=Paper=
{{Paper
|id=Vol-2962/paper18
|storemode=property
|title=Identification and Analyses of Variants associated with Covid-19 from Non-invasive Prenatal Testing in Slovak Population
|pdfUrl=https://ceur-ws.org/Vol-2962/paper18.pdf
|volume=Vol-2962
|authors=Natália Forgáčová,Juraj Gazdarica,Jaroslav Budiš,Martina Sekelská,Tomáš Szemes
|dblpUrl=https://dblp.org/rec/conf/itat/ForgacovaGBSS21
}}
==Identification and Analyses of Variants associated with Covid-19 from Non-invasive Prenatal Testing in Slovak Population==
https://ceur-ws.org/Vol-2962/paper18.pdf
Identification and Analyses of Variants Associated with COVID-19 from
Non-invasive Prenatal Testing in Slovak Population
Natalia Forgacova1, Juraj Gazdarica2,3,4, Jaroslav Budis1,3,4,
Martina Sekelska5,6 and Tomas Szemes1,2,3
1
Comenius University Science Park, Ilkovicova 8, Bratislava, 841 04, Slovakia
2
Faculty of Natural Sciences, Comenius University, Bratislava, 841 04, Slovakia
3
Geneton Ltd., Bratislava, 841 04, Slovakia
4
Slovak Centre of Scientific and Technical Information, Bratislava, 811 04, Slovakia
5
Trisomy Test Ltd., Bratislava, 841 04, Slovakia
6
Medirex Inc., Bratislava, 821 06, Slovakia
Abstract. Since December 2019, coronavirus disease 2019 human transmission is higher, as respiratory droplets and close
(COVID-19), caused by severe acute respiratory syndrome contact can primarily transmit it [4,6–10]. COVID-19 presents a
coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the broad spectrum of varied clinical manifestations, from
world and caused a large global pandemic which drastically asymptomatic or mild symptoms to serious health outcomes
changed our everyday lives. As the COVID-19 pandemic
progressed, a number of its characteristics showed enormous leading to death [11,12]. Even though the symptoms are highly
inter-individual and inter-population differences. Earlier heterogeneous, the most commonly observed in the large
genome-wide association studies (GWAS) have identified majority of infected persons are fever, cough, severe headache,
potential key genes and genetic variants associated with the risk muscle pain, fatigue, myalgia, shortness of breath, chest
and prognosis of COVID-19, but the underlying biological tightness, and loss of taste or smell [13–18]. Besides, several
interpretation is largely unclear. Our previous work described minor symptoms such as gastrointestinal complications,
genomic data generated through non-invasive prenatal testing including nausea, vomiting, and diarrhea, have also been
(NIPT) based on low-coverage massively parallel whole- reported [19]. In severe cases, breathing difficulties with
genome sequencing of total plasma DNA of pregnant women in dyspnea occur, with acute respiratory distress syndrome (ARDS)
Slovakia as a valuable source of population specific data. In the
present study, we have performed a literature search of studies being the most serious complication [20]. It is likely that a
and used NIPT data to determine the population allele frequency mixture of genetic and nongenetic factors interplays between
of risk COVID-19 variants that have been reported in GWAS virus and host genetic background and determines the severity of
studies to date. We also focused on variants located in the ACE2 COVID-19 outcome. Advanced age, male sex, some ethnicity
gene, encoding angiotensin-converting enzyme 2 (ACE2), which and blood type such as A and AB0 blood types, smoking,
is hypothesized to be a possible genetic risk factor for SARS- hypertension, diabetes mellitus, obesity, cardiovascular,
CoV-2 infection. Allele frequencies of identified variants were respiratory, and kidney disease or cancer have been identified as
compared with six world populations from the gnomAD risk factors associated with a higher risk of death COVID-19
database to detect significant differences between populations. [12,21–27]. Nevertheless, these factors do not explain the main
We interpreted variants and searched for functional
consequences and clinical significance of variants using publicly pathogenesis of COVID-19. Therefore, the host’s genetic
available databases. Finally, 2 COVID-19 risk variants were variations may partly provide novel insights into pathological
found that showed statistically significant differences in mechanisms underlying COVID-19. Recently, genome-wide
population allele frequencies - rs383510 and rs1801274. association studies (GWAS) have been performed to uncover
genetic risk factors associated with the diagnosis and prognosis
of COVID-19; however, the biological interpretation of their
1 Introduction findings has not yet been fully clarified [28–32].
Non-invasive prenatal testing (NIPT) based on low-coverage
The Coronavirus Disease (COVID-19), caused by the Severe massively parallel whole-genome sequencing of plasma DNA
Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is from pregnant women generates a large amount of data that
a complex, highly infectious disease involving the respiratory, provides the resources to investigate human genetic variations in
immune, cardiovascular, gastrointestinal, and neurological the population. In our previous studies, we described the re-use
systems [1–4]. The first case was registered in Wuhan, Hubei of the data from NIPT for genome-scale population specific
Province of China in December 2019, and it has rapidly evolved frequency determination of small DNA variants [33] and CNVs
into a global pandemic [5]. At the time of the writing (June [34]. Since pregnant women represent a relatively standard
2021), there have been more than 177 million confirmed cases sample of the local female population, we assumed this NIPT
and 3.8 million deaths worldwide (in Slovakia more than 390 data could also be used in the population study of COVID-19.
000 people were infected, with the total deaths exceeding 12 As of June 2021, there are more than 100 GWAS studies
000) (https://origin-coronavirus.jhu.edu/map.html). trying to identify possible candidate genes and human genetic
Although the mortality rate of COVID-19 (ranges between 1- variants that are likely involved in COVID-19 pathogenesis. The
7%) is lower than that of the other two types of coronaviruses, main aim of our study was an analysis of common variants
severe acute respiratory syndrome (SARS-CoV) and the Middle (MAF>0.05) that showed evidence of association with COVID-
East respiratory syndrome (MERS-CoV), the rate of human-to- 19 in studies and characterization of population variability from
data generated by NIPT. Allele frequencies of risk COVID-19
_______________________
Copyright ©2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0)
variants from studies identified in the Slovak population were in NIPT data were excluded from the analysis. All identified
compared with allele frequencies of risk COVID-19 variants in variants in the Slovak population used for further analyses were
6 worldwide populations. While previous studies have common (MAF>0.05). Subsequently, allele frequencies of
demonstrated the role of the ACE2 gene, encoding angiotensin- COVID-19 risk variants for each population (East Asian, South
converting enzyme 2, in host defense against COVID-19 [35– Asian, African, American, Finnish European and non-Finnish
37], our study aimed to analyze population allele frequencies and European) were extracted from the gnomAD database available
describe the clinical impacts of relevant variants located in the online (v3.0, downloaded from https://gnomad.
ACE2 gene. To our knowledge, this was the first population broadinstitute.org/downloads) and compared with our
study of COVID-19 using NIPT data conducted exclusively in frequencies determined for the Slovak population from NIPT
the Slovak population. data. Allele frequency in each population and allele frequency
differences were plotted using boxplots. Outliers of boxplots that
represent variants with highly different frequencies were
2 Methods annotated via published literature (in dbSNP
(https://www.ncbi.nlm.nih.gov/snp/). To assess the relations
2.1 Data source between allele frequency of COVID-19 risk variants in each
The laboratory procedure used, to generate the NIPT data, population, we also used Principal Component Analysis (PCA)
were as follows: DNA from plasma of peripheral maternal blood using matplotlib.pyplot library, which reduces the dimension of
was isolated for NIPT analysis from 1,501 pregnant women after the data to a graphically interpretable 2D or 3D dimension.
obtaining a written informed consent consistent with the Consequently, we obtained information on which populations
Helsinki declaration from the subjects. The population cohort have similar or different allele frequencies of the identified
consisted from women in reproductive age between 17-48 years COVID-19 risk variants.
with a median of 35 years. Genomic information from a sample
consisted of maternal and fetal DNA fragments. Each included 2.4 Analyses of variants located in ACE2 gene
individual agreed to use their genomic data in an anonymized While previous studies have demonstrated a possible
form for general biomedical research. The NIPT study (study ID important role of the ACE2 gene in COVID-19 infection, we also
35900_2015) was approved by the Ethical Committee of the focused on the study of variants located in the ACE2 gene in our
Bratislava Self-Governing Region (Sabinovska ul.16, 820 05 dataset of NIPT data. First, we filtered out a group of variants
Bratislava) on 30th April of 2015 under the decision ID located in this gene. The genomic locations of the gene were
03899_2015. Blood samples were collected to EDTA tubes and determined by the GeneCards database
plasma was separated in dual centrifugation procedure. DNA (https://www.genecards.org/). Allele frequencies of identified
was isolated from 700 μl of plasma using DNA Blood Mini kit variants for each population (East Asian, South Asian, African,
(Qiagen, Hilden, DE) according to standard protocol. American, Finnish European and non-Finnish European) were
Sequencing libraries were prepared from each sample using extracted from the gnomAD database (v3.0, downloaded from
TruSeq Nano kit HT (Illumina, San Diego, CA, USA) following https://gnomad.broadinstitute.org/downloads) and compared
standard protocol with omission of DNA fragmentation step. with frequencies of variants located in ACE2 gene determined
Individual barcode labelled libraries were pooled and sequenced for the Slovak population from NIPT data. Allele frequency in
using low-coverage whole-genome sequencing on an Illumina each population and allele frequency differences were plotted
NextSeq500 platform (Illumina, San Diego, CA, USA) by using boxplots. Outliers of boxplots were annotated via
performing paired end sequencing of 2×35 bases [38]. published literature and studies (in dbSNP
(https://www.ncbi.nlm.nih.gov/snp/).
2.2 Data analysis
The detailed information about mapping, exclusion of 3 Results
overlapping reads, quality control and filtering, realignment,
genomic coverage and variant calling is fully described in our
3.1 Analyses of common variants previously reported to be
previous study, in the section Methods and Results [33]. The
risk variants for COVID-19
datasets generated and analyzed during the current study are
available in the DSpace repository, We found 29 risk genetic variants associated with COVID-19
https://dspace.uniba.sk/xmlui/handle/123456789/27. disease in literature search of studies available online in PubMed
(Table 1).
2.3 Analyses of common variants previously reported to be After merging all identified variants from GWAS (29 risk
risk variants for COVID-19 variants) with our NIPT data, we identified 20 common risk
COVID-19 variants, while 9 risk variants that were not called in
We have performed a literature search and combined genotype
the Slovak population were excluded from further analysis. The
data from all previously published studies available online
allele frequencies of 20 variants identified in our population
(https://pubmed.ncbi.nlm.nih.gov/) for the years 2020-2021 with
sample (Slovak population) and the allele frequencies of variants
key words “COVID-19” and “GWAS”, specifically 114 studies
for 6 world populations (East Asian, South Asian, African,
focused on genetic variants associated with COVID-19 disease.
American, Finnish European and non-Finnish European)
Using data from these datasets, we have summarized 29 COVID-
obtained by gnomAD database are shown in graphical
19 risk variants, which were then merged with our data of
comparison by Boxplots (Figure 1) and PCA (Figure 2). The
identified variants from NIPT. Risk variants that were not found
median allele frequency for the Slovak population reached the
value of 0.2712, which is closest to the value of the median of Next, we compared known allele frequencies of 20 COVID-
the American population (MED=0.2972). PCA placed our 19 risk variants in our sample set from the Slovak population to
sample set most closely to the non-Finnish European population. allele frequencies of these variants in six world populations. The
final findings of allele frequency differences are shown in Figure
3. We identified 2 outliers, rs1801274 and rs383510, in Slovak-
Finnish European population comparison and Slovak-non-
Finnish European population comparison (Table 2). The
rs1801274 is a missense variant located in the FCGR2A gene
with interpretation in the ClinVar database
(https://www.ncbi.nlm.nih.gov/clinvar/), which aggregates
information about genomic variation and its relationship to
human health, as drug response. The rs383510 variant is
classified as an intron variant in the TMPRSS2 gene, not reported
in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/).
Fig. 1. Boxplots show allele frequency of 20 risk COVID-19 variants identified
from GWAS studies for the Slovak and the other six world populations. AFR,
African population; AMR, American population; EAS, East Asian population;
FIN, European (Finnish) population; NFE, European (non-Finnish) population;
SAS, South Asian population; SVK, Slovak population.
Table 1. 29 risk variants associated with COVID-19 in GWAS studies from
2020/2021.
ID_SNP GENE PMID
rs2252639 IFNAR2 33259846
rs200008298 DNAH7 33200144
rs183712207 DNAH7 33200144
rs191631470 DNAH7 33200144 Fig. 2. PCA plot illustrates the allele frequency of 20 risk COVID-19 variants
rs1024611 CCL2 33133166 identified from GWAS for the Slovak and the other six world populations.
rs1800450 MBL 33133166
rs2280788 CCL5 33133166
rs2248690 AHSG 33133166
rs2304237 ICAM3 33133166
rs2430561 IFN 33133166
rs4804803 CD209 33133166
rs2070874 IL4 33133166
rs2070788 TMPRSS2 33133166
rs383510 TMPRSS2 33133166
rs4932178 FURIN 33133166
rs16944 IL1B 33868239
rs2275913 IL17A 33868239
rs1800795 IL6 33868239
rs1800629 TNF 33868239
Fig. 3. Boxplots show allele frequency differences of Slovak and the other six
rs1143633 IL1B 33868239 world populations for 20 risk COVID-19 variants identified from GWAS. AFR,
rs3917332 IL1R1 33868239 Slovak-African population; AMR, Slovak-American population; EAS, Slovak-
rs2232354 IL1RN 33868239 East Asian population; FIN, Slovak-European (Finnish) population; NFE,
rs1800797 IL6 33868239 Slovak-European (non-Finnish) population; SAS, Slovak-South Asian
population.
rs1800796 IL6 33868239
rs1801274 FCGR2A 33868239
rs11385942 LZTFL1 33868239 Table 2. Outliers identified in boxplots that show allele frequency differences of
rs13078854 LZTFL1 33868239 Slovak and the other six world populations for 20 risk COVID-19 variants
identified from GWAS.
rs657152 ABO 33868239
rs9411378 ABO 33868239 ALLELE FREQUENCY DIFFERENCES
SVK- SVK- SVK- SVK- SVK- SVK-
SNP
AFR AMR EAS FIN NFE SAS
rs1801274 - - - - - -
0.3416 0.2869 0.1328 0.3118 0.3082 0.1901
rs383510 - - - - - -
0.2751 0.2149 0.2979 0.2701 0.1384 0.2049
Table 3. Outliers identified in boxplots that show differences of Slovak and the
. other six world populations in allele frequency for 79 variants located in the
3.2 Analyses of variants located in ACE2 gene ACE2 gene.
ALLELE FREQUENCY DIFFERENCES
In the analysis of variants located in the ACE2 gene, we
identified 79 common variants (MAF > 0.05) in our sample set SVK- SVK- SVK- SVK- SVK- SVK-
SNP
AFR AMR EAS FIN NFE SAS
from NIPT. The allele frequencies of 79 variants identified in our
population sample (Slovak population) and the allele frequencies rs1849701 - - - - - -
of these variants for 6 world populations (East Asian, South 0.2579 0.4862 0.7208 0.3828 0.3505 0.4449
Asian, African, American, Finnish European and non-Finnish rs869212298 - - - - - -
European) obtained by gnomAD database are shown in graphical 0.0101 0.2998 0.5512 0.2527 0.1906 0.2793
comparison by Boxplots (Figure 4). The median allele frequency rs35972066 - - - - - -
for the Slovak population reached the lowest value of 0.5634, 0.1318 0.3815 0.6282 0.3301 0.2670 0.3635
which is closest to the value of the median of the non-Finnish rs34730726 - - - - - -
European population (MED=0.622636). 0.5851 0.4906 0.6971 0.3936 0.3634 0.4999
In the next step, to identify variants having significantly
rs67054961 0.6549 0.6723 0.6723 0.6723 0.6697 0.6723
different frequencies, we compared known allele frequencies of
79 variants located in the ACE2 gene identified in our sample set rs111691073 0.7471 0.7544 0.7544 0.7489 0.7511 0.7544
from the Slovak population to allele frequencies of these variants rs2316904 - - - - - -
in six gnomAD world populations. The final findings of allele 0.4195 0.3384 0.5588 0.2404 0.1844 0.3429
frequency differences are shown in Figure 5. By comparing the rs114606371 0.1472 0.1474 0.1474 0.1474 0.1474 0.1474
allele frequency of variants of the Slovak and six world
populations, we identified a total of 8 outliers. The variation type
of all outliers was “intronic variant” and the clinical significance 4 Discussion
of all outliers was not reported in ClinVar
(https://www.ncbi.nlm.nih.gov/clinvar/).
We performed a literature overview of studies from 2020-
2021 that included genetic variants reported to be associated with
COVID-19 susceptibility and/or severity and others implicated
in the biological pathway of the COVID-19 disease. We
compared the allele frequencies of identified variants between
Slovak and 6 worldwide populations and, in addition, we also
focused on variants located in the ACE2 gene. To our
knowledge, the present study is the first population analysis of
COVID-19 variants worldwide and also in the Slovak population
using NIPT data. We illustrate the utility of these genomic data
for clinical genetics and population studies.
By pooling data of risk variants associated with COVID-19
and data variants in our population sample from NIPT, we have
identified 20 common risk variants (MAF> 0.05). When we
Fig. 4. Boxplots show allele frequency of 79 variants located in the ACE2 gene compared allele frequencies of these variants to allele
for the Slovak and the other six world populations. AFR, African population; frequencies in six gnomAD world populations, finally 2 variants
AMR, American population; EAS, East Asian population; FIN, European were found that showed statistically significant differences in
(Finnish) population; NFE, European (non-Finnish) population; SAS, South
Asian population; SVK, Slovak population. population allele frequencies - rs383510 and rs1801274.
The first intronic SNP, rs383510, is located in the gene
TMPRSS2 frequently discussed in the COVID-19 studies.
Together with the ACE2 gene, the gene TMPRSS2 is the main
host cell entry factor critical for SARS-CoV-2 infection. The
spike (S) glycoprotein of the virus binds to the ACE2 making it
essential for the entry of the virus into the host cell [37,39,40].
In addition, S-protein priming by the serine protease TMPRSS2
allows the fusion of viral and cellular membranes, resulting in
virus entry and replication in the host cells [41]. Cheng et al.
previously reported that the rs383510 variant, situated in the
putative regulatory region with enhancer activity, is significantly
associated with the susceptibility to influenzas such as A(H7N9)
and A(H1N1)pdm09 influenza [42]. Another study confirmed
Fig. 5. Boxplots show differences of Slovak and the other six world populations the effect of rs383510 on the expression of TMPRSS2 in lung
in allele frequency for 79 variants located in the ACE2 gene. AFR, Slovak- tissues, while the frequencies of variant alleles vary between
African population; AMR, Slovak-American population; EAS, Slovak-East populations. The rs383510 TT genotype was associated with
Asian population; FIN, Slovak-European (Finnish) population; NFE, Slovak- higher expression of TMPRSS2 in the lung compared to the CT
European (non-Finnish) population; SAS, Slovak-South Asian population.
and CC genotypes. In addition, the frequency of rs383510 T Conflict of interest
allele is lower in East Asian populations compared to European
and American populations suggesting that a relatively high
percentage of Europeans and Americans may have upregulated All authors declare that there is no conflict of interest and they
TMPRSS2 expression [43]. In our study, the frequency of have seen and approved the manuscript submitted.
rs383510 (T/C) was the lowest in Slovak population and the
highest frequency was identified in the East Asian population.
Schönlfelder et al. analyzed the association of the rs3835510
variant with susceptibility to SARS-CoV-2 infection and
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