SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

SARS-CoV-2 and Dysphagia: A Retrospective Analysis of COVID-19 Patients with Swallowing Disorders

BACKGROUND

COVID-19 can lead to impairment of neural networks involved in swallowing, since the act of swallowing is coordinated and performed by a diffuse brain network involving peripheral nerves and muscles. Dysphagia has been identified as a risk and predictive factor for the severest form of SARS-CoV-2 infection.

OBJECTIVES

To investigate the association between swallowing disorders and COVID-19 in patients hospitalized for COVID-19.

METHODS

We collected demographic data, medical information specific to dysphagia and data on medical treatments of patients with COVID-19.

RESULTS

A total of 43 hospitalized COVID-19 patients were enrolled in the study. Twenty (46%) were evaluated positive for dysphagia and 23 (54%) were evaluated negative. Neurocognitive disorders and diabetes were mostly associated with patients who resulted positive for dysphagia. Respiratory impairment caused by COVID-19 seems to be a cause of dysphagia, since all patients who needed oxygen-therapy developed symptoms of dysphagia, unlike patients who did not. In the dysphagic group, alteration of the swallowing trigger resulted in the severest form of dysphagia. An association was found between the severest form of COVID-19 and dysphagia. This group consisted predominantly of males with longer hospitalization.

CONCLUSIONS

Identification of COVID-19 patients at risk for dysphagia is crucial for better patient management.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Polymorphisms of IFN signaling genes and FOXP4 influence the severity of COVID-19

BACKGROUND

The clinical manifestations of COVID-19 range from asymptomatic, mild to moderate, severe, and critical disease. Host genetic variants were recognized to affect the disease severity. However, the genetic landscape differs among various populations. Therefore, we explored the variants associated with COVID-19 severity in the Guangdong population.

METHODS

A total of 314 subjects were selected, of which the severe and critical COVID-19 patients were defined as "cases", and the mild and moderate patients were defined as "control". Twenty-two variants in interferon-related genes and FOXP4 were genotyped using the MassARRAY technology platform.

RESULTS

IFN signaling gene MX1 rs17000900 CA + AA genotype was correlated with a reduced risk of severe COVID-19 in males (P = 0.001, OR = 0.050, 95%CI = 0.008-0.316). The AT haplotype comprised of MX1 rs17000900 and rs2071430 was more likely to protect against COVID-19 severity (P = 6.3E-03). FOXP4 rs1886814 CC genotype (P = 0.001, OR = 3.747, 95%CI = 1.746-8.043) and rs2894439 GA + AA genotype (P = 0.001, OR = 5.703, 95% CI = 2.045-15.903) were correlated with increased risk of severe COVID-19. Haplotype CA comprised of rs1886814 and rs2894439 was found to be correlated with adverse outcomes (P = 7.0E-04). FOXP4 rs1886814 CC (P = 0.0004) and rs2894439 GA + AA carriers had higher neutralizing antibody titers (P = 0.0018). The CA + AA genotype of MX1 rs17000900 tended to be correlated with lower neutralizing antibody titers than CC genotype (P = 0.0663), but the difference was not statistically significant.

CONCLUSION

Our study found a possible association between MX1 and FOXP4 polymorphisms and the severity of COVID-19. Distinguishing high-risk patients who develop severe COVID-19 will provide clues for early intervention and individual treatment strategies.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases

BACKGROUND

Comorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.

METHODS

By leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).

FINDINGS

Briefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.

INTERPRETATION

Five SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk.

METHODS

A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance.

RESULTS

We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk.

CONCLUSION

In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Blood-Type-A is a COVID-19 infection and hospitalization risk in a Turkish cohort

We have shown in an ethnically homogenous Turkey cohort with more than six thousand cases and 25 thousand controls that ABO blood types that contain anti-A antibody (O and B) are protective against COVID-19 infection and hospitalization, whereas those without the anti-A antibody (A and AB) are risks. The A + AB frequency increases from 54.7 % in uninfected controls to 57.6 % in COVID-19 outpatients, and to 62.5 % in COVID-19 inpatients. The odds-ratio (OR) for lacking of anti-A antibody risk for infection is 1.16 (95 % confidence interval (CI) 1.1-1.22, and Fisher test p-value 1.8 × 10^-7). The OR for hospitalization is 1.23 (95 %CI 1.06-1.42, Fisher test p-value 0.005). A linear regression treating controls, outpatients, inpatients as three numerical levels over anti-A antibody leads to a p-value of 5.9 × 10^-9. All these associations remain to be statistically significant after conditioning over age, even though age itself is a risk for both infection and hospitalization. We also attempted to correct the potential effect from vaccination, even though vaccination information is not available, by using the date of the data collection as a surrogate to vaccination status. Although no significant association between infection/hospitalization with Rhesus blood system was found, forest plots are used to illustrate possible trends.

Better safe than sorry-Whole-genome sequencing indicates that missense variants are significant in susceptibility to COVID-19

Undoubtedly, genetic factors play an important role in susceptibility and resistance to COVID-19. In this study, we conducted the GWAS analysis. Out of 15,489,173 SNPs, we identified 18,191 significant SNPs for severe and 11,799 SNPs for resistant phenotype, showing that a great number of loci were significant in different COVID-19 representations. The majority of variants were synonymous (60.56% for severe, 58.46% for resistant phenotype) or located in introns (55.77% for severe, 59.83% for resistant phenotype). We identified the most significant SNPs for a severe outcome (in AJAP1 intron) and for COVID resistance (in FIG4 intron). We found no missense variants with a potential causal function on resistance to COVID-19; however, two missense variants were determined as significant a severe phenotype (in PM20D1 and LRP4 exons). None of the aforementioned SNPs and missense variants found in this study have been previously associated with COVID-19.

A genome-wide association study identifies a novel association between SDC3 and apparent treatment-resistant hypertension

BACKGROUND

Compared with patients who require fewer antihypertensive agents, those with apparent treatment-resistant hypertension (aTRH) are at increased risk for cardiovascular and all-cause mortality, independent of blood pressure control. However, the etiopathogenesis of aTRH is still poorly elucidated.

METHODS

We performed a genome-wide association study (GWAS) in first cohort including 586 aTRHs and 871 healthy controls. Next, expression quantitative trait locus (eQTL) analysis was used to identify genes that are regulated by single nucleotide polymorphisms (SNPs) derived from the GWAS. Then, we verified the genes obtained from the eQTL analysis in the validation cohort including 65 aTRHs, 96 hypertensives, and 100 healthy controls through gene expression profiling analysis and real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

The GWAS in first cohort revealed four suggestive loci (1p35, 4q13.2-21.1, 5q22-23.2, and 15q11.1-q12) represented by 23 SNPs. The 23 significant SNPs were in or near LAPTM5, SDC3, UGT2A1, FTMT, and NIPA1. eQTL analysis uncovered 14 SNPs in 1p35 locus all had same regulation directions for SDC3 and LAPTM5. The disease susceptible alleles of SNPs in 1p35 locus were associated with lower gene expression for SDC3 and higher gene expression for LAPTM5. The disease susceptible alleles of SNPs in 4q13.2-21.1 were associated with higher gene expression for UGT2B4. GTEx database did not show any statistically significant eQTLs between the SNPs in 5q22-23.2 and 15q11.1-q12 loci and their influenced genes. Then, gene expression profiling analysis in the validation cohort confirmed lower expression of SDC3 in aTRH but no significant differences on LAPTM5 and UGT2B4, when compared with controls and hypertensives, respectively. RT-qPCR assay further verified the lower expression of SDC3 in aTRH.

CONCLUSIONS

Our study identified a novel association of SDC3 with aTRH, which contributes to the elucidation of its etiopathogenesis and provides a promising therapeutic target.

In-silico study of influence of HLA heterogeneity on CTL responses across ethnicities to SARS-CoV-2

Differences in outcome to COVID-19 infection in different individuals is largely attributed to genetic heterogeneity leading to differential immune responses across individuals and populations. HLA is one such genetic factor that varies across individuals leading to differences in how T-cell responses are triggered against SARS-CoV-2, directly influencing disease susceptibility. HLA alleles that influence COVID-19 outcome, by virtue of epitope binding and presentation, have been identified in cohorts worldwide. However, the heterogeneity in HLA distribution across ethnic groups limits the generality of such association. In this study, we address this limitation by comparing the recognition of CTL epitopes across HLA genotypes and ethnic groups. Using HLA allele frequency data for ethnic groups from Allele Frequency Net Database (AFND), we construct synthetic populations for each ethnic group and show that CTL epitope strength varies across HLA genotypes and populations. We also observe that HLA genotypes, in certain cases, can have high CTL epitope strengths in the absence of top-responsive HLA alleles. Finally, we show that the theoretical estimate of responsiveness and hence protection offered by a HLA allele is bound to vary across ethnic groups, due to the influence of other HLA alleles within the HLA genotype on CTL epitope recognition. This emphasizes the need for studying HLA-disease associations at the genotype level rather than at a single allele level.

Transcriptome-wide summary data-based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID-19

Severe COVID-19 has a poor prognosis, while the genetic mechanism underlying severe COVID-19 remains largely unknown. We aimed to identify genes that are potentially causally associated with severe COVID-19. We conducted a summary data-based Mendelian randomization (SMR) analysis using expression quantitative trait loci (eQTL) data from 49 different tissues as the exposure and three COVID-19-phenotypes (very severe respiratory confirmed COVID-19 [severe COVID-19], hospitalized COVID-19, and SARS-CoV-2 infection) as the outcomes. SMR using multiple SNPs was used as a sensitivity analysis to reduce false positive rate. Multiple testing was corrected using the false discovery rate (FDR) q-value. We identified 309 significant gene-trait associations (FDR q value < 0.05) across 46 tissues for severe COVID-19, which mapped to 64 genes, of which 38 are novel. The top five most associated protein-coding genes were Interferon Alpha and Beta Receptor Subunit 2 (IFNAR2), 2'-5'-Oligoadenylate Synthetase 3 (OAS3), mucin 1 (MUC1), Interleukin 10 Receptor Subunit Beta (IL10RB), and Napsin A Aspartic Peptidase (NAPSA). The potential causal genes were enriched in biological processes related to type I interferons, interferon-gamma inducible protein 10 production, and chemokine (C-X-C motif) ligand 2 production. In addition, we further identified 23 genes and 5 biological processes which are unique to hospitalized COVID-19, as well as 13 genes that are unique to SARS-CoV-2 infection. We identified several genes that are potentially causally associated with severe COVID-19. These findings improve our limited understanding of the mechanism of COVID-19 and shed light on the development of therapeutic agents for treating severe COVID-19.

Human genetic basis of severe or critical illness in COVID-19

Coronavirus Disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable morbidity and mortality worldwide. The clinical manifestation of COVID-19 ranges from asymptomatic or mild infection to severe or critical illness, such as respiratory failure, multi-organ dysfunction or even death. Large-scale genetic association studies have indicated that genetic variations affecting SARS-CoV-2 receptors (angiotensin-converting enzymes, transmembrane serine protease-2) and immune components (Interferons, Interleukins, Toll-like receptors and Human leukocyte antigen) are critical host determinants related to the severity of COVID-19. Genetic background, such as 3p21.31 and 9q34.2 loci were also identified to influence outcomes of COVID-19. In this review, we aimed to summarize the current literature focusing on human genetic factors that may contribute to the observed diversified severity of COVID-19. Enhanced understanding of host genetic factors and viral interactions of SARS-CoV-2 could provide scientific bases for personalized preventive measures and precision medicine strategies.

COVID-19 is associated with the risk of cardiovascular disease death: A two-sample Mendelian randomization study

Objective

This study aimed to estimate the causal effects of Coronavirus disease 2019 susceptibility and hospitalization on cardiovascular disease death using two-sample Mendelian randomization analysis.

Methods

We used statistics from a genome-wide association study. A total of 2,568,698 participants were assessed in this study, including 1,299,010 in Coronavirus disease 2019 susceptibility databases, 908,494 in Coronavirus disease 2019 hospitalization database, and 361,194 in a cardiovascular disease death database. We performed two-sample Mendelian randomization analysis using the inverse variance weighted method. As sensitivity analysis techniques, Mendelian randomization-Egger regression, heterogeneity analyses, and Leave-one-out analysis were employed. Reverse Mendelian randomization analysis was used to detect reverse causality. Statistical significance was defined as P < 0.05.

Results

Coronavirus disease 2019 susceptibility may be a causal factor for cardiovascular disease death (β = 2.188 × 10^–3, P = 0.002), which involves five common single nucleotide polymorphisms. Similarly, Coronavirus disease 2019 hospitalization may also be a causal factor for cardiovascular disease death (β = 8.626 × 10^–4, P = 0.010), which involves nine common single nucleotide polymorphisms. Furthermore, sensitivity and reverse Mendelian randomization analysis suggested that no heterogeneity, horizontal pleiotropy or reverse causality was found between Coronavirus disease 2019 and cardiovascular disease death.

Conclusion

Our bidirectional Mendelian randomization analysis showed a causal relationship between Coronavirus disease 2019 susceptibility and hospitalization associated with an increased risk of cardiovascular disease death.

Cellular heterogeneity in disease severity and clinical outcome: Granular understanding of immune response is key

During an infectious disease progression, it is crucial to understand the cellular heterogeneity underlying the differential immune response landscape that will augment the precise information of the disease severity modulators, leading to differential clinical outcome. Patients with COVID-19 display a complex yet regulated immune profile with a heterogeneous array of clinical manifestation that delineates disease severity sub-phenotypes and worst clinical outcomes. Therefore, it is necessary to elucidate/understand/enumerate the role of cellular heterogeneity during COVID-19 disease to understand the underlying immunological mechanisms regulating the disease severity. This article aims to comprehend the current findings regarding dysregulation and impairment of immune response in COVID-19 disease severity sub-phenotypes and relate them to a wide array of heterogeneous populations of immune cells. On the basis of the findings, it suggests a possible functional correlation between cellular heterogeneity and the COVID-19 disease severity. It highlights the plausible modulators of age, gender, comorbidities, and hosts' genetics that may be considered relevant in regulating the host response and subsequently the COVID-19 disease severity. Finally, it aims to highlight challenges in COVID-19 disease that can be achieved by the application of single-cell genomics, which may aid in delineating the heterogeneity with more granular understanding. This will augment our future pandemic preparedness with possibility to identify the subset of patients with increased diseased severity.

Host genomics of SARS-CoV-2 infection

SARS-CoV-2 infected a large fraction of humans in the past 2 years. The clinical presentation of acute infection varies greatly between individuals, ranging from asymptomatic or mild to life-threatening COVID-19 pneumonia with multi-organ complications. Demographic and comorbid factors explain part of this variability, yet it became clear early in the pandemic that human genetic variation also plays a role in the stark differences observed amongst SARS-CoV-2 infected individuals. Using tools and approaches successfully developed for human genomic studies in the previous decade, large international collaborations embarked in the exploration of the genetic determinants of multiple outcomes of SARS-CoV-2 infection, with a special emphasis on disease severity. Genome-wide association studies identified multiple common genetic variants associated with COVID-19 pneumonia, most of which in regions encoding genes with known or suspected immune function. However, the downstream, functional work required to understand the precise causal variants at each locus has only begun. The interrogation of rare genetic variants using targeted, exome, or genome sequencing approaches has shown that defects in genes involved in type I interferon response explain some of the most severe cases. By highlighting genes and pathways involved in SARS-CoV-2 pathogenesis and host-virus interactions, human genomic studies not only revealed novel preventive and therapeutic targets, but also paved the way for more individualized disease management.

There exists the "smartest" movement rate to control the epidemic rather than "city lockdown"

The emergency outbreak and spread of coronavirus disease 2019 (COVID-19) has left great damage to individuals over most of the world. Population mobility is the primary reason for the spread of the epidemic. A delayed stochastic epidemic susceptible-infected-recovered (SIR) model with Gaussian white noise is introduced. Compared with traditional models,this model is characterized by time delay, environmental noise and population mobility among municipalities with the convenient transportation network. The stochastic dynamic behavior of the SIR model is analyzed and the existence of the stochastic bifurcation of the system is proved. The effect of time delay and movement rate are investigated. Numerical simulations are performed to support the theoretical results. It is worth mentioning that the movement rate is not as low as possible and appropriate population mobility is conducive to alleviating the epidemic. Through simulation, we demonstrate the existence of the best movement rate named the "smartest" κ , which is helpful to control the epidemic. This model is also useful to prevent other infectious diseases.

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.

Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19

Aim: A human immunogenetics variation study was conducted in samples collected from diverse COVID-19 populations. Materials & methods: Whole-genome and whole-exome sequencing (WGS/WES), data processing, analysis and visualization pipeline were applied to identify variants associated with genes of interest. Results: A total of 2886 mutations were found across the entire set of 13 genomes. Functional annotation of the gene variants revealed mutation type and protein change. Many variants were found to be biologically implicated in COVID-19. The involvement of these genes was also found in multiple other diseases. Conclusion: The analysis determined that ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI had functional implications and TMPRSS4 was the gene most altered in virally infected patients.

The quest to establish an understanding of the genetics underlying COVID-19 is a central focus of life sciences today. COVID-19 is triggered by SARS-CoV-2, a single-stranded RNA respiratory virus. Several clinical-genomics studies have emerged positing different human gene mutations occurring due to COVID-19. A global analysis of these genes was conducted targeting major components of the immune system to identify possible variations likely to be involved in COVID-19 predisposition. Gene-variant analysis was performed on whole-genome sequencing samples collected from diverse populations. ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI were found to have functional implications and TMPRSS4 may have a role in the severity of clinical manifestations of COVID-19.

Structure of the 5' untranslated region in SARS-CoV-2 genome and its specific recognition by innate immune system via the human oligoadenylate synthase 1

2'-5'-Oligoadenylate synthetase 1 (OAS1) is one of the key enzymes driving the innate immune system response to SARS-CoV-2 infection whose activity has been related to COVID-19 severity. OAS1 is a sensor of endogenous RNA that triggers the 2'-5'-oligoadenylate/RNase L pathway. Upon SARS-CoV-2 infection, OAS1 is responsible for the recognition of viral RNA and has been shown to possess a particularly high sensitivity for the 5'-untranslated (5'-UTR) RNA region, which is organized in a double-strand stem loop motif (SL1). Here we report the structure of the SL1/OAS1 complex also rationalizing the high affinity for OAS1.