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. 2021 Mar 17;24(4):102322. doi: 10.1016/j.isci.2021.102322

Common variants at 21q22.3 locus influence MX1 and TMPRSS2 gene expression and susceptibility to severe COVID-19

Immacolata Andolfo 1,2, Roberta Russo 1,2, Vito Alessandro Lasorsa 1,2, Sueva Cantalupo 1,2, Barbara Eleni Rosato 1,2, Ferdinando Bonfiglio 3, Giulia Frisso 1,2, Pasquale Abete 4, Gian Marco Cassese 4, Giuseppe Servillo 5, Gabriella Esposito 1,2, Ivan Gentile 6, Carmelo Piscopo 7, Romolo Villani 8, Giuseppe Fiorentino 9, Pellegrino Cerino 10, Carlo Buonerba 10, Biancamaria Pierri 10,11, Massimo Zollo 1,2, Achille Iolascon 1,2, Mario Capasso 1,2,12,∗
PMCID: PMC7968217  PMID: 33748697

Summary

The established risk factors of coronavirus disease 2019 (COVID-19) are advanced age, male sex, and comorbidities, but they do not fully explain the wide spectrum of disease manifestations. Genetic factors implicated in the host antiviral response provide for novel insights into its pathogenesis.

We performed an in-depth genetic analysis of chromosome 21 exploiting the genome-wide association study data, including 6,406 individuals hospitalized for COVID-19 and 902,088 controls with European genetic ancestry from the COVID-19 Host Genetics Initiative. We found that five single nucleotide polymorphisms within TMPRSS2 and near MX1 gene show associations with severe COVID-19. The minor alleles of the five single nucleotide polymorphisms (SNPs) correlated with a reduced risk of developing severe COVID-19 and high level of MX1 expression in blood.

Our findings demonstrate that host genetic factors can influence the different clinical presentations of COVID-19 and that MX1 could be a potential therapeutic target.

Subject areas: Genetics, Genomics

Graphical abstract

graphic file with name fx1.jpg

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Highlights

  • •

    Genetic analysis was performed on 7,970 individuals hospitalized for COVID-19

  • •

    Five SNPs within TMPRSS2/MX1 locus (chr.21) are associated with severe COVID-19

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    The minor alleles of the five SNPs correlated with high level of MX1 expression in blood

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    MX1 could be a potential therapeutic target in patients with COVID-19

Genetics; Genomics

Introduction

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused so far more than over 2.5 million deaths (https://covid19.who.int/). The coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2, is associated with diverse clinical presentations, ranging from asymptomatic or mildly symptomatic infections to respiratory failure and death (Bellani et al., 2021; Grasselli et al., 2020, 2021; Richardson et al., 2020). Advanced age is an established risk factor, as well as male sex and comorbidities such as hypertension and diabetes (Zhou et al., 2020). Since these risk factors do not fully explain the wide spectrum of disease manifestations, dissecting the genetics of the host response to SARS-CoV-2 infection may provide novel insights into its pathogenesis (Anastassopoulou et al., 2020).

A genome-wide association study (GWAS) (Ellinghaus et al., 2020) identified two susceptibility loci of severe COVID-19: the first locus on chromosome 3 harbors multiple genes (SLC6A20, LZFTL1, CCR9, CXCR6, XCR1, FYCO1) that could be functionally implicated in COVID-19 pathology; the second on chromosome 9 that defines the ABO blood groups (Ellinghaus et al., 2020). Other very recent papers reported the results from the analysis of two large independent GWASs that validated the two previous risk loci and found novel risk variants at chromosome 19p13.3, 12q24.13, and 21q22.1 associated with severe COVID-19 (Pairo-Castineira et al., 2021; Shelton et al., 2020).

Two whole-exome sequencing studies showed that inactivating rare mutations in genes belonging to the type I interferon pathway predispose to life-threatening COVID-19 pneumonia (van der Made et al., 2020; Zhang et al., 2020). Additionally, preliminary results on a small set of Italian cases suggest that coding variants in TMPRSS2 and PCSK3 may contribute to the variability in infection susceptibility and severity (Latini et al., 2020).

In our previous opinion article, based on the analysis of allele frequencies across different populations and expression quantitative trait loci (eQTLs) data, we hypothesized that common variants on chromosome 21 near TMPRSS2 and MX1 genes may be genetic risk factors associated with the COVID-19 different clinical manifestations (Russo et al., 2020). Both TMPRSS2 and MX1 are involved in the host response to SARS-CoV-2 infection. ACE2 is the main entry receptor for SARS-CoV-2 (Wang et al., 2020). Entry depends on the binding of the surface unit S1 of the spike (S) protein of the virus to the receptor. SARS-CoV-2 engages ACE2 as the entry receptor and employs the host cellular TMPRSS2 for S-protein priming (Hoffmann et al., 2020b; Matsuyama et al., 2010). Particularly, binding of SARS-CoV-2 S-protein with ACE2 receptor is then followed by host TMPRSS2-mediated cleavage of the viral S-protein. This process, defined as priming, involves cleavage of the S-protein at S1/S2 and S2 sites which is essential for the viral fusion with the host cell membrane before entry into the cell (Hoffmann et al., 2020b; Matsuyama et al., 2020). SARS-CoV-2 can use other proteases such as cathepsin B/L for S-protein in the absence of TMPRSS2 receptors. However, in the lungs (the primary organ for SARS-CoV-2 infection), cathepsin B/L cannot substitute for TMPRSS2 protease activity as the latter is indispensable for viral entry as observed for SARS-CoV and MERS-CoV (Hoffmann et al., 2020a). MX1 is an interferon-α/β inducible gene that encodes a guanosine triphosphate metabolizing protein involved in the cellular antiviral response (Ciancanelli et al., 2016).

In this study, to further support our hypothesis, we exploited GWAS meta-analysis data from the COVID-19 Host Genetics Initiative (COVID-19 Host Genetics Initiative, 2020) and performed an in-depth genetic analysis of chromosome 21 using summary statistics where common variants at this chromosome were associated with severe COVID-19 at the genome-wide significance level (p ≤ 5 × 10−8). Using the cohort of 908,494 subjects with European origins, we found five SNPs at the TMPRSS2/MX1 locus showing suggestive association with the disease. All five SNPs replicated the association in two independent cohorts of Asian subjects, whereas two SNPs confirmed the association in African and one SNP in the Italian cohort. Significant eQTLs signals were found for the MX1 gene in blood.

Results

TMPRSS2/MX1 locus is associated with severe COVID-19

To prove that common variants at TMPRSS2/MX1 (21q22.3) locus may affect the susceptibility to severe COVID-19 onset, we analyzed the summary statistics of a large available GWAS dataset released by the COVID-19 Host Genetics Initiative (COVID-19 Host Genetics Initiative, 2020). The data set includes 6,406 hospitalized cases and 902,088 controls with European ancestry (Table S1). A region on chromosome 21 appears to be significantly associated with severe COVID-19 at the genome-wide level (https://www.covid19hg.org/results/) as also demonstrated in a recently published GWAS study (Pairo-Castineira et al., 2021). To investigate whether more than one association signals may exist at chromosome 21, we selected 74 SNPs showing a p ≤ 1 × 10−5 and we identified 3 independent loci among them (Table S2). The most significant signal was represented by rs13050728 (p = 2.76 × 10−12, OR = 0.83, Figure 1A) that maps within the INFRA2 gene. The other two signals showed a suggestive significance level (p ≤ 1 × 10−5) and were tagged by rs111783124 (p = 2.39 × 10−6, OR = 1.17, Figure 1B) and rs3787946 (p = 2.73 × 10−6, OR = 0.87, Figure 1C), respectively. The rs3787946 maps in an intronic region of TMPRSS2 and the first closest gene was MX1 (Figure 1C); herein, we named this locus as “TMPRSS2/MX1”. An in-depth inspection of the TMPRSS2/MX1 locus showed that 13 SNPs were in linkage disequilibrium (LD) with the lead rs3787946 (r2 > 0.8, Table 1) and that the 5 most significant SNPs (p values ranging from 2.7 × 10−6 to 5.8 × 10−6, Table 1) were in strong LD with each other (r2≥0.90, Figure S1). The other 9 SNPs showed an LD with the lead SNP rs3787946 ranging from 0.8 to 0.9 and p values ranging from 6 × 10−4 to 0.04 (Table 1). We then sought to replicate the associations of the 14 SNPs in three independent cohorts of cases and controls of GenOMMIC GWAS (Pairo-Castineira et al., 2021) with non-European ancestry. All the 11 available SNPs replicated in the east asian (EAS) population; the top five SNPs replicated in the South Asian (SAS) ancestry population, whereas two of five SNPs in the African (AFR) one (Table 1). By using the TaqMan assay, we typed the rs12329760 variant in samples from 226 hospitalized COVID-19 patients (Table S3) and 1848 controls from Southern Italy collected in our Institute. An additional Italian cohort of 1915 controls and 770 cases, typed for rs12329760 by whole-exome sequencing, was obtained from the Network for Italian Genomes (NIG) database (Daga et al., 2021). After combining the two cohorts, we confirmed the minor allele as a protective factor against the aggressive form of the disease (Table 2, ORallele = 0.89, Pallele = 0.07; ORdominant = 0.57, p = 0.01; ORCCvsTT = 0.57, p = 0.01). The results of our case-control study suggest that the protective effect against the severity of COVID-19 is mainly due to the TT genotype.

Figure 1.

Figure 1

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Regional association plots of the SNPs at three independent association signals of chromosome 21

Plots were generated using LocusZoom. Y axes represent the significance of association (−log10 transformed p values) and the recombination rate. SNPs are color-coded based on pairwise linkage disequilibrium (r2) with indicated lead SNPs: rs13050728 (A), rs111783124 (B) and rs3787946 (C).

Table 1.

Associations of SNPs at TMPRSS2/MX1 risk locus in linkage disequilibrium with the lead rs3787946 in different populations and prioritization scores

RS number EA OA MAF r2 OR P_EUR OR P_EAS OR P_SAS OR P_AFR aRegion score aTSS score bPredicted function bScore cCombined score
rs3787946 C G 0.23 1.00 0.87 2.73 × 10−6 0.63 0.026 0.71 0.02 0.74 0.07 0.16 0.29 INTRONIC 2 6
rs9983330 G A 0.23 0.91 0.88 3.12 × 10−6 0.54 0.004 0.73 0.04 0.79 0.16 0.31 0.64 REGULATORY 4 26
rs12329760 T C 0.24 0.90 0.88 3.13 × 10−6 0.64 0.029 0.76 0.08 0.78 0.14 0.32 0.41 MISSENSE 7 23
rs2298661 A C 0.23 0.99 0.88 4.51 × 10−6 0.63 0.030 0.67 0.01 0.60 0.01 0.18 0.35 INTRONIC 2 9
rs9985159 T C 0.23 0.98 0.88 5.80 × 10−6 0.61 0.018 0.75 0.06 0.98 0.89 0.16 0.46 INTRONIC 2 15
rs2298660 T C 0.20 0.82 0.88 0.001 NA NA NA NA NA NA 0.12 0.28 INTRONIC 2 4
rs7364088 A G 0.26 0.84 0.91 0.002 NA NA NA NA NA NA 0.19 0.23 INTRONIC 2 6
rs2298663 T C 0.25 0.87 1.08 0.005 1.49 0.052 1.12 0.40 0.94 0.66 0.26 0.37 REGULATORY 4 15
rs2094881 C T 0.25 0.87 1.08 0.005 1.47 0.058 1.10 0.47 0.93 0.60 0.29 0.26 REGULATORY 4 13
rs8131649 T C 0.25 0.85 0.92 0.007 0.64 0.035 0.90 0.46 1.01 0.93 0.26 0.35 REGULATORY 4 12
rs8134203 T C 0.26 0.85 1.08 0.007 1.49 0.058 1.09 0.54 0.91 0.50 0.26 0.41 REGULATORY 4 17
rs8134216 T C 0.26 0.85 1.08 0.007 1.54 0.038 1.11 0.43 0.91 0.49 0.28 0.4 REGULATORY 4 19
rs2104810 A G 0.26 0.85 1.08 0.008 1.54 0.040 1.10 0.47 0.90 0.48 0.23 0.35 REGULATORY 4 11
rs8131648 C T 0.26 0.85 1.07 0.036 NA NA NA NA NA NA 0.33 0.42 REGULATORY 4 26
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In bold the SNPs that replicated in at least one cohort.

EA: Effect Allele; OA: Other Allele; EUR: European; EAS: East Asian; SAS: South Asian; AFR: African; ITA: Italian; MAF: minor allele frequency; OR: odds ratio.

a

Scores from GWAVA predictor tool.

b

Scores from CADD predictor tool.

c

GWAVA and CADD scores were ranked from the smallest to largest and the obtained values were summed.

Table 2.

Association of rs12329760 SNP with severe COVID-19 in Italian population

Genotype SI cases
n = 226
 SI controls
n = 1848
 NIG cases
n = 770
 NIG controls
n = 1915
 All cases
n = 996
 All controls
n = 3763
 PSI OR (CI: 95%) PNIG OR (CI: 95%) PAll OR (CI: 95%)
n % n % n % n % n % n %
CC 164 72.6 1274 68.9 532 69.1 1289 67.3 696 69.9 2563 68.1 – – –
CT 57 25.2 497 26.9 220 28.6 554 28.9 277 27.8 1051 27.9 0.47 0.89 (0.64–1.22) 0.68 0.96 (0.79–1.15) 0.71 0.97 (0.83–1.13)
TT 5 2.2 77 4.2 18 2.3 72 3.8 23 2.3 149 4.0 0.14 0.50 (0.20–1.26) 0.06 0.60 (0.35–1.02) 0.01 0.57 (0.36–0.89)
Allele
C 385 85.2 3045 82.4 1284 83.4 3132 81.8 1669 83.8 6177 82.1 – – –
T 67 14.8 651 17.6 256 16.6 698 18.2 323 16.2 1349 17.9 0.14 0.81 (0.62–1.07) 0.16 0.89 (0.76–1.04) 0.07 0.89 (0.78–1.01)
Dominant
CC/CT 221 97.8 1771 95.8 752 97.7 1843 96.2 973 97.7 3614 96.0 – – –
TT 5 2.2 77 4.2 18 2.3 72 3.8 23 2.3 149 4.0 0.15 0.52 (0.20–1.30) 0.06 0.61 (0.36–1.03) 0.01 0.57 (0.37–0.89)
Recessive
CC 159 70.4 1274 68.9 532 69.1 1289 67.3 691 69.4 2563 68.1 – – –
CT/TT 62 27.4 574 31.1 238 30.9 626 32.7 300 30.1 1200 31.9 0.26 0.84 (0.61–1.14) 0.37 0.92 (0.76–1.10) 0.28 0.92 (0.79–1.07)
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NIG, Network for Italian Genomes; OR, odds ratio; CI, confidence interval; SI, Southern Italy.

In bold are highlighted the statistically significant results.

SNPs at TMPRSS2/MX1 locus are enriched in regulatory regions active in the thymus

We tested if the 14 SNPs (Table 1) and their proxy SNPs (r2 > 0.8) were significantly over-represented in active enhancers and promoters in multiple cell types and tissues by using HaploReg v4.1. These SNPs were enriched in the regulatory regions of several tissues (Table S4) but the best enrichment was found in induced pluripotent stem cells and thymus (Figure 2A).

Figure 2.

Figure 2

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Enrichment of SNPs in regulatory regions and eQTL analyses

The statistically significant fold enrichments (p < 0.05 after Bonferroni correction) of SNPs in regulatory DNA regions active in different tissues are shown (A). eQTL violin plots between genotypes of rs3787946 (B) and rs3787946 (C) with MX1 and TMPRSS2 expression from the Genotype-Tissue Expression (GTEx). The significance threshold adjusted for multiple comparisons is equal to 0.000055.

Functional role of the most significant SNPs at TMPRSS2/MX1 locus

We then investigated the predicted functional role of the 14 SNPs by GWAVA and CADD tools. We found that two of the five most significant SNPs, i.e. rs9983330 and rs12329760, showed the first (combined score = 26) and second (combined score = 23) most significant score (Table 1). The rs12329760 was classified as a coding variant (p.Val197Met) localized in the exon 6 of the TMPRSS2 gene and was predicted to be pathogenic (PolyPhen-2 = probably damaging and SIFT = deleterious).

The most significant disease-associated SNPs are eQTLs for MX1 in blood

We verified if the top five SNPs (Table 1) might cause gene expression alterations interrogating the GTEx portal for all the common variants within TMPRSS2/MX1 locus. We found that all the top five SNPs had eQTL signals for MX1 exclusively in blood tissue. Particularly, the minor alleles of these SNPs correlated with higher expression of MX1 compared to the major alleles (Figures 2B and S2A). Of note, all the other SNPs, except for rs2298660, did not have eQTL signals for MX1 in the blood (Table S5). The two SNPs rs12329760 and rs2298660 were confirmed as eQTLs for MX1 in the blood (p = 1.79 × 10−6 and 2.8 × 10−6, minor alleles correlated with a higher expression compared to the major alleles) by interrogation of another independent publicly available data set (Westra et al., 2013). TMPRSS2 is highly expressed in lung (Russo et al., 2020), so we investigated if the top five SNPs were eQTLs for TMPRSS2 in lung tissues at a nominally statistically significant level (p ≤ 0.05). We found that the minor alleles of four out of five SNPs correlated with lower expression of TMPRSS2 compared to the major alleles (Figures 2C and S2B). Notably, rs12329760 is also an eQTL for TMPRSS2 in osteoblasts treated with dexamethasone (Grundberg et al., 2011).

Discussion

Despite the substantial advances made in recent months in the field of SARS-CoV-2 infection, the major question remains about the identification of the factors that modulate the variable clinical spectrum of COVID-19.

Host genetic risk factors are emerging as a potential explanation for the clinical heterogeneity of COVID-19 and are also crucial to find new druggable therapeutic targets (Asselta et al., 2020; Beck and Aksentijevich, 2020; Benetti et al., 2020; Pairo-Castineira et al., 2021; Singh et al., 2020). The main host cell entry factors of SARS-CoV-2 are ACE2 and TMPRSS2 (Asselta et al., 2020; Benetti et al., 2020). The spike (S) glycoprotein of the virus binds to the ACE2 making it essential for the entry of the virus into the host cell. S-protein priming by the serine protease TMPRSS2 allows the fusion of viral and cellular membranes, resulting in virus entry and replication in the host cells (Singh et al., 2020). TMPRSS2 is emerging as a host cell factor that is critical for SARS-CoV-2 infection (Hoffmann et al., 2020b).

In our previous study, we hypothesized that common variants at chromosome 21, driving TMPRSS2 and MX1 expression, might have a mild-to-moderate effect on the susceptibility to SARS-CoV-2 infection. Particularly, genetic variants associated with reduced TMPRSS2 and elevated MX1 expression might confer less individual susceptibility to SARS-CoV-2 infection and favor a better outcome (Russo et al., 2020). Here, to further support our hypothesis, we exploited GWAS data of a cohort of 908,494 subjects with European origins from the COVID-19 Host Genetics Initiative (COVID-19 Host Genetics Initiative, 2020) and performed an in-depth genetic analysis of chromosome 21. We identified five common variants (rs3787946, rs9983330, rs12329760, rs2298661, and rs9985159) at locus 21q22.3 within TMPRSS2 and near the MX1 gene that showed suggestive associations with severe COVID-19. In particular, we found that the alleles with minor frequency were less recurrent among hospitalized patients when compared to the control individuals, suggesting their protective role against the progression of the disease. Interestingly, all five SNPs were replicated in two cohorts of Asian origin, whereas two SNPs replicated in a case series of African ancestry. Additionally, we replicated the association of the rs12329760 SNP in an independent case-control cohort of Italian origin. As “proof of concept”, the rs12329760 SNP was also detected in recent studies (Hou et al., 2020; Vargas-Alarcon et al., 2020). It was demonstrated that the SNP, in addition to its eQTL role, decreased the stability of the protein, which might impede viral entry (Vishnubhotla et al., 2020); moreover, in silico analysis demonstrated that it created a de novo pocket protein (Paniri et al., 2020). These results confirm 21q22.3 as a novel susceptibility locus to unfavorable outcome of COVID-19. Furthermore, molecular mechanisms underlying this genetic predisposition may be common among individuals with different ethnicity.

The results from our enrichment analysis for regulatory genomic regions suggested that the identified SNPs and other proxy SNPs located at 21q22.3 locus can be associated with different outcomes of COVID-19 by altering DNA elements that regulate the transcription of MX1 and likely of other genes relevant to the thymus functions. The thymus plays a significant role in the regulation of adaptive immune responses. The effect of aging on the thymus and immune senescence is well established, and the resulting inflammaging is found to be implicated in the development of many chronic diseases (Gunes et al., 2020; Kellogg and Equils, 2020). Both aging and diseases of inflammaging are associated with severe COVID-19, and a dysfunctional thymus may be implicated in the unfavorable outcome of disease (Gunes et al., 2020; Kellogg and Equils, 2020). Of note, MX1 plays an important role in the thymus as part of the innate antiviral immune response. Indeed, it is exclusively expressed after engagement of the type I interferon receptor by interferon-α/β in normal fetal and post-natal human thymus, but not in the periphery. The highest level of MX1 is properly found in mature thymocytes (Colantonio et al., 2011).

The five SNPs here identified had eQTL signals for MX1 exclusively in blood tissue. Particularly, the minor allele of these SNPs correlated with higher expression of MX1 and associated with a minor risk of developing severe COVID-19. These results support the evidence that MX1 can play a relevant role in determining less severe forms of disease and are in line with a recent study that suggests MX1 as an antiviral effector against SARS-CoV-2 (Bizzotto et al., 2020). Indeed, the expression of MX1 was found to be high in SARS-CoV-2 positive subjects, negatively correlated with age, and independently associated with increased viral load (Bizzotto et al., 2020). MX1 is part of the antiviral response induced by type I and III interferons (Zav'yalov et al., 2019). Inactivating mutations in genes belonging to type I interferon pathway and the consequently decreased levels of proteins have been shown to occur in patients with severe COVID-19 (Zhang et al., 2020).

Of note, within the region on chromosome 21, significantly associated with severe COVID-19 at the genome-wide level, the most significant signal was represented by rs13050728 that maps within the INFRA2 gene. Particularly, INFRA2 gene encodes for the type I membrane protein that forms the interferon-α/β receptor, involved in the canonical host antiviral signaling mediators (Duncan et al., 2015), so associated with interferon signaling like MX1. The SNP rs13050728 was previously identified as lead variant from the meta-analysis of overlapping SNPs between GenOMICC, The COVID-19 Host Genetics Initiative and 23andMe studies and its allele C was reported to reduce the odds of severe COVID-19 as associated with an increased expression of IFNAR2 (Pairo-Castineira et al., 2021). These findings, along with ours, further strength the protective role of IFN pathway against severe COVID-19.

We also report that the minor allele of four of the top five SNPs might reduce the expression of TMPRSS2 in lung tissues. In particular, the rs12329760 coding variant (p.Val197Met) is predicted to decrease the TMPRSS2 protein stability and ACE2 binding, thus decreasing virus entry into the cells (Vishnubhotla et al., 2020). Of note, this variant was recently found to be less frequent among Chinese patients with critical COVID-19 disease (Wang et al., 2020). Additionally, it correlates with lower expression of TMPRSS2 in osteoblast treated with dexamethasone (Grundberg et al., 2011), a drug currently used to inhibit an excessive inflammation response (Group et al., 2020). Together, these data suggest that even the functions of TMPRSS2 may be affected by the occurrence of protective variants against severe COVID-19.

Finally, we want to point out that our findings highlight the effectiveness of investigating other independent (putative) risk loci, when they do not pass genome-wide significance levels. These loci, usually overlooked in extensive meta-analysis and multi-cohorts efforts, might indeed contain important genetic variants associated with severe COVID-19 and map genes relevant to the pathogenesis of this disease. We then encourage post-GWAS genetic (re)analyses using multiple data sources to unravel novel COVID-19 risk loci and possible insights on the underlying biology.

In conclusion, our results provide evidence that common variants, regulating the expression of MX1, can predispose to the risk of developing severe COVID-19. Unraveling the role of regulatory variants at the TMPRSS2/MX1 locus could represent an important starting point for the treatment of COVID-19.

Limitations of the study

The data on eQTLs related to TMPRSS2 must be interpreted with caution as these eQTL signals in the lung (p = 0.019) do not pass the GTEx significance threshold adjusted for multiple comparisons (0.000055). Additional studies are required to further verify the role of genetic variants at TMPRSS2/MX1 locus in modulating the TMPRSS2 expression. Furthermore, the statistical approach adopted in this study did not include multivariate analyses to take into account confounding factors. Although this limitation does not affect the robustness of the presented genetic associations as replicated in multiple independent cohorts, we believe that future studies will help to better define the effect of genetic variants at TMPRSS2/MX1 locus on the clinical subgroups of COVID-19 disease; for instance, performing association analyses on patients stratified by disease aggressiveness or controlled for comorbidities in larger cohorts.

Methods

All methods can be found in the accompanying transparent methods supplemental file.

Resource availability

Lead contact

Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Prof. Mario Capasso, mario.capasso@unina.it.

Material availability

This study did not generate nor use any new or unique reagents.

Data and code availability

Manhattan plot and QQ plot of the results from the large GWAS “The COVID-19 Host Genetics Initiative website” are available at the website (https://www.covid19hg.org/results/). The 770 hospitalized COVID-19 cases and 1915 controls typed for rs12329760 by whole-exome sequencing were retrieved from the web database Network for Italian Genomes (NIG) available at the website (http://nigdb.cineca.it/index.php).

Prediction of the functional impact of 14 SNPs at TMPRSS2/MX1 locus was assessed by Genome Wide Annotation of VAriants (GWAVA) tool available at the website (https://www.sanger.ac.uk/sanger/StatGen_Gwava) and by Combined Annotation Dependent Depletion (CADD) tool at (https://cadd.gs.washington.edu/).

The Blood eQTL Browser is available at (https://www.genenetwork.nl/bloodeqtlbrowser/).

Acknowledgments

This study was supported by the project “CEINGE TASK-FORCE COVID19”, code D64I200003800 by Regione Campania for the fight against Covid-19 (DGR n. 140 del 17 Marzo 2020). This manuscript has been released as a pre-print at https://www.medrxiv.org/content/10.1101/2020.12.18.20248470v1.

The authors thank Roberta Campochiaro for her useful help in data management and analysis.

Author contributions

I.A., R.R., and M.C. designed and conducted the study, and prepared the manuscript; M.C., V.A.L., and F.B. analyzed the data; B.E.R. sampled genomic DNA from COVID-19 patients; S.C. genotyped COVID-19 patients and in-house controls; G.F., A.P., G.M.C., G.S., G.E., I.G., C.P., R.V., G.P., P.C., C.B., and B.P. cared for COVID-19 patients; M.Z. and A.I. provided a critical review of the manuscript. All the authors read and approved the final manuscript.

Declaration of interests

The authors declare that there are no competing interests.

Published: April 23, 2021

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2021.102322.

Supplemental information

Document S1. Transparent methods, Figures S1 and S2, and Tables S1–S5
mmc1.pdf (989.5KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Transparent methods, Figures S1 and S2, and Tables S1–S5
mmc1.pdf (989.5KB, pdf)

Data Availability Statement

Manhattan plot and QQ plot of the results from the large GWAS “The COVID-19 Host Genetics Initiative website” are available at the website (https://www.covid19hg.org/results/). The 770 hospitalized COVID-19 cases and 1915 controls typed for rs12329760 by whole-exome sequencing were retrieved from the web database Network for Italian Genomes (NIG) available at the website (http://nigdb.cineca.it/index.php).

Prediction of the functional impact of 14 SNPs at TMPRSS2/MX1 locus was assessed by Genome Wide Annotation of VAriants (GWAVA) tool available at the website (https://www.sanger.ac.uk/sanger/StatGen_Gwava) and by Combined Annotation Dependent Depletion (CADD) tool at (https://cadd.gs.washington.edu/).

The Blood eQTL Browser is available at (https://www.genenetwork.nl/bloodeqtlbrowser/).

Articles from iScience are provided here courtesy of Elsevier

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