GWAS methodology can identify highly pathogenic variants of the COVID-19

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Using genome-wide association studies (GWAS) methodology to analyze whole-genome sequencing data of SARS-CoV-2 mutations and COVID-19 mortality data can identify highly pathogenic variants of the virus that should be flagged for containment, according to Harvard T.H. Chan School of Public Health and MIT researchers.

Using this biostatistical methodology, the researchers pinpointed a mutation in the variant known as P.1, or Gamma, as being linked to increased mortality and, potentially, greater transmissibility, higher infection rates, and increased pathogenicity before the P.1 variant had been identified.

The team’s methodology is described online on June 23, 2021 in the journal Genetic Epidemiology.

“Based on our experience, GWAS methodology might provide suitable tools that could be used to analyze potential links between mutations at specific locations in viral genomes and disease outcome,” said Christoph Lange, professor of biostatistics at Harvard Chan School and senior author of the paper. “This could enable better real-time detection of novel, deleterious variants/new viral strains in pandemics.”

The first patients in Brazil with the P.1 variant were documented in January 2021 and within a few weeks the variant caused a spike in cases in Manaus, Brazil. The city had already been hard hit by the pandemic in May 2020, and researchers thought that the city’s residents had achieved population immunity because so many people in the area had developed antibodies for the virus during that initial wave.

Instead, P.1, which has several mutations in the spike protein the virus uses to attach to and invade a host cell, caused a second wave of infections and seemed to have higher transmissibility and be more likely to cause death than the earlier variants seen in the area.

In September 2020, several months before the first P.1 patient was documented, the Harvard Chan School and MIT team repurposed methodology used in GWAS, which are widely used to link certain genetic variations with specific diseases, to tease apart the relative pathogenicity of various SARS-CoV-2 mutations.

The team looked for links between each mutation of the SARS-CoV-2 virus’s single-stranded RNA and mortality in 7,548 COVID-19 patients. Data for the study came from the global initiative on sharing avian influenza data (GISAID) database, which contains the genetic sequence and related clinical and epidemiological data associated with SARS-CoV-2 and influenza viruses.

The researchers found one mutation—at locus 25,088bp in the virus’s genome—that alters the spike protein and was linked to a significant increase in mortality in COVID-19 patients. The team flagged the variant with this mutation, which was later identified as part of P.1.

The team’s biostatistical methodology should have broader applications beyond the P.1 variant and SARS-CoV-2, according to the researchers.

“We expect that this approach would work in similar scenarios involving other diseases, provided the quality of the data collected in public databases is sufficiently high,” said Georg Hahn, research associate and instructor of biostatistics at Harvard Chan School and co-first author of the paper.


Genetic Variants in the Study of COVID-19 Susceptibility and Severity
Variants in Genes Related to the Immune System
Human Leukocyte Antigens Gene Complex
The immune system is a complex and effective defense mechanism against pathogens, such as viruses and bacteria, mediated by cells and cytokines involved in the innate and adaptive immune responses (31). Human Leukocyte Antigens (HLA) are proteins encoded by the human MHC genes, which are the most highly polymorphic in the human genome. Individuals display between three and six different HLA alleles that present a variable distribution in the worldwide populations. The resulting HLA molecules’ variability affects the cellular immune response to peptides from human infecting-pathogens (32, 33). For instance, chronic viral infections can result if CD8+ or CD4+ T cells have difficulty identifying the HLA class I or II antigens on the cell surface or lower expression levels of the HLA molecules (34).

In patients with COVID-19, differences in the immune response of patients with mild and severe forms of the disease have been observed, including IgM and IgG levels (35). Also, a report considered the impact of the variation of the theoretical capacity for binding SARS-CoV-2 peptides to explain the HLA’s relation with the clinical heterogeneity of the disease (36). Therefore, this locus variability could explain differential risk susceptibility among populations considering the role of HLA molecules in the modulation of immune response to SARS-CoV-2 to identify risk subjects and the design of personalized therapy (37).

One study evaluated the HLA class I and II alleles in 82 Han individuals from Zhejiang with COVID-19. Authors reported that HLA-C07:29 and -B15:27 were found in a higher frequency among patients with COVID-19 than in previous analyzed controls, after correction with the Benjamini-Hochberg method. Other alleles also identified in different frequencies among compared groups, but with uncorrected tests, include HLA-C07:29, -C08:01G, -B15:27, -B40:06, -DRB104:06, and -DPB136:01 alleles, which were found more frequently among patients than in controls; and, -DRB112:02 and -DPB104:01 alleles, which were less common among individuals with COVID-19 than in the control group (38). In the Italian population, an investigation comprising 99 subjects found associated the HLA-DRB115:01, -DQB106:02, and -B27:07 alleles with COVID-19 susceptibility (39); while an ecological study strongly suggests a permissive role of HLA-C01 and B44 towards SARS-CoV-2 infection across Italy (40). Meanwhile, the HLA-A11:01, -B51:01, and -C14:02 alleles were related to the worst outcome among a Chinese population sample (41).

Regarding the severity of the disease, a study including 72 Spaniards with COVID-19 reported three HLA alleles associated with higher mortality (HLA-A11, -C01, and -DQB104) when the scores of Sequential Organ Failure Assessment (SOFA) and Acute Physiology And Chronic Health Evaluation II (APACHE II) were controlled (42). The HLA-DRB108 was correlated with mortality of COVID-19 in the Italian population, and the peptide binding prediction analyses showed that the allele was unable to bind any of the SARS-CoV-2 peptides with high affinity (43). The HLA-C*05 allele was also correlated with COVID-19 mortality in an ecological study (44).

Also, in a recent in silico analysis of the binding affinity between HLA class I molecules and all SARS-CoV-2 peptides, the HLA-B46:01 allele was identified as a vulnerability biomarker due to low predicting binding sites. In contrast, the HLA-B15:03 was considered a protector allele for showing the most significant capacity to present highly conserved SARS-CoV-2 peptides. The HLA-A25:01 and -C01:02 alleles were also related to a low predicted capacity for SARS-CoV-2 epitope presentations, whereas the highest predicted presentation capacity was observed for HLA-A02:02 and -C12:03 alleles (45). In agreement, another study using artificial neural networks identified the HLA-B46:01 and HLA-A25:01 as weakly binding alleles, while HLA-A02:02 was one of the HLA class I alleles found to present a strong binding to virus selected peptides (46). Interestingly, HLA-A02 alleles, among other class I and II alleles, were also identified as functional molecules for presenting SARS-CoV-2 peptides in a bioinformatic prediction study. In this same last report, an ecological study was also performed, and the HLA-DRB1*01 allele was found associated with COVID-19 fatality in a Mexican population; and, although the authors have addressed several limitations, the result must be taken with caution (47).

Nevertheless, other in silico analyses reported a possible association of HLA-A02:01 with increased risk for COVID-19 and a lower capacity of this allele to present SARS-CoV-2 antigens in comparison to other HLA variants (48). These results seem to be contradictory compared to those previously mentioned, in which HLA-A02 alleles were considered to have an adequate predicted capacity of antigens presentation. Therefore, the association should be taken with caution until the results of clinical studies were published.

Regarding HLA haplotypes, the study of regional frequencies for the most common Italian haplotypes reported that the HLA-A01:01-B08:01-C07:01-DRB103:01 and HLA-A02:01-B18:01-C07:01-DRB111:04 were correlated with COVID-19 incidence and mortality, suggesting risk and protection-related haplotypes, respectively (49). In an association study performed in a Sardinian population, the three-loci haplotype HLA-A30:02-B14:02-C*08:02 was more common among patients with COVID-19 (50).

Table 1 shows examples of worldwide populations where the mentioned HLA alleles are frequently found. Nevertheless, it is crucial to consider the results of a recent publication in which the relevance of the HLA alleles’ homozygosity and heterozygosity was observed. The authors evaluated the synthesis of influenza virus proteins and RNA in lymphocytes from serologically HLA-homozygous or -heterozygous donors after the cells were exposed to the virus. They found that specific HLA-A and HLA-B-homozygous lymphocytes did not synthesize influenza virus RNA or protein after virus exposure, suggesting an intrinsic resistance to influenza virus infection in homozygous but not for HLA-heterozygous cells (52).

Table 1

HLA alleles associated with SARS-CoV-2 infection susceptibility.

HLA allelesPopulations in which the allele is commonly found a
High-risk
-A*25:01Colombia Arhuaco.
-B*46:01Chinese populations, Hong Kong Chinese, Malaysia Peninsular Chinese, Singapore Chinese, Taiwan Han Chinese, Thailand Northeast, USA Chinese, Vietnam Hanoi Kinh.
 -C*01:02American Samoa, Australian Kimberly Aborigine, Chinese populations, Colombian populations, Hong Kong Chinese, Japanese populations, Malaysia Peninsular Chinese, Mexico Chihuahua Tarahumara, Mexico Hidalgo, Mezquital Valley/Otomi, Mexico Zapotec, New Caledonia, New Zealand populations, Papua New Guinea populations, South Korea, Taiwanese populations, USA Asian, USA Hawaii Okinawa, Venezuela Perja Mountain Bari, Vietnam Hanoi Kinh, Bolivia/Peru Quechua, Costa Rica populations.
Low-risk
 -A*02:02Cameroon Bamileke, Israel populations.
 -B*15:03Burkina Faso Rimaibe, Guinea Bissau.
 -C*12:03Azores Terceira Island, German populations, Greece, Italian populations, Lebanon Mixed, Pakistan Burusho, Papua New Guinea Wanigela Keapara, Poland, Portugal Azores Terceira Island, Spain populations, Sudan Mixed, China Jingpo Minority, Colombian populations.
Mortality/severity
 -A*11Myanmar, China, Thailand, Taiwan, Japan, Spain, Mexico, South Korea, Mongolia, France, United Arab Emirates, Iran.
 -B*51:01Italy North, Japan, China, Oman, Armenia, Greece, China, Saudi Arabia, Switzerland Lugano, United Arab Emirates, Portugal, USA South Dakota Lakota Sioux and North American Native, Germany, Croatia, Serbia, Mexico Sonora Seri and Chihuahua, Romania, China Guizhou Province Miao.
 -C*01China Wuhan, Japan, India Kerala Hindu Pulaya, Brazil Parana Japanese, Scotland Orkney, Thailand Northeast, South Korea, Norway Sami, Peru Arequipa Mestizo, Vietnam Hanoi, Mongolia Oold, Myanmar Mon.
 -C*05United Kingdom, England, France, Spain, Wales, Venezuela.
 -C*14:02Japan Kyoto and Osaka
 -DQB1*04Mexico populations, Norway Sami, Venezuela Zulia Maracaibo Mixed, Brazil Guarani Nandeva, Papua New Guinea Highland, Ecuador Amazonia Mixed Ancestry, USA OPTN Hispanic, Russia Siberia Chukchi, Malaysia Perak Rawa.
 -DRB1*08Taiwan, Brazil, Mexico, Chile, Sudan, Peru, Burkina Faso, Argentina, India, Japan, Venezuela, Colombia.
aRepresentative populations with reported frequencies >0.10 are included. Data from Allele Frequency Net Database http://www.allelefrequencies.net/ (51).

Cytokine Genes
The cytokine storm is a complex process that has been difficult to define and delimit. However, it refers to an immune system gone awry and an inflammatory response flaring out of control, which is associated with infectious and noninfectious diseases with a wide variety of consequences in the organism (53). As has been mentioned before, the cytokine storm plays a crucial role in severe COVID-19 cases. SARS-CoV-2 produces the activation of various immune cells (e.g., macrophages, monocytes, dendritic cells), which leads to the secretion of several cytokines, including the pro-inflammatory cytokine IL-6 (54). This cytokine plays a central role in cytokine storm with anti-inflammatory and pro-inflammatory effects by promoting T-cell proliferation and B-cell differentiation, affecting vascular disease’s hormone-like properties, lipid metabolism, insulin resistance, mitochondrial activity, neuroendocrine system, and neuropsychological behavior (55).

High levels of IL-6 can activate the coagulation pathway and vascular endothelial cells but inhibit the myocardial function (56). In severe COVID-19 patients, an increase of IL-6 levels has been observed and related to the disease’s poor prognosis (57). Several gene variants in IL6 (HGNC:6018) with differential cytokine expression and with different disorders have been reported. The rs1800795 (-174C) allele, as well as the promoter variant rs1800796 (-572C), have been associated with higher IL-6 plasma levels (58, 59) and with the risk of upper respiratory tract infections (60–62). Moreover, both IL6 variants have been related to the prognosis of different disorders such as sepsis (63), coronary heart disease (64), and diabetes (65). A third variant (rs1800797) on the IL6 promoter reported (66), and its role in studying the genetics of COVID-19 related-cytokine storm can be considered. In addition, seven variants in IL6 (rs140764737, rs142164099, rs2069849, rs142759801, rs190436077, rs148171375, rs13306435) and five variants in IL6R (rs2228144, rs2229237, rs2228145, rs28730735, rs143810642) have been predicted to alter the expression and interaction of IL6 and IL6R which can be implicated in the pathogenesis of COVID-19 and its complications (67).

Genetic variants in the regulatory regions of other cytokines genes have also been reported (68). For instance, non-synonymous variants affecting the final proteins of TGF-β and IFN-α, as well as variants modifying the transcriptional activity of TNF-α, IL-10, and IL-2, have been described (68, 69). Several of these variants have been previously related to infectious disease susceptibility, cytokine storm, and venous thrombosis. Reported variants in cytokines genes associated with those events and their frequencies are shown in Table 2.

Table 2

Frequency of allelic variants in cytokine genes associated with infectious disease susceptibility and COVID-19 manifestations.

Cytokine geneVariants studiedAllele frequency reference aRef
Infectious diseases susceptibility
IL1Brs16944
g.4490T>C
European A= 0.3499
African A= 0.5726
East Asian A= 0.4692
South Asian A= 0.6000
American A= 0.5500
(70)
IL17Ars2275913
g.4849G>C
European G= 0.6203
African G= 0.9508
East Asian G= 0.5069
South Asian G= 0.6200
American G= 0.7840
IL6rs1800795
g.4880C>G
European C= 0.4155
African C= 0.0182
East Asian C= 0.0010
South Asian C= 0.1390
American C= 0.1840
(60–62, 71)
TNFrs1800629
g.4682G>A
European G= 0.8658
African G= 0.8805
East Asian G= 0.9415
South Asian G= 0.9470
American G= 0.9310
(61, 62)
Venous thrombosis
IL1Brs1143633
g.8890G>A
European C= 0.6660
African C= 0.8260
East Asian C= 0.4613
South Asian C= 0.7480
American C= 0.7090
(72)
IL1R1rs3917332
g.102180064A>T
European A= 0.1938
African A= 0.0825
East Asian A= 0.0724
South Asian A= 0.1350
American A= 0.1540
IL1RNrs2232354
g.16866T>G
European T= 0.7962
African T= 0.9924
East Asian T= 0.9534
South Asian T= 0.7900
American T= 0.8310
Cytokine storm
IL6rs1800796
g.4481G>C
European G= 0.9523
African G= 0.8971
East Asian G= 0.2093
South Asian G= 0.6050
American G= 0.7050
(58, 59)
rs1800797
g.4456A>G
European A= 0.4076
African A= 0.0166
East Asian A= 0.0010
South Asian A= 0.1340
American A= 0.1840
(66)
FCGR2Ars1801274
g.9541A>G
European A= 0.4891
African A= 0.4743
East Asian A= 0.7222
South Asian A= 0.5810
American A= 0.5490
(73)

In an Iranian population, genotypes of IL1B (HGNC:5992) rs16944 and IL17A (HGNC:5981) rs2275913 were associated with severe influenza A/H1N1 and B cases, while the frequencies of IL10 (HGNC:5962) rs1800872 and IFNL3 (HGNC:18365) rs8099917 variants were not found different among patients and controls (70). The TNF (HGNC:11892) rs1800629 variant has also been associated with variation in the corresponding cytokine and respiratory infections (61, 62).

Regarding the risk of venous thrombosis, 18 single-nucleotide variants in IL1B (HGNC:5992), IL1RN (HGNC:6000), IL1R1 (HGNC:5993), and IL1R2 (HGNC:5994), as well as 25 haplotypes, were evaluated in a case-control study including patients with deep vein thrombosis and controls. Authors found associated the IL1B rs1143633, IL1R1 rs3917332, and IL1RN rs2232354 variants with different risks for venous thrombosis and an increased thrombotic risk for homozygous carriers of the IL1RN haplotype 5 GTGTA (rs3181052/rs419598/rs2232354/rs315952/rs315949) (72).

The Fc-gamma Receptors (FcγR) have been implicated in Fc-dependent cytokine release stimulation due to human leucocytes’ activation to secret various pro-inflammatory cytokines, as GM-CSF, IL-6, and IL-8 (75). The rs1801274 Fc fragment of IgG Receptor IIa (FCGR2A, HGNC:3616) gene was associated with severe pneumonia in patients with A/H1N1 infection. This variant produces a change of histidine to arginine at position 131 of the amino acid sequence. The frequency of homozygous individuals for p.His131 genotype was found to be increased in severe pneumonia patients (36.6%) in comparison to household contacts who did not develop respiratory illness (13.2%). Another gene reported in this study was the RPA Interacting Protein (RPAIN, HGNC:28641) and Complement C1q Binding Protein (C1QBP, HGNC:1243) (73).

Also, several in vivo and in vitro studies of influenza virus infection with lung damage due to cytokine storm have found a strong up-regulation on cytokine gene expressions, such as IL6, IL8 (CXCL8, HGNC:6025), CCL2 (HGNC:10618), CCL5 (HGNC:10632), CXCL9 (HGNC:7098), and CXCL10 (HGNC:10637); as well as a differential expression of inflammasome genes NLRP3 (HGNC:16400) and IL1B (HGNC:5992), cytokine genes TNF and IFNB1 (HGNC:5434), and cytokine receptor genes TNFRSF1B (HGNC:11917) and IL4R (HGNC:6015) (53). An investigation found inborn errors of Toll-like receptor 3 (TLR3, HGNC:11849)– and interferon regulatory factor 7 (IRF7, HGNC:6122)–dependent type I IFN immunity related to life-threatening COVID-19 pneumonia. Although the genetic variants were only found in 3.5% of the studied patients, the results suggested that other IFN variants were probably implicated in the COVID-19 severity and the use of type I IFN as a potential therapeutic strategy in those patients (76). Likewise, a nested case-control study reported that TLR7 (HGNC:15631) deleterious variants were found in 2.1% of severely affected males and none of the asymptomatic participants, and the corresponding functional gene expression analysis showed a reduction in the TLR7 expression in patients compared with controls suggesting an impairment in type I and II IFN responses (77).

It is worth mentioning that wide inter-ethnic variability in cytokine gene variants’ frequencies (IL2, IL6, IL10, TNF, TGFB1, and IFNG) has been reported (68, 78). For instance, significant differences in IL2 (HGNC:6001) alleles’ distribution among Africans, Caucasians, and Asians have been observed. Meanwhile, high expression alleles of IL6 and IL10 (HGNC:5962) have been more frequently found in Africans, Hispanics, and Asians, than Caucasians. Besides, low expression alleles of IFNG (HGNC:5438) have been more common among Asians than Caucasians (68).

Variants in Coding Genes for Human Receptors of SARS-CoV-2
SARS-CoV-2 presents a high binding affinity to the ACE2 receptor allowing the virus’s entry to the host cell cytosol through acid-dependent proteolytic cleavage of the S protein, with a contribution of the TMPRSS2 and CTSL (21). Besides its role in SARS-CoV-2 infection, ACE2 acts as a negative regulator of the renin-angiotensin system and a facilitator of amino acid transport. The ACE2 system is a critical protective pathway against heart failure with reduced and preserved ejection fraction, including myocardial infarction and hypertension, lung disease, and diabetes mellitus. Unfortunately, the function of ACE2 is lost following the binding of SARS-CoV-2 (79).

Increased ACE2 receptor levels and the two proteases have been associated with identified risk conditions (e.g., increasing age, male gender, and smoking) of COVID-19 susceptibility and clinical outcome (21). Also, genetic variants of ACE2 (HGNC:13557) that alter its transcriptional activity have been described (e.g., rs2285666, c.439+4G>A) (80, 81). An early study found higher allele frequencies of variants (e.g., rs143695310) associated with elevated expression of ACE2 among East Asian populations, which may suggest a higher susceptibility to COVID-19 individuals from this region (82).

A recent investigation has reported that genetic determinants of the highest expression of ACE2 can be observed in South Asian and East Asian populations, while the lowest expression levels of ACE2 were observed for Africans (83). Likewise, a genetic predisposition for the lowest TMPRSS2 (HGNC:11876) expression levels was observed for Africans and the highest for East Asians. Moreover, significant differences in TMPRSS2 expression levels among males and females were reported in the study (83).

Besides, variants with potential impact on the receptor stability have been reported. For instance, three common missense changes in ACE2 (p.Asn720Asp, p.Lys26Arg, and p.Gly211Arg) were predicted to interfere with protein structure and stabilization, while other two variants (p.Leu351Val and p.Pro389His) has been predicted to interfere with SARS-CoV-2 spike protein binding (84). Likewise, a study using web-based tools reported several variants in genes that encode proteins related to the SARS-CoV-2 entry into the host cells: the already mentioned ACE2 and TMPRSS2, as well as TMPRSS11A (HGNC:27954), ELANE (HGNC:3309), and CTSL (HGNC:2537). The authors found 48 variants in these genes with possible functional consequences, and some of them were reported to be shared among specific populations (85).

Nevertheless, the association results of the receptor variants with COVID-19 susceptibility remain controversial. For instance, Hou et al. found associated ACE2 variants, such as p.Arg514Gly, in the African/African-American populations with cardiovascular and pulmonary conditions due to the alteration of the angiotensinogen-ACE2 interactions. Additionally, the authors identified variants in TMPRSS2 (e.g., p.Val160Met, rs12329760) that could explain the COVID-19 susceptibility and some complication risk factors such as cancer and male gender (86).

Meanwhile, a study with multi-scale modeling approaches in combination with sequence and phylogenetic analysis evaluated eight relevant variants located at the interaction surface of ACE2 (i.e., rs961360700, rs143936283, rs146676783, rs759579097, rs370610075, rs766996587, rs73635825, and rs781255386). These SNPs are rare variants, except for European (non-Finnish) and African populations, and none of them would disrupt this receptor’s interaction with SARS-CoV-2 proteins (87).

Finally, the implication of an ACE1 (HGNC:2707) deletion/insertion (D/I, intron 16) variant in the ACE2 expression and the COVID-19 clinical course was also proposed at the early stages of the pandemic (88). Nevertheless, later studies have reported that this variant could be related to the COVID-19 severity, but only if the patients’ hypertension status is considered (89, 90).

Variants in Other Genes Related to COVID-19 Susceptibility and Severity
In addition to immune and SARS-CoV-2 receptors’ genes, variants in genes coding other proteins related to susceptibility and severity of COVID-19 have been identified. Recently, two independent genome-wide association studies (GWAS) had been performed among European populations (Italian and Spanish) (91) and individuals from the United States and the United Kingdom (92). In both cases, an association of loci 3p21.31 and 9q34.2 with COVID-19 severity were identified.

The first study by Ellinghaus et al. reported the associations of LZTFL1 (HGNC:6741) rs11385942, at locus 3p21.31, and ABO (HGNC:79) rs657152, at locus 9q34.2, with genetic susceptibility to COVID-19 (91). Meanwhile, Shelton et al. identified several non-genetic conditions as risk factors for hospitalization, and the genetic variants LZTFL1 rs13078854 and ABO rs9411378 were associated with COVID-19 outcome severity and diagnostic, respectively (92). LZTFL1 encodes the ubiquitously expressed protein leucine zipper transcription factor-like 1, and it is strongly expressed in human lung cells (91).

Nevertheless, none of the publications can explain this gene’s role in the susceptibility or severity of COVID-19, but there are several genes nearby in the 3p21.31 locus that could plausibly be driving the association, including SLC6A20 (HGNC:30927), CCR9 (HGNC:1610), FYCO1 (HGNC:14673), CXCR6 (HGNC:16647), and XCR1 (HGNC:1625) (92).

The role of ABO in COVID-19 susceptibility and clinical manifestations has been reported in genetic and non-genetic studies. Previous reports (93–95) and GWAS (91, 92) have observed a higher risk of COVID-19 infection among individuals with A group than other blood groups and a lower susceptibility for the O group. ABO blood group has been previously associated with infection susceptibility of other diseases such as influenza, malaria, schistosomiasis, and SARS-CoV. The hypotheses that blood groups can serve as receptors and/or co-receptors for bacteria, viruses, and parasites and that those blood antigens contribute to intracellular uptake, signal transduction, or adhesion have been stated (96). Besides, the idea that natural antibodies related to blood groups could contribute to the virus’s innate immune response has been proposed. Nevertheless, the ABO groups’ precise role in the SARS-CoV-2 infection mechanism still needs to be demonstrated (92).

Wang et al. also performed a GWAS among 332 Chinese patients and pedigree analysis. The authors reported the association with COVID-19 severity of the gene locus located in TMEM189 (PEDS1, HGNC:16735)–UBE2V1 (HGNC:12494), which is involved in the IL-1 signaling pathway. In the pedigree analysis, a potential monogenic effect of loss of function variants in GOLGA3 (HGNC:4426) and DPP7 (HGNC:14892) was suggested when authors looked for rare variants in families where a differential clinical outcome was observed among siblings (41).

One more GWAS performed in 2,244 critically ill patients with COVID-19 from intensive care units in the United Kingdom found significant associations in several loci: in a gene cluster that encodes antiviral restriction enzyme activators OAS1 (HGNC:8086), OAS2 (HGNC:8087), and OAS3 (HGNC:8088); near the gene that encodes tyrosine kinase 2 (TYK2, HGNC:12440); within the gene that encodes dipeptidyl peptidase 9 (DPP9, HGNC:18648); and in the interferon receptor gene IFNAR2 (HGNC:5433) (97).

Patients with critical COVID-19 can present venous thromboembolism and/or systemic coagulopathies such as Disseminated Intravascular Coagulation (DIC) (13). This complication is characterized by the combined occurrence of activation of the extrinsic coagulation pathway and decreased activity of the protein C-protein S and Antithrombin (AT) inhibitory pathways, and it can be presented with excessive or inhibited fibrinolysis (98). DIC’s clinical and laboratory characteristics in COVID-19 are different from the typical presentation of these conditions, and a timely diagnosis is required to avoid the deterioration of pulmonary oxygen exchange (13).

In this sense, a genetic marker that could predict coagulation complications could help to start appropriate treatment. For instance, the involvement of Mannose-Binding Lectin (MBL) and MBL-associated serine protease (MASP)-1/3 in coagulation has been reported, and its deficiency has been considered as a risk factor for DIC during sepsis complication; therefore, genetic variants producing a decrease of these proteins or their activity could be positively related with coagulopathies secondary to COVID-19 (99).

Besides, other genes with risk variants for DIC have been identified. In the anticoagulant pathways, variants in protein C gene (PROC, HGNC:9451), factor V Leiden (F5, HGNC:3542), and deficiencies of AT (SERPINC1, HGNC:775) have been related to an impaired function of the coagulation. While variants in the serpin plasminogen activator inhibitor 1 (SERPINE1, HGNC:8583) could impact the encoded protein levels, which is considered one of the main inhibitors of fibrinolysis, and it is related to DIC development. Additionally, variants in fibrinogen genes that promote the pro-coagulant pathways leading to microvascular thrombi formation in various organs have been described (98).

reference link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047200/


More information: “Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain,” Genetic Epidemiology, online June 23, 2021.

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