Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

Systematic assessment of COVID-19 host genetics using whole genome sequencing data

Courses of SARS-CoV-2 infections are highly variable, ranging from asymptomatic to lethal COVID-19. Though research has shown that host genetic factors contribute to this variability, cohort-based joint analyses of variants from the entire allelic spectrum in individuals with confirmed SARS-CoV-2 infections are still lacking. Here, we present the results of whole genome sequencing in 1,220 mainly vaccine-naïve individuals with confirmed SARS-CoV-2 infection, including 827 hospitalized COVID-19 cases. We observed the presence of autosomal-recessive or likely compound heterozygous monogenic disorders in six individuals, all of which were hospitalized and significantly younger than the rest of the cohort. We did not observe any suggestive causal variants in or around the established risk gene TLR7. Burden testing in the largest population subgroup (i.e., Europeans) suggested nominal enrichments of rare variants in coding and non-coding regions of interferon immune response genes in the overall analysis and male subgroup. Case-control analyses of more common variants confirmed associations with previously reported risk loci, with the key locus at 3p21 reaching genome-wide significance. Polygenic scores accurately captured risk in an age-dependent manner. By enabling joint analyses of different types of variation across the entire frequency spectrum, this data will continue to contribute to the elucidation of COVID-19 etiology.

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147-173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%-60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features," inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGS p h 1 and IPGS p h 2 ). By applying a logistic regression with both IPGS, (IPGS p h 2 (or indifferently IPGS p h 1 ) and age as inputs, we reached an accuracy of 84%-86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147-173) by a factor of 10%.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best. This architecture was composed of two layers with, respectively, 7 and 46 units per layer implementing respective drop-out rates of 0.210 and 0.358. The classification of the test data resulted in AUC = 0.750, accuracy = 0.650, sensitivity = 0.600, and specificity = 0.700. Significant SNPs were selected based on the SHapley Additive exPlanation (SHAP). As a final result, one GO term related to the biological process and thirteen GO terms related to molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Our findings revealed that the optimal approach can correctly predict susceptibility or resistance status for approximately 65% of cows. Genes marked by the most significant SNPs are related to the immune response and protein synthesis.

COVID-19 severity: does the genetic landscape of rare variants matter?

Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

Ultra-rare RTEL1 gene variants associate with acute severity of COVID-19 and evolution to pulmonary fibrosis as a specific long COVID disorder

BACKGROUND

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused an ongoing pandemic of a pathology termed Coronavirus Disease 19 (COVID-19). Several studies reported that both COVID-19 and RTEL1 variants are associated with shorter telomere length, but a direct association between the two is not generally acknowledged. Here we demonstrate that up to 8.6% of severe COVID-19 patients bear RTEL1 ultra-rare variants, and show how this subgroup can be recognized.

METHODS

A cohort of 2246 SARS-CoV-2-positive subjects, collected within the GEN-COVID Multicenter study, was used in this work. Whole exome sequencing analysis was performed using the NovaSeq6000 System, and machine learning methods were used for candidate gene selection of severity. A nested study, comparing severely affected patients bearing or not variants in the selected gene, was used for the characterisation of specific clinical features connected to variants in both acute and post-acute phases.

RESULTS

Our GEN-COVID cohort revealed a total of 151 patients carrying at least one RTEL1 ultra-rare variant, which was selected as a specific acute severity feature. From a clinical point of view, these patients showed higher liver function indices, as well as increased CRP and inflammatory markers, such as IL-6. Moreover, compared to control subjects, they present autoimmune disorders more frequently. Finally, their decreased diffusion lung capacity for carbon monoxide after six months of COVID-19 suggests that RTEL1 variants can contribute to the development of SARS-CoV-2-elicited lung fibrosis.

CONCLUSION

RTEL1 ultra-rare variants can be considered as a predictive marker of COVID-19 severity, as well as a marker of pathological evolution in pulmonary fibrosis in the post-COVID phase. This notion can be used for a rapid screening in hospitalized infected people, for vaccine prioritization, and appropriate follow-up assessment for subjects at risk. Trial Registration NCT04549831 ( www.

CLINICALTRIAL

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TLRs: Innate Immune Sentries against SARS-CoV-2 Infection

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been responsible for a devastating pandemic since March 2020. Toll-like receptors (TLRs), crucial components in the initiation of innate immune responses to different pathogens, trigger the downstream production of pro-inflammatory cytokines, interferons, and other mediators. It has been demonstrated that they contribute to the dysregulated immune response observed in patients with severe COVID-19. TLR2, TLR3, TLR4 and TLR7 have been associated with COVID-19 severity. Here, we review the role of TLRs in the etiology and pathogenesis of COVID-19, including TLR7 and TLR3 rare variants, the L412F polymorphism in TLR3 that negatively regulates anti-SARS-CoV-2 immune responses, the TLR3-related cellular senescence, the interaction of TLR2 and TLR4 with SARS-CoV-2 proteins and implication of TLR2 in NET formation by SARS-CoV-2. The activation of TLRs contributes to viral clearance and disease resolution. However, TLRs may represent a double-edged sword which may elicit dysregulated immune signaling, leading to the production of proinflammatory mediators, resulting in severe disease. TLR-dependent excessive inflammation and TLR-dependent antiviral response may tip the balance towards the former or the latter, altering the equilibrium that drives the severity of disease.

Mendelian inheritance revisited: dominance and recessiveness in medical genetics

Understanding the consequences of genotype for phenotype (which ranges from molecule-level effects to whole-organism traits) is at the core of genetic diagnostics in medicine. Many measures of the deleteriousness of individual alleles exist, but these have limitations for predicting the clinical consequences. Various mechanisms can protect the organism from the adverse effects of functional variants, especially when the variant is paired with a wild type allele. Understanding why some alleles are harmful in the heterozygous state - representing dominant inheritance - but others only with the biallelic presence of pathogenic variants - representing recessive inheritance - is particularly important when faced with the deluge of rare genetic alterations identified by high throughput DNA sequencing. Both awareness of the specific quantitative and/or qualitative effects of individual variants and the elucidation of allelic and non-allelic interactions are essential to optimize genetic diagnosis and counselling.

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19.

Genome-wide association studies of COVID-19: Connecting the dots

Genome-wide association studies (GWASs) are a research approach used to identify genetic variants associated with common diseases, like COVID-19. The lead genetic variants (n = 41) reported by the eleven largest COVID-19 GWASs are mapped to 22 different chromosomal regions. The loci 3q21.31 (LZTFL1 and chemokine receptor genes) and 9q34.2 (ABO), associated with disease severity and susceptibility to infection, respectively, were the most replicated findings across studies. Genes involved with mucociliary clearance (CEP97, FOXP4), viral-entry (ACE2, SLC6A20) and mucosal immunity (MIR6891) are associated with the risk of SARS-CoV-2 infection while genes of antiviral immune response (IFNAR2, OAS1), leukocyte trafficking (CCR9, CXCR6) and lung injury (DPP9, NOTCH4) are associated with severe disease. The biological processes underlying the risk of infection occur prominently, but not exclusively, in the upper airways whereas the severe COVID-19-associated processes in alveolar-capillary interface. The COVID-19 GWASs has unraveled key genetic mechanisms of SARS-CoV-2 pathogenesis, although the genetic basis of other COVID-19 related phenotypes (long COVID and neurological impairment) remains to be elucidated.

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.

Geographical distribution of cystic fibrosis carriers as population genetic determinant of COVID-19 spread and fatality in 37 countries

COVID-19 has shown a relevant heterogeneity in spread and fatality among countries together with a significant variability in its clinical presentation, indicating that host genetic factors may influence COVID-19 pathogenicity. Indeed, subjects carrying single pathogenic variants of the Cystic Fibrosis (CF) Transmembrane Conductance Regulator (CFTR) gene - i.e. CF carriers - are more susceptible to respiratory tract infections and are more likely to undergo severe COVID-19 with higher risk of 14-day mortality. Given that CF carrier prevalence varies among ethnicities and nations, an ecological study in 37 countries was conducted, in order to determine to what extent the diverse CF carrier geographical distribution may have affected COVID-19 spread and fatality during the first pandemic wave. The CF prevalence in countries, as indicator of the geographical distribution of CF carriers, significantly correlated in a direct manner with both COVID-19 prevalence and its Case Fatality Rate (CFR). In a regression study weighted for the number of tests performed, COVID-19 prevalence positively correlated with CF prevalence, while CFR correlated with population percentage older than 65-year, cancer and CF prevalence. Multivariate regression model also confirmed COVID-19 CFR to be associated with CF prevalence, after adjusting for elderly, cancer prevalence, and weighting for the number of tests performed. This study suggests a putative contribution of population genetics of CFTR in understanding the spatial distribution of COVID-19 spread and fatality.

Estimating the number of diseases - the concept of rare, ultra-rare, and hyper-rare

At the dawn of the personalized medicine era, the number of rare diseases has been estimated at 10,000. By considering the influence of environmental factors together with genetic variations and our improved diagnostic capabilities, an assessment suggests a considerably larger number. The majority would be extremely rare, and hence, we introduce the term "hyper-rare," defined as affecting <1/108 individuals. Such disorders would potentially outnumber all currently known rare diseases. Because autosomal recessive disorders are likely concentrated in consanguineous populations, and rare toxicities in rural areas, establishing their existence necessitates a greater reach than is currently viable. Moreover, the randomness of X-linked and gain-of-function mutations greatly compound this challenge. However, whether concurrent diseases actually cause a distinct illness will depend on if their pathological mechanisms interact (phenotype conversion) or not (phenotype maintenance). The hyper-rare disease concept will be important in precision medicine with improved diagnosis and treatment of rare disease patients.

The genetic and evolutionary determinants of COVID-19 susceptibility

Devastating pandemics, such as that due to COVID-19, can provide strong testimony to our knowledge of the genetic and evolutionary determinants of infectious disease susceptibility and severity. One of the most remarkable aspects of such outbreaks is the stunning interindividual variability observed in the course of infection. In recent decades, enormous progress has been made in the field of the human genetics of infectious diseases, and an increasing number of human genetic factors have been reported to explain, to a great extent, the observed variability for a large number of infectious agents. However, our understanding of the cellular, molecular, and immunological mechanisms underlying such disparities between individuals and ethnic groups, remains very limited. Here, we discuss recent findings relating to human genetic predisposition to infectious disease, from an immunological or population genetic perspective, and show how these and other innovative approaches have been applied to deciphering the genetic basis of human susceptibility to COVID-19 and the severity of this disease. From an evolutionary perspective, we show how past demographic and selection events characterizing the history of our species, including admixture with archaic humans, such as Neanderthals, facilitated modern human adaptation to the threats imposed by ancient pathogens. In the context of emerging infectious diseases, these past episodes of genetic adaptation may contribute to some of the observed population differences in the outcome of SARS-CoV-2 infection and the severity of COVID-19 illness.

Pathogen-sugar interactions revealed by universal saturation transfer analysis

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an "end-on" manner. uSTA-guided modeling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis.

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage.

p53/NF-kB Balance in SARS-CoV-2 Infection: From OMICs, Genomics and Pharmacogenomics Insights to Tailored Therapeutic Perspectives (COVIDomics)

SARS-CoV-2 infection affects different organs and tissues, including the upper and lower airways, the lung, the gut, the olfactory system and the eye, which may represent one of the gates to the central nervous system. Key transcriptional factors, such as p53 and NF-kB and their reciprocal balance, are altered upon SARS-CoV-2 infection, as well as other key molecules such as the virus host cell entry mediator ACE2, member of the RAS-pathway. These changes are thought to play a central role in the impaired immune response, as well as in the massive cytokine release, the so-called cytokine storm that represents a hallmark of the most severe form of SARS-CoV-2 infection. Host genetics susceptibility is an additional key side to consider in a complex disease as COVID-19 characterized by such a wide range of clinical phenotypes. In this review, we underline some molecular mechanisms by which SARS-CoV-2 modulates p53 and NF-kB expression and activity in order to maximize viral replication into the host cells. We also face the RAS-pathway unbalance triggered by virus-ACE2 interaction to discuss potential pharmacological and pharmacogenomics approaches aimed at restoring p53/NF-kB and ACE1/ACE2 balance to counteract the most severe forms of SARS-CoV-2 infection.

Host genetic basis of COVID-19: from methodologies to genes

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is having a massive impact on public health, societies, and economies worldwide. Despite the ongoing vaccination program, treating COVID-19 remains a high priority; thus, a better understanding of the disease is urgently needed. Initially, susceptibility was associated with age, sex, and other prior existing comorbidities. However, as these conditions alone could not explain the highly variable clinical manifestations of SARS-CoV-2 infection, the attention was shifted toward the identification of the genetic basis of COVID-19. Thanks to international collaborations like The COVID-19 Host Genetics Initiative, it became possible the elucidation of numerous genetic markers that are not only likely to help in explaining the varied clinical outcomes of COVID-19 patients but can also guide the development of novel diagnostics and therapeutics. Within this framework, this review delineates GWAS and Burden test as traditional methodologies employed so far for the discovery of the human genetic basis of COVID-19, with particular attention to recently emerged predictive models such as the post-Mendelian model. A summary table with the main genome-wide significant genomic loci is provided. Besides, various common and rare variants identified in genes like TLR7, CFTR, ACE2, TMPRSS2, TLR3, and SELP are further described in detail to illustrate their association with disease severity.

Associations and Disease-Disease Interactions of COVID-19 with Congenital and Genetic Disorders: A Comprehensive Review

Since December 2019, the COVID-19 pandemic, which originated in Wuhan, China, has resulted in over six million deaths worldwide. Millions of people who survived this SARS-CoV-2 infection show a number of post-COVID complications. Although, the comorbid conditions and post-COVID complexities are to some extent well reviewed and known, the impact of COVID-19 on pre-existing congenital anomalies and genetic diseases are only documented in isolated case reports and case series, so far. In the present review, we analyzed the PubMed indexed literature published between December 2019 and January 2022 to understand this relationship from various points of view, such as susceptibility, severity and heritability. Based on our knowledge, this is the first comprehensive review on COVID-19 and its associations with various congenital anomalies and genetic diseases. According to reported studies, some congenital disorders present high-risk for developing severe COVID-19 since these disorders already include some comorbidities related to the structure and function of the respiratory and cardiovascular systems, leading to severe pneumonia. Other congenital disorders rather cause psychological burdens to patients and are not considered high-risk for the development of severe COVID-19 infection.