Abstract
Since the first report of COVID-19 in December 2019, more than 100
million people have been infected with SARS-CoV-2. Despite ongoing
research, there is still limited knowledge about the genetic causes of
COVID-19. To resolve this problem, we applied the SMR method to analyze
the genes involved in COVID-19 pathogenesis by the integration of
multiple omics data. Here, we assessed the SNPs associated with
COVID-19 risk from the GWAS data of Spanish and Italian patients and
lung eQTL data from the GTEx project. Then, GWAS and eQTL data were
integrated by summary-data-based (SMR) methods using SNPs as
instrumental variables (IVs). As a result, six protein-coding and five
non-protein-coding genes regulated by nine SNPs were identified as
significant risk factors for COVID-19. Functional analysis of these
genes showed that UQCRH participates in cardiac muscle contraction,
PPA2 is closely related to sudden cardiac failure (SCD), and OGT, as
the interacting gene partner of PANO1, is associated with neurological
disease. Observational studies show that myocardial damage, SCD, and
neurological disease often occur in COVID-19 patients. Thus, our
findings provide a potential molecular mechanism for understanding the
complications of COVID-19.
Keywords: SMR, COVID-19, eQTL, GWAS, UQCRH, PPA2, OGT, PANO1
Introduction
In December 2019, SARS-CoV-2 was first reported to lead to the
respiratory disease coronavirus disease 2019 (COVID-19) ([25]Zhou et
al., 2020). Subsequently, COVID-19 quickly spread to all parts of the
world and became a worldwide public health event. As of February 17,
2021, more than 100 million people had been infected, and more than 2.4
million people had died of COVID-19. At present, a total of seven types
of coronaviruses that can infect humans have been discovered, including
SARS-CoV, SARS-CoV-2, and MERS-CoV, which have high case fatality rates
(CFRs) ([26]Gussow et al., 2020). The other four coronaviruses,
HCoV-HKU1, HCoV-NL63, HCoV-OC43, and HCoV-229E, only cause mild
symptoms in humans. Although the CFR of SARS-CoV-2 is relatively lower
than those of SARS-CoV and MERS-CoV, it is still highly infectious.
Exploring the origin of the virus would help to increase the
understanding of SARS-CoV-2 ([27]Cui et al., 2019; [28]Narang et al.,
2019; [29]Benetti et al., 2020; [30]Meng et al., 2020; [31]Wan et al.,
2020; [32]Qi et al., 2021). Previous studies have shown that bats are
the natural host of the evolved coronavirus ([33]Jiang et al., 2020;
[34]Kwon et al., 2020). Based on the alignment of the reference genome
sequence, a phylogenetic tree was constructed, indicating that the
genes are very similar between SARS-CoV-2 and members of the bat
Sarbecovirus subgenus Betacoronavirus ([35]Wu et al., 2020). According
to sequence mapping, the whole-genome sequence of SARS-CoV-2 has the
highest similarity with SARS-CoV BatCoV RaTG13, reaching over 96%. The
similarity between SARS-CoV-2 and the coronaviruses SARS-CoV and
MERS-CoV is only 79 and 50%, respectively ([36]Andersen et al., 2020).
Although the specific route of transmission of SARS-CoV-2 from its
natural host to humans is not yet clear, researchers have discovered
that the key functional sites of the SARS-CoV-2 spike protein are
almost the same as those of the virus isolated from pangolins.
Therefore, a pangolin coronavirus may have provided part of the spike
gene for SARS-CoV-2.
Computational methods with omics data have shown strong power in
identifying disease-related genes ([37]Zhao T. et al., 2020c).
SARS-CoV-2 is a single-stranded RNA virus ([38]Chen et al., 2020). The
reference genome shows that it has 29,903 nucleic acid base pairs,
including seven conserved unstructured protein domains and four
structural protein domains, including the spike protein. The sequence
of SARS-CoV-2 has mutated since its discovery. As early as March 2020,
researchers analyzed 160 early virus strains and found three important
single-point mutations, T8782C, C28144T, and G26144T ([39]Forster et
al., 2020). Among them, the mutations T8782C and C28144T are used to
distinguish between type A and B viruses, and the mutation G26144T
represents a newer virus type (type C). In May 2020, Cheng et al.
analyzed more than 1,800 strains of viruses in the Americas, Europe,
and Asia and verified that type B viruses are more infectious and have
almost replaced the type A viruses in current circulation ([40]Cheng et
al., 2021). Through comparison with the reference genome, it was found
that each virus mutated at approximately 1.75 sites per month. The
overall mutations were silent mutations and would not cause major
functional changes in the virus. Cheng et al. proposed to analyze the
mutation rules of the virus over time and identified seven dominant
mutations. According to functional bioinformatics analysis, these
mutations likely caused the virus to decrease in toxicity and increase
in infectivity. In addition, mutation clustering information ([41]Zhu
X. et al., 2019; [42]Qi et al., 2020; [43]Zhao T. et al., 2020b;
[44]Zhao X. et al., 2020; [45]Zou et al., 2020; [46]Zhao et al., 2021)
indicated that the virus circulating in the Americas is more similar to
RaTG13.
By searching for the origin of SARS-CoV-2, researchers also found that
SARS-CoV-2 and SARS-CoV use the same receptor, angiotensin-converting
enzyme II (ACE2). SARS-CoV-2 invades human cells by binding to ACE2. By
interacting with proteins in human cells, SARS-CoV-2 affects human
health. In August 2020, researchers found hundreds of interactions
between human proteins and SARS-CoV-2 proteins. Although many proteins
that interact with SARS-CoV-2 have been discovered so far, the genes
associated with SARS-CoV-2 pathogenesis are still unknown. Since
genomics data of many COVID-19 patients have been reported, there is an
opportunity to find potential risk genes through analysis and
integration of omics data of susceptible populations ([47]Cheng, 2019;
[48]Cheng et al., 2019; [49]Li F. et al., 2020; [50]Wang et al., 2020).
SMR is a method used to determine the causal association between
genetically determined traits and diseases, and eQTLs are genetic
variations related to the expression of traits. Since eQTL data are
tissue specific, it is possible to correlate the eQTL data of
disease-related tissues with disease GWAS data and use the SMR method
to find causal genes for diseases. Therefore, in our study, we used
lung eQTL data of GTEx and GWAS data of COVID-19 patients to identify
the genes related to COVID-19 pathogenesis using the SMR method.
Materials and Methods
Risk SNPs for Severe COVID-19
According to current knowledge, some people are more susceptible than
others to COVID-19. To assess the impact of SNPs on the risk of
COVID-19, researchers conducted a genome-wide association study (GWAS)
on two groups of European patients in seven hospitals in Italy and
Spain and performed a meta-analysis of the results of the two groups
([51]Ellinghaus et al., 2020). First, quality control was conducted on
1980 severe COVID-19 patients, and 1,610 patients remained after
removing population outliers. Then, the researchers performed a GWAS on
835 Italian patients and 1,255 control group members ([52]Sun et al.,
2019), as well as 775 Spanish patients and 950 control group members.
As a result, they obtained severe COVID-19 risk data for 8,582,968
SNPs. Finally, the two sets of experimental results were meta-analyzed
to obtain the final risk SNPs. [53]Figure 1 shows the distribution of
risk betas of SNPs in COVID-19 patients. Most SNPs had no impact on the
risk of COVID-19. In addition, researchers found that COVID-19
high-risk SNPs clustered on 3p21.31.
FIGURE 1.
[54]FIGURE 1
[55]Open in a new tab
The distribution of GWAS beta in severe COVID-19 patients.
Lung eQTL
To date, many GWAS have been performed. These studies have identified
thousands of disease-related risk SNPs. Since more than 90% of SNP
sites exist outside of protein-coding genes, it is difficult to
understand the mechanisms by which these SNPs affect diseases. To this
end, researchers investigate expressed quantitative trait loci (eQTLs)
to reveal the genes regulated by SNPs in the blood, lung and other
tissues ([56]Zhao T. et al., 2020a). Therefore, the NIH launched the
gene type-tissue expression (GTEx) project, with the goal of
establishing the relationships between SNPs and gene expression in
different tissues. Currently, the project has accepted more than 900
post-mortem donors. Sequencing of different tissues in the donors has
identified a large number of SNP-regulated genes. Summarized GTEx data
could be obtained from the project’s website. [57]Figure 2 shows the
distribution of beta values of lung SNPs on gene expression.
FIGURE 2.
[58]FIGURE 2
[59]Open in a new tab
The distribution of eQTL beta on lung SNPs.
Identification of Potential Causal Genes of COVID-19 Based on the SMR Method
In the domain of biomedicine, many causal disease associations have
been discovered through observational research, such as the association
between smoking and lung cancer ([60]Ghosh and Yan, 2020; [61]Li J. et
al., 2020; [62]Yuan et al., 2020). However, the associations between
phenotypes and diseases found in these observational studies cannot
reflect causality. However, because observational studies are usually
disturbed by external factors and often face practical problems related
to long time frames and high costs, there are large errors in the
analysis of pathogenic factors of diseases. Mendelian randomization
follows the Mendelian inheritance law of allele separation and free
recombination of nonalleles and makes causal inferences based on
genetic variation, which does not change with environment or age, so
this method is widely used in causal inference of pathogenic factors.
Sometimes, due to the influence of confounding factors, the correlation
found is not accurate. This greatly limits the development of the
field. To solve this problem, the statistician [63]Katan (1986)
introduced the concept of Mendelian randomization (MR) in 1986 to study
whether low serum cholesterol levels can increase the risk of cancer.
MR uses genotype as an instrumental variable (IV) and applies the
two-stage least squares method to infer the pathogenicity of diseases.
With the gradual deepening of GWAS research, this method has been
widely applied using SNPs as IVs. This method assumes that Z is an
instrumental variable (SNP), X represents exposure factors or gene
expression levels, and Y represents the disease. According to the
two-stage least squares method, the effect of X on Y is evaluated as
follows.
[MATH:
BetaXY=BetaZX/BetaZY :MATH]
(1)
Here, Beta[ZX] represents the least-squares estimate of Z on X, and
Beta[ZY] is the least-squares estimate of Z on Y. Then, we estimate the
significance of X on Y as T[MR].
[MATH: TMR=(BetaXY<
/mrow>)2/var(BetaXY<
/mrow>)
:MATH]
(2)
Theoretically, GWAS and eQTL need to target the same sample, but GWAS
and eQTL are performed independently, so we use the two-sample MR
method, and T[SMR] is obtained as the final evaluation value ([64]Zhao
S. et al., 2019; [65]Zhao T. et al., 2019):
[MATH: TSM
R=ZZY2*ZZX<
/none>2/(ZZ<
mo>Y2ZZX2) :MATH]
(3)
where Zzy represents the Z statistics from the GWAS, and Zzx represents
the z statistics from the eQTL study. Since the true values of Beta[ZX]
and Beta[ZY] cannot be obtained, we can use estimations to replace
them. The T[SMR] yields an approximate Chi-square test statistic.
Here, we used the SMR method to evaluate the mechanism by which SNPs
affect COVID-19 and identify potential pathogenic genes in the lungs.
The specific process is shown in [66]Figure 3. The GWAS data of
COVID-19 patients and the lung eQTL data were obtained from a public
dataset. T[SMR] was calculated based on the two-sample MR method, which
was then further evaluated using the chi-square test to identify
significant SNPs and genes.
FIGURE 3.
[67]FIGURE 3
[68]Open in a new tab
The workflow of identifying causal genes of COVID-19 using the SMR
method.
Results
Causal SNPs and Genes of COVID-19
A total of 1,072 SNPs appeared in both GWAS and eQTL data. After
application of the SMR method, 1,072 SNPs were determined to regulate
the P value distribution of COVID-19 through genes ([69]Figure 4). The
P values of SMR for most SNPs were concentrated in the range of
0.02–0.08. Here, the threshold was set as 0.003, and then 11 genes
(UQCRH, PPA2, PAPSS1, ABO, AP006621.5, CMB9-55F22.1, AP006621.6, PANO1,
CTD-2027I19.2, LINC01273, and ARSA) regulated by nine SNPs (rs41292543,
rs35258888, rs70947091, rs8176719, rs10678686, rs10678686, rs10678686,
rs7104929, rs10407383, rs6122883, and rs6151429) with P values lower
than the threshold were considered to increase the risk of COVID-19
([70]Table 1). [71]Figure 5 shows the GWAS P value, eQTL P value, and
SMR P-value of all SNPs.
FIGURE 4.
[72]FIGURE 4
[73]Open in a new tab
The distribution of SMR p-values of SNPs.
TABLE 1.
Nine Causal SNPs and eleven pathogenic genes of COVID-19.
SNP P_SMR Gene chr pos P_GWAS P_eQTL
rs41292543 0.002574 UQCRH 1 46309111 0.001553 2.65E−16
rs35258888 0.002419 PPA2 4 105355205 0.001035 2.61E−10
rs70947091 0.002154 PAPSS1 4 107694523 0.001899 2.28E−56
rs8176719 3.07E-06 ABO 9 133257521 8.76E−07 1.27E−34
rs10678686 4.68E-05 AP006621.5 11 780321 3.96E−05 1.28E−100
rs10678686 4.34E-05 CMB9-55F22.1 11 780321 3.96E−05 9.62E−143
rs10678686 6.40E-05 AP006621.6 11 780321 3.96E−05 1.12E−44
rs7104929 0.001973 PANO1 11 784340 3.73E−05 0.0111629
rs10407383 0.001423 CTD-2027I19.2 19 24134099 0.000412 4.61E−09
rs6122883 0.000702 LINC01273 20 50172836 0.000494 3.43E−34
rs6151429 0.000645 ARSA 22 50625049 0.000522 4.70E−50
[74]Open in a new tab
FIGURE 5.
[75]FIGURE 5
[76]Open in a new tab
The experimental results based on SMR.
Functional Analysis of Causal Genes
Among the identified pathogenic genes, there were a total of six
protein-coding genes (UQCRH, PPA2, PAPSS1, ABO, PANO1, and ARSA) and
five noncoding genes. We then performed functional analysis of these
six protein-coding genes to identify their functions, related diseases
and pathways.
UQCRH participates in cardiac muscle contraction. A large number of
studies have found that approximately 8–12% of COVID-19 patients have
myocardial damage ([77]Lippi et al., 2020). Although heart problems are
usually not the most prominent or deadly feature of COVID-19, they are
common and are severe enough that most people admitted to the hospital
for COVID-19 are now being screened for myocardial damage. There are
many potential causes of COVID-19-related myocardial damage, but it is
often difficult to determine the specific cause in a specific
individual. The UQCRH gene found here may be a potential cause. In
addition, according to Gene Ontology annotation, UQCRH has
ubiquinol-cytochrome-c reductase activity.
PPA2 is closely related to sudden cardiac failure (SCD). At present,
there have been reports of COVID-19 patients who have died of SCD. In
July 2020, Samira et al. diagnosed three patients with COVID-19
according to the reverse transcriptase-polymerase chain reaction of
nasopharyngeal swabs and radiological examinations ([78]Shirazi et al.,
2021) who eventually died of SCD. Thus, the authors recommended that it
is necessary to monitor the heart conditions of COVID-19 patients.
Although there is no direct causal link between SCD and COVID-19,
analysis of current data shows that there is a reasonable link between
them. According to the latest studies, the incidence of SCD in the
community and hospital environment has increased since the outbreak of
COVID-19 ([79]Yadav et al., 2020). Based on our findings, PPA2 can
increase the risk of COVID-19, so PPA2 may be a potential factor that
results in SCD in COVID-19 patients.
Interaction Between the Causal Gene PANO1 and OGT
We searched for the interactions between causal genes of COVID-19 and
other genes. As a result, we found that OGT can interact with PANO1.
Then, we further investigated the function of OGT to explore the
potential mechanism of PANO1 in the risk of COVID-19. At the start of
the COVID-19 epidemic, some patients experienced neurological symptoms,
such as feeling confused, being unable to discern direction, and
feeling restless ([80]Marshall, 2020). A total of 0.2% of the patients
of two other SARS-CoV-2-related coronaviruses, SARS-CoV and MERS-CoV,
have neurological disease. Given the number of COVID-19 patients,
hundreds of thousands of patients may have neurological complications.
As genes related to neurological disease and risk genes for COVID-19,
OGT, and PANO1 must be considered further.
Conclusion
In this article, we used the SMR method to analyze the genes involved
in COVID-19 pathogenesis. Here, the risk SNPs for COVID-19 were derived
from the GWAS data of Spanish and Italian patients. Lung eQTL data were
acquired from the GTEx project. In the postgenomic era, MR and SMR
methods have been widely used ([81]Liu et al., 2018). Currently, a
large number of pathogenic phenotypes and genes have been identified
based on these methods. Through SMR, this article discovered six
protein-coding genes and five noncoding genes that can increase the
risk of COVID-19. Finally, nine SNPs that met the threshold conditions
were identified, and the SMR method was used to determine that these
SNPs regulated 11 disease-causing genes that could increase the risk of
COVID-19. Then, disease pathway enrichment analysis was performed on
these genes.
Through functional analysis, we found that UQCRH participates in
cardiac muscle contraction, PPA2 is closely related to SCD, and
myocardial damage and SCD occurred in patients with COVID-19.
Therefore, our findings provide a potential molecular mechanism for
these processes. Further analysis revealed an interaction between OGT
and PANO1. OGT is associated with neurological disease. This may
explain the neurological complications in COVID-19 patients.
Data Availability Statement
The original contributions presented in the study are included in the
article/supplementary material, further inquiries can be directed to
the corresponding author/s.
Ethics Statement
Ethical review and approval was not required for the study on human
participants in accordance with the local legislation and institutional
requirements. Written informed consent for participation was not
required for this study in accordance with the national legislation and
the institutional requirements.
Author Contributions
YZ and XL wrote the manuscript and did the experiments. XL provided
ideas of this work. YZ analyzed the data. Both authors approved the
submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
References