Evaluation and Limitations of Different Approaches Among COVID-19 Fatal Cases Using Whole-exome Sequencing Data

doi:10.21203/rs.3.rs-1403472/v1

Download PDF

Research Article

Evaluation and Limitations of Different Approaches Among COVID-19 Fatal Cases Using Whole-exome Sequencing Data

https://doi.org/10.21203/rs.3.rs-1403472/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 10 Jan, 2023

Read the published version in BMC Genomics →

You are reading this latest preprint version

Background: COVID-19 caused by SARS-CoV-2 infection may result in various disease symptoms and severity, ranging from asymptomatic, through mild, up to very severe and fatal cases. Although environmental, clinical, and social factors play important roles in both susceptibility to SARS-CoV-2 infection and COVID-19 disease progress, it is becoming evident that both pathogen and host genetic factors are important too. Here we report whole-exome sequencing (WES) findings of 27 individuals who died as a result of COVID-19 infection, especially focusing on frequencies of DNA variants in genes previously associated with SARS-CoV-2 infection and COVID-19 severity.

Results: We selected risk DNA variants/alleles or target genes using four different approaches: 1) aggregated GWAS results from the GWAS Catalog; 2) selected publications from PubMed; 3) the aggregated results of the Host Genetics Initiative database; and 4) a commercial DNA variant annotation/interpretation tool providing its own knowledgebase. We divided these variants/genes into those reported to influence the susceptibility to SARS-CoV-2 infection and those influencing COVID-19 severity. Based on these, we compared frequencies of alleles among the fatal COVID-19 cases to frequencies identified in two population control datasets (non-Finnish European population from the gnomAD database and genomic frequencies specific for the Slovak population from our own database). Our comparisons delineated a trend of higher frequencies of severe COVID-19 associated risk alleles among fatal COVID-19 cases, when compared to both control population datasets. This trend reached statistical significance specifically when using the HGI derived variant list. We also analyzed other approaches to WES data evaluation, where we showed their usage as well as limitations.

Conclusions: Although our results proved the likely involvement of host genetic factors pinned out by previous studies for COVID-19 disease severity, careful considerations about the molecular-testing strategies and the evaluated genomic positions may have a strong impact on the utility of genomic testing.

SARS-CoV-2

COVID-19

whole-exome sequencing

genetic association

polymorphisms

gnomAD

non-invasive prenatal testing

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 (February 2022), there have been more than 410 million confirmed cases and 5.8 million deaths worldwide (in Slovakia more than 1.8 million people were infected, with the total deaths exceeding 18 000) (https://origin-coronavirus.jhu.edu/map.html).

Although the mortality rate of COVID-19 (ranges between 1–7%) 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-to-human 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, 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].

SARS-CoV-2 infection exhibits varied infectivity and mortality rates in different worldwide populations [21, 22]. An obvious possible explanation for these findings is that a mixture of genetic and nongenetic factors interplays between virus and host genetic background, which determines the severity of COVID-19 outcome. Advanced age, male sex, blood type, 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 caused by COVID-19 [12, 23–29]. In addition, the host’s genetic variations affecting the structure or function of essential proteins with an active role in the entry and spread of SARS-CoV-2 can influence the host's susceptibility or responses to the infection. This assumption is supported by a study of twins reporting 50% heritability of COVID-19 risk [30].

In this direction, the COVID-19 Host Genetics Initiative, 2020 (available at https://www.covid19hg.org/) is currently leading a worldwide public effort to analyze COVID-19 information for millions of individuals in relation with genotype data to identify genetic variants associated with SARS-CoV-2 infection as well as COVID-19 hospitalization and disease severity. A recent study by Baggen et al. summarizes the wealth of information on proviral SARS-CoV-2 host factors that have been produced by genome-wide functional genetic screens and interactome analyses with further discussion about their roles in cellular processes [31]. Moreover, worldwide genome-wide association studies (GWAS) have identified many risk genes and loci that could be functionally implicated in COVID-19 disease [32–36]. Recent whole-exome sequencing (WES)-based studies in several countries also aimed to identify the genetic COVID-19 susceptibility factors. However, it is to be noted that these studies are focused only on investigating genetic variants in small groups of risk genes, mainly related to the initial stages of infection [37–42].

In this study, we summarized available resources of known risk variants and genes associated with COVID-19 to explain genetic predispositions for a severe course or higher mortality of COVID-19 in patients. We collected and analyzed genomic variants that either: a.) showed evidence of association with COVID-19 in GWAS studies b.) were located in genes previously reported to be a risk in selected studies by literature search c.) showed strong association in meta-analyses conducted by the COVID-19 HGI Browser and d.) were located in genes associated with COVID-19 by QIAGEN Clinical Insight (QCI™) Analyze software. We present a comprehensive comparative assessment of variants identified in these four groups compared to our cohort of genotyped patients. In addition, allele frequencies of identified variants were compared with two types of control groups, worldwide public data of the non-Finnish European population from the gnomAD database (NFE) and our genetic data from Non-invasive prenatal testing (NIPT) in the Slovak population. Despite growing knowledge about genetic factors associated with the risk of COVID-19, there is a lack of studies that elucidate the multitude of factors that influence this disease. Here we provide different approaches to analyzing genetic variants in WES data from Slovak patients who have died of COVID-19.

Analyses of variants associated with COVID-19 in GWAS Catalog

The first goal of our analysis was to investigate the variants associated with COVID-19 in GWAS Catalog (https://www.ebi.ac.uk/gwas/). Overall, 413 risk variants in a total of 403 genes were present in the GWAS dataset after entering the keyword “COVID-19” into the GWAS Catalog Browser. After merging all identified variants from GWAS (413 risk variants) with our WES data of dead patients, we identified only 3 common variants - a missense variant rs11147040, synonymous variant rs72472161, and coding sequence variant rs8176719. Other identified variants belonged to the intron, non-coding, and UTR variants group.

Analyses of risk COVID-19 variants by literature search

After a literature search, we choose 3 types of studies: A. a study by Baggen et al. [31] included cellular host factors for SARS-CoV-2 infection, B. studies by the COVID-19 Host Genetics Initiative [43] and Pairo-Castineira et al. [35], which reported genome-wide loci associated with severe manifestations of COVID-19 and C. Ackermann et al. [44] which analyzed respiratory failure-associated gene sets (Table S1).

When we merged all identified variants with WES data of the dead Slovak patients, we identified 208 (A.), 154 (B.), and 437 (C.) common risk variants, respectively. Next, we compared these data with two control groups - the NIPT data from the Slovak population, where we identified 103 (A.), 96 (B.), and 245 (C.) common risk variants and NFE data (Table S2). Using NFE data, we performed two types of analyses, first with risk variants found in the overlap publications data/WES/NIPT and the second with risk variants from the overlap publications data/WES. The violin-swarm plots in Fig. 1 represent the graphical comparison of the allele frequencies of risk variants from the overlap publications data/WES/NIPT/NFE (103, 96, and 245 common risk variants). The comparison of the allele frequencies of risk variants from the overlap publication data/WES/NFE (208, 155, and 437 risk variants) is shown in Figure S1. Using the Mann–Whitney U test, we did not find any significant difference between the groups in both types of comparisons.

Subsequently, allele counts of risk variants identified in our sample sets from WES data were compared to allele counts of these variants in Slovak NIPT data and NFE data using Fisher's exact test. By comparing risk variants allele counts in individual groups of publications (A., B., C.), we have found 9, 12, and 10 risk variants with p-value under 0.05 in comparison between NIPT and WES data and 4, 21, and 15 risk variants in comparison between WES and NFE data, respectively (Table S2). After the Bonferroni correction, a risk variant rs35048651 was identified with a significantly different representation (p-value = 0.000005) in the A. group and a risk variant rs2498800 (p-value = 0.000035) in the C. group in comparison between NIPT and WES data and risk variants rs7853989, rs8176741, rs8176743, rs8176746, rs8176747, rs8176749, and rs8176751 in B. group in comparison between WES and NFE data in the overlap publications data/WES/NIPT/NFE (Table 2). Using Fisher's exact test, we also compared allele counts of risk variants from the overlap publications data/WES/NFE, where we found 43, 30, and 92 risk variants, respectively with a p-value under 0.05. After the Bonferroni correction risk variants rs78472109, rs4024453, rs594178, rs146238849, rs62478356, rs10454320, rs56850341, rs150756699 and rs377124451 in the A. group, risk variants rs8176751, rs8176749, rs8176747, rs8176746, rs8176743, rs8176741 and rs7853989 in the B. group and risk variants rs373030497, rs9521779, rs1226613997, rs540909253, rs9521780, rs9515217, rs201005516 and rs201495169 in the C. group were identified with a significantly different representation (Table S2).

Analyses of risk COVID-19 variants by COVID-19 Host Genetics Initiative

Here we analyzed the COVID-19 risk variants identified in meta-analyses conducted by the COVID-19 HGI Browser. We focused only on missense variants from all four genome-wide meta-analysis results available online (https://app.covid19hg.org/). A total number of missense risk COVID-19 variants found in each HGI group are summarized in Table 1.

Table 1. Total number of risk variants that were common in individual comparative analyses.

	Source of data	Total number of all variants (publications)/Total number of missense risk variants (COVID-19 HGI Group)	Number of risk variants in comparison: WES/NFE **	*Number of risk variants in comparison: NIPT/WES/pub or HGI data (in parentheses *)	QCI analysis - infection group (376 risk variants)		QCI analysis - critical group (1738 risk variants)
	Source of data		Number of risk variants in comparison: WES/NFE **		Risk variants in comparison: WES/NFE/pub or HGI data (in parentheses **)	*Risk variants in comparison: NIPT/WES/NFE/pub or HGI data (in parentheses ***)	Risk variants in comparison: WES/NFE/pub or HGI data (in parentheses **)	*Risk variants in comparison: NIPT/WES/NFE/pub or HGI data (in parentheses ***)
publications (pub)	A infection	1745372	208	103	0 (0 %)	0 (0 %)	0 (0 %)	0 (0 %)
	B severity	277165	154	96	0 (0 %)	0 (0 %)	14 (9.1 %)	7 (7.3 %)
	C respiratory failure	3076573	437	245	0 (0 %)	0 (0 %)	43 (9.8 %)	22 (9 %)
COVID-19 HGI group (HGI)	A2: critically ill Covid-19+ vs. population	62	51	45 (72.6 %)	3 (5.9 %)	2 (4.4 %)	1 (2 %)	1 (2.2 %)
	B1: hospitalized Covid-19+ vs. non-hospitalized Covid-19+	16	13	13 (81.3 %)	0 (0 %)	0 (0 %)	0 (0 %)	0 (0 %)
	B2: hospitalized Covid-19+ vs. population	72	58	51 (70.8 %)	3 (5.2 %)	2 (3.9 %)	1 (1.7 %)	1 (2 %)
	C2: reported SARS-CoV-2 infected vs. population	83	63	52 (62.7 %)	0 (0 %)	0 (0 %)	13 (20.6 %)	9 (17.3 %)

* Comparison results from NIPT/WES/NFE/pub or HGI data/QCI analysis published in Results; ** Comparison results from NFE/WES/pub or HGI data/QCI analysis published in supplementary files; *** % of variants from the comparison number of WES/NFE/QCI/pub or HGI data risk variants to the total number of risk variants in comparison: WES/pub or HGI data; **** % of variants from the comparison number of NIPT/WES/NFE/QCI/pub or HGI data risk variants to the total number of risk variants in comparison: NIPT/WES/pub or HGI data.

After merging all missense risk variants identified in individual HGI groups with WES data of the dead patients, we determined 51 common risk variants in the A2 group, 13 risk variants in the B1 group, 58 risk variants in the B2 group, and 63 risk variants in C2 group (Table S2). These data were compared with two controls, first with the Slovak NIPT data, where we identified 45 common risk variants in the A2 group, 13 in the B1 group, 51 in the B2 group and 52 in the C2 group (Table S2), and the second with the NFE data, where we performed two different analyses, first with risk variants that were found in the overlap HGI groups data/WES/NIPT and second with risk variants from the overlap HGI groups data/WES. The violin-swarm plots graphical comparison of the allele frequencies of risk variants from the overlap HGI groups data/WES/NIPT/NFE are shown in Fig. 2. The graphical comparison of the allele frequencies of risk variants from the overlap HGI groups data/WES/NFE is shown in Figure S2.

Using Mann–Whitney U test, we observed a significant difference in the comparison between WES data and NIPT data (p-value = 0.02609) and WES data and NFE (p-value = 0.01872), both in the B2 group in the HGI groups data/WES/NIPT/NFE overlap (Fig. 2). Another significant difference was between WES data and NFE in the B2 group in the HGI groups data/WES/NFE overlap (p-value = 0.03132, Figure S2). The difference in the other comparisons was negligible.

Using Fisher's exact test, the allele count of risk variants identified in our sample set from the WES data to allele counts of these variants in Slovak NIPT data and the NFE data were compared. We identified 3 variants in the A2 group, 4 variants in the B2, and 2 risk variants in the C2 group in comparison between WES and NIPT and 2 risk variants in the A2 group, 2 risk variants in the B2 group, and 7 risk variants in C2 group in comparison between WES and NFE with p-value under 0.05 in the overlap HGI groups data/WES/NIPT/NFE (Table S2). After the Bonferroni correction, a variant rs3130984 was identified with a significantly different representation (p-value = 0.000056) in the comparison between WES and NIPT data in the A2 group and 4 risk variants rs8176747, rs8176746, rs8176743 and rs7853989 (p-value = 0.00025) in the comparison between WES and NFE data in C2 group (Table 2). We did not find any significant differences in the B1 and B2 groups. After using Fisher's exact test to compare allele count of the risk variants in the overlap HGI groups data/WES/NFE, we found 2 risk variants in the A2 group, 2 risk variants in the B2 group, and 8 risk variants in the C2 group with p-value under 0.05. The results after the Bonferroni correction correspond with the results in comparison between WES and NFE in the overlap HGI groups data/WES/NIPT/NFE.

Analyses of variants by QIAGEN Clinical Insight (QCI™) Analyze software

After analyzing the VCF files of each patient and then applying a biological filter, we selected 4 filters from the 8 offered COVID-19 filters. Variants are divided into two categories to follow the concept of other analyses: QCI infection group, included 376 risk variants with biological filter "Mild COVID-19" and QCI critical group, included 1738 variants with biological filters "Critical COVID-19", "Severe COVID-19" and "COVID-19 related immunodeficiency 74". Other filters were excluded from further analyses as it was not possible to clearly determine their classification into infected or critical groups. Subsequently, a number of risk variants in comparison WES/NFE or in comparison NIPT/WES/pub or HGI data were compared with QCI analysis of infection and critical group. Results of individual comparisons of the number of variants are shown in Table 1.

In the present article, we attempted to elaborate on the spectrum of risk variants and genes identified in different ways and their possible relationship to COVID-19 severity and/or mortality. We investigated the frequencies of these variants and evaluated their possible role using a cohort of 27 Slovak patients who have died of COVID-19. 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 [45], CNVs [46], and variants associated with colorectal cancer and Lynch syndrome [47]. Therefore, we assumed that NIPT data can be utilized as a control group in this population study of COVID-19. As a second control group, we chose genetic data of NFE, which contains a total of 125,748 exomes and 71,702 genomes. To our knowledge, the present study is the first population analysis of COVID-19 variants worldwide and also in the Slovak population that provides different approaches to the analysis of genetic variants in WES data from patients who have died of COVID-19.

Over the past two years, GWAS has offered the opportunity to uncover genetic susceptibility factors for COVID-19 disease and provide insights into the biological basis of SARS-CoV-2 etiology. To date, a large number of risk genetic variants and genes have been identified by the GWAS approach, which has been intimately connected to the COVID-19 susceptibility and severity [32–35, 48]. However, after merging all identified risk COVID-19 variants from GWAS Catalog with our WES data of dead patients, we identified only 3 common variants - a missense variant rs11147040, synonymous variant rs72472161, and coding sequence variant rs8176719. Other identified variants belonged to the intron, non-coding, and UTR variants group. Due to this limitation, we have found that risk variants from the GWAS Catalog are not useful for analyzing and comparing our data obtained from WES.

In 2021, a large consortium organized highly expected studies published last year. The COVID-19 HGI presented results from three genome‐wide association meta‐analyses of up to 49,562 COVID‐19 patients from 46 studies across 19 countries [49]. They reported 13 genome‐wide significant loci. The 3p21.31 region seemed to be associated with infection susceptibility, which was also confirmed in study by Ellinghaus et al. This study also confirmed a potential involvement of the ABO blood-group system [32]. Similar results were also found in a study conducted by 23andMe company using their biobank. After the Bonferroni correction in the analysis of COVID-19 missense risk variants conducted by the COVID-19 HGI Browser, we identified 5 variants with a significantly different representation; a missense variant rs3130984 located in the CDSN gene and four variants (rs8176747, rs8176746, rs8176743, and rs7853989) all located in the ABO gene. Two missense variants, rs8176747 and rs8176746, were found in the comparison between WES and NFE data in the C2 HGI group and the B group in the analysis by literature search. Recently, GWAS found COVID-19-association signal at locus 9q34.2 coincident with the ABO-blood group (rs8176747, rs41302905, rs8176719) in Italian and Spanish severe COVID-19 patients with respiratory failure [32, 50]. Another study published a genetic hypothesis on the role of RAS-pathway genes (ACE1 and ACE2) and ABO-locus (rs495828, rs8176746) in COVID-19 prognosis, suspecting inherited genetic predispositions to be predictive of COVID-19 severity [51].

Since 2017, the QIAGEN Clinical Insight (QCI™) Analyze software has offered itself as an integrated clinical decision support solution for the annotation, interpretation and reporting of NGS data. QCI seeks to extract clinical significance and actionability from sequencing data [52]. After a literature search of studies using QIAGEN Clinical Insight (QCI™) Analyze software, we found only a few studies, mainly focused on cancer research [52–54]. As we expected, a comparison of the QCI results showed an overlap in the number of risk variants found in the QCI critical group with the variants in groups B. and C., publications associated with the severity and mortality of COVID-19. However, we did not identify any overlap with group A.; the publication included cellular host factors for SARS-CoV-2 infection. In the B1 HGI group, no overlap was found in the number of variants with the QCI infection and critical group, which may be due to the relatively small number of missense variants found (only 13 risk variants). In the C2 HGI group, we expected to overlap with both groups, but we only identified with the QCI critical group. Although using QIAGEN Clinical Insight (QCI ™) Analyze software requires further optimization, this software may be a promising diagnostic, prognostic, or predictive tool in future research of COVID-19.

Our study has several key shortcomings. First, although we used different approaches to analyze WES data of patients who died of COVID-19, we were able to identify only 10 risk variants with significant difference (after Bonferroni correction) in allele distribution of WES data and data of Slovak NIPT and NFE. Next, the sample size of dead patients was relatively small. Moreover, data from NIPT are strongly biased towards the healthy population of females. It should also be noted that the total number of analyzed risk variants identified by COVID-19 HGI was also relatively small as we focused only on missense variants. We focused on missense variants to identify as many risk variants as possible since most of the identified variants from HGI belonged to the group of non-coding, which in GWAS analysis proved to be useless for the analysis of our WES data.

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 are linked to the clinical outcomes. Therefore, many 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.

The method consists of four main steps, presented on the diagram in Fig. 3. The exome sequencing data derived from the 27 (13 men, 14 women) dead patients, 66 years mean age with a confirmed diagnosis of SARS-CoV-2 infection, were analyzed in the first step. Laboratory processing and bioinformatic analysis of the data were performed as described in Additional file (part Methods).

In the second step, we performed a selection of risk variants and/or genes associated with COVID-19. All risk variants associated with COVID-19 found in the GWAS Catalog were downloaded from https://www.ebi.ac.uk/gwas/search?query=COVID-19. We also performed a literature search of risk genes and genetic variants associated with infection and severity of COVID-19 published in 2020 and 2021 via the PubMed database (https://pubmed.ncbi.nlm.nih.gov/). More details in supplementary material. COVID-19 HGI GWAS meta-analysis release 6 data was downloaded from https://app.covid19hg.org/. Details for each study are provided on the HGI website and in the consortium paper [43].

In the third step, we compared each group of found risk variants with our WES data of dead patients and with two groups of controls: NIPT data from the Slovak population and data of NFE (v3.1.2, downloaded from https://gnomad.broadinstitute.org/downloads). The detailed information about the NIPT dataset generated and analyzed in this study is fully described in our previous studies [45, 55]. Allele frequencies of identified variants for each group (Slovak NIPT data, WES data, and data of NFE) in each analysis were plotted using violin-swarm plots.

In the fourth step, QIAGEN Clinical Insight (QCI™) Analyze software was used to analyze WES data of dead patients. Variants were imported as industry-standard variant call format (VCF) into the QCI-A web interface, which enables data interpretation. We performed a variant filtering based on biological context with the keyword “COVID-19,” which included eight filters.

Statistical analysis

Python language libraries scikit-allele were used for analyzing genetic variation data and matplotlib and seaborn for the graphic representation. The significance of our findings was evaluated using statistical tests implemented in the Python scipy package. We used the Mann-Whitney U test to compare risk variants allele frequencies from COVID-19 died patients and Slovak NIPT data, respectively gnomAD data as controls. In addition, observed allele frequencies for each risk variant in all groups were tested using Fisher's exact test.

AVAILABILITY OF DATA AND MATERIALS

The NIPT datasets generated and analyzed during the current study are available in the DSpace repository, https://dspace.uniba.sk/xmlui/handle/123456789/27. WES datasets are available from the corresponding author on reasonable request.

AUTHORS´ CONTRIBUTIONS

NF was responsible for the literature search and manuscript writing. TSe prepared samples for whole-exome sequencing. RH, ZH, JG and ZP performed data analysis. JG, JB and JR were responsible for designing the study and supervising the work. TSz conceived the idea of the project and TSz, JB, PB, JR and PR performed proofreading of the manuscript. LK, PJ, KMK were collecting the samples for analysis. All authors have read and approved the final manuscript.

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; co financed by the Slovak Research and Development Agency grant PP-COVID-20-051.

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki, revised in 2013, and approved by the Institutional Review Board of the Department of Pathology, Faculty of Medicine, Comenius University in Bratislava, Slovakia, protocol code 21–49, on 8 April 2021.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgement

Not applicable.

Driggin E, Madhavan MV, Bikdeli B, Chuich T, Laracy J, Biondi-Zoccai G, et al. Cardiovascular Considerations for Patients, Health Care Workers, and Health Systems During the COVID-19 Pandemic. J Am Coll Cardiol. 2020;75: 2352–2371.
Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ, et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020;395: 1033–1034.
Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, et al. Hematological findings and complications of COVID-19. Am J Hematol. 2020;95: 834–847.
Fricke-Galindo I, Falfán-Valencia R. Genetics Insight for COVID-19 Susceptibility and Severity: A Review. Front Immunol. 2021;12: 622176.
Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395: 565–574.
Majumder J, Minko T. Recent Developments on Therapeutic and Diagnostic Approaches for COVID-19. AAPS J. 2021;23: 14.
Guarner J. Three Emerging Coronaviruses in Two Decades. American Journal of Clinical Pathology. 2020. pp. 420–421. doi:10.1093/ajcp/aqaa029
Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many estimates of the COVID-19 case fatality rate. Lancet Infect Dis. 2020;20: 776–777.
Sun Q, Qiu H, Huang M, Yang Y. Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province. Ann Intensive Care. 2020;10: 33.
Vincent J-L, Taccone FS. Understanding pathways to death in patients with COVID-19. The Lancet. Respiratory medicine. 2020. pp. 430–432.
Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA. 2020;323: 1574–1581.
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020. p. 2052. doi:10.1001/jama.2020.6775
Xu X-W, Wu X-X, Jiang X-G, Xu K-J, Ying L-J, Ma C-L, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ. 2020;368: m606.
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395: 507–513.
Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020;323: 1239–1242.
Han C, Duan C, Zhang S, Spiegel B, Shi H, Wang W, et al. Digestive Symptoms in COVID-19 Patients With Mild Disease Severity: Clinical Presentation, Stool Viral RNA Testing, and Outcomes. Am J Gastroenterol. 2020;115: 916–923.
Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al. Characteristics of COVID-19 infection in Beijing. J Infect. 2020;80: 401–406.
Singhal T. A Review of Coronavirus Disease-2019 (COVID-19). Indian J Pediatr. 2020;87: 281–286.
Pan L, Mu M, Yang P, Sun Y, Wang R, Yan J, et al. Clinical Characteristics of COVID-19 Patients With Digestive Symptoms in Hubei, China: A Descriptive, Cross-Sectional, Multicenter Study. Am J Gastroenterol. 2020;115: 766–773.
Cantalupo S, Lasorsa VA, Russo R, Andolfo I, D’Alterio G, Rosato BE, et al. Regulatory Noncoding and Predicted Pathogenic Coding Variants of Predispose to Severe COVID-19. Int J Mol Sci. 2021;22. doi:10.3390/ijms22105372
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20: 533–534.
Ali H, Alshukry A, Marafie SK, AlRukhayes M, Ali Y, Abbas MB, et al. Outcomes of COVID-19: Disparities by ethnicity. Infect Genet Evol. 2021;87: 104639.
Liu X, Zhou H, Zhou Y, Wu X, Zhao Y, Lu Y, et al. Risk factors associated with disease severity and length of hospital stay in COVID-19 patients. The Journal of infection. 2020. pp. e95–e97.
Scully EP, Haverfield J, Ursin RL, Tannenbaum C, Klein SL. Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol. 2020;20: 442–447.
Gebhard C, Regitz-Zagrosek V, Neuhauser HK, Morgan R, Klein SL. Impact of sex and gender on COVID-19 outcomes in Europe. Biology of Sex Differences. 2020. doi:10.1186/s13293-020-00304-9
Jutzeler CR, Bourguignon L, Weis CV, Tong B, Wong C, Rieck B, et al. Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 2020;37: 101825.
Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382: 1708–1720.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020. pp. 1054–1062. doi:10.1016/s0140-6736(20)30566-3
Cummings MJ, Baldwin MR, Abrams D, Jacobson SD, Meyer BJ, Balough EM, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. medRxiv. 2020. doi:10.1101/2020.04.15.20067157
Williams FMK, Freidin MB, Mangino M, Couvreur S, Visconti A, Bowyer RCE, et al. Self-Reported Symptoms of COVID-19, Including Symptoms Most Predictive of SARS-CoV-2 Infection, Are Heritable. Twin Res Hum Genet. 2020;23: 316–321.
Baggen J, Vanstreels E, Jansen S, Daelemans D. Cellular host factors for SARS-CoV-2 infection. Nat Microbiol. 2021;6: 1219–1232.
Severe Covid-19 GWAS Group, Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, et al. Genomewide Association Study of Severe Covid-19 with Respiratory Failure. N Engl J Med. 2020;383: 1522–1534.
Wu P, Ding L, Li X, Liu S, Cheng F, He Q, et al. Trans-ethnic genome-wide association study of severe COVID-19. Commun Biol. 2021;4: 1034.
Dubé M-P, Lemaçon A, Barhdadi A, Lemieux Perreault L-P, Oussaïd E, Asselin G, et al. Genetics of symptom remission in outpatients with COVID-19. Sci Rep. 2021;11: 10847.
Pairo-Castineira E, Clohisey S, Klaric L, Bretherick AD, Rawlik K, Pasko D, et al. Genetic mechanisms of critical illness in COVID-19. Nature. 2021;591: 92–98.
Hu J, Li C, Wang S, Li T, Zhang H. Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data. Hum Genomics. 2021;15: 10.
Curtis D. Variants in ACE2 and TMPRSS2 Genes Are Not Major Determinants of COVID-19 Severity in UK Biobank Subjects. Hum Hered. 2020;85: 66–68.
Baldassarri M, Fava F, Fallerini C, Daga S, Benetti E, Zguro K, et al. Severe COVID-19 in Hospitalized Carriers of Single Pathogenic Variants. J Pers Med. 2021;11. doi:10.3390/jpm11060558
Ravikanth V, Sasikala M, Naveen V, Latha SS, Parsa KVL, Vijayasarathy K, et al. A variant in is associated with decreased disease severity in COVID-19. Meta Gene. 2021;29: 100930.
Monticelli M, Hay Mele B, Benetti E, Fallerini C, Baldassarri M, Furini S, et al. Protective Role of a Variant on Severe COVID-19 Outcome in Young Males and Elderly Women. Genes . 2021;12. doi:10.3390/genes12040596
Al-Mulla F, Mohammad A, Al Madhoun A, Haddad D, Ali H, Eaaswarkhanth M, et al. and variants are potential predictors of SARS-CoV-2 outcome: A time to implement precision medicine against COVID-19. Heliyon. 2021;7: e06133.
Latini A, Agolini E, Novelli A, Borgiani P, Giannini R, Gravina P, et al. COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells. Genes . 2020;11. doi:10.3390/genes11091010
COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature. 2021;600: 472–477.
Ackermann M, Verleden SE, Kuehnel M, Haverich A, Welte T, Laenger F, et al. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N Engl J Med. 2020;383: 120–128.
Budis J, Gazdarica J, Radvanszky J, Harsanyova M, Gazdaricova I, Strieskova L, et al. Non-invasive prenatal testing as a valuable source of population specific allelic frequencies. J Biotechnol. 2019;299: 72–78.
Pös O, Budis J, Kubiritova Z, Kucharik M, Duris F, Radvanszky J, et al. Identification of Structural Variation from NGS-Based Non-Invasive Prenatal Testing. Int J Mol Sci. 2019;20. doi:10.3390/ijms20184403
Forgacova N, Gazdarica J, Budis J, Radvanszky J, Szemes T. Repurposing non-invasive prenatal testing data: Population study of single nucleotide variants associated with colorectal cancer and Lynch syndrome. Oncol Lett. 2021;22: 779.
Mousa M, Vurivi H, Kannout H, Uddin M, Alkaabi N, Mahboub B, et al. Genome-wide association study of hospitalized COVID-19 patients in the United Arab Emirates. EBioMedicine. 2021;74: 103695.
COVID-19 Host Genetics Initiative. 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. Eur J Hum Genet. 2020;28: 715–718.
Gemmati D, Tisato V. Genetic Hypothesis and Pharmacogenetics Side of Renin-Angiotensin-System in COVID-19. Genes . 2020;11. doi:10.3390/genes11091044
Chung C-M, Wang R-Y, Chen J-W, Fann CSJ, Leu H-B, Ho H-Y, et al. A genome-wide association study identifies new loci for ACE activity: potential implications for response to ACE inhibitor. Pharmacogenomics J. 2010;10: 537–544.
Perakis SO, Weber S, Zhou Q, Graf R, Hojas S, Riedl JM, et al. Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer. ESMO Open. 2020;5: e000872.
Erdem HB, Bahsi T. Spectrum of germline cancer susceptibility gene mutations in Turkish colorectal cancer patients: a single center study. Turk J Med Sci. 2020;50: 1015–1021.
Kolostova K, Pospisilova E, Pavlickova V, Bartos R, Sames M, Pawlak I, et al. Next generation sequencing of glioblastoma circulating tumor cells: non-invasive solution for disease monitoring. Am J Transl Res. 2021;13: 4489–4499.
Gazdarica J, Budis J, Duris F, Turna J, Szemes T. Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions. Int J Mol Sci. 2019;20. doi:10.3390/ijms20143414
Babraham bioinformatics - FastQC A quality control tool for high throughput sequence data. [cited 20 Dec 2021]. Available: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv [q-bio.GN]. 2013. Available: http://arxiv.org/abs/1303.3997
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25: 2078–2079.
Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31: 2032–2034.
Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics. 2016;32: 292–294.
Poplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36: 983–987.
Budis J, Krampl W, Kucharik M, Hekel R, Goga A, Lichvar M, et al. SnakeLines: integrated set of computational pipelines for sequencing reads. arXiv [q-bio.GN]. 2021. Available: http://arxiv.org/abs/2106.13649
Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10. doi:10.1093/gigascience/giab008
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29: 308–311.
McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17: 122.

Table 2 is available in the Supplementary Files section.

No competing interests reported.

Download PDF

Journal Publication

published 10 Jan, 2023

Read the published version in BMC Genomics →

Editorial decision: Major revision
28 Jul, 2022
Reviews received at journal
17 Jul, 2022
Reviewers agreed at journal
06 Jul, 2022
Reviews received at journal
23 Jun, 2022
Reviewers agreed at journal
07 Jun, 2022
Reviewers invited by journal
21 Mar, 2022
Editor assigned by journal
21 Mar, 2022
Editor invited by journal
21 Mar, 2022
Submission checks completed at journal
21 Mar, 2022
First submitted to journal
28 Feb, 2022

You are reading this latest preprint version

Evaluation and Limitations of Different Approaches Among COVID-19 Fatal Cases Using Whole-exome Sequencing Data

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Analyses of variants associated with COVID-19 in GWAS Catalog

Analyses of risk COVID-19 variants by literature search

Analyses of risk COVID-19 variants by COVID-19 Host Genetics Initiative

Analyses of variants by QIAGEN Clinical Insight (QCI™) Analyze software

Discussion

Conclusions

Methods

Statistical analysis

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1