key: cord-0861701-mgpmf4j1
authors: Shen, Yiran; Khatri, Bhuwan; Rananaware, Santosh; Li, Danmeng; Ostrov, David A.; Jain, Piyush K; Lessard, Christopher J.; Nguyen, Cuong Q.
title: Ancestral origins are associated with SARS-CoV-2 susceptibility and protection in a Florida patient population
date: 2022-03-30
journal: bioRxiv
DOI: 10.1101/2022.03.30.486345
sha: fd4ad7ef8f01f7aea778c8e18e33a4c6e0b06170
doc_id: 861701
cord_uid: mgpmf4j1

COVID-19 is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The severity of COVID-19 is highly variable and related to known (e.g., age, obesity, immune deficiency) and unknown risk factors. The widespread clinical symptoms encompass a large group of asymptomatic COVID-19 patients, raising a crucial question regarding genetic susceptibility, e.g., whether individual differences in immunity play a role in patient symptomatology and how much human leukocyte antigen (HLA) contributes to this. To reveal genetic determinants of susceptibility to COVID-19 severity in the population and further explore potential immune-related factors, we performed a genome-wide association study on 284 confirmed COVID-19 patients (cases) and 95 healthy individuals (controls). We compared cases and controls of European (EUR) ancestry and African American (AFR) ancestry separately. We identified two loci on chromosomes 5q32 and 11p12, which reach the significance threshold of suggestive association (p<1×10-5 threshold adjusted for multiple trait testing) and are associated with the COVID-19 susceptibility in the European ancestry (index rs17448496: odds ratio [OR] = 0.173; 95% confidence interval [CI], 0.08–0.36 for G allele; p=5.15× 10-5 and index rs768632395: OR = 0.166; 95% CI, 0.07–0.35 for A allele; p= 4.25×10-6, respectively), which were associated with two genes, PPP2R2B at 5q32, and LRRC4C at 11p12, respectively. To explore the linkage between HLA and COVID-19 severity, we applied fine-mapping analysis to dissect the HLA association with mild and severe cases. Using In-silico binding predictions to map the binding of risk/protective HLA to the viral structural proteins, we found the differential presentation of viral peptides in both ancestries. Lastly, extrapolation of the identified HLA from the cohort to the worldwide population revealed notable correlations. The study uncovers possible differences in susceptibility to COVID-19 in different ancestral origins in the genetic background, which may provide new insights into the pathogenesis and clinical treatment of the disease.

In December 2019, a novel coronavirus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), emerged in Wuhan, Hubei Province, China, initiating a breakout of atypical acute respiratory disease, termed coronavirus disease 2019 . SARS-CoV-2 is a betacoronavirus in the family of Coronaviridae; the virus contains four structural proteins: S (spike), E (envelope), M (membrane), and N (nucleocapsid), sixteen non-structural proteins (nsp1− 16) and eleven accessory proteins, which support viral essential physiological function and evasion from the host immune system [1] . The complex structure of the virus provides multiple possible targets for antiviral prevention and treatment; however, the lack of comprehensive knowledge of viral infection and host immune response has hampered efforts to predict the disease course and identify effective therapeutic candidates. One of the most striking features of SARS-CoV-2 is consequence variability, ranging from asymptomatic to symptomatic viral pneumonia and finally to life-threatening acute respiratory distress syndrome [2] . A majority of patients recovered during early infection, but a smaller percentage of patients were more likely to progress and eventually die from severe systemic inflammatory response syndrome. Several factors are associated with disease severity, e.g., age, gender, pre-existing conditions [3, 4] , and race [5] [6] [7] .

To reveal the underlying pathogenesis of SARS-CoV-2 susceptibility and disease progression, genome-wide association studies (GWAS) provide additional clues regarding the pathogenesis of complex diseases by identifying potential susceptible allelic variants. Several loci on the different chromosomes have been previously reported to be associated with COVID-19 severity [8] [9] [10] .

Some studies have focused on the genetic linkage between HLA alleles and SARS-CoV-2 6 infection. The class I (HLA-A, -B, and -C) and class II HLA (HLA-DR, -DQ, and -DP) exhibit a high degree of polymorphism, and CD4 + T cells and CD8 + T cells respond to pathogens by recognizing different classes of HLA molecules (I or II, respectively) on the cell surface. Specific HLA genotypes have been associated with T-cell mediated immunity and viral clearance. Several class I and II alleles have been identified to be related to SARS-CoV-2 infection, protection, and severity through in silico prediction [11] [12] [13] [14] , patient genotyping [15] [16] [17] [18] , and whole-genome sequencing [19] .

These studies suggested that genetic variants, especially HLA alleles, were associated with disease morbidity, mortality, and prognosis.

To study the COVID-19 consequences variability, we applied GWAS on 284 SARS-CoV-2 positive samples and 89 negative samples composed of different ancestry origins to determine if a specific genetic factor was associated with susceptibility to SARS-CoV-2 infection and severity of COVID-19 on different genetic background. We also applied fine-mapping to reveal potential disease-associated HLA alleles in European and African ancestral populations. In addition, we applied in silico prediction and structural modeling to identify and map the structural epitopes presented by the associated protective and risk HLAs. Lastly, we extrapolated the finding to the worldwide population and the result showed significant correlations with other countries. 

To predict the SARS-CoV-2 peptides in which selective HLA alleles will bind, HLA peptidebinding prediction algorithms netMHCpan (v4.1) and netMHCIIpan (v4.0) were utilized for HLA class I and class II alleles, respectively [24] . Full-length amino acid sequences of structural proteins from SARS-CoV-2 whole-genome proteome (SnapGene, EPI_ISL_7196120_B.1.1.529, EPI_ISL_7196121_B.1.1.529) were used to infer all possible potentially relevant peptides (9mers for class I and 15mers for class II). Default rank thresholds (%Rank values) were used to define strong (0.5% for netMHCpan and 2% for netMHCIIpan) and weak (2% for netMHCpan and 10%

for netMHCIIpan) binders. The prediction binding pattern was compared among different alleles to predict the immunogenic portion for further application. In selecting the alleles of interest, %Rank was used as a reference value to compare the ability of the same peptide to be presented by risk and protective alleles among predicted alleles that can be presented by at least one of the candidate alleles (Class I MHC: %Rank < 2%, Class II MHC: %Rank < 10), the top three alleles have the greatest difference in presentation ability (%Rank) from the other allele are selected.

Models for all the HLA molecules were made using SWISS-MODEL. 7RTD was used as templates for HLA-B*27:05 and HLA-C*13:02, and 3PDO for HLA-DRB1*13:02. 6PX6 and 5KSA were used as templates for HLA-DQA1*05:01 and HLA-DQB1*03:01, respectively. Then, they were superposed into 5KSU and merged into one final model for the HLA-DQ structure in COOT. The peptide in the template structure was mutated in COOT, too. The geometry of the result complexes 10 was regularized in PHENIX.

The data of allele frequencies among countries/regions and ethnicities were obtained from the Allele Frequency Net Database (http://allelefrequencies.net), global susceptibility among countries/regions (cases per one million population) were obtained from Worldometer (https://www.worldometers.info), and global death rate (case-mortality) were obtained from John Hopkins Coronavirus resource center (https://coronavirus.jhu.edu/data/mortality). Databases were searched on March 24 th , 2022. When multiple data points of an allele frequency were available for a country, the weighted Mean was calculated according to the sample size.

The association between the allele frequency of each HLA gene and cases and mortality were assessed by linear regression in GraphPad Prism (v9.3.1). An adjusted p-value of <0.05 was considered statistically significant.

The state of Florida in the United States (US) is one of the most ethnically diverse states. 53% of Floridians are White (Non-Hispanic), with 21.6% being White (Hispanic), Black or African American (Non-Hispanic) (15.2%), Asian (Non-Hispanic) (2.73%), and Other (Hispanic) (2.97%).

20.1% of Floridians were born outside the US, which is higher than the national average of 13.7% in 2019 (https://datausa.io/profile/geo/florida). Due to the ethnically diverse nature of Florida residents, we performed QC of the dataset as described previously. Following QC, we stratified the population based on their genetic ancestry (COVID-19 cases: 56% European (EUR) ancestry and 37% African (AFR) ancestry. Non-COVID-19 controls: 77% EUR ancestry and 15% AFR ancestry ( Supplementary Figures 1 and 2) . Since EUR and AFR ancestries are distinct populations, we performed GWAS analysis on the two populations separately. As presented in confidence interval [CI], 0.08-0.36 for G allele; p<5.15x10 −6 ) and rs768632395 at locus 11p12 (OR = 0.166; 95% CI, 0.07-0.35 for A allele; p<4.25 ×10 −6 ) that were suggestive GWAS with COVID-19 susceptibility in the EUR ancestral patients. There were no SNP association signals in the AFR ancestral patients that met the significance threshold of suggestive association ( Figure   2 ). In summary, the limited sample size was sufficient to distinguish two different genetic ancestries. In addition, we identified two interesting SNPs localized in the EUR ancestry. These two SNPs showed OR values of less than 1, suggesting that they are possibly involved to some extent in protection against SARS-CoV-2 infection (i.e., to a greater extent, would be present in 12 unconfirmed healthy individuals) in contrast to the AFR ancestral patients where the absence of prominent SNPs, which can be interpreted as a protective mechanism with no apparent contribution to SARS-CoV-2 infection.

As presented in Figure 1 , the GWAS showed a suggestive association with serine/threonine- [26] , and dysregulation of PPP2R2B may contribute to the development and progression of breast cancer [27] . Importantly, PPP2R2B interacted with PPP1R15A in the ERK signaling pathway, which is on chr9 and was associated with SARS-CoV-2 infection in the C5 phenotype from HGI (COVID-19 Host Genetics Initiative group) [28] . LRRC4, also known as netrin-G ligand-2 (NGL-2), belongs to the superfamily of LRR proteins and is a receptor for netrin-G2 [29] , regulates excitatory synapse formation and promotes axonal differentiation. LRRC4 can also act as a tumor suppressor gene to significantly inhibit glioblastoma cell proliferation by interacting with extracellular and intracellular signaling pathways [30] . LRRC4 binds to phosphotyrosinedependent protein kinase 1 (PDPK1), promotes NF-κB activation in glioblastoma cells and secretion of Interleukin 6 (IL-6), C-C Motif Chemokine Ligand 2 (CCL2) and Interferon-gamma (IFN-γ), thereby inhibiting the expansion of tumor-infiltrating regulatory T cells and the growth of glioblastoma cells [31] . Although not as significant as the findings of the previous meta-analysis, the loci we observed in the EUR ancestral patients were associated with host-adaptive, especially T-cell-related immunity, and the lack of such a significance in AFR ancestral patients may explain the susceptibility of the population.

Specific HLA alleles have played significant roles in many bacterial and viral infections. We applied the fine-mapping to the extended HLA region (chromosome 6, 25 to 34 Mb) to determine their association with the EUR and AFR ancestral patients. We determined no significant SNP association signal on the HLA complex that achieved the threshold of significance for suggestive association (Supplementary Figure 2) . Due to the substantial overlap in bound peptides among HLA alleles and their co-dominant expression, additive GWAS association tests may not capture the full functional role of HLA in COVID-19 risk. Therefore, we further analyzed the association of COVID-19 with HLA alleles through HLA imputation. The results showed multiple alleles present 14 for HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 loci through imputation-based methods using the SNPs data from GWAS. Indeed, it is well established that minority groups of different races and ethnic groups in the US are disproportionately affected by COVID-19. Minorities endured a higher risk for infection, hospitalization, and death [6, 32] . The allele distributions were compared between COVID-19 patients and control individuals in EUR and AFR ancestries, respectively. As presented in Table 1 

It is well documented that COVID-19 patients exhibited a whole range of symptoms and severity. To further define the HLA association with the severity of COVID-19, we subdivided the patient cases into mild and severe according to the source of sample acquisition (saliva: mild; tracheal aspirates: hospitalized/severe) and performed the same HLA imputation. Stratified by disease severity showed significant alleles changed in both EUR and AFR ancestral patient populations. In the EUR ancestral patients, HLA-C*12:03, -B*35, -B*38:01 were associated with 15 an increased risk of SARS-CoV-2 severity (OR >1) ( Table 2) . Interestingly, HLA-B*38 showed a significant odds ratio (OR=1639). Whereas, as presented in Table 3 , in the AFR ancestral patients, HLA-DQB1*03 was associated with an increased risk of SARS-CoV-2 severity. In contrast, HLA-B*58, -DRB1*13:02, and -DQB1*06 were associated with decreased risk of SARS-CoV-2 severity.

The results suggest that there is a higher frequency of risk and protective alleles in mild and severe cases of different ancestral patient populations, and this frequency could explain the demographic differences in the affected population.

As presented in Table 1 and Table 2 , the protective and risk alleles of the EUR ancestry encode class I MHC molecules. To map which viral epitopes are presented by the protective and risk HLAs, we applied the prediction tool NetMHCpan -4.1 provided by DTU Health. As a model, we chose HLA-B*27:05 (protective) and HLA-C*12:03 (risk) based on the significance and mapped the SARS-CoV-2 structural proteins (S, M, N, and E). Both stronger (%Rank less than 0.5%) and weaker (%Rank less than 2%) binders of HLA-B*27:05 (46 peptides in total) and HLA-C*12:03 (46 peptides in total) were selected and mapped across the structural proteins ( Figure   3 ). Based on the presented differences in protective and risk alleles, the top three alleles that have the most significant difference in presentation ability (%Rank) from the other allele are selected and highlighted in Figure 3 and the sequences are listed in Table 4 Table 1 , 2). In summary, the results demonstrated that in the EUR ancestry group, HLA-B*27:05 (protective) and HLA-C*12:03 (risk) present multiple structural proteins of SASR-CoV-2 and the protective alleles lack presentation of the E protein.

The most protective and risk alleles were class II HLA in AFR ancestry, unlike the EUR ancestry (Table 3) . To determine which viral epitopes are presented by the protective and risk HLAs, we again applied the prediction tool NetMHCIIpan -4.0. As a model, we chose HLA-DRB1*13:02 (protective) and HLA-DQA1*05:01-DQB1*03:01 (risk) and mapped them for SARS-CoV-2 structural proteins. Due to only the DQB1*03 being detected in the AFR group, the haplotype with the highest frequency was selected for DQA1 prediction. Both stronger (%Rank less than 2%) and weaker (%Rank less than 10%) binders of HLA-DRB1*13:02 (248 peptides in total) and HLA-DQB1*03:01 (206) were selected and mapped across the structural proteins ( Figure 5) . Again, the top three alleles that have the most significant difference in presentation ability (%Rank) from the other allele were selected and highlighted in Figure 5 , and the sequences were listed in Table 5 Table 3 , 4) structural proteins. We found that this property was still retained. In summary, the result demonstrated that in the AFR ancestry group, the antigens presented by protective alleles (HLA-DRB1*13:02) are more diverse, with different presentation sites against the S, M, and N proteins. In contrast, the antigens presented by risk alleles (HLA-DQB1*03:01) 18 are more at a single site and lack presentation of the E protein.

To further evaluate whether the identified risk and protective HLA in the studied cohort in the state of Florida can be extrapolated to determine the association with cases and mortalities worldwide, we employed linear regression to determine the association of each allele frequency, number of COVID-19 cases per one million population, and case-mortality due to COVID-19. In alleles predicted by disease severity ( Table 2 and Table 3 ), We selected two protective alleles (A*11:01 and DPB1*11:01) and two risk alleles (B*38:01 and DQB1*03) based on their ORs. As presented in Figure 7 , the protective allele A*11:01 showed a significant association. As the allele frequencies increase, the lesser case frequency per one million population (p=0.0267); however, there was no significant correlation with case mortality. There was no statistical significance with the protective DPB1*11:01 allele for case mortality and case/1M pop ( Figure 7A) . Interestingly, when we evaluated the risk B*38:01 allele, which has the largest odds ratio (OR=1639.00), it showed a positive or upward trend between the increase in the allele frequencies and rise in both case/1M population (p=0.0101) and mortality (p=0.6092) (Figure 7B) . The risk allele DQB1*03 showed a significant association with case-mortality (p = 0.0099) but no significant correlation with case/1M population (p=0.0782). The results suggest that extrapolation of specific protective and risk HLA alleles identified in the studied AFR and EUR cohort can be applicable to determine the association with worldwide COVID-19 cases and mortalities.

In this study, the GWAS study showed two plausible genome-wide significant associations on chromosomes 5q32 and 11p12 in EUR ancestral group in our overall susceptibility model by ancestral stratification. Using HLA imputation, the results suggest that several class I MHC alleles in EUR ancestral group and a mixed class I and II MHC alleles in AFR ancestral group were likely to be associated with disease susceptibility or severity. was associated with an increased risk of SARS-CoV-2 severity. Using in silico prediction and modeling of SARS-CoV-2 structural proteins to identify the epitopes and binding strength with risk and protective HLA alleles showed that a different presentation pattern may activate the immune response to varying degrees, leading to changes in the course of the disease. Overall, the study sheds important insight into COVID-19 by stratifying ancestral origin, leading to better disease understanding and prevention strategies.

The first reported genome-wide association signals were at loci 3p21.31 and 9q34.2, which revealed the association of protein-coding genes that regulate viral attachment and host immune response (LC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1) and ABO blood group to severe 20 COVID-19 disease, respectively [33] . These associations were replicated in subsequent studies [10, 34, 35] . Results of a genetic study of 2244 critically ill patients with COVID-19 in intensive care units across the UK identified associations on chromosomes 12q24. 13, 19p13.2, 19p13 .3, and 21q22.1, revealed the genes with innate immunity (IFNAR2 and OAS) and hostdriven lung inflammatory injury (DPP9, TYK2, and CCR2) [8] . A large GWAS study from HGI, including three genome-wide association meta-analyses comprising 49,562 COVID-19 patients from 46 studies in 19 countries, identified 13 human genomic loci associated with infection or severe COVID-19, including TYK2, DPP9, and FOXP4 which corresponded to previously documented associations with lung or autoimmune diseases and inflammatory disorders [36] .

Another GWAS study in Thai suggested a protective effect of IL17B on 5q32 against disease [37] , and a recent release from IHG showed that MUC5B and ELF5 on chr11 might be associated with immune system regulation in the lungs in the development of COVID-19. Our study identified two loci on chromosomes 5q,32, and 11p12 in European ancestry who reached the significance threshold of suggestive association with SARS-CoV-2 infection. We could not define a similar association in the AFR group, and this variation in gene-level may be responsible for the difference. [11] . Amoroso et al. have shown that HLA-DRB1*08, which was predicted to bind SARS-CoV-2 peptides with low affinity, was correlated to mortality. To confirm this, a study with the Sardinian population found HLA-DRB1*08:01 allele only existed in hospitalized patients [16, 17] . A group from South Asia found HLA-B*35 was more among the mildly infected group than the fatal group and owned a high peptide loading capacity compared to other HLA-B proteins [14, 18] . Another evidence may come from cytokine storms, where an unbalanced immune response leads to higher morbidity, while a well-regulated immune response allows patients to recover more quickly. Although not studied in detail in COVID-19, it was shown in a previous study that class II MHC secretes different types of cytokines when binding to different peptides, thus triggering T cell differentiation in divergent ways (TH1/TH17 by DRB1*0401, risk; TH1/TH2 by DRB *0402, protective) in 22 rheumatoid arthritis [40] . In our studies, we identified in EUR group HLA-C*12:03, -B*35, -B*38:01 were associated with an increased risk of SARS-CoV-2 severity (OR >1). And in the AFR group, HLA-DQB1*03 was associated with an increased risk of SARS-CoV-2 severity, while HLA-B*58, -DRB1*13:02, and -DQB1*06 were associated with a decreased risk of SARS-CoV-2 severity.

HLA-C*12:03 and -B*35 identified in the severed EUR group were previously shown to be associated with less severity, and several class II MHC molecules were associated with increased or decreased severity in the AFR group, altogether suggesting a complicated disease progression mechanism that may involve in multiple alleles as well as host immune-regulated factors.

T cells have an important role in the outcome and maintenance of SARS-CoV-2 immunity normally during viral infection by recognizing viral antigens in short peptides present by HLA. A previous study identified epitope megapools (MPs) containing SARS-CoV-2 T cell epitopes derived from various viral proteins, which successfully induced viral-specific T cells responses in patients [41] and CoVac-1 vaccinated individuals [42] . CoVac-1 is a peptide-based vaccine candidate composed of predicted MPs (S, N, N, E, and ORF8); this prediction is based on the most prominent class I HLA-A and -B and class II HLA-DR alleles to protect a broad population.

The success of phase 1 clinical trial of this vaccine demonstrates the potential and importance of epitope screening, as it can stimulate long-lasting T-cell immune responses in the fight against COVID-19. We performed in silico epitope mapping of the identified HLAs in different ancestry groups hoping that further experiments will lead to an affected-population-based understanding of immune defense mechanisms against SARS-CoV-2 and aid in the development of vaccines and immunotherapies. To understand the disease outcome in different ancestral origins, we 23 applied a similar strategy; instead of utilizing common alleles, we used alleles associated with increased or decreased COVID-19 severity in both EUR and AFR groups (identified protective and risk alleles). We found the overall number of peptides that protective and risk alleles present differed, 46 vs. 151 in the EUR ancestry group and 248 vs. 206 in the AFR ancestry group. Due to the presentation difference in class I and class II MHC, the number could not simply explain the various disease outcome of different ancestry origins. In addition, the percentage of predicted stronger binders of protective and risk alleles is approximately 41% vs. 30% in the EUR ancestry group and 25% vs. 21% in the AFR ancestry group. The lesser portions to be presented in protective alleles in the AFR ancestry group might become an issue of ineffective immunity activation. Meanwhile, we observed some differences in the regions to be presented of protective and risk alleles, and we also observed that the E protein is only presented by class I MHC risk alleles in the EUR group, whereas it is presented by class II MHC protective alleles in the AFR group. This suggests that besides well-known RBD, certain regions of SARS-CoV-2 are also immunogenic, and peptides with insufficient immunogenicity should be considered for exclusion when developing vaccines or drugs for specific affected groups.

In conclusion, this study provides the first insight of group analysis regarding the effects of SARS-CoV-2 infection in different ancestry origins from the same region, including the susceptibility and disease severity. Due to the limited sample size, further validation is needed by in vitro experiment. This study demonstrated that the contributions of genetic factors and comorbidities are helpful in identifying potential severe COVID-19 cases.

The authors declare no competing financial interests. 25 

YS isolated genomic DNAs and conducted the imputation. PJ and SR obtained patient samples.

BK and CJL performed the GWAS analyses. YS, DO, and CN conceptualized the study, performed data analysis, and prepared the manuscript. All authors have read and approved the final manuscript.

CQN is supported financially in part by PHS grants DE028544 and DE028544-02S1 from the National Institute of Dental and Craniofacial Research. We thanked Ms. Maria Cecilia Lopez from the UF Genetics Institute for performing the GWAS assays. We appreciated Dr. Patrick Concannon at the UF Genetics Institute for the insightful discussion. Tables   Table 1 Significant 

SARS-CoV-2: Structure, Biology, and Structure-Based Therapeutics Development

COVID-19 illness in native and immunosuppressed states: A clinicaltherapeutic staging proposal

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

Gender Difference Is Associated With Severity of Coronavirus Disease 2019 Infection: An Insight From a Meta-Analysis

Factors associated with COVID-19-related death using OpenSAFELY

Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19 -Georgia

Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019

Genetic mechanisms of critical illness in COVID-19

AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci

Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity

Human leukocyte antigen susceptibility map for severe acute respiratory syndrome coronavirus 2

A bioinformatic prediction of antigen presentation from SARS-CoV-2 spike protein revealed a theoretical correlation of

Mexican population: An ecological approach

… of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide

HLA class I genotypes customize vaccination strategies in immune simulation to combat COVID-19

Population Difference in Allele Frequency of HLA-C*05 and Its Correlation with COVID-19 Mortality

HLA and AB0 Polymorphisms May Influence SARS-CoV-2 Infection and COVID-19 Severity

Human Leukocyte Antigen Complex and Other Immunogenetic and Clinical Factors Influence Susceptibility or Protection to SARS-CoV-2 Infection and Severity of the Disease Course

Association between the HLA genotype and the severity of COVID-19 infection among South Asians

Initial Whole Genome Sequencing and Analysis of the Host Genetic Contribution to COVID-19 Severity and Susceptibility

PLINK: a tool set for whole-genome association and population-based linkage analyses

Variants at multiple loci implicated in both innate and adaptive immune responses are associated with Sjögren's syndrome

Principal components analysis corrects for stratification in genome-wide association studies

Next-generation genotype imputation service and methods

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

PP2A as the Main Node of Therapeutic Strategies and Resistance Reversal in Triple-Negative Breast Cancer

PPP2R2B hypermethylation causes acquired apoptosis deficiency in systemic autoimmune diseases

PPP2R2B downregulation is associated with immune evasion and predicts poor clinical outcomes in triple-negative breast cancer

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

The NGL family of leucine-rich repeat-containing synaptic adhesion molecules

LRRC4, a putative tumor suppressor gene, requires a functional leucine-rich repeat cassette domain to inhibit proliferation of glioma cells in vitro by modulating the extracellular signal-regulated kinase/protein kinase B/nuclear factor-kappaB pathway

Novel Therapy for Glioblastoma Multiforme by Restoring LRRC4 in Tumor Cells: LRRC4 Inhibits Tumor-Infitrating Regulatory T Cells by Cytokine and Programmed Cell Death 1-Containing Exosomes

Trends in Racial and Ethnic Disparities in COVID-19 Hospitalizations, by Region -United States

Genomewide Association Study of Severe Covid-19 with Respiratory Failure

Genomewide analysis in 756,646 individuals provides first genetic evidence that ACE2 expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease

Genetic risk factors and Covid-19 severity in Brazil: results from BRACOVID Study

Mapping the human genetic architecture of COVID-19

Host genetic factors of COVID-19 susceptibility and disease severity in a Thai population

Distribution of HLA allele frequencies in 82 Chinese individuals with coronavirus disease-2019 (COVID-19)

Association of HLA-B22 serotype with SARS-CoV-2 susceptibility in Hong Kong Chinese patients

DRB1*0402 may influence arthritis by promoting naive CD4+ T-cell differentiation in to regulatory T cells

Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals

A COVID-19 peptide vaccine for the induction of SARS-CoV-2 T cell immunity

Ancestral origins are associated with SARS-CoV-2 susceptibility and protection in a

Supplementary