key: cord-0732906-75ggmj4w
authors: Namkoong, H.; Edahiro, R.; Fukunaga, K.; Shirai, Y.; Sonehara, K.; Tanaka, H.; Lee, H.; Hasegawa, T.; Kanai, M.; Naito, T.; Yamamoto, K.; Saiki, R.; Hyugaji, T.; Shimizu, E.; Katayama, K.; Takahashi, K.; Harada, N.; Hiki, M.; Matsushita, Y.; Takagi, H.; Aoki, R.; Nakamura, A.; Harada, S.; Sasano, H.; Kabata, H.; Masaki, K.; Kamata, H.; Ikemura, S.; Chubachi, S.; Okamori, S.; Terai, H.; Morita, A.; Asakura, T.; Sasaki, J.; Morisaki, H.; Uwamino, Y.; Nanki, K.; Mikami, Y.; Uchida, S.; Uno, S.; Ishihara, R.; Matsubara, Y.; Nishimura, T.; Ogawa, T.; Ishiguro, T.; Isono, T.; Shibata, S.
title: Japan COVID-19 Task Force: a nation-wide consortium to elucidate host genetics of COVID-19 pandemic in Japan
date: 2021-05-18
journal: nan
DOI: 10.1101/2021.05.17.21256513
sha: 8e6d865ee5116637fee2ca20be4abd677a916b54
doc_id: 732906
cord_uid: 75ggmj4w

To elucidate the host genetic loci affecting severity of SARS-CoV-2 infection, or Coronavirus disease 2019 (COVID-19), is an emerging issue in the face of the current devastating pandemic. Here, we report a genome-wide association study (GWAS) of COVID-19 in a Japanese population led by the Japan COVID-19 Task Force, as one of the initial discovery GWAS studies performed on a non-European population. Enrolling a total of 2,393 cases and 3,289 controls, we not only replicated previously reported COVID-19 risk variants (e.g., LZTFL1, FOXP4, ABO, and IFNAR2), but also found a variant on 5p35 (rs60200309-A at DOCK2) that was significantly associated with severe COVID-19 in younger (<65 years of age) patients with a genome-wide significant p-value of 1.2 x 10-8 (odds ratio = 2.01, 95% confidence interval = 1.58-2.55). This risk allele was prevalent in East Asians, including Japanese (minor allele frequency [MAF] = 0.097), but rarely found in Europeans. Cross-population Mendelian randomization analysis made a causal inference of a number of complex human traits on COVID-19. In particular, obesity had a significant impact on severe COVID-19. The presence of the population-specific risk allele underscores the need of non-European studies of COVID-19 host genetics.

Japan COVID-19 Task Force is a nation-wide consortium to overcome COVID-19 pandemic in Japan, which was established in early 2020. Japan COVID-19 Task Force consists of >100 hospitals (red dots) led by core academic institutes (blue labels), and collected DNA, RNA, and plasma from >3,400 COVID-19 cases along with detailed clinical information.

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In this study, we enrolled unrelated 2,393 patients with COVID-19 who required hospitalization from April 2020 to January 2021, from >100 hospitals participating in the Japan COVID-19 Task Force. COVID-19 diagnoses of all cases were confirmed by physicians of each affiliated hospital based on clinical manifestations and a positive PCR test result. As for the control, we enrolled unrelated 3,289 subjects ahead of the COVID-19 pandemic who represent a general Japanese population. All the participants were confirmed to be of Japanese origin on the basis of a principal component analysis (Supplementary Table   2 .

Of the 2,393 COVID-19 cases, 990 ultimately had severe infection as defined by oxygen support, artificial respiration, and/or intensive-care unit hospitalization), while 1,391 cases had non-severe diseases. Severity information was not available for the remaining 12. As reported previously 3, 18 , the severe COVID-19 cases were relatively more aged (65.3 ± 13.9 years [mean ± SD]) and a higher proportion of males (73.9%), compared with non-severe cases (49.3 ± 19.2 years and 57.2 of males, respectively).

We conducted a GWAS of COVID-19 in a Japanese population. After applying stringent quality control (QC) filters and genome-wide genotype imputation using a population-specific reference panel of Japanese 19-21 , we obtained 13,485,123 variants with minor allele frequency (MAF) ≥ 0.001 and imputation score (Rsq) ≥ 0.5 (13,116,003, 368,566 and 554 variants for autosomal, X-chromosomal, and mitochondrial variants, respectively). As illustrated in the follow-up analysis stratified by deep clinical information at the LZTFL1 locus, several COVID-19 risk variants are expected to confer relatively larger effects in severe and younger cases than in mild (and self-reported) cases or elder cases 14 . This suggests that COVID-19 GWAS likely to have a higher statistical power when focusing on severe and younger cases 12, 16, 22 . We thus separately conducted stratified GWAS of severe COVID-19 cases (nCase = 990), younger cases (age < 65, nCase = 1,484), and their combinations (nCase = 440), as well as all the cases (nCase = 2,393), in comparisons with the controls. We selected the age of 65 as a threshold, since ages ≥65 years is defined as a aggravation risk factor in the clinical management guide of patients with COVID-19 in Japan 4 . We did not observe . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint inflation of GWAS test statistics (λGC < 1.007; Supplementary Figure 2 ), suggesting no evidence of population stratification, as well as potential biases, in our GWAS.

GWAS between all COVID-19 cases vs. controls yielded no positive signals satisfying a genome-wide significance threshold of P < 5.0 × 10 -8 (Supplementary Figure 2) 23 . By contrast, when the comparison was made between younger cases with sever COVID-19 and respective controls, where the highest prior probability of discovery of a positive association was expected, we identified a genetic locus on 5q35 that satisfied genome-wide significance (P = 1.2 × 10 -8 at rs60200309; Figure 2a) to the trisected recruitment periods of April 2020 -July 2020, August 2020 -October 2020, and November 2020 -January 2021, respectively; OR = 2.00 and P = 2.0 × 10 -8 in the allperiod meta-analysis; Supplementary Table 3 ). This allele, however, did not seem to confer any significant COVID-19 risk in elder cases (P > 0.069). These results suggest a susceptibility of patients with the rs60200309-A allele to severe COVID-19 in the Japanese population, particularly in younger cases with severe COVID-19.

We then looked up COVID-19 risk of the DOCK2 variant in different ancestries (3, 138 hospitalized COVID-19 cases vs 891,375 controls from the pan-ancestry meta-analysis available at https://rgc-covid19.regeneron.com/) 24,25 . We observed the same directional effect with a marginal association signal (OR = 1.73, 95%CI = 0.95-3.15, P = 0.072, MAFCase = 0.0025, MAFControl = 0.0008; Supplementary Table 4) . Meta-analysis of the Japanese discovery GWAS and the replication study from the pan-ancestry study yielded a genomewide significant association showing an OR = 1.97 (95%CI = 1.57-2.46, P = 1.2 × 10 -9 ;

Supplementary Table 5). We note that rs60200309 does not exist in the public summary statistics provided by the COVID-19 Host Genetics Initiative (release 5) 16 . Nevertheless, . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint given the low allele frequencies of the relevant DOCK2 allele in non-East Asian populations, further population-specific and cross-population replication studies are warranted.

Of interest, the risk allele at DOCK2 (rs60200309-A) identified in this study was common in East Asians (= 0.097) with the highest frequency in Japanese (= 0.125), less frequent in native Americans (= 0.049), but very rare in Europeans, African, and south Asians (< 0.005; from 1000 Genomes Project Phase3v5 database; Figure 2c ). When we referred to the results of WGS-based natural selection screening in Japanese 19 , the rs60200309-A allele marginally positively selected in Japanese (PSDS = 0.051). It is suggested that the rs60200309-A rapidly increased its frequency among the Japanese population during the past several thousand years. These population-specific features of the DOCK2 variant partly explain the reason why it was not identified in the previous European COVID-19 GWAS studies despite their larger sample sizes, and should provide a rationale for further accelerating COVID-19 host genetics researches on non-European populations.

. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (b) A regional association plot at the DOCK2 locus. Dots represent SNPs with colors according to linkage disequilibrium (r 2 ) with the lead SNP of rs60200309. (c) Allele frequency spectra of the rs60200309-A allele in the 1000 Genomes Project Phase3v5 database.

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We then conducted cross-population comparisons of allele frequency spectra and genetic risk of the previously reported COVID-19-associated variants 12, 16, 22 . Of the 11 associated variants evaluated in our Japanese GWAS, we replicated the associations with 7 variants (P < 0.05 in any 4 phenotypes of the case-control GWAS; LZTFL1, FOXP4, TMEM65, ABO, TAC4, DPP9, and IFNAR2; Figure 3 and Supplementary Table 6 ). For all nominally associated signals (P < 0.05), we observed same directional effects of the alleles as in Europeans. ORs for severe and younger COVID-19 cases were highest among the phenotype patterns in six of the 7 loci, confirming our strategy that focusing on such cases should efficiently highlight the host genetic risk of COVID-19.

The most significant replication was observed at the FOXP4 locus, where the risk allele was known to be more prevalent in East Asians than in Europeans, and expected to have a higher power to be detected in East Asians 16 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Odds ratios of the COVID-19-associated variants in the Japanese population are indicated.

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Given their critical impact on immune responses and contribution to host genetics of various infectious diseases, 26, 27 HLA gene variants have been investigated for their possible role in the response to COVID-19 infection with controversial discussions 28, 29 . To address this issue, we applied in silico imputation of both classical and non-classical HLA variants using the HLA reference panel of Japanese (n = 1,118) 30, 31 . After imputing the HLA variants, we did not observe association signals satisfying neither of the genome-wide significance (P < 5.0 × 10 -8 ) or HLA-wide significance thresholds (P < 0.05/2,482 variants = 2.0 × 10 -5 ; Supplementary 

ABO blood types are defined by the variants on the coding region of the ABO gene on 9q34 32 , which are pleiotropic on various complex human traits including infectious diseases (e.g., malaria resistance of blood type O 33 ). Motivated by replicated COVID-19 risk of the ABO locus in Japanese, we conducted ABO blood type-based risk analysis. 34 Among the four major ABO blood types (A, B, AB, and O with 39.0%, 21.8%, 9.5%, and 29.7% in our Japanese GWAS, respectively), the O blood type was consistently associated with a protective effect on COVID-19 in case-control phenotypes (P < 0.05), most evidently in severe and younger cases (OR = 0.73, 95%CI = 0.56-0.93, P = 0.014; Figure 4 and Supplementary Table 8 ), as reported previously 12 . we found increased risk of the AB blood type, especially in severe cases (OR = 1.41, 95%CI = 1.10-1.81, P = 0.0065 for all ages, and OR = 1.40, 95%CI = 1.00-1.94, P = 0.048 for age < 65, in comparison with the other blood types). Increased severity risk of the AB blood type was also significant when compared with the A or B blood types (OR > 1.34, P < 0.041 for all ages). To our knowledge, this is the initial study to report severe COVID-19 risk of the AB blood type. The ABO blood type distributions are heterogeneous among worldwide populations, and Japanese is the one with the highest . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint AB blood type frequency 35 , which might have provided statistical power to detect its risk on severe COVID-19 in our study.

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Odds ratios of the ABO blood types in the Japanese population are indicated.

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COVID-19 pandemic has exposed global populations to an emergent health risk. To predict individuals' risk on COVID-19-related outcomes, elucidation of the medical conditions that can affect COVID-19 susceptibility is warranted. While medical record-based epidemiological studies assessing comorbidity have identified multiple risk factors, there remains various clinical status where causal inference on COVID-19 is controversial 3 .

To make a causal inference, we applied cross-population two-sample Mendelian randomization (MR) analysis. Two-sample MR utilizes GWAS summary statistics to infer causality between correlated phenotypes. 36 In the Japanese population, MR results were contrastive between the severe COVID-19 cases and all COVID-19 cases (Figure 5 and Supplementary Table 10) . As for the severe COVID-19 cases, a causal effect was demonstrated only for obesity (P = 0.0067 and 0.0074 for all age and age < 65, respectively). By contrast, we observed causal effects of asthma (P = 0.0061 and 0.018 for all age and age < 65, respectively), UA (P = 0.019 for age < 65), and gout (P = 0.0048 and 0.0027 for all age and age < 65, respectively), while SLE (P = 0.0014 for all age) showed a protective effect.

We then looked up the MR results in Europeans by using publicly released GWAS summary statistics of COVID-19 Host Genetics Initiative (release 5) 16 . We observed significant causal inferences of obesity consistent with those in Japanese. The causal effect of obesity was observed for self-reported, hospitalized, and severe COVID-19 in Europeans (P = 8.5 × 10 -9 , 3.2 × 10 -11 , and 6.2 × 10 -6 , respectively) as previously reported 40 , while effect sizes were twice as high in hospitalized and severe COVID-19 (β > 0.398) when compared with self-reported COVID-19 (β = 0.175). Obesity is one of the major risk factors for COVID-19 severity and critical outcomes 3-5 , and our cross-population MR analysis provided evidence of causality on this link. Causal inference of decreased renal function (P = 0.043 for severe COVID-19) and

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The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint T2D (P = 0.019 for self-reported COVID-19 and P = 0.0078 for hospitalized COVID-19) was also observed in Europeans.

Our cross-population MR analysis provided several phenotypes with significant MR results observed only in Japanese (i.e., risk of asthma, UA, and gout, and protective role of SLE).

This suggests existence of populational heterogeneity in the impacts of causal links from the baseline clinical manifestations to COVID-19 susceptibility. Hyperuricemia is reported as one of the major risk factors of severe COVID-19 in Japan 18 , which is consistent with a Japanesespecific causal inference of UA and gout in the Japanese MR analysis. There exist controversial discussions on the risk of SLE patients on COVID-19 infection 38,39 . Our results suggest a possibility that genetically-determined susceptibility to SLE, and its underlying immunophenotypes, could make patients protective against COVID-19 infection.

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The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint with the largest sample size (i.e., "COVID-19 vs control" for Japanese and "Self-reported COVID-19 vs control (C2)" for Europeans) was set to be 0.1. Abbreviations of the exposure phenotypes and the detailed MR results are in Supplementary Table 10. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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In this study, we reported GWAS of COVID-19 in a Japanese population led by the Japan COVID-19 Task Force, a nation-wide consortium to battle against the COVID-19 pandemic. This is one of the initial and largest COVID-19 host genetics studies in non-European populations to date. Our study highlighted multiple genetic variants associated with the COVID-19 risk shared across populations such as LZTFL1, ABO, and FOXP4, as well as the identification of a population-specific risk variant at the DOCK2 locus. Stratified analysis of these susceptibility loci supported the expectation that host genetics of COVID-19 should be enhanced when focusing the analysis on younger cases with severe COVID-19. Rather unexpectedly, contribution of HLA variants to COVID-19 host susceptibility, if ever present, was not remarkable, compared with previous findings on other infectious diseases. As for the ABO blood type classification, we newly identified the risk of the AB blood type to severe patients 46 . Given that LoF caused by an inborn error is a key to fine-map host susceptible genes for infectious diseases 26 , DOCK2 could be considered as one of the key genes to determine the risk for, as well as potential targets, of COVID-19 therapy and drug discovery. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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All the cases affected with COVID-19 were recruited through Japan COVID-19 Task Force.

We enrolled the hospitalized cases diagnosed as COVID-19 by physicians using the clinical manifestation and PCR test results, who were recruited from April 2020 to January 2021 at any of the >100 the affiliated hospitals (Supplementary Table 1 

We performed GWAS genotyping of the 2,520 COVID-19 cases and 3,341 controls using Infinium Asian Screening Array (Illumina, CA, USA). We applied stringent QC filters to the samples (sample call rate < 0.97, excess heterozygosity of genotypes > mean + 3SD, related samples with PI_HAT > 0.175, or outlier samples from East Asian clusters in principal component analysis with 1000 Genomes Project samples), and variants (variant call rate < 0.99, significant call rate differences between cases and controls with P < 5.0 × 10 -8 , deviation from Hardy-Weinberg equilibrium with P < 1.0 × 10 -6 , or minor allele count < 5), as described elsewhere 48 . Details of the QC for the mitochondrial variants are described elsewhere 21 . After QC, we obtained genotype data of 489,539, 15,161, and 217 autosomal, X-chromosomal, and mitochondrial variants, respectively, for 2,393 COVID-19 cases and 3,289 controls.

We used SHAPEIT4 software (version 4.1.2) for haplotype phasing of autosomal genotype data, and SHAPEIT2 software (v2.r904) for X-chromosomal genotype data. After phasing, we used Minimac4 software (version 1.0.1) for genome-wide genotype imputation. We used the population-specific imputation reference panel of Japanese (n = 1,037) combined with 1000 Genomes Project Phase3v5 samples (n = 2,504) 19,20 . Imputations of the mitochondrial variants were conducted as described elsewhere 21 , using the population-specific reference panel (n = 1,037). We applied post-imputation QC filters of MAF ≥ 0.1% and imputation score (Rsq) > 0.5. We note that the genotypes of the lead variant in the GWAS (rs60200309) were . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted May 18, 2021. ; https://doi.org/10.1101/2021.05.17.21256513 doi: medRxiv preprint obtained by imputation (Rsq = 0.88). We assessed accuracy by comparing the imputed dosages with WGS data for the part of the controls (n = 236), and confirmed high concordance rate of 97.5%.

HLA genotype imputation was performed using DEEP*HLA software (version 1.0), a multitask convolutional deep learning method 31 . We used the population-specific imputation reference panel of Japanese (n = 1,118), which included both classical and non-classical HLA gene variants for imputation 30 . Before imputation, we removed the overlapping samples between the GWAS controls and the reference panel (n = 649), from the GWAS data side.

We imputed HLA alleles (2-digit and 4-digit) and the corresponding HLA amino acid polymorphisms, and applied post-imputation QC filters of MAF ≥ 0.5% and imputation score (r 2 in cross-validation) > 0.7.

We conducted GWAS of COVID-19 by using logistic regression of the imputed dosages of each of the variants on case-control status, using PLINK2 software (v2.00a3LM AVX2 Intel [6 Jul 2020] ). We included sex, age, and the top five principal components as covariates in the regression model. We set the genome-wide association significance threshold of P < 5.0 × 10 -8 23 . We obtained the association of the DOCK2 variant (rs60200309) from the panancestry meta-analysis available at https://rgc-covid19.regeneron.com/ 24,25 . We obtained the meta-analysis results of the phenotype of "hospitalized COVID-19 vs COVID-19 negative or COVID-19 status unknown" with the largest case sample size. Meta-analysis of the Japanese discovery GWAS and the pan-ancestry analysis was conducted using an inverse-variance method assuming a fixed-effects model.

As for the imputed HLA variants, we conducted (i) association test of binary HLA markers (2-digit and 4-digit HLA alleles, respectively amino acid residues) and (ii) an omnibus test of each of the HLA amino acid positions, as described elsewhere 30 . Binary maker test was conducted using the same logistic regression model and covariates as in the GWAS.

Omnibus test was conducted by a log likelihood ratio test between the null model and the fitted model, followed by a χ 2 distribution with m-1 degree(s) of freedom, where m is the number of the residues. R statistical software (version 3.6.0) was used for the HLA association test. In addition to the genome-wide significance threshold, we set the HLA-wide significance threshold based on Bonferroni's correction for the number of the HLA tests (α = 0.05).

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We estimated the ABO blood types of the GWAS subjects based on the five coding variants at the ABO gene (rs8176747, rs8176746, rs8176743, rs7853989, and rs8176719) 32,33 . We phased the haplotypes of these five variants based on the best-guess genotypes obtained by genome-wide imputation, and estimated the ABO blood type as described elsewhere 34 .

We could unambiguously determine the ABO blood type of 99.1 % of the subjects. 

We conducted two-sample MR analysis as described elsewhere 36, 37 . As an outcome phenotype, we utilized the GWAS summary statistics of Japanese (current study) and

Europeans (release 5 from COVID-19 Host Genetics Initiative 16 ). Lists of the Japanese and European GWAS studies used as the exposure phenotypes are in Supplementary Table 9 .

We extracted the independent lead variants with genome-wide significance (or the proxy variants in linkage disequilibrium r 2 ≥ 0.8 in the EAS or EUR subjects of the 1000 Genomes Project Phase3v5 databases) from the GWAS results of the exposure phenotypes. We applied the inverse variance weighted (IVW) method using the TwoSampleMR package (version 0.5.5) in R statistical software (version 4.0.2).

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A Novel Coronavirus from Patients with Pneumonia in China

SARS-CoV-2 Variants of Concern in the United States-Challenges and Opportunities

Factors associated with COVID-19-related death using OpenSAFELY

Ministry of Health Labour and Welfare. Clinical Management of Patients with COVID-19 A guide for front-line

Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis

Prevalence and impact of diabetes among people infected with SARS-CoV-2

Why does Japan have so few cases of COVID-19?

Polymorphisms of large effect explain the majority of the host (JPMJCR20H2), MHLW (20CA2054), Takeda Science Foundation, the Mitsubishi Foundation, and Bioinformatics Initiative of Osaka University Graduate School of Medicine, Osaka University. The super-computing resource was provided by Human Genome

Okada designed the study

Toshiro Sato 150 , Naoki Hasegawa 1 , Katsushi Tokunaga 143 , Makoto Ishii 4 , Ryuji Koike 151

Division of Health Medical Intelligence

20. Division of Gastroenterology and Hepatology

Department of Infection Control

Allergic Diseases Internal Medicine, Tosei General Hospital

Department of Infectious Diseases

Division of Infection Control

Department of Integrative Physiology and Bio-Nano Medicine

Japan 53. Department of Otolaryngology and Head and Neck Surgery

Japan Community Health care Organization Kanazawa Hospital, Kanazawa, Japan. 63. Department of Respiratory Medicine

Japan Organization of Occupational Health and Safety

Japan Organization of Occupational Health and Safety

Department of General Internal Medicine and Infectious Diseases, National Hospital Organization Tokyo Medical Center

Department of respiratory medicine, Sapporo, Japan. 76. Division of General Internal Medicine, Department of Internal Medicine

Department of Rheumatology, National Hospital Organization Hokkaido Medical Center

National Hospital Organization Hokkaido Medical Center

Department of Emergency and Critical Care Medicine, National Hospital Organization Hokkaido Medical Center

Musashino Red Cross Hospital

Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital

Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College

Department of Pulmonary Medicine

Department of Infectious Disease and Clinical Research Institute, National Hospital Organization Kyushu Medical Center

Department of Infectious Disease, National Hospital Organization Kyushu Medical Center

Daini Osaka Police Hospital

KINSHUKAI Hanwa The Second Hospital

National Hospital Organization Tokyo Hospital Hospital

Division of Infectious Diseases

National hospital organization Saitama Hospital

We would like to sincerely thank all the participants involved in this study, and all the members of Japan COVID-19 Task Force for their supports. We thank Mr. Johji Kitano, e-Parcel Corporation, and Ascend Corporation for voluntarily supporting Japan COVID-19 Task Force.We thank COVID-19 Host Genetics Initiative for publicly sharing the GWAS summary statistics of COVID-19. This study was supported by AMED (JP20nk0101612, JP20fk0108415, JP21jk0210034, JP21km0405211, and JP21km0405217), JST CREST

The authors declare no conflicts of interests.