Identification and Analyses of Variants Associated with COVID-19 from Non-invasive Prenatal Testing in Slovak Population

Abstract

Since December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world and caused a large global pandemic which drastically changed our everyday lives. As the COVID-19 pandemic progressed, a number of its characteristics showed enormous inter-individual and inter-population differences. Earlier genome-wide association studies (GWAS) have identified potential key genes and genetic variants associated with the risk and prognosis of COVID-19, but the underlying biological interpretation is largely unclear. Our previous work described genomic data generated through non-invasive prenatal testing (NIPT) based on low-coverage massively parallel wholegenome sequencing of total plasma DNA of pregnant women in Slovakia as a valuable source of population specific data. In the present study, we have performed a literature search of studies and used NIPT data to determine the population allele frequency of risk COVID-19 variants that have been reported in GWAS studies to date. We also focused on variants located in the ACE2 gene, encoding angiotensin-converting enzyme 2 (ACE2), which is hypothesized to be a possible genetic risk factor for SARSCoV-2 infection. Allele frequencies of identified variants were compared with six world populations from the gnomAD database to detect significant differences between populations. We interpreted variants and searched for functional consequences and clinical significance of variants using publicly available databases. Finally, 2 COVID-19 risk variants were found that showed statistically significant differences in population allele frequencies - rs383510 and rs1801274.

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The Coronavirus Disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a complex, highly infectious disease involving the respiratory, immune, cardiovascular, gastrointestinal, and neurological systems [ 1–4 ]. The first case was registered in Wuhan, Hubei Province of China in December 2019, and it has rapidly evolved into a global pandemic [ 5 ]. At the time of the writing (June 2021), there have been more than 177 million confirmed cases and 3.8 million deaths worldwide (in Slovakia more than 390 000 people were infected, with the total deaths exceeding 12 000) (https://origin-coronavirus.jhu.edu/map.html).

Although the mortality rate of COVID-19 (ranges between 17%) is lower than that of the other two types of coronaviruses, severe acute respiratory syndrome (SARS-CoV) and the Middle East respiratory syndrome (MERS-CoV), the rate of human-tohuman transmission is higher, as respiratory droplets and close contact can primarily transmit it [ 4,6–10 ]. COVID-19 presents a broad spectrum of varied clinical manifestations, from asymptomatic or mild symptoms to serious health outcomes leading to death [ 11,12 ]. Even though the symptoms are highly heterogeneous, the most commonly observed in the large majority of infected persons are fever, cough, severe headache, muscle pain, fatigue, myalgia, shortness of breath, chest tightness, and loss of taste or smell [ 13–18 ]. Besides, several minor symptoms such as gastrointestinal complications, including nausea, vomiting, and diarrhea, have also been reported [ 19 ]. In severe cases, breathing difficulties with dyspnea occur, with acute respiratory distress syndrome (ARDS) being the most serious complication [ 20 ]. It is likely that a mixture of genetic and nongenetic factors interplays between virus and host genetic background and determines the severity of COVID-19 outcome. Advanced age, male sex, some ethnicity and blood type such as A and AB0 blood types, smoking, hypertension, diabetes mellitus, obesity, cardiovascular, respiratory, and kidney disease or cancer have been identified as risk factors associated with a higher risk of death COVID-19 [ 12,21–27 ]. Nevertheless, these factors do not explain the main pathogenesis of COVID-19. Therefore, the host’s genetic variations may partly provide novel insights into pathological mechanisms underlying COVID-19. Recently, genome-wide 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 findings has not yet been fully clarified [ 28–32 ].

Non-invasive prenatal testing (NIPT) based on low-coverage massively parallel whole-genome sequencing of plasma DNA from pregnant women generates a large amount of data that provides the resources to investigate human genetic variations in the population. In our previous studies, we described the re-use of the data from NIPT for genome-scale population specific frequency determination of small DNA variants [ 33 ] and CNVs [ 34 ]. Since pregnant women represent a relatively standard sample of the local female population, we assumed this NIPT data could also be used in the population study of COVID-19.

As of June 2021, there are more than 100 GWAS studies trying to identify possible candidate genes and human genetic variants that are likely involved in COVID-19 pathogenesis. The main aim of our study was an analysis of common variants (MAF>0.05) that showed evidence of association with COVID19 in studies and characterization of population variability from data generated by NIPT. Allele frequencies of risk COVID-19 variants from studies identified in the Slovak population were compared with allele frequencies of risk COVID-19 variants in 6 worldwide populations. While previous studies have demonstrated the role of the ACE2 gene, encoding angiotensinconverting enzyme 2, in host defense against COVID-19 [ 35– 37 ], our study aimed to analyze population allele frequencies and describe the clinical impacts of relevant variants located in the ACE2 gene. To our knowledge, this was the first population study of COVID-19 using NIPT data conducted exclusively in the Slovak population. 2 2.1

Methods

Data source

The laboratory procedure used, to generate the NIPT data, were as follows: DNA from plasma of peripheral maternal blood was isolated for NIPT analysis from 1,501 pregnant women after obtaining a written informed consent consistent with the Helsinki declaration from the subjects. The population cohort consisted from women in reproductive age between 17-48 years with a median of 35 years. Genomic information from a sample consisted of maternal and fetal DNA fragments. Each included individual agreed to use their genomic data in an anonymized form for general biomedical research. The NIPT study (study ID 35900_2015) was approved by the Ethical Committee of the Bratislava Self-Governing Region (Sabinovska ul.16, 820 05 Bratislava) on 30th April of 2015 under the decision ID 03899_2015. Blood samples were collected to EDTA tubes and plasma was separated in dual centrifugation procedure. DNA was isolated from 700 μl of plasma using DNA Blood Mini kit (Qiagen, Hilden, DE) according to standard protocol. Sequencing libraries were prepared from each sample using TruSeq Nano kit HT (Illumina, San Diego, CA, USA) following standard protocol with omission of DNA fragmentation step. Individual barcode labelled libraries were pooled and sequenced using low-coverage whole-genome sequencing on an Illumina NextSeq500 platform (Illumina, San Diego, CA, USA) by performing paired end sequencing of 2×35 bases [ 38 ]. 2.2

Data analysis

The detailed information about mapping, exclusion of overlapping reads, quality control and filtering, realignment, genomic coverage and variant calling is fully described in our previous study, in the section Methods and Results [ 33 ]. The datasets generated and analyzed during the current study are available in the DSpace repository, https://dspace.uniba.sk/xmlui/handle/123456789/27. 2.3

Analyses of common variants previously reported to be risk variants for COVID-19

We have performed a literature search and combined genotype data from all previously published studies available online (https://pubmed.ncbi.nlm.nih.gov/) for the years 2020-2021 with key words “COVID-19” and “GWAS”, specifically 114 studies focused on genetic variants associated with COVID-19 disease. Using data from these datasets, we have summarized 29 COVID19 risk variants, which were then merged with our data of identified variants from NIPT. Risk variants that were not found in NIPT data were excluded from the analysis. All identified variants in the Slovak population used for further analyses were common (MAF>0.05). Subsequently, allele frequencies of COVID-19 risk variants for each population (East Asian, South Asian, African, American, Finnish European and non-Finnish European) were extracted from the gnomAD database available online (v3.0, downloaded from https://gnomad. broadinstitute.org/downloads) and compared with our frequencies determined for the Slovak population from NIPT 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 annotated via published literature (in dbSNP (https://www.ncbi.nlm.nih.gov/snp/). To assess the relations between allele frequency of COVID-19 risk variants in each population, we also used Principal Component Analysis (PCA) using matplotlib.pyplot library, which reduces the dimension of the data to a graphically interpretable 2D or 3D dimension. Consequently, we obtained information on which populations have similar or different allele frequencies of the identified COVID-19 risk variants. 2.4

Analyses of variants located in ACE2 gene

While previous studies have demonstrated a possible important role of the ACE2 gene in COVID-19 infection, we also focused on the study of variants located in the ACE2 gene in our dataset of NIPT data. First, we filtered out a group of variants located in this gene. The genomic locations of the gene were determined by the GeneCards database (https://www.genecards.org/). Allele frequencies of identified variants for each population (East Asian, South Asian, African, American, Finnish European and non-Finnish European) were extracted from the gnomAD database (v3.0, downloaded from https://gnomad.broadinstitute.org/downloads) and compared with frequencies of variants located in ACE2 gene determined for the Slovak population from NIPT data. Allele frequency in each population and allele frequency differences were plotted using boxplots. Outliers of boxplots were annotated via published literature and studies (in dbSNP (https://www.ncbi.nlm.nih.gov/snp/). 3 3.1

Results

Analyses of common variants previously reported to be risk variants for COVID-19

We found 29 risk genetic variants associated with COVID-19 disease in literature search of studies available online in PubMed (Table 1).

After merging all identified variants from GWAS (29 risk variants) with our NIPT data, we identified 20 common risk COVID-19 variants, while 9 risk variants that were not called in the Slovak population were excluded from further analysis. The allele frequencies of 20 variants identified in our population sample (Slovak population) and the allele frequencies of variants for 6 world populations (East Asian, South Asian, African, American, Finnish European and non-Finnish European) obtained by gnomAD database are shown in graphical comparison by Boxplots (Figure 1) and PCA (Figure 2). The median allele frequency for the Slovak population reached the value of 0.2712, which is closest to the value of the median of the American population (MED=0.2972). PCA placed our sample set most closely to the non-Finnish European population.

Next, we compared known allele frequencies of 20 COVID19 risk variants in our sample set from the Slovak population to 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 SlovakFinnish European population comparison and Slovak-nonFinnish 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. 3. Boxplots show allele frequency differences of Slovak and the other six world populations for 20 risk COVID-19 variants identified from GWAS. AFR, Slovak-African population; AMR, Slovak-American population; EAS, SlovakEast Asian population; FIN, Slovak-European (Finnish) population; NFE, Slovak-European (non-Finnish) population; SAS, Slovak-South Asian population.

SVKAFR

0.3416

0.2751

ALLELE FREQUENCY DIFFERENCES

SVK- SVK- SVK- SVKAMR EAS FIN NFE

- - - 0.2869 0.1328 0.3118 0.3082

- - - 0.2149 0.2979 0.2701 0.1384 SVKSAS

0.1901

0.2049 3.2

.

Analyses of variants located in ACE2 gene

In the analysis of variants located in the ACE2 gene, we identified 79 common variants (MAF > 0.05) in our sample set from NIPT. The allele frequencies of 79 variants identified in our population sample (Slovak population) and the allele frequencies of these variants for 6 world populations (East Asian, South Asian, African, American, Finnish European and non-Finnish European) obtained by gnomAD database are shown in graphical comparison by Boxplots (Figure 4). The median allele frequency for the Slovak population reached the lowest value of 0.5634, which is closest to the value of the median of the non-Finnish European population (MED=0.622636).

In the next step, to identify variants having significantly different frequencies, we compared known allele frequencies of 79 variants located in the ACE2 gene identified in our sample set from the Slovak population to allele frequencies of these variants in six gnomAD world populations. The final findings of allele frequency differences are shown in Figure 5. By comparing the 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 of all outliers was not reported in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/).

Fig. 5. Boxplots show differences of Slovak and the other six world populations in allele frequency for 79 variants located in the ACE2 gene. AFR, SlovakAfrican population; AMR, Slovak-American population; EAS, Slovak-East Asian population; FIN, Slovak-European (Finnish) population; NFE, SlovakEuropean (non-Finnish) population; SAS, Slovak-South Asian population.

We performed a literature overview of studies from 20202021 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 compared allele frequencies of these variants to allele frequencies in six gnomAD world populations, finally 2 variants were found that showed statistically significant differences in 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 the effect of rs383510 on the expression of TMPRSS2 in lung tissues, while the frequencies of variant alleles vary between populations. The rs383510 TT genotype was associated with higher expression of TMPRSS2 in the lung compared to the CT and CC genotypes. In addition, the frequency of rs383510 T 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 TMPRSS2 expression [ 43 ]. In our study, the frequency of 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 severity of COVID-19 in 239 SARS-CoV-2-positive and 253 SARS-CoV-2-negative patients. The results showed that the CC genotype of TMPRSS2 rs383510 was associated with a 1.73-fold increased SARS-CoV-2 infection risk in a German cohort [44].

The second identified variant in the FCGR2A gene, rs1801274, is a missense variant leading to an amino-acid substitution of histidine by arginine at position 131 (H131R). This variant was significantly associated with the risk of severe pneumonia in A/H1N1 influenza infection, bacteremic pneumococcal pneumonia infection, and the severity of community-acquired pneumonia [45]. Another meta-analysis demonstrated that the rs1801274 polymorphism was associated with the susceptibility to multiple autoimmune diseases, including Kawasaki disease and Ulcerative colitis. It has also been reported that the rs1801274 polymorphism may be associated with susceptibility to multiple autoimmune diseases in the Asia population [46].

Regarding genetic variants located in the ACE2 gene, we did not observe any association between identified variants and SARS-CoV-2 infection. On the one hand, the sample size was relatively small, and it is strongly biased towards the healthy population of females. On the other hand, this could be due to the biological function of ACE2 as a mediator of cell entry for SARS-CoV-2, so further analyses are needed to conclusively clarify the influence of ACE2 variants on disease severity.

As the COVID-19 pandemic creates a global crisis and has already had a serious impact on the world, it is crucial to determine how host genetic factors link to clinical outcomes. Several GWAS studies have focused on discovering the influence of host genetic factors on SARS-CoV-2 infection risk or COVID-19 severity. Nevertheless, with the information available to date, not everything has been resolved about the genetic involvement in COVID-19 susceptibility or severity, and new knowledge in the field is continuously generated. Since NIPT expands rapidly to millions of individuals each year, the reuse of these data reduces the cost of large-scale population studies and likely provides an acceptable background for information about genomic variation.

Funding

This publication is the result of support from the Operational Programme Integrated Infrastructure for the project: Pangenomics for personalized clinical management of infected persons based on identified viral genome and human exoma (Code ITMS:313011ATL7), co-financed by the European Regional Development Fund.

Conflict of interest

All authors declare that there is no conflict of interest and they have seen and approved the manuscript submitted. cohorts from multiple continents,” Biochem Biophys Res Commun. 2020;529: 263–269. [44] Schönfelder K, Breuckmann K, Elsner C, Dittmer U, Fistera D, Herbstreit F, et al. “Polymorphisms and Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Type 2 Infection: A German Case-Control Study,” Front Genet. 2021;12: 667231. [45] Shi X, Ma Y, Li H, Yu H. “Association between FCGR2A rs1801274 and MUC5B rs35705950 variations and pneumonia susceptibility,” BMC Med Genet. 2020;21: 71. [46] Zhang C ’e, Wang W, Zhang H ’e, Wei L, Guo S. “Association of FCGR2A rs1801274 polymorphism with susceptibility to autoimmune diseases: A meta-analysis,” Oncotarget. 2016;7: 39436–39443.

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