. However, COVID-19 remains an important global health issue.

Researchers quickly identified risk factors associated with COVID-19 disease. Several risk factors such as diabetes mellitus, obesity, smoking, cardiovascular disease, hypertension, cancer, chronic obstructive pulmonary disease, and chronic renal failure were identified and placed in a broad spectrum [2-10]. It became clear that COVID-19 is a new multifactorial disease. Three categories of factors play important roles in both the susceptibility to develop COVID-19 after exposure to SARS-CoV-2 virus and the outcome of the disease, which are SARS-CoV-2 virus-related factors, host-related factors, and environmental factors. Subsequently, many ecological and case-control studies were conducted to identify the genetic polymorphisms associated with the disease [11-17]. Several research groups also used the new molecular biology technology of genome-wide association studies (GWAS) [18-21]. People were in a very wide range after being infected with the virus, the severity of COVID-19 varies from a mild course with few or no symptoms and some people who need simple medical care, to a group of patients whose disease was so severe that they had to be hospitalized in the special care unit of the hospital. Some patients also died because of the severity of the disease and the problems it caused. What the researchers did was to find the genes involved in each of these conditions. The result was a relatively long list of polymorphic genes associated with susceptibility to, hospitalization for, severity of, and death from COVID-19. It should be noted that in addition to host genetic makeup, genetic changes in the virus are also important in the pathogenesis and outcome of infection [22-25].

Enrichment analysis is now used to identify classes of genes that are overrepresented in a large set of genes [26-30]. In the present study, enrichment analysis was performed using GWAS-predicted polymorphic protein-coding loci associated with various aspects of SARS-CoV-2 virus infection, such as susceptibility to, hospitalization for, severity of, and death from COVID-19, which are abbreviated as susceptibility, hospitalization, severity, and death, respectively, in this article.

2.1. Search for polymorphic loci associated with susceptibility to, hospitalization for, severity of, and death from COVID-19: Genes encoding proteins whose polymorphisms were associated with the risk of susceptibility, hospitalization, severity, and death from COVID-19 were extracted from the GWAS database (https://www.ebi.ac.uk/gwas) on July 7, 2024. Any gene for which at least one of its polymorphisms was statistically associated with outcomes in humans exposed to SARS-CoV-2 virus (e.g., susceptibility to, hospitalization for, severity of, and death from COVID-19) in at least one of the GWAS studies was included in the present analysis.

2.2. Enrichment analysis: Several tools, including web-based tools, are available for computational enrichment statistical analysis [26-30], such as the Enrichr online server (http://maayanlab.cloud/Enrichr) used in this study.

Reactome and KEEG are open source software whose primary goal is to provide intuitive bioinformatics tools for biological pathway analysis. It can be used in various research disciplines, including molecular biology and genetics projects, to interpret the results of experimental studies. In the present study, the Reactome 2022 and the KEEG 2021 database were used. Gene Ontology (GO) enrichment analysis was performed, including molecular functions, cellular components, and biological processes. Enrichment analysis for loci associated with each aspect of COVID-19 that were shared with other multifactorial traits was performed using GWAS Catalog 2023. Adjusted p-values were used to eliminate the effects of multiple comparisons on the results of statistical tests. To reduce false positives (type I statistical error), an adjusted p-value less than or equal to 0.001 was considered statistically significant.

Only protein-coding genes were included in this study. For some genes, more than one single nucleotide polymorphism was examined. Any gene with at least one significant polymorphism association with susceptibility to, hospitalization for, severity of, and death from COVID-19 was included in the analysis. A total of 177, 91, 67, 67, and 22 loci were identified whose genetic polymorphisms were associated with susceptibility, hospitalization, severity, and death, respectively (Table S1). Reactome and KEEG pathway analysis and gene ontology (GO) enrichment analysis were performed. No significant results were found (Table 1).

The data sets were then further analyzed using Venn diagrams to find the common loci among susceptibility, hospitalization, severity, and death of COVID-19. Figure 1 shows the results in a Venn diagram.

The common genes among the different aspects of COVID-19 are summarized in Table 2. The two sets of genes related to disease severity (with 67 genes) and death (with 22 genes) had 11 shared genes (including CNTNAP5, LZTFL1, SNTG2, CERKL, CTRC, GRIN2D, LRRC4C, CADM2, NCAM1, TRIOBP, and APOC1).

Shared loci of susceptibility to, hospitalization for, severity of, and death from COVID-19 with other multifactorial traits were investigated using the GWAS Catalog 2023. The results are summarized in Table 3. Very interestingly, the polymorphic loci that associated with susceptibility, hospitalization, severity, and death of COVID-19 were associated with smoking initiation (Table 3). It should be noted that smoking initiation is one of the most important smoking behavior traits which used in many epidemiological studies. It is a binary trait expressed as ever versus never smokers. Ever smokers were defined as individuals who smoked >99 cigarettes in their lifetime, which is consistent with the Centers for Disease Control and Prevention (2008) definition [31]. In much of the literature, smoking imitation is referred to as smoking behavior, or simply as smoking habit, etc. Out of 177 genes involved in susceptibility, 39 genes; out of 91 genes involved in hospitalization, 22 genes; out of 67 genes involved in disease severity, 19 genes; and finally, out of 22 genes involved in death, 8 genes shared with smoking initiation. This means that 21.7, 24.1, 28.3, and 34.6% of the loci associated with susceptibility to, hospitalization for, severity of, and death from COVID-19 were shared with smoking initiation trait.

Numerous lines of evidence suggest that the influence of various factors on susceptibility, hospitalization, severity, and death from COVID-19 is a very complex and multifaceted issue that requires a comprehensive understanding of the interplay between the following items: 1) individual factors such as age, occupation, workplace, pre-existing diseases and lifestyle; 2) social factors such as socioeconomic status and the Human Development Index (HDI) of the country of residence, access to medical and nursing services, hospital facilities, etc.; 3) host genetic polymorphisms; and 4) genetic mutations in the SARS-CoV-2 virus. The present study focused on host’s genetic polymorphisms.

The present study identified a total of 177, 91, 67, 67, and 22 polymorphic protein-coding loci associated with susceptibility to, hospitalization for, severity of, and death from COVID-19, respectively. It was hypothesized that the mentioned genes were enriched in some biological pathways. Unexpectedly, no significant results were found in the enrichment analysis. It seems that the present results did not support our hypothesis. On the other hand, the low number of common genes among the four sets of susceptibility, hospitalization, disease severity, and death due to COVID-19 indicates the very high diversity in the disease course and the involvement of factors other than the genetic background of the host, including many environmental factors, in the disease and its outcome. It appears that susceptibility, hospitalization, disease severity, and death due to COVID-19 are distinct multifactorial traits.

As mentioned in the Results section, susceptibility to, hospitalization for, severity of, and death from COVID-19 shared 39, 22, 19, and 9 loci, respectively, with the smoking initiation trait. Statistical analysis indicated that the levels of sharing were highly significant (see Table 3). Several risk factors associated with susceptibility, hospitalization, severity, and death from COVID-19, including chronic medical conditions (such as hypertension, heart failure, coronary artery disease, diabetes mellitus, and cancer), smoking habit, and obesity (Docherty et al., 2020), have been identified from the epidemiologic evidence accumulated during the COVID-19 pandemic. Almost from the beginning of the pandemic, studies have emphasized the role of smoking habits and their relationship with susceptibility to infection with SARS-CoV-2 virus, hospitalization, severity of illness, and death due to COVID-19. Interestingly, epidemiological studies have reported that smoking habit is an important risk factor for many respiratory infections, including influenza, Middle East Respiratory Syndrome (MERS), and COVID-19, and may also accelerate disease progression in patients [2, 33-36].

Smoking is a major risk factor for many diseases. Because of its high prevalence in human populations and its positive association with a wide range of diseases, it remains a serious health concern. Smoking initiation has been shown to have significant heritability [37], and many polymorphic loci have been identified that show significant association with the trait [38]. It should be noted, however, that this is a modifiable trait.

Evidence suggests that smoking initiation is significantly associated with increased risk of cancer (e.g., breast, lung, colorectal, etc.) ([3-41], 14 different cardiovascular diseases (e.g., coronary heart disease, heart failure, ischemic stroke, transient ischemic attack, and arterial hypertension [42], atrial fibrillation[43], type 2 diabetes mellitus [44], increased abdominal fat distribution [45], and so on.

Finally, I would like to draw your attention to the following evidence.

1. Smoking initiation is associated with susceptibility to, hospitalization for, severity of, and death from COVID-19.

2. Shared genes between smoking initiation and susceptibility to, hospitalization for, severity of, and death from COVID-19.

3. Associations between smoking and chronic diseases related to COVID-19, such as cancer, diabetes mellitus, cardiovascular disease, etc.

Taken together, it is reasonable to conclude that factors such as chronic cardiovascular disease and diabetes mellitus are not independent risk factors for COVID-19 and its outcomes, and that smoking is the most important and influential risk factor. To confirm or reject this conclusion, further analysis using Mendelian randomization on the available information is needed.

Ethics approval: This is an enrichment analysis study based on published data in the GWAS database and does not require ethical approval.

Consent for publication: The author read and approved the final manuscript for submission.

Declaration of competing interest: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding: None

Data availability: The author confirms that the data supporting the findings of this study are available within the article as supplementary materials.

Contributions: Idea for and design of the study, data collection, statistical analysis, preparation of the first draft and critical revision of the manuscript were done by the author (MS).

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Table 1: Results of Enrichment Analysis of polymorphic genes for susceptibility to, hospitalization for, severity of, and death from COVID-19

Index	Name	Adjusted p-value	Odds Ratio	Combined score
Susceptibility to COVID-19
KEGG	Circadian entrainment	0.2578	6.23	39.70
Reactome	Regulation Of KIT Signaling R-HSA-1433559	0.2009	26.27	209.03
GO Biological Process	Synaptic Membrane Adhesion (GO:0099560)	0.0959	37.96	336.46
GO Cellular Component	Adherens Junction (GO:0005912)	0.0583	5.67	44.57
GO Molecular Function	Alpha-Actinin Binding (GO:0051393)	0.2701	17.07	116.94

Hospitalization of patients
KEGG	Focal adhesion	0.0341	7.14	57.57
Reactome	Voltage Gated Potassium Channels R-HSA-1296072	0.0976	16.93	117.22
GO Biological Process	Regulation of Intracellular Signal Transduction (GO:1902531)	0.0444	6.54	63.30
GO Cellular Component	Sarcolemma (GO:0042383)	0.0108	19.02	176.83
GO Molecular Function	Transmembrane Receptor Protein Kinase Activity (GO:0019199)	0.0547	19.93	146.77

Severity of COVID-19
KEGG	Salivary secretion	0.1502	10.33	57.70
Reactome	Plasma Lipoprotein Assembly R-HSA-8963898	0.1248	43.78	291.36
GO Biological Process	Regulation of Neuron Projection Development (GO:0010975)	0.0013	13.81	182.11
GO Cellular Component	Chylomicron (GO:0042627)	0.0244	102.19	827.06
GO Molecular Function	Phosphatidylcholine-Sterol O-acyltransferase Activator Activity (GO:0060228)	0.0148	204.41	1863.51

Death due to COVID-19
KEGG	Aldosterone synthesis and secretion	0.1163	20.71	109.12
Reactome	G Alpha (Z) Signaling Events R-HSA-418597	0.0576	44.30	297.48
GO Biological Process	Positive Regulation of Cellular Metabolic Process (GO:0031325)	0.0728	36.90	234.95
GO Cellular Component	NMDA Selective Glutamate Receptor Complex (GO:0017146)	0.1147	158.51	771.89
GO Molecular Function	Phosphatidylcholine-Sterol O-acyltransferase Activator Activity (GO:0060228)	0.0852	237.79	1237.70

Table 2: Shared polymorphic loci between any two traits under study (susceptibility to, hospitalization for, severity of, and death from COVID-19)

Traits	Number of shared loci	Symbols of genes
Susceptibility-Hospitalization	6	SSPN, DMD, SFTPD, RBFOX1, DPP9, XCR1
Susceptibility-Severity	5	CNTNAP5, LZTFL1, RFLNA, PRKG1, ADGRE1
Susceptibility-Death	3	CNTNAP5, LZTFL1, SCFD2
Hospitalization-Severity	0	-
Hospitalization-Death	1	ADAM33
Severity-Death	11	CNTNAP5, LZTFL1, SNTG2, CERKL, CTRC, GRIN2D, LRRC4C, CADM2, NCAM1, TRIOBP, APOC1

Table 3: Polymorphic genes associated with susceptibility to, hospitalization for, severity of, and death from COVID-19 that are shared with the smoking initiation trait

Traits	Number of loci	Adjusted p-value	Odds Ratio	Combined score	Genes
Susceptibility	39	5.0e-10	4.52	124.49	PRKN; RYR2; SETD2; GALNT13; MCTP1; EHMT1; CACNA1D; FRY; FHIT; CDH4; DACH1; GLRA3; HERC1; POLR2A; CDH22; DLEU1; MACROD2; ZNF385D; CSMD1; PRKG1; MAGI1; RBFOX1; SZT2; CA10; NEGR1; NUBPL; MAGI2; LSAMP; KSR2; PTPRD; OLFM1; KANSL1; TRAF3; ELMO1; KCNQ3; SSPN; PRKD1; CNTNAP5; SOX2-OT
Hospitalization	22	2.3e-6	5.05	90.82	ROBO2; CADPS2; RBFOX1; TENM3; CPNE4; CELF2; ULK4; HSF2BP; DCAF5; KALRN; SGCZ; DNM3; TAFA5; ERBB4; KCNMA1; SSPN; CAMTA1; WDR7; HIVEP2; ASIC2; ANKS1B; RAPGEF4
Severity	19	1.3e-6	6.26	116.16	GUCY1A2;CADM2;STK39;AGAP1;RORA;CDH8;EMCN;NFIA;RARB;SPOCK1;NCAM1;MAPT;GALNTL6;ASTN2;ZFPM2;BEND7;LRRC4C;PRKG1;CNTNAP5
Death	8	9.7e-4	8.97	94.88	RGS17; CADM2; IQSEC1; PRKCE; CTNNA3; NCAM1; LRRC4C; CNTNAP5

No competing interests reported.

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Enrichment analysis of polymorphic loci associated with SARS-CoV-2 infection and its outcomes

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Abstract

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1. Introduction

2. Method

3. Results

4. Discussion

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Consent for publication: The author read and approved the final manuscript for submission.

References

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