# Mapping the human genetic architecture of COVID-19

Covid-19 Host Genetics Initiative, Mari E K Niemi, Juha Karjalainen, Rachel G Liao, Benjamin M Neale, Mark Daly, Andrea Ganna, Gita A Pathak, Shea J Andrews, Masahiro Kanai, et al.

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Covid-19 Host Genetics Initiative, Mari E K Niemi, Juha Karjalainen, Rachel G Liao, Benjamin M Neale, et al. Mapping the human genetic architecture of COVID-19. Nature, 2021, 600 (7889), pp.472-477. 10.1038/s41586-021-03767-x. pasteur-03282354

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# Mapping the human genetic architecture of COVID-19

https://doi.org/10.1038/s41586-021-03767-x

Received: 2 March 2021

Accepted: 23 June 2021

Published online: 8 July 2021

Open access

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COVID-19 Host Genetics Initiative*

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19¹,², host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases³⁻⁷. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

The COVID-19 pandemic, caused by infection with SARS-CoV-2, has resulted in an enormous health and economic burden worldwide. One of the most remarkable features of SARS-CoV-2 infection is the variation in consequences, which range from asymptomatic to life-threatening, viral pneumonia and acute respiratory distress syndrome⁸. Although established host factors correlate with disease severity (for example, increasing age, being a man and higher body-mass index¹), these risk factors alone do not explain all of the variability in disease severity observed across individuals.

Genetic factors contributing to COVID-19 susceptibility and severity may provide new biological insights into disease pathogenesis and identify mechanistic targets for therapeutic development or drug repurposing, as treating the disease remains a highly important goal despite the recent development of vaccines. Further supporting this line of inquiry, rare loss-of-function variants in genes involved in the type I interferon response may be involved in severe forms of COVID-19⁹⁻¹¹. At the same time, several genome-wide association studies that investigate the contribution of common genetic variation¹²⁻¹⁵ to COVID-19 have provided robust support for the involvement of several genomic loci associated with COVID-19 severity and susceptibility, with the strongest and most robust finding for severity being at the 3p21.31 locus¹²⁻¹⁶. However, much remains unknown about the genetic basis of susceptibility to SARS-CoV-2 and severity of COVID-19.

The COVID-19 Host Genetics Initiative (COVID-19 HGI) (https://www.covid19hg.org/)¹⁷ is an international, open-science collaboration to share scientific methods and resources with research groups across the world with the goal to robustly map the host genetic determinants of SARS-CoV-2 infection and the severity of the resulting COVID-19 disease. Here, we report the latest results of meta-analyses of 46 studies from 19 countries (Fig. 1) for COVID-19 host genetic effects.

## Meta-analyses of COVID-19

Overall, the COVID-19 HGI combined genetic data from 49,562 cases and 2 million controls across 46 distinct studies (Fig. 1). The data included studies from populations of different genetic ancestries, including European, admixed American, African, Middle Eastern, South Asian and East Asian individuals (Supplementary Table 1). An overview of the study design is provided in Extended Data Fig. 1. We performed case-control meta-analyses in three main categories of COVID-19 disease according to predefined and partially overlapping phenotypic criteria. These included (1) critically ill cases of COVID-19 defined as those individuals who required respiratory support in hospital or who died due to the disease; (2) cases of moderate or severe COVID-19 defined as those participants who were hospitalized due to symptoms associated with the infection; and (3) all cases with reported SARS-CoV-2 infection

<footnote>*Lists of authors and their affiliations appear in the online version of the paper.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** | Geographical overview of the contributing studies to the COVID-19 HGI and composition by major ancestry groups. Populations are defined as African (AFR), admixed American (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID) and South Asian (SAS).</figcaption></figure>

regardless of symptoms (Methods). Controls for all three analyses were selected as genetically ancestry-matched samples without known SARS-CoV-2 infection, if that information was available (Methods). The average age of the participants with COVID-19 across studies was 55 years (Supplementary Table 1). We report quantile-quantile plots in Supplementary Fig. 1 and ancestry principal component plots for contributing studies in Extended Data Fig. 2.

Across our three analyses, we reported a total of 13 independent genome-wide significant loci associated with COVID-19 (the threshold of $P < 1.67 \times 10^{-8}$ is adjusted for multiple trait testing) (Supplementary Table 2), most of which were shared between two or more COVID-19 phenotypes. Two of these loci are in very close proximity within the 3p21.31 region, which was previously reported as a single locus associated with COVID-19 severity$^{12–16}$ (Extended Data Fig. 3). Overall, we find six genome-wide significant associations for critical illness due to COVID-19, using data from 6,179 cases and 1,483,780 controls from 16 studies (Extended Data Fig. 4). Nine genome-wide significant loci were detected for moderate to severe hospitalized COVID-19 (including five of the six critical illness loci) from an analysis of 13,641 cases of COVID-19 and 2,070,709 controls across 29 studies (Fig. 2a, top). Finally, seven loci reached genome-wide significance in the analysis using data for all available 49,562 reported cases of SARS-CoV-2 infection and 1,770,206 controls, using data from a total of 44 studies (Fig. 2a, bottom). The proportion of cases with non-European genetic ancestry for each of the three analyses was 23%, 29% and 22%. We report the results for the lead variants at the 13 loci in different ancestry-group meta-analyses in Supplementary Table 3. We note that two loci, tagged by lead variants rs1886814 and rs72711165, had higher allele frequencies in southeast Asian (rs1886814; 15%) and East Asian genetic ancestry (rs72711165; 8%) whereas the minor allele frequencies in European populations were less than 3%. This highlights the value of including data from diverse populations for genetic discovery. We discuss the replication of previous findings and the new discoveries from these three analyses in the Supplementary Note.

## Variant effects on severity and susceptibility

We found no genome-wide significant sex-specific effects at the 13 loci. However, we did identify significant heterogeneous effects ($P < 0.004$) across studies for 3 out of the 13 loci (Methods), which probably reflects the differential ascertainment of cases (Supplementary Table 2). There was a small number of overlapping samples ($n = 8,380$ European ancestry; $n = 745$ East Asian ancestry) between controls from the genOMICC and the UK Biobank studies, but leave-one-out sensitivity analyses did not reveal any bias in the corresponding effect sizes or $P$ values (Extended Data Fig. 5 and Supplementary Information).

We next wanted to better understand whether the 13 significant loci were acting through mechanisms that increased the susceptibility to infection or that affected the progression of symptoms towards more severe disease. For all 13 loci, we compared the lead variant (strongest association $P$ value) odds ratios (ORs) for the risk-increasing allele across our different COVID-19 phenotype definitions.

Focusing on the two better powered analyses: all cases with a reported SARS-CoV-2 infection and all cases hospitalized due to COVID-19, we find that four of the loci have similar odds ratios between these two analyses (Methods and Supplementary Table 2). Such consistency suggests a stronger link to susceptibility to SARS-CoV-2 infection rather than to the development of severe COVID-19. The strongest susceptibility signal was the previously reported ABO locus (rs912805253)$^{12,13,15,16}$. Notably, and in agreement with a previously reported study$^{15}$, we also report a locus within the 3p21.31 region that was more strongly associated with susceptibility to SARS-CoV-2 than progression to more severe COVID-19 phenotypes. rs2271616 showed a stronger association with a reported SARS-CoV-2 infection ($P = 1.79 \times 10^{-34}$; OR (95% confidence interval (CI)) = 1.15 (1.13–1.18)) than hospitalization ($P = 1.05 \times 10^{-5}$; OR (95% CI) = 1.12 (1.06–1.19)). For this locus—which contains additional independent signals—the linkage-disequilibrium (LD) pattern is discordant with the $P$-value expectation (Extended Data Fig. 6 and Supplementary Note), pointing to a key missing causal variant or to a potentially undiscovered multi-allelic or structural variant in this locus.
a

<figure><img src="image_4.png" /><figcaption>**Fig. 2** | Genome-wide association results for COVID-19. a, Top, results of a genome-wide association study of hospitalized cases of COVID-19 (n = 13,641 cases and n = 2,070,709 controls). Bottom, the results of reported SARS-CoV-2 infections (n = 49,562 cases and n = 1,770,206 controls). Loci highlighted in yellow (top) represent regions associated with the severity of the COVID-19 manifestation—that is, increased odds of more severe COVID-19 phenotypes. Loci highlighted in green (bottom) are regions associated with susceptibility to a SARS-CoV-2 infection—that is, the effect is the same across mild and severe</figcaption></figure>

b

<figure><img src="image_5.png" /></figure>

COVID-19 phenotypes. We highlight in red genome-wide significant variants that had high heterogeneity across contributing studies and that were therefore excluded from the list of loci found. **b**, Results of gene prioritization using different evidence measures of gene annotation. Genes in the LD region, genes with coding variants and eGenes (fine-mapped *cis*-eQTL variant PIP > 0.1 in GTEx Lung) are annotated if in LD with a COVID-19 lead variant (r² > 0.6). V2G, highest gene prioritized by the V2G score of Open Target Genetics.

By contrast, 9 out of the 13 loci were associated with increased risk of severe symptoms with significantly larger odds ratios for hospitalized COVID-19 compared with the mildest phenotype of reported SARS-CoV-2 infection (eight loci were below the threshold of P < 0.004 (test for effect size difference) and, in addition, the lead variant rs10774671 had a clear increase in odds ratios despite not passing this threshold) (Supplementary Table 2). We further compared the odds ratios for these nine loci for critical illness due to COVID-19 versus hospitalized due to COVID-19, and found that these loci exhibited a general increase in effect risk for critical illness (Methods, Extended Data Fig. 7a and Supplementary Table 4), but the lower power for association analysis of critically ill COVID-19 means that these results should be considered as suggestive. Overall, these results indicated that these nine loci were more likely to be associated with progression of the disease and worse outcome from SARS-CoV-2 infection compared to being associated with susceptibility to SARS-CoV-2 infection.

For some of these analyses, the controls were simply existing population controls without knowledge of SARS-CoV-2 infection or COVID-19 status, which may bias effect size estimates as some of these individuals may have either become infected with SARS-CoV-2 or developed COVID-19. We perform several sensitivity analyses (Extended Data Fig. 7b, Supplementary Note and Supplementary Table 4) in which we show that using population controls can be a valid and powerful strategy for host genetic discovery of infectious disease, and particularly those that are widespread and with rare severe outcomes.

## Gene prioritization and association with other traits

To better understand the potential biological mechanism of each locus, we applied several approaches to prioritize candidate causal genes and

explore additional associations with other diseases and traits. Of the 13 genome-wide significant loci, we found that nine loci implicated biologically plausible genes (Supplementary Tables 2, 5). Protein-altering variants in LD with lead variants implicated genes at six loci, including *TYK2* (chromosome and cytogenetic band (chr.) 19p13.2) and *PPP1R15A* (chr. 19q13.33). The COVID-19 lead variant rs74956615T>A in *TYK2*, which confers risk for critical illness (OR (95% CI) = 1.43 (1.29–1.59), P = 9.71 × 10⁻¹²) and hospitalization due to COVID-19 (OR (95% CI) = 1.27 (1.18–1.36), P = 5.05 × 10⁻¹⁰) is correlated with the missense variant rs34536443:G>C (p.Pro1104Ala; r² = 0.82). This is consistent with the primary immunodeficiency described with complete *TYK2* loss of function³ as this variant is known to reduce function¹⁸,¹⁹. By contrast, this missense variant was previously reported to be protective against autoimmune diseases (Extended Data Fig. 8 and Supplementary Table 6), including rheumatoid arthritis (OR = 0.74, P = 3.0 × 10⁻⁸; UK Biobank SAIGE) and hypothyroidism (OR = 0.84, P = 1.8 × 10⁻¹⁰; UK Biobank). At the 19q13.33 locus, the lead variant rs4801778, which was significantly associated with a reported SARS-CoV-2 infection (OR (95% CI) = 0.95 (0.93–0.96), P = 2.1 × 10⁻⁸), is in LD (r² = 0.93) with a missense variant rs11541192:G>A (p.Gly312Ser) in *PPP1R15A*.

A lung-specific *cis*-expression quantitative trait loci (*cis*-eQTLs) from GTEx v.8²⁰ (n = 515) and the Lung eQTL Consortium²¹ (n = 1,103) provided further support for a subset of loci (Supplementary Table 7), including *FOXP4* (chr. 6p21.1) and *ABO* (chr. 9q34.2), *OAS1/OAS3/OAS2* (chr. 12q24.13) and *IFNAR2/IL1ORB* (21q22.11), where the COVID-19-associated variants modify gene expression in lung. Furthermore, our phenome-wide association study (PheWAS) analysis (Supplementary Table 6) implicated three additional loci related to lung function, with modest lung eQTL evidence—that is, the lead variant was not fine-mapped but significantly associated. An intronic variant
<figure><img src="image_6.png" /><figcaption>**Fig. 3 |** Genetic correlations and Mendelian randomization causal estimates between 38 traits and COVID-19 critical illness, hospitalization and reported SARS-CoV-2 infection. Larger squares correspond to *P* values with higher significance, with genetic correlations ($r_g$) or Mendelian randomization (MR) causal estimates significantly different from zero. The size of each coloured square indicates the magnitude of the *P* value, with *P* < 0.05 shown as a full-sized square, *P* = 0.05–0.1 as a large square, *P* = 0.1–0.5</figcaption></figure>

as a medium square and *P* > 0.5 as a small square. Genetic correlations or causal estimates that are significantly different from zero at an FDR of 5% are marked with an asterisk. Two-sided *P* values were calculated using LDSC for genetic correlations and inverse-variance-weighted analysis for Mendelian randomization. ADHD, attention-deficit hyperactivity disorder; BMI, body mass index; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate.

rs2109069:G>A in *DPP9* (chr. 19p13.3), which is positively associated with critical illness, was previously reported to be risk-increasing for interstitial lung disease (tag lead variant rs12610495:A>G (p.Leu8Pro); OR = 1.29, *P* = 2.0 × 10<sup>-12</sup>)<sup>5</sup>. The COVID-19 lead variant rs1886814:A>C in the *FOXP4* locus is correlated (*r*<sup>2</sup> = 0.64) with a lead variant of lung adenocarcinoma (tag variant is rs7741164; OR = 1.2, *P* = 6.0 × 10<sup>-13</sup>)<sup>6,22</sup> and similarly with a lead variant reported for subclinical interstitial lung disease<sup>23</sup>. In severe COVID-19, lung cancer and interstitial lung disease, the minor, expression-increasing allele is associated with increased risk. We also found that intronic variants (chr. 1q22) and rs1819040:T>A in *KANSL1* (chr. 17q21.31), associated with protection against hospitalization due to COVID-19, were previously reported for reduced lung function (for example, tag lead variant rs141942982:G>T; OR (95% CI) = 0.96 (0.95–0.97), *P* = 1.00 × 10<sup>-20</sup>)<sup>7</sup>. Notably, the 17q21.31 locus is a well-known locus for structural variants containing a megabase inversion polymorphism (H1 and inverted H2 forms) and complex copy-number variations, in which the inverted H2 forms were shown to be positively selected in European individuals<sup>24,25</sup>.

Lastly, there are two loci in the 3p21.31 region with varying genes prioritized by different methods for different independent signals. For the severity lead variant rs10490770:T>C, we prioritized *CXCR6* with the Variant2Gene (V2G) algorithm<sup>26</sup>, although *LZTFL1* is the closest gene. The *CXCR6* has a role in chemokine signalling<sup>27</sup> and *LZTFL1* has been implicated in lung cancer<sup>28</sup>. rs2271616:G>T, which is associated with susceptibility, tags a complex region including several independent signals (Supplementary Note) that are all located within the gene body of *SLC6A20*, which encodes a protein that is known to functionally interact with the SARS-CoV-2 receptor ACE2<sup>29</sup>. However, none of the lead variants in the 3p21.31 region has been previously associated with other traits or diseases in our PheWAS analysis. Although these results provide supporting in silico evidence for candidate causal gene prioritization, further functional characterization is needed. Detailed locus descriptions and LocusZoom plots are provided in Supplementary Fig. 2.

## Polygenic architecture of COVID-19

To further investigate the genetic architecture of COVID-19, we used results from meta-analyses including samples from European ancestries

(sample sizes are described in the Methods and Supplementary Table 1) to estimate the heritability explained by common single-nucleotide polymorphisms—that is, the proportion of variation in the two phenotypes that was attributable to common genetic variants—and to determine whether heritability of COVID-19 phenotypes was enriched in genes that were specifically expressed in certain tissues<sup>30</sup> from the GTEx dataset<sup>31</sup>. We detected low, but significant, heritability across all three analyses (<1% on observed scale, all *P* values were *P* < 0.0001) (Supplementary Table 8). The values are low compared to previously published studies<sup>14</sup>, but may be explained by differences in the reported estimate scale (observed versus liability), the specific method used, disease-prevalence estimates, phenotypic differences between patient cohorts or ascertainment of controls. Despite the low reported values, we found that heritability of a reported SARS-CoV-2 infection was significantly enriched in genes that were specifically expressed in the lung (*P* = 5.0 × 10<sup>-4</sup>) (Supplementary Table 9). These findings, together with the genome-wide significant loci identified in the meta-analyses, suggest that there is a significant polygenic architecture that can be better leveraged with future, larger, sample sizes.

## Genetic correlation and Mendelian randomization

Genetic correlations ($r_g$) between the three COVID-19 phenotypes was high, although lower correlations were observed between hospitalized COVID-19 and reported SARS-CoV-2 infection (critical illness versus hospitalized: $r_g$ (95% CI) = 1.37 (1.08–1.65), *P* = 2.9 × 10<sup>-21</sup>; critical illness versus reported SARS-CoV-2 infection, $r_g$ (95% CI) = 0.96 (0.71–1.20), *P* = 1.1 × 10<sup>-14</sup>; hospitalized versus reported SARS-CoV-2 infection: $r_g$ (95% CI) = 0.85 (0.68–1.02), *P* = 1.1 × 10<sup>-22</sup>). To better understand which traits are genetically correlated and/or potentially causally associated with COVID-19 hospitalization, critical illness and reported SARS-CoV-2 infection, we chose a set of 38 disease, health and neuropsychiatric phenotypes as potential COVID-19 risk factors based on their clinical correlation with disease susceptibility, severity or mortality (Supplementary Table 10).

We found evidence (false-discovery rate (FDR) < 0.05) of significant genetic correlations between nine traits and hospitalized COVID-19 and reported SARS-CoV-2 infection (Fig. 3, Extended Data Fig. 9 and Supplementary Table 11). Notably, genetic liability to ischaemic
stroke was only significantly positively correlated with critical illness or hospitalization due to COVID-19, but not with a higher likelihood of reported SARS-CoV-2 infection (infection $r_g$ = 0.019 versus hospitalization $r_g$ = 0.41, $z$ = 2.7, *P* = 0.006; infection $r_g$ = 0.019 versus critical illness $r_g$ = 0.40, $z$ = 2.49, *P* = 0.013).

We next used two-sample Mendelian randomization to infer potentially causal relationships between these traits. After correcting for multiple testing (FDR < 0.05), eight exposure-COVID-19 trait pairs showed suggestive evidence of a causal association (Fig. 3, Extended Data Fig. 10, Supplementary Table 12 and Supplementary Fig. 3). Five of these associations were robust to potential violations of the underlying assumptions of Mendelian randomization. Corroborating our genetic correlation results and evidence from epidemiological studies, genetically predicted higher body-mass index (OR (95% CI) = 1.4 (1.3–1.6), *P* = 8.5 × 10⁻¹¹) and smoking (OR (95% CI) = 1.9 (1.3–2.8), *P* = 0.0012) were associated with increased risk of COVID-19 hospitalization, with body-mass index also being associated with increased risk of SARS-CoV-2 infection (OR (95% CI) = 1.1 (1.1–1.2), *P* = 4.8 × 10⁻⁷). Genetically predicted increased height (OR (95% CI) = 1.1 (1–1.1)), *P* = 8.9 × 10⁻⁴) was associated with an increased risk of reported SARS-CoV-2 infection, whereas a genetically predicted higher red-blood-cell count (OR (95% CI) = 0.93 (0.89–0.96), *P* = 5.7 × 10⁻⁵) was associated with a reduced risk of reported SARS-CoV-2 infection. Despite evidence of a genetic correlation between type II diabetes and COVID-19 outcomes, there was no evidence of a causal association in the Mendelian randomization analyses, which suggests that the observed genetic correlations are due to pleiotropic effects between body-mass index and type 2 diabetes. Further sensitivity analyses relating to sample overlap are discussed in the Supplementary Information.

## Discussion

The COVID-19 HGI has brought together investigators from across the world to advance genetic discovery for SARS-CoV-2 infection and severe COVID-19 disease. We report 13 genome-wide significant loci associated with some aspect of SARS-CoV-2 infection or COVID-19. Many of these loci overlap with previously reported associations with lung-related phenotypes or autoimmune or inflammatory diseases, but some loci have no obvious candidate gene.

Four out of the thirteen genome-wide significant loci showed similar effects in the reported SARS-CoV-2 infection analysis (a proxy for disease susceptibility) and all-hospitalized COVID-19 (a proxy for disease severity). Of these, one locus was in close proximity to, yet independent of, the major genetic signal for COVID-19 severity at the 3p21.31 locus. Notably, this locus was associated with COVID-19 susceptibility rather than severity. The locus overlaps *SLC6A20*, which encodes an amino acid transporter that interacts with ACE2. Nonetheless, we caution that more data are needed to resolve the nature of the relationship between genetic variation and COVID-19 at this locus, particularly as the physical proximity, LD structure and patterns of association suggest that untagged genetic variation could drive the association signal in the region. Our findings support the notion that some genetic variants, most notably at the *ABO* and *PPPIR15A* loci, in addition to *SLC6A20*, can indeed affect susceptibility to infection rather than progression to severe COVID-19 once infected.

Several of the loci reported here—as noted in previous publications¹²,¹⁴—intersect with well-known genetic variants that have established genetic associations. Examples of these include variants at *DPP9* and *FOXP4*, which show previous evidence of increasing risk for interstitial lung disease⁵, and missense variants within *TYK2* that show a protective effect on several autoimmune-related diseases³²⁻³⁵. Together with the heritability enrichment observed in genes expressed in lung tissues, these results highlight the involvement of lung-related biological pathways in the development of severe COVID-19. Several other loci show no previously documented genome-wide significant

associations, despite the high significance and attractive candidate genes for COVID-19 (for example, *CXCR6*, *LZTFL1*, *IFNAR2* and *OAS1/OAS2/OAS3* loci). The previously reported associations for the strongest association for COVID-19 severity at the 3p21.31 locus and monocytes count are likely to be due to proximity and not a true co-localization.

Increasing the global representation in genetic studies enhances the ability to detect novel associations. Two of the loci that affect disease severity were only discovered by including the four studies of individuals with East Asian ancestry. One of these loci—close to *FOXP4*—is common particularly in East Asian participants (32%) as well as admixed American participants in the Americas (20%) and Middle Eastern participants (7%), but has a low frequency in most European ancestries (2–3%) in our data. Although we cannot be certain of the mechanism of action, the *FOXP4* association is an attractive biological target, as it is expressed in the proximal and distal airway epithelium³⁶ and has been shown to have a role in controlling epithelial cell fate during lung development³⁷. The COVID-19 HGI continues to pursue expansion of the datasets included in the analyses of the consortium to populations from underrepresented populations in upcoming data releases. We plan to release ancestry-specific results in full once the sample sizes allow for a well-powered meta-analysis.

Care should be taken when interpreting the results from a meta-analysis because of challenges with case and control ascertainment and collider bias (see Supplementary Note for a more detailed discussion on study limitations). Drawing a comprehensive and reproducible map of the host genetics factors associated with COVID-19 severity and SARS-CoV-2 requires a sustained international effort to include diverse ancestries and study designs. To accelerate downstream research and therapeutic discovery, the COVID-19 HGI regularly publishes meta-analysis results from periodic data freezes on the website https://www.covid19hg.org/ and provides an interactive explorer through which researchers can browse the results and the genomic loci in more detail. Future work will be required to better understand the biological and clinical value of these findings. Continued efforts to collect more samples and detailed phenotypic data should be endorsed globally, allowing for more thorough investigation of variable, heritable symptoms, particularly in light of the newly emerging strains of SARS-CoV-2, which may provoke different host responses that lead to disease.

## Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-021-03767-x.

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© The Author(s) 2021
## COVID-19 Host Genetics Initiative

### Leadership

Mari E. K. Niemi¹,¹²⁸⁰, Juha Karjalainen¹,¹²⁸⁰, Rachel G. Liao², Benjamin M. Neale⁴,¹²⁸⁰ [2], Mark Daly¹,²,³,¹²⁸⁰ [2] & Andrea Ganna¹,²,³,¹²⁸⁰ [2]

### Writing group

### Writing group leaders

Mari E. K. Niemi¹,¹²⁸⁰, Gita A. Pathak⁶, Shea J. Andrews⁷ & Masahiro Kanai²

### Writing group members

Kumar Veerapen², Israel Fernandez-Cadenas⁸, Eva C. Schulte⁹,¹⁰,¹⁰¹, Pasquale Striano¹,²,¹³, Minttu Marttila⁷⁵, Camelia Minica², Eirini Marouli¹⁴, Mohd Anisul Karim¹⁵,³⁸, Frank R. Wendt⁶, Jeanne Savage¹⁶, Laura Sloofman⁷, Guillaume Butler-Laporte¹⁷,⁵³, Han-Na Kim¹⁸, Stavroula Kanoni¹⁹, Yukinori Okada²⁰, Jinyoung Byun²⁰, Younghun Han²⁰, Mohammed Jashim Uddin²¹, George Davey Smith²², Cristen J. Willer²³,²⁴,²⁵ & Joseph D. Buxbaum²⁶

### Analysis group

### Manuscript analyses team leader

Juha Karjalainen¹,¹²⁸⁰

### Manuscript analyses team member: meta-analysis

Juha Mehtonen¹

### Manuscript analyses team member: heritability, methods and supplements

Mari E. K. Niemi¹,¹²⁸⁰

### Manuscript analyses team member: PHEWAS

Gita A. Pathak⁶

### Manuscript analyses team member: Mendelian randomization

Shea J. Andrews⁷

### Manuscript analyses team member: PC projection and gene prioritization

Masahiro Kanai²

### Manuscript analyses team member: gene prioritization

Hilary Finucane³,²⁹

### Manuscript analyses team member: sensitivity analysis

Mattia Cordioli¹

### Manuscript analyses team members: PC projection

Alicia R. Martin³,²⁹ & Wei Zhou¹,²⁹

### In silico analysis team members

Mattia Cordioli¹, Bogdan Pasanuic³, Hanna Julienne³³, Hugues Aschard³³, Huwenbo Shi³⁴, Loic Yengo³⁵, Renato Polimanti⁶, Maya Ghoussaini¹⁵,³⁸, Jeremy Schwartzentruber¹⁵,³⁸, Ian Dunham¹⁵,⁴⁰ & Joseph D. Buxbaum²⁹

### Project management group

### Project management leader

Rachel G. Liao²

### Project management support

Karolina Chwialkowska⁴², Margherita Francescatto⁴³, Amy Trankiem² & Mary K. Balaconis²

### Phenotype steering group

Lea Davis⁴⁴, Andrea Ganna¹,²,³, Sulggi Lee⁴⁵, James Priest⁴⁶, Alessandra Renieri⁴²,⁶,⁵, Vijay G. Sankaran⁴⁹, David van Heel⁵⁰, Patrick Deelen⁵¹,⁵², J. Brent Richards¹⁷,⁵³,⁵⁵,⁵⁶, Tomoko Nakanishi¹⁷,⁵⁶,⁵⁷, Les Biesecker⁵⁹, V. Eric Kerchberger⁴⁴ & J. Kenneth Baillie⁶⁰,⁶¹,⁶²

### Data dictionary

Alessandra Renieri⁴⁷,⁶⁵, Francesca Mari⁶³,⁶⁴,⁶⁵, Anna Bernasconi⁶⁶, Stefano Ceri⁷⁸ & Arif Canakoglu⁶⁷

### Scientific communication group

### Scientific communication leaders

Kumar Veerapen² & Brooke Wolford⁶⁸

### Scientific communication members

Amy Trankiem², Annika Faucon⁶⁹, Atanu Kumar Dutta⁷⁰, Claudia Schurmann⁷¹, Emi Harry⁷², Ewan Birney⁷³, Gita A. Pathak⁶, Huy Nguyen², Jamal Nasir⁷⁴, James Priest⁴⁶, Mari Kaunisto¹, Minttu Marttila⁷⁵, Matthew Solomonon², Nicole Dueker⁷⁶, Nirmal Vadgama⁴⁶, Shea J. Andrews⁷, Sophie Limou⁷⁸, Rachel G. Liao² & Andrea Ganna¹,²,³

### Translators

Souad Rahmouni⁷⁰, Hamdi Mbarek⁸⁰, Dima Darwish⁸⁰, Atanu Kumar Dutta⁷⁰, Md Mesbah Uddin⁸²,⁸³, Israel Fernandez-Cadenas⁸, Raquel Albertos⁸⁴, Jordi Pérez-Tur⁸⁵,⁸⁶,⁸⁷, Ruolin Li⁸⁸, Lasse Folkersen⁸⁹, Ida Moltke⁹⁰, Nils Koelling⁹¹, Eva C. Schulte⁸¹,⁰¹,¹¹, Claudia Schurmann⁷¹, Alexander Teumer⁹³,⁹⁴, Athanasios Kousathanas⁹⁵, Eirini Marouli¹⁴,

Alicia Utrilla⁹⁶, Ricardo A. Verdugo⁹⁷, Ruth Zárate⁹⁸, Carolina Medina-Gómez⁹⁸, David Gómez-C abrero¹⁰⁰,¹⁰¹, Elena Carnero-Montoro¹⁰², Jordi Pérez-Tur⁸⁵,⁸⁶,⁸⁷, Israel Fernandez-Cadenas⁸, Carmen L. Cadilla¹⁰³, Andrés Moreno-Estrada¹⁰⁴, Adriana Garmendia¹⁰⁵, Leire Moya¹⁰⁵, Bahar Sedaghati-Khayat⁸⁸, Minttu Marttila⁷⁵, Palwendé Romuald Boua¹⁰⁶, Guillaume Butler-Laporte¹⁷,⁵³, Marie-Julie Favé¹⁰⁰, Laurent Francioli², Audrey Lemaçon¹⁰⁸, Sophie Limou⁷⁸, Isabelle Migeotte¹⁰⁰, Atanu Kumar Dutta⁷⁰, Sanjay Patel⁷⁰, Reka Varnai¹¹², Jozsef L. Szentpeteri¹¹², Csilla Sipeky¹¹³, Francesca Colombo¹¹⁴, Margherita Francescatto⁴³, Kathrin von Hohenstaufen¹¹⁵, Pietro Lio¹¹⁶, Costanza Vallerga¹⁸⁸, Pasquale Striano¹²,¹³, Qingbo Wang⁶, Masahiro Kanai², Yosuke Tanigawa⁴⁶, Tomoko Nakanishi¹⁷,⁵⁶,⁵⁷, Hogune Im¹¹⁷, Chulho Han¹¹⁷, Han Song¹¹⁷, Jiwoo Lim¹¹⁷, Younhe Lee¹¹⁷, Sugyeong Kim¹¹⁷, Sangyoon Im¹²⁸¹, Biljana Atanasovska¹³⁹, Hajar Fauzan Ahmad¹²⁰, Kumar Veerapen², Cindy Boer⁸⁸, Philip Jansen¹²², Lude Franke¹²³, Karolina Chwialkowska²⁴, Elżbieta Kaja¹²⁴, Dorota Pasko⁹⁵, Ingrid Kennis-Szilagyi⁸⁸, Sergey A. Kornilov¹²⁵, Vid Prijatelj⁸⁸, Ivana Prokić⁸⁸, Ilangkumaran Sivanadhan¹²⁶, Sarala Perumal¹²⁷, Sahar Esmaeeli¹²⁸, Nathaniel M. Pearson¹²⁹, Ruth Zárate⁹⁸ & Mohd Anisul Karim¹⁵,³⁸

### Website Development

### Website development leaders

Huy Nguyen² & Matthew Solomonon²

### 23andMe

### Analysis team leader

Adam Auton¹³⁰

### Data collection leaders

23andMe COVID-19 Team*, Janie F. Shelton¹³⁰ & Anjali J. Shastri¹³⁰

### Analysis team members

Teresa Filshtein-Sonmez¹³⁰, Daniella Coker¹³⁰, Antony Symons¹³⁰, Jorge Esparza-Gordillo¹³¹, Stella Aslibekyan¹³⁰ & Jared O'Connell¹³⁰

### Data collection members

Chelsea Ye¹³⁰ & Catherine H. Weldon¹³⁰

### 23andMe COVID-19 Team

Adam Auton¹³⁰

### ACCOuNT

### Analysis team leader

Minoli Perera¹³²

### Data collection leaders

Kevin O'Leary¹³³, Matthew Tuck¹³⁴, Travis O'Brien¹³⁵, David Meltzer¹³⁶, Peter O'Donnell¹³⁷ & Edith Nutescu¹³⁸

### Analysis team members

Guang Yang¹³²

### Data collection members

Cristina Alarcon¹³², Stefanie Herrmann¹³², Sophia Mazurek¹³³, Jeff Banagan¹³⁴, Zacharia Hamidi¹³⁴, April Barbour¹⁴⁴, Noora Raffat¹³⁶ & Diana Moreno¹³⁸

### Admin team member

Paula Friedman¹³²

### Amsterdam UMC COVID Study Group

### Analysis team leader

Bart Ferwerda¹⁴⁸

### Data collection leaders

Diederik van de Beek¹⁴⁸, Matthijs C. Brouwer¹⁴⁸, Alexander P. J. Vlaar¹⁴⁹ & W. Joost Wiersinga¹⁵⁰

### Analysis team members

Danielle Posthuma¹⁶, Elleke Tissink¹⁶, A. H. Koos Zwinderman¹⁵¹ & Emil Uffelmann¹⁶

### Data collection members

Michiel van Agtmael¹⁵⁰, Anne Geke Algera¹⁴⁹, Frank van Baarle¹⁴⁹, Diane Bax¹⁵², Martijn Beudel¹⁴⁸, Harm Jan Bogaard¹⁵³, Marije Bomers¹⁵⁰, Peter I. Bonta¹⁵³, Lieuwe Bos¹⁴⁹, Michela Botta¹⁴⁹, Justin de Brabander¹⁵⁰, Godelieve de Bree¹⁵⁰, Sanne de Bruin¹⁴⁹, Marianna Bugiani¹⁵⁴, Esther Bulle¹⁴⁹, Osoul Chouchane¹⁵⁰, Alex Cloherty¹⁵², Dave Dongelmans¹⁴⁹, Paul Elbers¹⁴⁹, Lucas Fleuren¹⁴⁹, Suzanne Geerlings¹⁵⁰, Bart Geerts¹⁵⁵, Theo Geijenbeek¹⁵², Armand Girbes¹⁴⁹, Bram Goorhuis¹⁵⁰, Martin P. Grobusch¹⁵⁰, Florianne Hafkamp¹⁵², Laura Hagens¹⁴⁹, Jorg Hannam¹⁵⁶, Vanessa Harris¹⁵⁰, Robert Hemke¹⁵⁷, Sabine M. Hermans¹⁵⁰, Leo Heunks¹⁴⁹, Markus Hollmann¹⁵⁶, Janneke Horn¹⁴⁹, Joppe W. Hovius¹⁵⁰, Menno D. de Jong¹⁵⁸, Rutger Koning¹⁴⁸, Niels van Mourik¹⁴⁹, Jeannine Nellen¹⁵⁰, Esther J. Nossent¹⁵³, Frederique Paulus¹⁴⁹, Edgar Peters¹⁵⁰, Tom van der Poll¹⁵⁰, Bennedikt Preckel¹⁵², Jan M. Prins¹⁵⁰, Jorinde Raasveld¹⁴⁹, Tom Reijnders¹⁵⁰, Michiel Schinkel¹⁵⁰, Marcus J. Schultz¹⁴⁹, Alex Schuurman¹⁵⁰, Kim Sigaloff¹⁵⁰, Marry Smit¹⁴⁹, Cornelis S. Tjinis¹⁵⁰, Willemke Stilma¹⁵⁰, Charlotte Teunissen¹⁴⁹, Patrick Thoral¹⁴⁹, Anissa Tsonas¹⁵⁰, Marc van der Valk¹⁵⁰, Denise Veelo¹⁵⁵, Heder de Vries¹⁴⁹, Michèle van Vugt¹⁵⁰, Dorien Wouters¹⁵⁰, René P. Minnaar¹⁶⁰, Adrie Kromhout¹⁶⁰, Kees W. J. van Uffelen¹⁶⁰ & Ruud A. Wolterman¹⁶¹
# AncestryDNA COVID-19 Research Study

## Analysis team leader

Genevieve Roberts¹⁶²

## Data collection leader

Danny Park¹⁶²

## Admin team leader

Catherine A. Ball¹⁶²

## Analysis team members

Marie Coignet¹⁶², Shannon McCurdy¹⁶², Spencer Knight¹⁶², Raghavendran Partha¹⁶² & Brooke Rhead¹⁶²

## Data collection members

Miao Zhang¹⁶², Nathan Berkowitz¹⁶², Michael Gaddis¹⁶², Keith Noto¹⁶², Luong Ruiz¹⁶² & Milos Pavlovic¹⁶²

## Admin team members

Eurie L. Hong¹⁶², Kristin Rand¹⁶², Ahna Girshick¹⁶², Harendra Guturu¹⁶² & Asher Haug Baltzell¹⁶²

## BelCovid

### Analysis team leader

Mari E. K. Niemi¹,²,³,⁸,⁹

## Data collection leaders

Isabelle Migeotte¹⁰⁹, Souad Rahmouni¹⁶³ & Julien Guntz¹⁶⁴

## Admin team leader

Yves Beguin¹⁶⁵

## Analysis team members

Mattia Cordioli¹, Sara Pigazzini¹ & Lindokuhle Nkambule³,⁲,⁹,¹⁶,¹⁷

## Data collection members

Youssef Bouysran¹⁶⁸, Adeline Busson¹⁶⁸, Xavier Peyrassol¹⁶⁸, Françoise Wilkin¹⁶⁸, Bruno Pichon¹⁶⁸, Guillaume Smits¹⁶⁸, Isabelle Vanderroot¹⁶⁸, Jean-Christophe Goffard¹⁶⁹, Michel Georges¹⁶⁸, Michel Moutschen¹⁷⁰, Benoit Misset¹⁷⁰, Gilles Darcis¹⁷⁰, Julien Guiot¹⁷⁰, Laurent Jadot¹⁶⁴, Samira Azzar¹⁷⁰, Patricia Dellot¹⁷⁰, Stéphanie Gofflot¹⁶⁵, Sabine Claassen¹⁶⁴, Axelle Bertrand¹⁷⁰, Gilles Parzibut¹⁷⁰, Mathilde Clarinval¹⁷⁰, Catherine Moermans¹⁷⁰, Olivier Malaise¹⁷⁰, Kamilia El Kandoussi¹⁶⁵, Raphaël Thonon¹⁶⁵, Pascale Huynen¹⁷⁰, Alyssia Mesdagh¹⁷⁰, Sofia Melo¹⁶³, Nicolas Jacques¹⁶³, Emmanuel Di Valentin¹⁶³, François Giroule¹⁶³, Alice Collignon¹⁶³, Corалиne Radermecker¹⁶³, Marielle Lebrun¹⁶³, Alice Collignon¹⁶³, Hélène Pérée¹⁶³, Samuel Latour¹⁶³, Olivia Barada¹⁶³, Judit Sanchez¹⁶³, Claire Josse¹⁷⁰, Bouchra Boujemla¹⁷⁰, Margot Meunier¹⁶³, Emeline Mariavelle¹⁶³, Sandy Anania¹⁶³, Hélène Gazon¹⁶³, Danusia Juszczak¹⁷⁰, Marjorie Fadeur¹⁷⁰, Séverine Camby¹⁷⁰, Christelle Meuris¹⁷⁰, Marie Thys¹⁷⁰, Jessica Jacques¹⁷⁰, Monique Henket¹⁷⁰, Philippe Léonard¹⁷⁰, Frederic Frippiat¹⁷⁰, Jean-Baptiste Giot¹⁷⁰, Anne-Sophie Sauvage¹⁷⁰, Christian Von Frenckell¹⁷⁰, Myriam Mni¹⁶³, Marie Wéry¹⁶⁵, Alicia Staderoli¹⁷⁰, Yasmine Belhaj¹⁶³ & Bernard Lambermont¹⁷⁰

# Biobanque Quebec COVID-19

## Analysis team leader

Tomoko Nakanishi¹⁷,⁵,⁶,⁷

## Data collection leader

David R. Morrison¹⁷

## Admin team leaders

Vincent Mooser⁵⁶,¹⁷,⁴ & J. Brent Richards¹⁷,⁵,⁳,⁵,⁵,⁶

## Analysis team members

Guillaume Butler-Laporte¹⁷,⁵³, Vincenzo Forgetta¹⁷ & Rui Li⁵⁶,¹⁷⁴

## Data collection members

Biswarup Ghosh¹⁷, Laetitia Laurent¹⁷, Alexandre Belisle⁵⁶,¹⁷⁴, Danielle Henry¹⁷, Tala Abdullah¹⁷, Olumide Adeleye¹⁷, Noor Mamlouk¹⁷, Nofar Kimchi¹⁷, Zaman Afrasiabi¹⁷, Nardin Rezk¹⁷, Branka Vulesevic¹⁷, Meriem Bouab¹⁷, Charlotte Guzman¹⁷, Louis Petitjean¹⁷, Chris Tselios¹⁷, Xiaoqing Xue¹⁷, Jonathan Afilalo¹⁷, Marc Afilalo¹⁷,¹⁷⁷, Maureen Oliveira¹⁷⁹, Bluma Brenner¹⁹⁰, Nathalie Brassard¹⁸¹ & Madeleine Durand¹⁸²,¹⁸³

## Admin team members

Erwin Schurr¹⁸⁴, Pierre Lepage⁵⁶,¹⁷⁴, Jiannis Ragoussis⁵⁶,¹⁷⁴, Daniel Auld⁵⁶,¹⁷⁴, Michaël Chasse¹⁶³,¹⁸⁵, Daniel E. Kaufmann¹⁶³,¹⁸⁶, G. Mark Lathrop⁵⁶,¹⁷⁴ & Darin Adra¹⁷

# BioVU

## Analysis team leaders

Lea K. Davis¹⁶⁷,¹⁸⁶, Nancy J. Cox¹⁶⁷,¹⁸⁸ & Jennifer E. Below¹⁶⁷,¹⁸⁸

## Analysis team members

Julia M. Sealock¹⁶⁷,¹⁸⁸, Annika B. Faucon¹⁶⁷,¹⁸⁸, Megan M. Shuey¹⁶⁷,¹⁸⁸, Hannah G. Polikowsky¹⁶⁷,¹⁸⁸, Lauren E. Petty¹⁶⁷,¹⁸⁸, Douglas M. Shaw¹⁶⁷,¹⁸⁸, Hung-Hsin Chen¹⁶⁷,¹⁸⁸ & Wanying Zhu¹⁶⁷,¹⁸⁸

# Bonn Study of COVID-19 Genetics

## Data collection leader

Kerstin U. Ludwig¹⁶⁹

## Analysis team members

Julia Schröder¹⁸⁹ & Carlo Maj¹⁹⁰

## Data collection members

Selina Rolker¹⁸⁰, Markus M. Nöthen¹⁸⁰, Julia Fazaal¹⁸⁰, Verena Keitel¹⁹¹, Björn-Erik Ole Jensen¹⁹¹, Torsten Feldt¹⁹¹, Ingo Kurth¹⁹², Nikolaus Marx¹⁹³, Michael Dreher¹⁹⁴, Isabell Pink¹⁹⁵, Markus Cornberg¹⁹⁶, Thomas Illig¹⁹⁷, Clara Lehmann¹⁹⁸,¹⁹⁹,²⁰⁰, Philipp Schommers¹⁹⁸,¹⁹⁹,²⁰⁰, Max Augustin¹⁹⁸, Jan Rybnik¹⁹⁸, Lisa Knopp¹⁹⁹, Thomas Eggermann¹⁹², Sonja Volland¹⁹⁷, Janine Altmüller²⁰², Marc M. Berger²⁰³, Thorsten Brenner²⁰³, Anke Hinney²⁰⁴, Oliver Witzke²⁰⁵, Robert Bals²⁰⁶, Christian Herr²⁰⁶, Nicole Ludwig²⁰⁷ & Jörn Walter²⁰⁸

# CHRIS

## Analysis team leader

Christian Fuchsberger²⁰⁹

## Data collection leaders

Cristian Pattaro²⁰⁹ & Alessandro De Grandi²⁰⁹

## Admin team leader

Peter Pramstaller²⁰⁹

## Analysis team members

David Emmert²⁰⁹, Roberto Melotti²⁰⁹ & Luisa Foco²⁰⁹

## Admin team members

Deborah Mascalzoni²⁰⁹, Martin Gögele²⁰⁹, Francisco Domingues²⁰⁹ & Andrew Hicks²⁰⁹

# Colorado Center for Personalized Medicine (CCPM)

## Analysis team leader

Christopher R. Gignoux²¹⁰

## Data collection leaders

Stephen J. Wicks²¹⁰ & Kristy Crooks²¹⁰

## Admin team leader

Kathleen C. Barnes²¹⁰

## Analysis team members

Michelle Daya²¹⁰, Jonathan Shortt²¹⁰, Nicholas Rafaels²¹⁰ & Sameer Chavan²¹⁰

# Columbia University COVID-19 Biobank

## Analysis team leaders

David B. Goldstein²¹¹ & Krzysztof Kiryluk²¹²

## Data collection leaders

Soumitra Sengupta²¹³, Wendy Chung²¹⁴ & Muredach P. Reilly²¹⁵

## Analysis team members

Atlas Khan²¹⁵, Chen Wang²¹⁵, Gundula Povysil²¹⁶, Nitin Bhardwaj²¹⁶, Ali G. Gharavi²¹⁵ & Iuliana Ionita-Laza²¹⁷

## Data collection members

Ning Shang²¹⁵, Sheila M. O'Byrne²¹⁵, Renu Nandakumar²¹⁵, Amritha Menon²¹³, Yat S. So²¹³ & Eldad Hod²¹⁸

## Admin team member

Danielle Pendrick²¹⁸

# Corea (Genetics of COVID-19-related Manifestation)

## Analysis team leader

Han-Na Kim²¹⁰,²²⁰

## Data collection leaders

Soo-Kyung Park²²¹, Hyung-Lae Kim²²², Chang Kyung Kang²²³, Hyo-Jung Lee²²⁴ & Kyoung-Ho Song²²⁵

## Admin team leaders

Kyung Jae Yoon²²⁶,²²⁷,²²⁸ & Nam-Jong Paik²²⁹,²³⁰

## Analysis team members

Woojin Seok²³¹ & Heejun Yoon²³²

## Data collection members

Eun-Jeong Joo²³³, Yoosoo Chang²³⁴,²³⁵, Seungho Ryu²³⁴,²³⁵, Wan Beom Park²²³, Jeong Su Park²³⁶, Kyoung Un Park²³⁶, Sin Young Ham²²⁵, Jongtak Jung²²⁵, Eu Suk Kim²²⁵ & Hong Bin Kim²²⁵
Data collection leaders

Pierre-Yves Bochud³⁹⁸, Stéphanie Bibert³⁹⁸, Noémie Boillat³⁹⁸, Semira Gonseth Nussle³⁹⁸ & Werner Albrich³⁹⁸

Analysis team members

Mathieu Quinodoz⁶⁴,³⁸⁴ & Dhryata Kamdar³⁸⁴,³⁸⁵

Data collection members

Noémie Suh³⁹⁰, Dionysios Neofytos³⁹¹, Véronique Erard³⁹², Cathy Voide³⁹³, FHoGID*, RegCOVID*, P-PredictUs*, SeroCOVID* & CRiPSI*

FHoGID

P. Y. Bochud³⁹⁴,³⁹⁵,³⁹⁸, C. Rivolta³⁹⁴, S. Bibert³⁹⁴, M. Quinodoz³⁹⁴, D. Kamdar³⁹⁴, D. Neofytos³⁹⁴, V. Erard³⁹⁴, C. Voide³⁹⁴, R. Friolet³⁹⁴, P. Vollenweider³⁹⁴,³⁹⁵, J. L. Pagani³⁹⁴,³⁹⁵, M. Oddo³⁹⁴, F. Meyer zu Bentrup³⁹⁴, A. Conen³⁹⁴, O. Clerc³⁹⁴, O. Marchetti³⁹⁴, A. Guillet³⁹⁴, C. Guyat-Jacques³⁹⁴, S. Foucas³⁹⁴, M. Rime³⁹⁴, J. Chassot³⁹⁴, M. Jaquet³⁹⁴, R. Merlet Viollet³⁹⁴, L. Yannepouden³⁹⁴ & L. Portopena³⁹⁴

RegCOVID

P. Y. Bochud³⁹⁴,³⁹⁵,³⁹⁸, P. Vollenweider³⁹⁴,³⁹⁵, J. L. Pagani³⁹⁴,³⁹⁵, F. Desgranges³⁹⁵, P. Filippidis³⁹⁵, B. Guéry³⁹⁵, D. Haefliger³⁹⁵, E. E. Kampouri³⁹⁵, O. Manuel³⁹⁵, A. Munting³⁹⁵, M. Papadimitr ou-Olivgeris³⁹⁵, J. Regina³⁹⁵, L. Rochat-Stettler³⁹⁵, V. Suttels³⁹⁵, E. Tadini³⁹⁵, J. Tschopp³⁹⁵, M. Van Singer³⁹⁵ & B. Viala³⁹⁵

P-PredictUs

N. Boillat-Blanco³⁹⁶, T. Brahier³⁹⁶, O. Hügli³⁹⁶, J. Y. Meuwly³⁹⁶ & O. Pantet³⁹⁶

SérocoVID

S. Gonseth Nussle³⁹⁷, M. Bochud³⁹⁷, V. D'Acremont³⁹⁷ & S. Estoppey Younes³⁹⁷

CRiPSI

W. C. Albrich³⁹⁸, N. Suh³⁹⁸, A. Cerny³⁹⁸, L. O'Mahony³⁹⁸, C. von Mering, P. Y. Bochud³⁹⁴,³⁹⁵,³⁹⁸, M. Frischknecht³⁹⁸, G.-R. Kleger³⁹⁸, M. Filipovic³⁹⁸, C. R. Kahler³⁹⁸, H. Wozniak³⁹⁸, T. Rochat Negro³⁹⁸, J. Pugin³⁹⁸, K. Bouras³⁹⁸, C. Knapp³⁹⁸, T. Egger³⁹⁸, A. Perret³⁹⁸, P. Montillier³⁹⁸, C. di Bartolomeo³⁹⁸ & B. Barda³⁹⁸

GCAT Genomes For Life

Analysis team leader

Rafael de Cid³⁹⁹

Data collection leaders

Anna Carreras³⁹⁹, Victor Moreno⁴⁰⁰ & Manolis Kogevinas³³⁵,³³⁶,³³⁷,³³⁸

Analysis team members

Iván Galván-Femenia³⁹⁹, Natalia Blay³⁹⁹, Xavier Farré³⁹⁹ & Lauro Sumoy³⁹⁹

Data collection members

Beatriz Cortés³⁹⁹, Josep Maria Mercader⁴⁰¹,⁴⁰²,⁴⁷⁷,³²⁷,¹²⁷,³⁷⁸, Marta Guindo-Martinez⁴⁰¹, David Torrents⁴⁰¹, Judith Garcia-Aymerich³³⁵,³³⁶,³³⁷, Gemma Castaño-Vinyals³³⁵,³³⁶,³³⁷,³³⁸ & Carlota Dobaño³³⁵,³³⁶

GEN-COVID Multicenter Study

Analysis team leaders

Marco Gori⁴⁰⁴,⁴⁰⁵ & Mari E. K. Niemi¹¹²⁸⁰

Data collection leaders

Alessandra Renieri⁶³,⁶⁴,⁶⁵, Francesca Mari⁶³,⁶⁴,⁶⁵, Mario Umberto Mondelli⁴⁰⁸,⁴⁰⁹, Francesco Castelli⁴¹⁰, Massimo Vaghi⁴¹¹, Stefano Rusconi⁴¹²,⁴¹³, Francesca Montagnani⁶⁵,⁴¹⁴, Elena Bargagli⁶⁵, Federico Franchi⁶¹⁸, Maria Antonietta Mazzei⁴¹⁹, Luca Cantarini⁴¹⁹, Danilo Tacconi⁴¹⁹, Marco Ferri⁴²⁰, Raffaele Scala⁴²⁰, Genni Spargi⁴²², Cesira Nencioni⁴²³, Maria Bandini⁴²⁴, Gian Piero Caldarelli⁴²⁵, Maurizio Spagnesi⁴²⁴, Anna Canacci⁴²⁶, Agostino Ognibene⁴²⁷, Antonella D'Arminio Monforte⁴²⁸, Massimo Girardis⁴²⁹, Andrea Antinori⁴³⁰, Daniela Francisci⁴³¹,⁴³², Elisabetta Schiaroli⁴³¹,⁴³², Pier Giorgio Scotton⁴³³, Sandro Panese⁴³⁴, Renzo Scaggiante⁴³⁵, Matteo Della Monica⁴³⁶, Mario Capasso⁴³⁷,⁴³⁸,⁴³⁹, Giuseppe Fiorentino⁴⁴⁰, Marco Castori⁴⁴¹, Filippo Acucella⁴⁴², Antonio Di Biagio⁴⁴³, Luca Masucci⁴⁴,⁴⁴,⁴⁵, Serafina Valente⁴⁴⁶, Marco Mandalà⁴⁴⁷, Patrizia Zuchi⁴⁴⁸, Ferdinando Giannattasio⁴⁴⁹, Domenico A. Coviello¹²,⁴⁵⁰, Cristina Mussini⁴⁵¹, Giancarlo Bosio⁴⁵², Luisa Tavecchia⁴⁵³, Licia Crotti⁴⁵,⁴⁵,⁴⁵,⁴⁶,⁴⁷, Marco Rizzi⁴⁵⁸, Maria Teresa La Rovere⁴⁵⁹, Simona Sarzi-Braga⁴⁶⁰, Maurizio Bussotti⁴⁶¹, Sabrina Ravaglia⁴⁶², Rosangela Artuso⁴⁶³, Antonio Perrella⁴⁶⁴, Davide Romani⁴⁶⁵, Paola Bergomi⁴⁶⁶, Emanuele Catena⁴⁶⁶, Antonella Vincenti¹²³³, Claudio Ferri¹²³², Davide Grassi¹²³², Gloria Pessina¹²³³, Mario Tumbarello⁸⁵,⁴⁴¹,⁴⁵, Massimo Di Pietro¹²³⁴, Ravaglia Sabrina⁴⁶², Sauro Luchi¹²³⁵, Chiara Barbieri¹²³⁵, Donatella Acquilini¹²³⁵, Elena Andreucci¹²³⁵, Francesco Paciosi¹²³⁵, Francesco Vladimiro Segala¹²³⁸, Giusy Tiseo¹²³⁶, Marco Falcone¹²³⁶, Mirjam Lista⁶³,⁶⁵, Monica Poscente¹²³⁵, Oreste De Vivo⁴⁴⁶, Paola Petrocelli¹²³⁵, Alessandra Guarnaccia⁴⁴,⁴⁴,⁴⁵, Silvia Baroni¹²³⁹ & Valentina Perticaroli⁶³,⁶⁴,⁶⁵

Admin team leaders

Simone Furin⁵⁶⁰ & Simona Dei⁴⁶⁷

Analysis team members

Elisa Benetti⁵⁵, Nicola Picchiotti⁴⁰⁴,⁴⁶⁸, Maurizio Sanarico⁴⁶⁹, Stefano Ceri⁵⁶, Pietro Pinol⁵⁶, Francesco Raimondi⁴⁷⁰, Filippo Biscarini⁴⁷¹, Alessandra Stella⁴⁷¹, Mattia Bergomi⁴⁷³,

Kristina Zguro⁶⁵, Katia Capitani⁶⁵,⁴⁷⁵, Mattia Cordioli¹, Sara Pigazzini¹, Lindokuhle Nkambule³,²⁹,³⁶⁷ & Marco Tanfoni⁴⁶⁸

Data collection members

Chiara Fallerini⁶³,⁶⁵, Sergio Daga⁶³,⁶⁵, Margherita Baldassarri⁶³,⁶⁵, Francesca Fava⁶³,⁶⁴,⁶⁵, Elisa Frullanti⁶³,⁶⁵, Floriana Valentino⁶³,⁶⁵, Gabriella Doddato⁶³,⁶⁵, Annarita Gilberti⁶³,⁶⁵, Rossella Tita⁶⁴, Sara Amitrano⁶⁴, Mirella Bruttini⁶³,⁶⁵,⁶⁴, Susanna Croci⁶³,⁶⁵, Ilaria Meloni⁶³,⁶⁵, Maria Antonietta Mencarelli⁶⁴, Caterina Lo Rizzo⁶⁴, Anna Maria Pinto⁶⁴, Giada Beligni⁶³,⁶⁵, Andrea Tommasi⁶³,⁶⁴,⁶⁵, Laura Di Sarno⁶³,⁶⁵, Maria Palmieri⁶³,⁶⁵, Miriam Lucia Carriero⁶³,⁶⁵, Diana Alaverdian⁶³,⁶⁵, Nicola Iuso⁶³,⁶⁵, Gabriele Inchingolo⁶³,⁶⁵, Stefano Busani⁴²⁹, Raffaele Bruno⁴⁰⁸,⁴⁰⁹, Marco Vecchia⁴⁷⁸, Mary Ann Belli⁴⁵³, Stefania Mantovani⁴⁷⁸, Serena Ludovisi⁴⁰⁸,⁴⁰⁹, Eugenia Quiros-Roldan⁴⁰⁰, Melanie Degli Antoni⁴¹⁰, Isabella Zanella⁴⁷⁹,⁴⁸⁰, Matteo Siano⁴¹³, Arianna Emiliozzi⁴³⁰, Massimiliano Fabbiani⁴¹⁴, Barbara Rossetti⁴¹⁴, Giacomo Zanelli⁶⁵,⁴¹⁴, Laura Bergantini⁴¹⁵, Miriana D'Alessandro⁴¹⁵, Paolo Cameli⁴¹⁵, David Bennet⁴¹⁵, Federico Anedda⁴¹⁶, Simona Marcantonio⁴¹⁶, Sabino Scolletta⁴¹⁶, Susanna Guerrini⁴¹⁷, Edoardo Conticini⁴¹⁸, Bruno Frediani⁴¹⁸, Chiara Spertilli⁴¹⁹, Alice Donati⁴²⁰, Luca Guidelli⁴²¹, Marta Corridi⁴²², Leonardo Croci⁴²³, Paolo Piacentini⁴³⁵, Elena Desanctis⁴²⁴, Silvia Cappelli⁴²⁴, Agnese Verzuri⁴²⁶, Valentina Anemoli⁴²⁶, Alessandro Pancrazi⁴²⁷, Maria Lorubbio⁴²⁷, Esther Merlini⁴²⁸, Federica Gaia Miraglia⁴²⁸, Sophie Venturelli⁴²⁹, Andrea Cossarizza⁴⁸¹, Alessandra Vergori⁴³⁰, Arianna Gabriel¹⁴¹, Agostino Riva¹⁴¹,⁴²,¹⁴³, Francesco Paciosi⁴³², Francesca Andretta⁴³³, Francesca Gatti⁴³³, Saverio Giuseppe Parisi⁴³⁵, Stefano Baratti⁴³⁵, Carmelo Piscopo⁴³⁶, Roberta Russo⁴³⁷,⁴³⁸, Immacolata Andolfo⁴³⁷,⁴³⁸, Achille Iolascon⁴³⁷,⁴³⁸, Massimo Carella⁴⁴¹, Giuseppe Merla⁴⁵,⁴⁷,⁴⁸, Gabriella Maria Squeo⁴⁸³, Pamela Raggi⁴⁸⁴, Carmen Marciano⁴⁸⁴, Rita Perna⁴⁸⁴, Matteo Bassetti⁴⁴,⁴³,⁴⁸⁵, Maurizio Sanguinetti⁴⁴,⁴⁴,⁴⁵, Alessia Giorli⁴⁴⁷, Lorenzo Salerni⁴⁴⁷, Pierpaolo Parravicini⁴⁴⁸, Elisabetta Menatti⁴⁸⁸, Tullio Trotta⁴⁴⁹, Gabriella Coiro⁴⁴⁹, Fabio Lena⁴⁷⁸, Enrico Martinelli⁴⁵², Sandro Mancarella⁴⁵³, Chiara Gabbi⁴⁸⁸, Franco Maggiolo⁴⁵⁸, Diego Ripamonti⁴⁵⁸, Tiziana Bachetti⁴⁸⁹, Claudia Suardi⁴⁹⁰, Gianfranco Parati⁴⁵,⁴⁵,⁴⁹, Giordano Bottà⁴⁹¹, Paolo Di Domenico⁴⁹¹, Ilaria Rancan¹⁴¹, Francesco Bianchi⁸⁵,⁴⁶⁴, Riccardo Colombo⁴⁶⁶, Chiara Barbieri¹²³⁵, Donatella Acquilini¹²³⁵, Elena Andreucci¹²³⁵, Francesco Paciosi¹²³⁵, Francesco Vladimiro Segala¹²³⁸, Giusy Tiseo¹²³⁶, Marco Falcone¹²³⁶, Mirjam Lista⁶³,⁶⁵, Monica Poscente¹²³⁵, Oreste De Vivo⁴⁴⁶, Paola Petrocelli¹²³⁵, Alessandra Guarnaccia⁴⁴,⁴⁴,⁴⁵, Silvia Baroni¹²³⁹ & Valentina Perticaroli⁶³,⁶⁴,⁶⁵

Genes & Health

Analysis team leader

David A. van Heel⁵⁰

Data collection leader

Karen A. Hunt⁵⁰

Admin team leader

Richard C. Trembath⁴⁹³

Analysis team members

Qin Qin Huang⁴⁹⁴ & Hilary C. Martin⁴⁹⁴

Data collection members

Dan Mason⁴⁹⁶, Bhavi Trivedi⁵⁰ & John Wright⁴⁹⁵

Admin team members

Sarah Finer⁴⁹⁷, Genes & Health Research Team* & Christopher J. Griffiths⁴⁹⁹

Genes & Health Research Team

Shaheen Akhtar⁴⁰⁸, Mohammad Anwar⁴⁰⁸, Elena Arciero⁴⁰⁸, Samina Ashraf⁴⁰⁸, Gerome Breen⁴⁰⁸, Raymond Chung⁴⁰⁸, Charles J. Curtis⁴⁰⁸, Maharun Chowdhury⁴⁰⁸, Grainne Colligan⁴⁰⁸, Panos Deloukas⁴⁰⁸, Ceri Durham⁴⁰⁸, Sarah Finer⁴⁰⁸, Chris Griffiths⁴⁰⁸, Qin Qin Huang⁴⁰⁸, Matt Hurles⁴⁰⁸, Karen A. Hunt⁴⁰⁸, Shapna Hussain⁴⁰⁸, Kamrul Islam⁴⁰⁸, Ahsan Khan⁴⁰⁸, Amara Khan⁴⁰⁸, Cath Lavery⁴⁰⁸, Sang Hyuck Lee⁴⁰⁸, Robin Lerner⁴⁰⁸, Daniel MacArthur⁴⁰⁸, Bev MacLaughlin⁴⁰⁸, Hilary Martin⁴⁰⁸, Dan Mason⁴⁰⁸, Shefa Miah⁴⁰⁸, Bill Newman⁴⁰⁸, Nishat Safa⁴⁰⁸, Farah Tahmasebi⁴⁰⁸, Richard C. Trembath⁴⁰⁸, Bhavi Trivedi⁴⁰⁸, David A. van Heel⁴⁰⁸ & John Wright⁴⁰⁸

Genes for Good

Analysis team leader

Albert V. Smith⁵⁰⁰

Data collection members

Andrew P. Boughton⁵⁰⁰, Kevin W. Li⁵⁰⁰, Jonathon LeFaive⁵⁰⁰ & Aubrey Annis⁵⁰⁰

Genetic determinants of COVID-19 complications in the Brazilian population

Analysis team leader

Mari E. K. Niemi¹¹²⁸⁰

Data collection leader

Cinthia E. Jannes⁵⁰¹

Admin team leaders

Jose E. Krieger⁵⁰¹ & Alexandre C. Pereira⁵⁰¹

Analysis team members

Mariliza Velho⁵⁰¹, Emanuelle Marques⁵⁰¹, Mattia Cordioli¹, Sara Pigazzini¹ & Lindokuhle Nkambule³,²⁹,³⁶⁷
**Data collection members**

Isabella Ramos Lima⁵⁰¹, Mauricio Teruo Tada⁵⁰¹ & Karina Valino⁵⁰¹

**Genetic influences on severity of COVID-19 illness in Korea**

**Analysis team leaders**

Mark McCarthy⁵⁰² & Carrie Rosenberger⁵⁰²

**Data collection leader**

Jong Eun Lee⁵⁰³

**Analysis team members**

Diana Chang⁵⁰², Christian Hammer⁵⁰², Julie Hunkapiller⁵⁰², Anubha Mahajan⁵⁰², Sarah Pendergrass⁵⁰², Lara Sucheston-Campbell⁵⁰² & Brian Yaspan⁵⁰²

**Data collection members**

Hyun Soo Lee⁵⁰³, Eunsoon Shin⁵⁰³, Hye Yoon Jang⁵⁰³, Sunmie Kim⁵⁰⁴, Sungmin Kym⁵⁰⁵, Yeon-Sook Kim⁵⁰⁵, Hyeongseok Jeong⁵⁰⁵, Ki Tae Kwon⁵⁰⁷, Shin-Woo Kim⁵⁰⁷, Jin Yong Kim⁵⁰⁸, Young Rock Jang⁵⁰⁸, Hyun ah Kim⁵⁰⁹, Ji Yeon Lee⁵⁰⁹, Jeong Eun Lee⁵¹⁰, Shinwon Lee⁵¹⁰, Kang-Won Choe⁵¹¹, Yu Min Kang⁵¹¹, Sun Ha Jee⁵¹² & Keum Ji Jung⁵¹²

**Genomic epidemiology of SARS-CoV-2host genetics in coronavirus disease 2019**

**Data collection leaders**

Victoria Parikh⁵¹³, Euan Ashley⁵¹⁴,⁵¹⁵, Matthew Wheeler⁵¹³, Manuel Rivas⁵¹⁶, Carlos Bustamante⁵¹⁵,⁵¹⁶, Benjamin Pinsky⁵¹⁸, Phillip Febbo⁵¹⁹, Kyle Farh⁵¹⁹, Gary P. Schroth⁵¹⁹ & Francis deSouza⁵¹⁹

**Admin team leaders**

Karen Dalton⁵¹³ & Jeff Christle⁵¹³

**Analysis team members**

Christopher Deboever⁵²⁰, Sándor Szalma⁵²⁰, Yosuke Tanigawa⁵¹⁶, Simone Rubinacci⁵²¹ & Olivier Delaneau⁵²¹

**Data collection members**

John Gorzynski⁵¹³, Hannah de Jong⁵¹³, Shirley Sutton⁵¹³, Nathan Youtlton⁵¹³, Ruchi Joshi⁵¹³, David Jimenez-Morales⁵¹³, Christopher Hughes⁵¹³, David Amar⁵¹³, Alex Ioannidis⁵¹⁸, Steve Hershman⁵¹³, Anna Kirillova⁵¹³, Kinya Seo⁵¹³, Yong Huang⁵¹³, Massa Shoura⁵¹⁸, Nathan Hammond⁵¹⁸, Nathaniel Watson⁵¹⁸, Archana Raja⁵¹³, ChunHong Huang⁵¹⁸, Malaya Sahoo⁵¹⁸ & Hannah Wang⁵¹⁸

**Admin team member**

Jimmy Zhen⁵¹³

**Genotek COVID-19 study**

**Analysis team leader**

Alexander Rakitko⁹⁷⁴

**Admin team leader**

Valery Ilinsky⁹⁷⁴

**Analysis team members**

Danat Yermakovich⁹⁷⁵, Iaroslav Popov⁹⁷⁵, Alexander Chernitsov⁹⁷⁵, Elena Kovalenko⁹⁷⁵, Anna Krasnenko⁹⁷⁴, Nikolay Plotnikov⁹⁷⁴, Ivan Stetsenko⁹⁷⁴ & Anna Kim⁹⁷⁴

**Helix & Healthy Nevada Project Exome+ COVID-19 Phenotypes**

**Analysis team leader**

Elizabeth T. Cirulli⁹⁷⁵

**Analysis team members**

Kelly M. Schiabor Barrett⁹⁷⁵, Alexandre Bolze⁹⁷⁵, Simon White⁹⁷⁵, Nicole L. Washington⁹⁷⁵ & James T. Lu⁹⁷⁵

**Data collection members**

Stephen Riffle⁹⁷⁵, Francisco Tanudjaja⁹⁷⁵, Xueqing Wang⁹⁷⁵, Jimmy M. Ramirez III⁹⁷⁵, Nicole Leonetti⁹⁷⁵, Efren Sandoval⁹⁷⁵, Iva Neveux⁹⁷⁶, Shaun Dabe⁹⁷⁷ & Joseph J. Grzymski⁹⁷⁶

**24Genetics & IdiPaz Genomic Variants associated to COVID-19 infection outcome**

**Analysis team leader**

Juan Ignacio Esteban Miñano⁹⁷⁸

**Data collection leader**

Luis A. Aguirre⁹⁷⁹

**Admin team leader**

Eduardo López-Collazo⁹⁷⁹

**Analysis team members**

Manuel de la Mata Pazos⁹⁷⁸, Luciano Cerrato⁹⁷⁸ & Lasse Folkersen⁹⁷⁸

**Data collection members**

Roberto Lozano-Rodríguez⁹⁷⁹, José Avendaño-Ortiz⁹⁷⁹, Verónica Terrón Arcos⁹⁷⁹, Karla Marina Montalbán-Hernández⁹⁷⁹, Jaime Valentin Quiroga⁹⁷⁹ & Alejandro Pascual-Iglesias⁹⁷⁹

**Admin team members**

Charbel Marouel-Eid⁹⁷⁹ & Alejandro Martín-Quirós⁹⁷⁹

**Japan Coronavirus Taskforce**

**Analysis team leaders**

Ho Namkoong⁹⁸⁰, Yukinori Okada⁹⁸¹,⁹⁸²,⁹⁸³ & Seiya Imoto⁹⁸⁴

**Data collection leaders**

Kazuhiko Katayama⁹⁸⁵, Koichi Fukunaga⁹⁸⁰, Yuko Kitagawa⁹⁸⁶, Toshiro Sato⁹⁸⁷, Naoki Hasegawa⁹⁸⁸, Atsushi Kumanogoh⁹⁸³,⁹⁸⁹,⁹⁹⁰, Akinori Kimura⁹⁸¹, Masumi Ai⁹⁹² & Katsushi Tokunaga⁹⁹³

**Admin team leaders**

Takanori Kanai⁹⁹⁴, Satoru Miyano⁹⁹⁵ & Seishi Ogawa⁹⁹⁶,⁹⁹⁷

**Analysis team members**

Ryuya Edahiro⁹⁸¹,⁹⁸⁹, Kyuto Sonehara⁹⁸¹, Yuya Shira¹⁹⁷,⁹⁸⁰,⁹⁸¹ & Masahiro Kanai²⁷⁴

**Data collection members**

Makoto Ishii⁹⁸⁰, Hiroki Kabata⁹⁸⁰, Katsunori Masaki⁹⁸⁰, Hirofumi Kamata⁹⁸⁰, Shinnosuke Ikemura⁹⁸⁰, Shotaro Chubachi⁹⁸⁰, Satoshi Okamori⁹⁸⁰, Hideki Terai⁹⁸⁰, Hiromu Tanaka⁹⁸⁰, Atsuho Morita⁹⁸⁰, Ho Lee⁹⁸⁰, Takanori Asakura⁹⁸⁰, Junichi Sasaki⁹⁹⁹, Hiroshi Morisaki¹⁰⁰⁰, Yoshifumi Uwamino¹⁰⁰¹, Kosaku Nanki⁹⁹⁴, Yohei Mikami⁹⁹⁴, Kazunori Tomono¹⁰⁰², Kazuto Kato¹⁰⁰³, Fumihiko Matsuda¹⁰⁰⁴, Meiko Takahashi¹⁰⁰⁴, Nobuyuki Hizawa¹⁰⁰⁵, Yoshito Takeda⁹⁸⁹, Haruhiko Hirata⁹⁸⁹, Takayuki Shiroyama⁹⁸⁹, Satoru Miyawaki¹⁰⁰⁸, Ken Suzuki⁹⁸¹, Yuichi Maeda⁹⁸⁹,¹⁰⁰⁷, Takuro Niij⁹⁸⁰,¹⁰⁰⁷, Yoshimi Noda⁹⁸⁹, Takayuki Niitsu⁹⁸⁸, Yuichi Adachi⁹⁸⁸, Takatoshi Enomoto⁹⁸⁸, Saori Amiya⁹⁸⁸, Reina Hara⁹⁸⁸, Kunihiko Takahashi⁹⁹⁵, Tatsuhiko Anzai⁹⁹⁶, Takanori Hasegawa⁹⁸⁵, Satoshi Ito⁹⁹⁵, Ryuji Koike¹⁰⁰⁹, Akifumi Endo¹⁰¹⁰, Yuji Uchimura¹⁰¹¹, Yasunari Miyazaki¹⁰¹², Takayuki Honda¹⁰¹², Tomoya Tateishi¹⁰¹², Shuji Tohda¹⁰¹³, Naoya Ichimura¹⁰¹³, Kazunari Sonobe¹⁰¹³, Chihiro Sassa¹⁰¹³, Jun Nakajima¹⁰¹³, Yasuhito Nanny¹⁰¹⁴, Yosuke Omae⁹⁹³, Kazuhisa Takahashi¹⁰¹⁵, Norihiro Harada¹⁰¹⁵, Makoto Hiki¹⁰¹⁶,¹⁰¹⁷, Haruhi Takagi¹⁰¹⁵, Ai Nakamura¹⁰¹⁵, Etsuko Tagaya¹⁰¹⁸, Masatoshi Kawana¹⁰¹⁹, Ken Arimura¹⁰¹⁸, Takashi Ishiguro¹⁰²⁰, Noboru Takayanagi¹⁰²⁰, Taisuke Isono¹⁰²⁰, Yotaro Takaku¹⁰²⁰, Kenji Takano¹⁰²⁰, Ryusuke Anan¹⁰²¹, Yukiko Nakajima¹⁰²¹, Yasushi Nakano¹⁰²¹, Kazumi Nishio¹⁰²¹, Soichiro Ueda¹⁰²², Reina Hayashi¹⁰²², Hiroki Teno¹⁰²³, Isano Hase¹⁰²³, Shuichi Yoshida¹⁰²³, Shoji Suzuki¹⁰²³, Keiko Mitamura¹⁰²⁴, Fumitake Saito¹⁰²⁵, Tetsuya Ueda¹⁰²⁶, Masanori Azuma¹⁰²⁶, Tadao Nagasaki¹⁰²⁶, Yoshinori Yasu¹⁰²⁸, Yoshinori Hasegawa¹⁰²⁸, Yoshikazu Mutoh¹⁰²⁹, Takashi Yoshiyama¹⁰³⁰, Tomohisa Shoko¹⁰³¹, Mitsuaki Kojima¹⁰³¹, Tomohiro Adachi¹⁰³¹, Motonao Ishikawa¹⁰³², Kenichiro Takahashi¹⁰³³, Kazuyoshi Watanabe¹⁰³⁴, Tadashi Manabe¹⁰³⁵, Fumimaro Ito¹⁰³⁵, Takahiro Fukui¹⁰³⁵, Yohei Funatsu¹⁰³⁵, Hidefumi Koh¹⁰³⁵, Yoshihiro Hirai¹⁰³⁶, Hidetoshi Kawashima¹⁰³⁶, Atsuya Narita¹⁰³⁶, Kazuki Niwa¹⁰³⁷, Yoshiyuki Sekikawa¹⁰³⁷, Fukuki Saito¹⁰³⁸, Kazuhisa Yoshiya¹⁰³⁸, Tomoyuki Yoshihara¹⁰³⁸, Yusuke Suzuki¹⁰³⁹, Sohei Nakayama¹⁰³⁹, Keita Masuzawa¹⁰³⁹, Koichi Nishi¹⁰⁴⁰, Masaru Nishitsuji¹⁰⁴⁰, Maiko Tani¹⁰⁴⁰, Takashi Inoue¹⁰⁴¹, Toshiyuki Hirano¹⁰⁴¹, Keigo Kobayashi¹⁰⁴¹, Naoki Miyazawa¹⁰⁴², Yasuhiro Kimura¹⁰⁴², Reiko Sado¹⁰⁴², Takashi Ogura¹⁰⁴³, Hideya Kitamura¹⁰⁴³, Kota Murohashi¹⁰⁴³, Ichiro Nakachi¹⁰⁴⁴, Rie Baba¹⁰⁴⁴, Daisuke Arai¹⁰⁴⁴, Satoshi Fuke¹⁰⁴⁵, Hiroshi Saito¹⁰⁴⁵, Naota Kuwahara¹⁰⁴⁶, Aikiko Fujiwara¹⁰⁴⁶, Takenori Okada¹⁰⁴⁶, Tomoya Baba¹⁰⁴⁷, Junya Noda¹⁰⁴⁷, Shuko Mashimo¹⁰⁴⁷, Kazuma Yagiri¹⁰⁴⁸, Tetsuya Shiomi¹⁰⁴⁸, Mitsuha Hashiguchi¹⁰⁴⁸, Toshio Odani¹⁰⁴⁹, Takao Mochimaru¹⁰⁵⁰,¹⁰⁵¹, Yoshitaka Oyamada¹⁰⁵⁰,¹⁰⁵¹, Nobuaki Mori¹⁰⁵², Namiki Izumi¹⁰⁵³, Kaoru Nagata¹⁰⁵³, Reiko Taki¹⁰⁵⁵, Koji Murakami¹⁰⁵⁴, Mitsuhiro Yamada¹⁰⁵⁴, Hisatoshi Sugiura¹⁰⁵⁴, Kentaro Hayashi¹⁰⁵⁵, Tetsuo Shimizu¹⁰⁵⁵, Yasuhiro Gon¹⁰⁵⁵, Shigeki Fujitani¹⁰⁵⁶, Tomoya Tsuchida¹⁰⁵⁶, Toru Yoshida¹⁰⁵⁶, Takashi Kagaya¹⁰⁵⁸, Toshiyuki Kita¹⁰⁵⁸, Satoru Sakagami¹⁰⁵⁸, Yoshifumi Kimizuka¹⁰⁵⁹, Akihiko Kawana¹⁰⁵⁹, Yoshihiko Nakamura¹⁰⁶⁰, Hiroyasu Ishikura¹⁰⁶⁰, Tohru Takata¹⁰⁶¹, Takahide Kikuchi¹⁰⁶², Daisuke Taniyama¹⁰⁶², Morio Nakamura¹⁰⁶², Nobuhiro Kodama¹⁰⁶³, Yasunari Kaneyama¹⁰⁶³, Shunsuke Maeda¹⁰⁶³, Yoji Nagasaki¹⁰⁶⁴, Masaki Okamoto¹⁰⁶⁵,¹⁰⁷², Sayoko Ishihara¹⁰⁶⁴, Akihiro Ito¹⁰⁶⁷, Yusuke Chihara¹⁰⁶⁸, Mayumi Takeuchi¹⁰⁶⁸, Keisuke Ono¹⁰⁶⁸, Naozumi Hashimoto¹⁰⁶⁹, Keiko Wakahara¹⁰⁶⁹, Akira Ando¹⁰⁶⁹, Makoto Masuda¹⁰⁷⁰, Aya Wakabayashi¹⁰⁷⁰, Hiroki Watanabe¹⁰⁷⁰, Hisako Sageshima¹⁰⁷¹, Taka-Aki Nakada¹⁰⁷², Ryuzo Abe¹⁰⁷², Tadanaga Shimada¹⁰⁷², Kodai Kawamura¹⁰⁷³, Kazuya Ichikado¹⁰⁷³, Kenta Nishiyama¹⁰⁷³, Masaki Yamasaki¹⁰⁷⁴, Satoru Hashimoto¹⁰⁷⁴, Yu Kusaka¹⁰⁷⁵, Takehiko Ohba¹⁰⁷⁵, Susumu Isogai¹⁰⁷⁵, Minoru Takada¹⁰⁷⁶, Hidenori Kanda¹⁰⁷⁶, Yuko Komase¹⁰⁷⁷, Fumiaki Sano¹⁰⁷⁸, Koichiro Asano¹⁰⁷⁹, Tsuyoshi Oguma¹⁰⁸⁰, Masahiro Harada¹⁰⁸¹, Takeshi Takahashi¹⁰⁸¹, Takayuki Shibusawa¹⁰⁸¹, Shinji Abe¹⁰⁸², Yuta Kono¹⁰⁸², Yuki Togashi¹⁰⁸², Takehiro Izumo¹⁰⁸³, Minoru Inomata¹⁰⁸³, Nobuyasu Awano¹⁰⁸³, Shinichi Ogawa¹⁰⁸⁴, Tomouki Ogata¹⁰⁸⁴, Shoichiro Ishihara¹⁰⁸⁴, Arihiko Kanehiro¹⁰⁸⁵, Shinji Ozaki¹⁰⁸⁵, Yasuko Fuchimoto¹⁰⁸⁵, Yuichiro Kitagawa¹⁰⁸⁶, Shozo Yoshida¹⁰⁸⁶, Shinji Ogura¹⁰⁸⁶, Kei Nishiyama¹⁰⁸⁷, Kousuke Yoshida¹⁰⁸⁸, Satoru Beppu¹⁰⁸⁸, Satoru Fukuyama¹⁰⁸⁸, Yoshihiro Eriguchi¹⁰⁹⁰, Akiko Yonekawa¹⁰⁹⁰, Yoshiaki Inoue¹⁰⁹¹, Kunihiro Yamagata¹⁰⁹², Shigeru Chiba¹⁰⁹³, Osamu Narumoto¹⁰⁹⁴, Hideaki Nagai¹⁰⁹⁴, Nobuharu Oshima¹⁰⁹⁴, Mitsuru Motegi¹⁰⁹⁵, Hironori Sagara¹⁰⁹⁶, Akihiko Tanaka¹⁰⁹⁶, Shin Ohta¹⁰⁹⁶, Yoko Shibata¹⁰⁹⁷, Yoshinori Tanino¹⁰⁹⁷, Yuki Sato¹⁰⁹⁷, Yuichiro Yamada¹⁰⁹⁸, Takuya Hashino¹⁰⁹⁸, Masato Shinoki¹⁰⁹⁸, Hajime Iwagoe¹⁰⁹⁹, Tomonori Imamura¹⁰⁰⁰, Akira Umeda¹⁰⁰¹, Hisato Shimada¹⁰¹¹, Mayu Endo¹⁰¹², Shinichi Hayashi¹⁰¹³, Mai Takahashi¹⁰¹³, Shigefumi Nakano¹⁰¹³, Masakiyo Yatomi¹⁰¹⁴, Toshitaka Maeno¹⁰¹⁴, Tomoo Ishii¹⁰¹⁵, Mitsuyoshi Utsugi¹⁰¹⁶, Akihiro Ono¹¹⁰⁰, Kensuke Kanaoka¹¹⁰⁷, Shoichi Ihara¹¹⁰⁷ & Kiyoshi Komuta¹¹⁰⁷

**Lifelines**

**Analysis team leader**

Lude Franke⁵¹
**Data collection leader**

Marike Boezen¹¹⁰⁹

**Analysis team members**

Patrick Deelen⁵¹,⁵², Annique Claringbould⁵³, Esteban Lopera⁵⁴, Robert Warmerdam⁵⁵, Judith. M. Vonk¹¹⁰⁹ & Irene van Blokland⁵¹

**Data collection members**

Pauline Lanting⁵¹ & Anil P. S. Ori¹¹¹²,¹¹¹³

**Lung eQTL Consortium**

**Data collection members**

Ma'en Obeidat¹¹¹⁴, Ana I. Hernández Cordero¹¹¹⁴, Don D. Sin¹¹⁴,¹¹¹⁵, Yohan Bossé¹¹¹⁶, Philippe Joubert¹¹¹⁶, Ke Hao¹¹¹⁷, David Nickle¹¹¹⁸,¹¹¹⁹, Wim Timens¹¹²⁰,¹¹²¹ & Maarten van den Berge¹¹²¹,¹¹²²

**Mass General Brigham-Host Vulnerability to COVID-19**

**Analysis team leaders**

Yen-Chen Anne Feng¹¹²³ & Josep Mercader²⁰,¹¹²³

**Data collection leaders**

Scott T. Weiss¹¹²⁶, Elizabeth W. Karlson¹¹²⁷, Jordan W. Smoller¹¹²⁸, Shawn N. Murphy¹¹²⁹, James B. Meigs¹¹³⁰,¹¹²⁴,¹¹²⁵ & Ann E. Woolley¹¹²⁷

**Admin team leader**

Robert C. Green²,²⁷³

**Data collection member**

Emma F. Perez²⁷³

**Michigan Genomics Initiative (MGI)**

**Analysis team leader**

Brooke Wolford¹¹³²

**Admin team leader**

Sebastian Zöllner⁵⁰⁰

**Analysis team members**

Jiongming Wang⁵⁰⁰ & Andrew Beck⁵⁰⁰

**Mount Sinai Health System COVID-19 Genomics Initiative**

**Analysis team leader**

Laura G. Sloofman²⁶

**Data collection leaders**

Steven Ascolillo¹¹³³, Robert P. Sebra¹¹⁷,¹¹³⁵, Brett L. Collins²⁶ & Tess Levy²⁶

**Admin team leaders**

Joseph D. Buxbaum²⁶ & Stuart C. Sealfon⁷

**Analysis team members**

Shea J. Andrews⁷, Daniel M. Jordan¹¹⁷,¹¹³⁷, Ryan C. Thompson¹¹³³,¹¹⁴⁰,¹¹⁴¹, Kyle Gettler¹¹¹⁷, Kumardeep Chaudhary¹¹¹⁷,¹¹⁴⁰, Gillian M. Belbin¹¹⁴⁴, Michael Preuss¹¹⁴³,¹¹⁴⁶, Clive Hoggart¹¹⁴⁷,¹¹⁴⁸,¹¹⁴², Sam Choi¹¹⁴⁷,¹¹⁴⁸,¹¹⁴²,¹¹⁴⁹ & Slayton J. Underwood²⁶

**Data collection members**

Irene Salib¹¹¹⁷, Bari Britvan²⁶, Katherine Keller²⁶, Lara Tang²⁶, Michael Peruggia²⁶, Liam L. Hiester²⁶, Kristi Niblo²⁶, Alexandra Aksentijevich²⁶, Alexander Labkowsky²⁶, Avromie Karp²⁶, Menachem Zlatopolsky²⁶ & Marissa Zyndorf¹¹⁷

**Admin team members**

Alexander W. Charney¹⁴¹,¹¹⁵⁰, Noam D. Beckmann¹¹³³, Eric E. Schadt¹¹¹⁷,¹¹³⁵, Noura S. Abul-Husn¹⁴⁴, Judy H. Choi¹¹¹⁷,¹¹⁴⁳, Yuval Itan¹¹⁷,¹¹⁴⁳, Eimear E. Kenny¹⁴⁴, Ruth J. F. Loos¹⁴³,¹¹⁴⁶,¹¹⁵¹, Girish N. Nadkarni¹¹³³,¹¹⁴³,¹¹⁵³,¹¹⁵⁴,¹¹⁵⁵, Ron Do¹¹¹⁷,¹¹⁴³, Paul O'Reilly¹¹⁴⁷,¹¹⁴⁸,¹¹⁴²,¹¹⁴⁹ & Laura M. Huckins¹¹⁴⁷,¹¹⁴⁸,¹¹⁴²

**MyCode Health Initiative**

**Analysis team leaders**

Manuel A. R. Ferreira¹¹⁵⁷ & Goncalo R. Abecasis¹¹⁵⁷

**Data collection leaders**

Joseph B. Leader¹¹⁵⁸ & Michael N. Cantor¹¹⁵⁷

**Admin team leaders**

Anne E. Justice¹¹⁵⁹ & Dave J. Carey¹¹⁶⁰

**Analysis team members**

Geetha Chittoor¹¹⁵⁹, Navya Shilpa Josyula¹¹⁵⁹, Jack A. Kosmicki¹¹⁵⁷, Julie E. Horowitz¹¹⁵⁷ & Aris Baras¹¹⁵⁷

**Data collection members**

Matthew C. Gass¹¹⁵⁸ & Ashish Yadav¹¹⁵⁷

**Admin team member**

Tooraj Mirshahi¹¹⁶⁰

**Netherlands Twin Register**

**Analysis team leader**

Jouke Jan Hottenga²²

**Data collection leader**

Meike Bartels¹²²

**Admin team leader**

Eco J. C. de Geus¹²²

**Analysis team member**

Michel G. Nivard¹²²

**Penn Medicine Biobank**

**Analysis team leaders**

Anurag Verma¹¹⁶² & Marylyn D. Ritchie¹¹⁶²

**Admin team leader**

Daniel Rader¹¹⁶²

**Analysis team members**

Bingtan Li¹¹⁶³, Shefali S. Verma¹¹⁶², Anastasia Lucas¹¹⁶² & Yuki Bradford¹¹⁶²

**Population controls**

**Analysis team leader**

Federico Zara¹²

**Analysis team members**

Vincenzo Salpietro¹², Marcello Scala¹¹⁷², Michele Iacomino¹², Paolo Scudieri¹² & Renata Bocciardi¹²

**Data collection members**

Carlo Minetti¹², Antonella Riva¹¹⁷², Maria Stella Vari¹², Myriam Mni¹⁶³, Jean-François Rahier¹¹⁷³, Elisa Giorgio¹¹⁷⁴, Federico Zara¹² & Diana Carli¹¹⁷⁵

**Data collection leaders**

Pasquale Triiano¹²,¹³, Edouad Louis¹⁷⁰, Michel Georges¹⁶³, Souad Rahmouni¹⁶³, Cynthia M. Bulik⁷⁰⁹,¹¹⁶⁶,¹¹⁶⁷, Mikael Landén⁷⁰⁹,¹¹⁶⁸, Alfredo Brusco¹¹⁶⁹ & Giovanni Battista Ferrero¹¹⁷⁰

**Admin team leaders**

Francesca Madia¹² & Bengt Fundin⁷⁰⁹

**Qatar Genome Program**

**Analysis team leader**

Hamdi Mbarek⁸⁰

**Data collection leader**

Said I. Ismail⁸⁰

**Analysis team members**

Chadi Saad⁸⁰ & Yaser Al-Sarraj⁸⁰

**Data collection members**

Radja Messai Badji⁸⁰, Wadha Al-Muftah⁸⁰, Asma Al Thani⁸⁰ & Nahla Afifi¹¹⁷⁶

**Study of the COVID-19 host genetics in the population of Latvia**

**Analysis team leader**

Janis Klovins¹¹⁷⁷

**Data collection leader**

Vita Rovite¹¹⁷⁷

**Analysis team members**

Raimonds Rescenko¹¹⁷⁷ & Raitis Peculis¹¹⁷⁷

**Data collection member**

Monta Ustinova¹¹⁷⁷

**The genetic predisposition to severe COVID-19**

**Analysis team leader**

Mari E. K. Niemi¹,¹²⁸⁰

**Data collection leader**

Hugo Zeberg¹¹⁷⁸,¹¹⁷⁹
**Analysis team members**

Mattia Cordioli¹, Sara Pigazzini¹ & Lindokuhle Nkambule³,⁹,¹⁶,¹⁷

**Data collection members**

Robert Frithiof¹¹⁸⁰, Michael Hultström¹¹⁸⁰,¹¹⁸¹ & Miklos Lipcsey¹¹⁸⁰,¹¹⁸²

**UCLA Precision Health COVID-19 Host Genomics Biobank**

**Analysis team leader**

Ruth Johnson¹¹⁸³

**Data collection leader**

UCLA Health ATLAS & Data Mart Working Group*

**UCLA Health ATLAS & Data Mart Working Group**

Daniel H. Geschwind¹¹⁸⁴

**Admin team leaders**

Nelson Freimer¹¹⁸⁵, Manish J. Butte¹⁸⁶,¹⁷,¹⁷,¹¹⁸⁴, Daniel H. Geschwind¹¹⁸⁸,¹¹⁸⁷,¹¹⁹,¹⁵² & Bogdan Pasaniuc¹¹⁸⁹,¹¹⁹,¹⁰⁰,¹¹³⁹

**Analysis team members**

Yi Ding¹⁹¹, Alec Chiu¹⁹¹, Timothy S. Chang¹⁹² & Paul Boutros¹⁹³,¹¹³⁹

**UK 100,000 Genomes Project (Genomics England)**

**Analysis team leader**

Loukas Moutsianas¹⁴,⁹⁵

**Data collection leaders**

Mark J. Caulfield⁹⁵,⁶⁹,⁹⁵ & Richard H. Scott⁹⁵,¹¹⁹,¹⁵⁹,¹¹⁹⁶

**Analysis team members**

Athanasios Kousathanas⁹⁵, Dorota Pasko⁹⁵, Susan Walker⁹⁵, Alex Stuckey⁹⁵, Christopher A. Odhams⁹⁵ & Daniel Rhodes⁹⁵

**Data collection members**

Tom Fowler⁹⁶, Augusto Rendon⁹⁵,¹¹⁹,⁷, Georgia Chan⁹⁵ & Prabhu Arumugam⁹⁵

**UK Biobank**

**Analysis team leaders**

Tomoko Nakanishi¹⁷,⁵⁶,⁵⁷, Konrad J. Karczewski³,²⁹, Alicia R. Martin³,²⁹, Daniel J. Wilson¹¹⁹⁹ & Chris A. Spencer⁹¹

**Data collection leaders**

Derrick W. Crook¹²⁰¹, David H. Wyllie¹²⁰¹,¹²⁰² & Anne Marie O'Connell¹²⁰³

**Admin team leader**

J. Brent Richards¹⁷,⁵³,⁵⁵,⁵⁶

**Analysis team members**

Guillaume Butler-Laporte¹⁷,⁵³, Vincenzo Forgetta¹⁷, Elizabeth G. Atkinson³,²⁹, Masahiro Kanai³,²⁹,¹²⁰⁰⁴, Kristin Tsuo³,²⁹,¹²⁰⁰⁵, Nikolas Baya³,²⁹, Patrick Turley³,²⁹, Rahul Gupta³,²⁹, Raymond K. Walters³,²⁹, Duncan S. Palmer³,²⁹, Gopal Sarma³,²⁹, Matthew Solomonomon³,²⁹, Nathan Cheng³,²⁹, Wenhan Lu³,²⁹, Claire Churchhouse³,²⁹, Jacqueline I. Goldstein³,²⁹, Daniel King³,²⁹, Wei Zhou³,²⁹, Cotton Seed³,²⁹, Mark J. Daly¹,²,³, Benjamin M. Neale³,²⁹, Hilary Finucane³,²⁹, Sam Bryant², F. Kyle Satterstrom³,²⁹, Gavin Band⁷⁰⁰, Sarah G. Earle¹¹⁹⁹, Shang-Kuan Lin¹¹⁹⁹, Nicolas Arning¹¹⁹⁹ & Nils Koelling⁹¹

**Data collection members**

Jacob Armstrong¹¹⁹⁹ & Justine K. Rudkin¹¹⁹⁹

**Admin team members**

Shawneequa Callier¹²⁰⁷, Sam Bryant³,²⁹ & Caroline Cusick²⁹

**UK Blood Donors Cohort**

**Analysis team leaders**

Nicole Soranzo¹²⁰⁸,¹²⁰⁹,¹²¹⁰ & Jing Hua Zhao¹²¹¹

**Data collection leaders**

John Danesh¹²¹¹,¹²¹²,¹²¹³,¹²¹⁴,¹²¹⁵ & Emanuele Di Angelantonio¹²¹¹,¹²¹²,¹²¹³,¹²¹⁴

**Analysis team member**

Adam S. Butterworth¹²¹¹,¹²¹²,¹²¹³,¹²¹⁴,¹²¹⁵

**VA Million Veteran Program (MVP)**

**Analysis team leaders**

Yan V. Sun¹²¹⁶,¹²¹⁷ & Jennifer E. Huffman¹²¹⁸

**Data collection leader**

Kelly Cho¹²¹⁹

**Admin team leaders**

Christopher J. O'Donnell¹²¹⁸, Phil Tsao¹²²⁰,¹²²¹ & J. Michael Gaziano¹²¹⁹

**Analysis team member**

Gina Peloso¹²¹⁸,¹²²²

**Data collection member**

Yuk-Lam Ho¹²¹⁹

**Val Gardena**

**Analysis team leader**

Christian Fuchsberger²⁰⁹

**Data collection leader**

Michael Mian²²³

**Data collection member**

Federica Scaggiante²²⁴

**Admin team members**

Cristian Pattaro²⁰⁹ & Peter Pramstaller²⁰⁹

**CHOP_CAG**

Xiao Chang¹²²⁷, Joseph R. Glessner¹²²⁷,¹²²⁸ & Hakon Hakonarson¹²²⁷,¹²²⁸,¹²²⁹

**GenOMIC/SARIC4C**

**Data collection leaders**

J. Kenneth Baillie⁶⁰,⁶¹,⁶², Peter J. McGuigan⁵²³, Luke Stephen Prockter Moore⁵²⁴, Marcela Paola Vizcaychipi⁵²⁴, Kathryn Hall⁵²⁵, Andy Campbell⁵²⁶, Ailstair Nichol⁵²⁷, Geraldine Ward⁵²⁸, Valerie Joan Page⁵²⁹, Malcolm G. Semple⁵³⁰, Kayode Adenijin³¹, Daniel Agranoff⁵³², Ken Agwu⁵³³, Dhiraj All⁵³⁴, Erin L. Aldera⁵³⁵, Ana Alegria⁵³⁶,⁵⁰⁶, Brian Angus⁵³⁷, Abdul Ashish⁵³⁸, Dougal Atkinson⁵³⁹, Shahedal Bari⁵⁴⁰, Gavin Barlow⁵⁴¹, Stella Barnass⁵⁴², Nicholas Barrett⁵⁴³, Christopher Bassford⁵⁴⁴, Sneha Basude⁵⁴⁵, David Baxter⁵⁴⁶, Michael Beadsworth⁵⁴⁷, Jolanta Bernatoniene⁵⁴⁸, John Berridge⁵⁴⁹, Nicola Best⁵⁵⁰, Pieter Bothma⁵⁵¹, David Chadwick⁵⁵², Robin Brittain-Long⁵⁵³, Naomi Bulteel⁵⁵⁴, Tom Burden⁵⁵⁵, Andrew Burtenshaw⁵⁵⁶, Vikki Caruth⁵⁵⁷, David Chadwick⁵⁵², Duncan Chambler⁵⁵⁸, Nigel Chees⁵⁵⁹, Jenny Child⁵⁶⁰, Srikanth Chukkambotla⁵⁶¹, Tom Clark⁵⁶², Paul Collini⁵⁶³, Catherine Cosgrove⁵⁶⁴, Jason Cupitt⁵⁶⁵, Maria-Teresa Cutino-Moguel⁵⁶⁶, Paul Dark⁵⁶⁷, Chris Dawson⁵⁶⁸, Samir Dervisevic⁵⁶⁹, Phil Donnison⁵⁶⁷, Sam Douthwaite⁵⁷³, Andrew Drummond⁵⁷²,⁷⁷³, Ingrid DuRand⁵⁷³, Ahilanadan Dushianthan⁵⁷⁴, Tristan Dyer⁵⁷⁵, Cariad Evans⁵⁶³, Chi Eziefula⁵³², Christopher Fegan⁵⁷⁶, Adam Finn⁵⁷⁷, Duncan Fullerton⁵⁷⁸, Sanjeev Garg⁵⁷⁹, Atul Garg⁵⁸⁰, Effrossyni Gkrania-Klotas⁵⁸¹, Jo Godden⁵⁸², Arthur Goldsmith⁵⁸³, Clive Graham⁵⁸⁴, Elaine Hardy⁵⁸⁵, Stuart Hartshorn⁵⁸⁸, Daniel Harvey⁵⁸⁷, Peter Havalda⁵⁸⁸, Daniel B. Hawcutt⁵⁸⁹, Maria Hobrok⁵⁹⁰, Luke Hodgson⁵⁹¹, Anil Hormis⁵⁹², Michael Jacobs⁵⁹³, Susan Jain⁵⁹⁴, Paul Jennings⁵⁹⁵, Agilan Kaliappan⁵⁹⁶, Vidya Kasipalan⁵⁹⁷, Stephen Kegg⁵⁹⁸, Michael Kelsey⁵⁹⁹, Jason Kendall¹²⁸¹, Caroline Kerrison⁶⁰⁰, Ian Kerslake⁶⁰¹, Oliver Koch⁶⁰², Gouri Koduri⁶⁰³, George Koshy⁶⁰⁴, Shondipon Laha⁶⁰⁵, Steven Laird⁶⁰⁶, Susan Larkin⁶⁰⁷, Tomas Leiner⁶⁰⁴, Patrick Lillie⁶⁰⁸, James Limb⁶⁰⁹, Vanessa Linnett⁶¹⁰, Jeff Little⁶¹¹, Mark Lyttle⁶¹², Michael MacMahon¹²⁸¹, Emily MacNaughton⁶¹³, Ravish Mankregod⁶¹⁴, Huw Masson⁶¹⁵, Elijah Matovu⁶¹⁷, Katherine McCullough⁶¹⁶, Ruth McEwen⁶¹⁷, Manjula Media⁶¹⁸, Gary H. Mills⁵⁶³, Jane Minton⁶²⁰, Karl Ward⁶²⁰, Mariyam Mirfenderesky⁶²¹, Kavya Mohandas⁶²², Quen Mok⁶²³, James Moon⁶²⁴, Elinoor Moore⁵⁸⁰, Patrick Morgan⁶²⁵, Craig Morris⁶²⁶, Katherine Mortimore⁶²⁰, Samuel Moses⁶²⁷, Mbiye Mpenge⁶²⁸, Rohinton Mulla⁶²⁹, Michael Murphy⁶³⁰, Megan Nagel⁶³¹, Thapas Nagarajan⁶³², Mark Nelson⁶³³, Matthew K. O'Shea⁶³⁴, Igor Otahal⁶³⁵, Marlies Ostermann⁵⁴³, Mark Pais⁶³⁶, Selva Panchatsharam⁶³⁷, Danai Papakonstantinou⁶³⁸, Hassan Paraiso⁶³⁹, Brij Patel⁶⁴⁰, Natalie Pattison⁶⁴¹, Justin Pepperell⁶⁴², Mark Peters¹²⁸¹, Mandeep Phull⁶⁴³, Stefania Pintus⁶⁴⁴, Jagtur Singh Pooni⁶⁴⁵, Frank Post⁶⁴⁶, David Price⁶⁴⁷, Rachel Proutt⁶⁴⁸, Nikolaas Rae⁶⁴⁹, Henrik Reschreiter⁶⁵⁰, Tim Reynolds⁶⁵¹, Neil Richardson⁶⁵², Mark Roberts⁶⁵³, Devender Roberts⁶⁵⁴, Alistair Rose⁶⁵⁵, Guy Rousseau⁶⁵⁶, Brendan Ryan⁶⁵⁷, Taranprit Saluja⁶⁵⁸, Aarti Shah⁶⁵⁹, Prad Shanmuga⁶⁶⁰, Anil Sharma⁶⁶¹, Anna Shawcross⁶⁶², Jeremy Sizer⁶⁶³, Manu Shankar-Hari⁵⁴³, Richard Smith⁶⁶⁴, Catherine Snelson⁶⁶⁵, Nick Spittle⁶⁶⁶, Nikki Staines⁶⁶⁷, Tom Stambach⁶⁶⁸, Richard Stewart⁶⁶⁹, Pradeep Subdhi⁶⁷⁰, Tomas Szakmany⁶⁷¹, Kate Tatham⁶⁷², Jo Thomas⁶⁷³, Chris Thompson⁶⁷⁴, Robert Thompson¹²⁸¹, Ascanio Tridente⁶⁷⁵, Darell Tupper-Carey²⁵⁰, Mary Twagira⁶⁷⁶, Andrew Ustianowski⁷⁷², Nick Vallotton⁶⁷⁷, Lisa Vincent-Smith⁶⁷⁸, Shico Visuvanathan⁶⁷⁹, Alan Vuylsteke⁶⁷⁹, Sam Waddy⁶⁸⁰, Rachel Wake⁶⁸¹, Andrew Walden⁶⁸², Ingeborg Welters⁶⁷⁴, Tony Whitehouse⁶⁸⁵, Paul Whittaker⁶⁸³, Ashley Whittington⁶⁸⁴, Padmasayee Papineni⁶⁸⁵, Meme Wijesinghe⁶⁸⁶, Martin Williams¹²⁸¹, Lawrence Wilson⁶⁸¹, Sarah Cole⁶⁸⁸, Stephen Winchester⁶⁸⁷, Martin Wiselka⁶⁸⁸, Adam Voltwerson⁶⁸⁹, Daniel G. Wooton⁶⁹⁰, Andrew Workman⁶⁸⁸, Bryan Yates⁶⁹¹ & Peter Young⁶⁹²

**Analysis team members**

J. Kenneth Baillie⁶⁰,⁶¹,⁶², Rupert Beale⁶⁹³, Andrew D. Bretherick⁶², Mark J. Caulfield⁶⁵,⁶⁹⁵, Sara Clohisey⁶⁰, Max Head Fourman⁶⁰, James Furniss⁶⁰, Elvina Gountouna⁶⁹⁶, Graeme Grimes⁶², Chris Haley⁶⁰, David Harrison⁶⁹⁷, Caroline Hayward⁶²,⁶⁹⁶, Sean Keating⁶¹, Lucija Klaric⁶², Paul Klenerman⁷⁰⁰, Athanasios Kousathanas⁶⁹⁸, Andy Law⁶⁰, Alison M. Meynert⁶², Jonathan Millar⁶⁰, Loukas Moutsianas¹⁴,⁹⁵, Erola Pairo-Castineira⁶⁰,⁶²⁰, Nicholas Parkinson⁶⁰, Dorota Pasko⁹⁵, Chris P. Ponting⁶², David J. Porteous⁶⁹⁶, Konrad Rawlik⁶⁰, Anne Richmond⁶², Kathy Rowan⁶⁹⁷, Clark D. Russell⁶⁰,⁷⁰⁵, Richard H. Scott⁹⁵,⁷⁰⁶, Xia Shen⁷⁰,⁷⁰,⁷⁰⁹, Barbara Shih⁶⁰, Albert Tenesa⁶⁰,⁶²,⁷⁰⁸, Veronique Vitart⁶², Susan Walker⁹⁰, Bo Wang⁶⁰, James F. Wilson⁶²,⁷⁰⁸, Yang Wu⁷¹⁰, Jian Yang⁷¹¹,⁷¹², Zhijian Yang⁷⁰⁷, Marie Zechner⁶⁰, Ranran Zhai⁷⁰⁷, Chenqing Zheng⁷⁰⁷, Lisa Norman⁷¹⁴, Riinu Pius⁷¹⁴, Thomas M. Drake⁷¹⁴, Cameron J. Fairfield⁷¹⁴, Stephen R. Knight⁷¹⁴, Kenneth A. Mclean⁷¹⁴, Derek Murphy⁷¹⁴, Catherine A. Shaw⁷¹⁴, John Dalton⁷¹⁵, Michelle Girvan⁷¹⁵, Egle Saviciute⁷¹⁵, Stephanie Roberts⁷¹⁵, Janet Harrison⁷¹⁵, Laura Marsh⁷¹⁵, Marie Connor⁷¹⁵,
Sophie Halpin⁷¹⁵, Clare Jackson⁷¹⁵, Carrol Gamble⁷¹⁶, Gary Leeming⁷¹⁶, Andrew Law⁶⁰, Murray Wham⁷¹⁷, Sara Clohisey⁶⁰, Ross Hendry⁶⁰ & James Brown⁷¹⁸

Data collection members

Colin Begg⁷¹⁰, Sara Clohisey⁶⁰, Charles Hinds⁶⁹⁵, Antonia Ding Uai Ho⁷²⁰, Peter W. Horby⁷²², Kevin Knight⁷²⁰, Lowell Ling⁷²⁴, David Maslow⁷²⁵, Nancy Yung Mai⁷²⁶,272, Jonathan Millard⁶⁰, Hugh Montgomery⁷²⁸, Alistair Nichol⁷²⁹, Peter J. M. Openshaw⁷³⁰,731, Chris P. Ponting⁶², Timothy Walsh⁷³¹, Lisa Armstrong⁷³⁶, Hayley Bates⁷³⁶, Fiona Dooks⁷³⁶, Fiona Jarrett⁷³⁶, Bridg Hairsine⁷³⁶, C. McParland⁷³⁶, Sophie Packham⁷³⁶, Zoe Alldos⁷³⁷, Raine Astin-Chamber Iain Berth⁷³, Fatima Bibi⁷³, Jack Biddle⁷³, Mary Blow⁷³, David Collie⁷³, Matthew Borra⁷³, Ruth Bowles⁷³, Maudrian Burton⁷³, Yasmin Choudhury⁷³, David Collier⁷³, Amber Grau⁷³, Amy Easthope⁷³⁷, Patrizia Eban⁷³, Stavros Fotiadis⁷³⁷, Jana Gurasavhill⁷³⁹, Rosslyn Halls⁷³⁷, Pippa Hartridge⁷³⁷, Delordson Kallon⁷³⁷, Jamila Kassam⁷³⁷, Ivone Landac-Malcolm⁷³⁷, Minderpal Matharu⁷³⁷, Peter May⁷³, Oliver Mitchelmore⁷³⁷, Tabitha Newman⁷³⁷, Mital Patel⁷³, Amy Pheby⁷³⁷, Irene Pinzuti⁷³⁷, Zoe Prime⁷³⁷, Oleksandra Prysyzazhna⁷³⁷, Julian Shiell⁷³⁷, Melanie Taylor⁷³⁷, Carey Tierney⁷³⁷, Suzanne Wood⁷³⁷, Anne Zak⁷³⁷, Olivier Zongo⁷³⁷, Miranda Forsey⁷³⁸, Agilan Kaliappan⁷³⁸, Anne Nicholson⁷³⁸, Joanne Riches⁷³⁸, Mark Vertue⁷³⁸, Christopher Wasson⁵²³, Stephanie Finn⁵²³, Jackie Green⁵²³, Erin Collins⁵²³, Bernadette King⁵²³, Lina Graslyte⁷³⁹, Musarat Hussan⁷³⁹, Mandeep Prull⁷³⁹, Tatiana Pogrebne⁷³⁹, Lace Rosaroso⁷³⁹, Erika Salcire⁷³⁹, George Franke⁷³⁹, Joanna Wong⁷³⁹, Aparna Varees⁷³⁹, Louise Akeroyd⁷⁴⁰, Shereen Bano⁷⁴⁰, Matt Bromley⁷⁴⁰, Lucy Gurr⁷⁴⁰, Tom Lawton⁷⁴⁰, James Morgan⁷⁴⁰, Kirsten Sellick⁷⁴⁰, Deborah Warren⁷⁴⁰, Brian Wilkinson⁷⁴⁰, Janet McGowan⁷⁴⁰, Camilla Ledgard⁷⁴⁰, Amelia Stacey⁷⁴⁰, Kate Pye⁷⁴⁰, Ruth Bellwood⁷⁴⁰, Michael Bentley⁷⁴⁰, Maria Hobrok⁷⁴⁰, Ronda Losley⁷⁴⁰, Heather McGuinness⁷⁴¹, Helen Tench⁷⁴¹, Angela Wolf-Roberts⁷⁴¹, San Gibson⁷⁴², Amanda Lyte⁷⁴², Anna McNeela⁷⁴², Jayachandran Radhakrishnan⁷⁴², Alistair Hughes⁷⁴², Asifa Ali⁷⁴³, Megan Brady⁷⁴³, Sam Dale⁷⁴³, Annalisa Dance⁷⁴³, Lisa Gledhill⁷⁴², Jill Greig⁷⁴³, Mathy Hanson⁷⁴³, Kelly Holdroyd⁷⁴³, Marie Home⁷⁴³, Diane Kelly⁷⁴³, Ross Kitson⁷⁴³, Lear Matapure⁷⁴³, Deborah Jarrett⁷⁴³, Samantha Mellor⁷⁴³, Tonika Nortcliffe⁷⁴³, Jez Pinnell⁷⁴³, Matthew Robinson⁷⁴³, Lisa Shaw⁷⁴³, Ryan Shaw⁷⁴³, Lesley Thomis⁷⁴³, Alison Wilson⁷⁴³, Tracy Wood⁷⁴³, Lee-An Bayo⁷⁴³, Eka Merwaha⁷⁴³, Tahira Ishaq⁷⁴³, Sarah Hanley⁷⁴³, David Antcliffe⁷⁴⁴, Dorota Banach⁷⁴⁴, Stephen Brett⁷⁴⁴, Phoebe Coghlan⁷⁴⁴, Ziorzta Fernandez⁷⁴⁴, Anthony Gordon⁷⁴⁴, Roceld Rojo⁷⁴⁴, Sonia Sousa Arias⁷⁴⁴, Michael Templeton⁷⁴⁴, Rajeev Jha⁷⁴⁴, Vinod Krishnamurthy⁷⁴⁴, Lai Lim⁷⁴⁴, Rehana Bi⁷⁴⁶, Barney Schofield⁷⁴⁶, Lydia Ashton⁷⁴⁶, Alison Williams⁷⁴⁶, Claire Cheyne⁷⁴⁶, Anne Saunders⁷⁴⁶, Angela Allan⁷⁴⁶, Felicity Anderson⁷⁴⁶, Callum Kaye⁷⁴⁶, Jade Liew⁷⁴⁶, Jasmine Medhora⁷⁴⁸, Teresa Scott⁷⁴⁸, Erin Trumper⁷⁴⁸, Adriana Botello⁷⁴⁸, Petra Polgarova⁷⁴⁹, Kazina Stroud⁷⁴⁹, Eogh Meaney⁷⁴⁹, Megan Jones⁷⁴⁹, Anthony Ng⁷⁴⁹, Shruti Agrawal⁷⁴⁹, Natarina Pathan⁷⁴⁹, Deborah White⁷⁴⁹, Esther Daubney⁷⁴⁹, Kay Elston⁷⁴⁹, Robert Parker⁷⁵⁰, Amy Redme⁷⁵⁰, Ian Turner-Bone⁷⁵⁰, Laura Wilding⁷⁵⁰, Peter Hanley⁷⁵⁰, Reni Jacob⁷⁵², Cathy Jones⁷⁵², Craig Denrad⁷⁵², Maria Croft⁷⁵², Ian White⁷⁵³, Rajeev Jha⁷⁴⁵, Vinod Krishnamurthy⁷⁴⁵, Li Lim⁷⁴⁵, Denise Griffin⁷⁵⁴, Niyola Muchenje⁷⁵⁴, McDonald Mupudzi⁷⁵⁴, Richard Partridge⁷⁵⁴, Jo-Anna Conyngway⁷⁵⁴, Rachel Thomas⁷⁵⁴, Mary Wright⁷⁵⁴, Maria Alvarez Corral⁷⁵⁴, Victoria Bastion⁷⁵⁶, Daphne Corral⁷⁵⁶, Beena David⁷⁵⁶, Harriet Kent⁷⁵⁶, Rachel Lorusso⁷⁵⁶, Gamu Lubimbí⁷⁵⁶, Sophie Murdoch⁷⁵⁶, Melchizedek Poracenarra⁷⁵⁶, Alastair Thomas⁷⁵⁶, Jennifer Valentine⁷⁵⁶, Ana Voch⁷⁵⁶, Retno Wulandari⁷⁵⁶, Brice Djeugam⁷⁵⁶, Joy Dawson⁷⁵⁵, Sweyn Garroicio⁷⁵⁵, Melanie Tolson⁷⁵⁵, Jonathan Aldridge⁷⁵⁶, Laura Gomes de Almeida Martins⁵²⁴,JA Carungoddar⁵²⁴, Vicky Beavis⁷⁵⁶, Katie Dale⁷⁵⁶, Rachel Gascoyne⁷⁵⁶, Joana Haves⁷⁵⁶, Kelly Pritchard⁷⁵⁶, Lesley Stevenenson⁷⁵⁶, Amanda Whileman⁷⁵⁶, Judith Highgate⁷⁵⁷, Rikki Crawley⁷⁵⁶, Abigail Crew⁷⁵⁶, Mishell Cunningham⁷⁵⁶, Allison Daniels⁷⁵⁶, Laura Harrison⁷⁵⁶, Susan Hope⁷⁵⁸, Ken Inweregbu⁷⁵⁸, Sian Jones⁷⁵⁸, Nicola Lancaster⁷⁵⁸, Jamie Matthews⁷⁵⁸, Alice Nicholson⁷⁵⁸, Gemma Wray⁷⁵⁸, Leonie Benham⁷⁵⁸, Stephen Bradshaw⁷⁵⁸, Joanna Brown⁷⁵⁸, Melanie Caswell⁷⁵⁸, Jason Cupitt⁷⁵⁸, Sarah Mell⁷⁵⁸, Zena Preston⁷⁵⁸, Martin Slawson⁷⁵⁸, Emma Stoddard⁷⁵⁸, Scott Warden⁷⁵⁸, Edward Combes⁷⁶⁰, Teish Joefield⁷⁶⁰, Sonja Monnery⁷⁶⁰, Valerie Beech⁷⁶⁰, Sallyn Trotman⁷⁶⁰, Bridget Hopkins⁷⁶⁰, James Scriven⁷⁶¹, Laura Thrasyvoulou⁷⁶¹, Heather Willis⁷⁶¹, Susan Anderson⁷⁶², Janine Birch⁷⁶², Emma Collins⁷⁶², Kate Hammerton⁷⁶², Ryan O'Leary⁷⁶², Caroline Abernathy⁷⁶², Louise Foster⁷⁶², Andrew Graixtr⁷⁶², Vicky Martinson⁷⁶², Priya Parkinson⁷⁶², Elizabeth Stones⁷⁶², Joana Carbal-Ortegá, Ritoo Kapoor⁷⁶², David Loader⁷⁶², Karen Castle⁷⁶², Craig Brandwood⁷⁶², Lara Smith⁷⁶², Richard Clark⁷⁶², Katie Bauchall⁷⁶², Rachel Kokalauri⁷⁶², Deborah Bawes⁷⁶², Anila Sukumaran⁷⁶², Isheunesh Mapfunde⁵²⁵, Megan Meredith⁷⁶⁷, Lucy Morris⁷⁶⁷, Amy Clark⁷⁶⁷, Shuja Sampson⁷⁶⁷, Cecilia Peters⁷⁶⁷, Martin Dent⁷⁶⁷, Margaret Langley⁷⁶⁷, Saima Rafraf⁷⁶⁷, Shuying Wei⁷⁶⁷, Angela Peters⁷⁶⁷, Manish Chabnani⁷⁶⁷, Amy Kirby⁷⁶⁷, Kimberley Netherthony⁷⁶⁸, Michelle Bates⁷⁶⁹, Jo Dasgin⁷⁶⁹, Jaspert Gill⁷⁶⁹, Annette Nilsson⁷⁶⁹, James Scriven⁷⁶⁰, Elena Apetri⁷⁷⁰, Cathrine Basikolo⁷⁷⁰, Bethalage⁷⁷⁰, Jo Catlow⁷⁷⁰, Bethan Charles⁷⁷⁰, Paul Dark⁷⁷⁰, Reece Doonan⁷⁷⁰, Jade Harris⁷⁷⁰, Alice Harvey⁷⁷⁰, Daniel Horner⁷⁷⁰, Karen Knowles⁷⁷⁰, Stephanie Lee⁷⁷⁰, Diane Lomas⁷⁷⁰, Chloe Lyons⁷⁷⁰, Tracy Marsden⁷⁷⁰, Danielle McLaughlan⁷⁷⁰, Liam Morrow⁷⁷⁰, Jessica Pendlebury⁷⁷⁰, Perez Jonez⁷⁷⁰, Maria Poulaka⁷⁷⁰, Nicola Proudfoot⁷⁷⁰, Melanie Slaughter⁷⁷⁰, Kathryn Slevin⁷⁷⁰, Melanie Taylor⁷⁷⁰, Vicky Thomas⁷⁷⁰, Danielle Walker⁷⁷⁰, Angiy Michael⁷⁷⁰, Matthew Collis⁷⁷⁰, Martin Collie⁷⁷⁰, Martina Coulding⁷⁷⁰, Edward Jude⁷⁷⁰, Jacqueline McCormick⁷⁷⁰, Oliver Mercer⁷⁷⁰, Darsh Potla⁷⁷⁰, Hafiz Rehman⁷⁷⁰, Heather Savill⁷⁷⁰, Victoria Turner⁷⁷⁰, Miriam Davey⁷⁷², David Golden⁷²², Rebecca Seaman⁷²², Jodie Hunt⁷²², Joy Dearden⁷²², Emma Dobson⁷²², Andrew Drummond⁵⁷²,773, Michelle Mulcahy⁷³³, Sheila Munt⁷³³, Grainne O'Connor⁷³³, Jennifer Philbin⁷³³, Chloe Riston⁷³³, Redmond Tully⁷³³, Sarah Winnard⁷³³, Lenka Cagovka⁷³³, Adama Fofano⁷³³, Lucie Garner⁷³³, Helen Holcombe⁷³⁴, Sue Mepham⁷³⁴, Alice Michael Mitchell⁷³⁴, Lucy Mwaura⁷³⁴, K. Praman⁷³⁴, Alain Vuylsteke⁷³⁴, Julie Zamikula⁷³⁴, Miriam Davey⁷²⁰, David Golden⁷²⁰, Rebecca Seaman⁷²², Georgia Bercades⁷²⁰, David Brealey⁷²⁰, Ingrid Hass⁷²⁰, Niall MacCallum⁷²⁰, Gladsy Martin⁷²⁰, Eamon Raith⁷²⁰, Anna Reyes⁷²⁰, Deborah Smyth⁷²⁰, Abigail Taylor⁷²⁰, Rachel Hughes⁷²⁰, Helen Thomas⁷²⁰, Alun Rees⁷²⁰, Michaela Duskova⁷²⁰, Janet Phipps⁷²⁰, Suzanne Brooks⁷²⁰, Michelle Edwards⁷²⁰, Peter Alexander⁷²⁰, Schvune Allen⁷²⁰, Joanne Bradley-Potts⁷²⁰, Stuart Brantwood⁷²⁰, Jasmine Egan⁷²⁰, Timothy Felton⁷²⁰, Grace Boarden⁷²⁰, Luke WR, Stuart Moss⁷²⁰, Susannah Glasgow⁷²⁰, Kate Beesley⁷²⁰, David Padd⁷²⁰, Agnieszka Kubisz-Pudelko⁷²⁰, Alison Lewis⁷²⁰, Jess Perry⁷²⁰, Lucy Pippard⁷²⁰, Di Wood⁷²⁰, Clare Buckley⁷²⁰, Alison Brown⁷²⁰, Susan O'Connell⁷²⁰, Tim Smith⁷²⁰, Zakaula Belagodu⁷²⁰, Bridget Fuller⁷⁸⁰, Anca Gherman⁷²⁰, Olumide Olufuwa⁷⁸⁰

Remi Paramaharan⁷⁸⁰, Carmel Coller⁷⁸⁰, Naomi Oakley⁷⁸⁰, Charlotte Coran⁷⁸⁰, David TyL⁷⁸⁰, Katy Collins⁷⁸⁰, Pedro Silva⁷⁸⁰, June Taylor⁷⁸⁰, Laura King⁷⁸⁰, Charlotte Coates⁷⁸⁰, Maria Crowley⁷⁸⁰, Phillipa Wakefield⁷⁸⁰, Jane Beadle⁷⁸⁰, Laura Johnson⁷⁸⁰, Janet Sargeant⁷⁸⁰, Madeleine Anderson⁷⁸⁰, Catherine Jardine⁷⁸⁰, Dewi Williams⁷⁸¹, Victoria Parris⁷⁸², Sheena Quid⁷⁸², Ekateryna Watson⁷⁸², Julie Melville⁷⁸³, Jay Naibitt⁷⁸³, Rosanna Joseph⁷⁸³, Maria Lazo⁷⁸³, Olivia Walton⁷⁸³, Alan Neal⁷⁸³, Michaela Hill⁷⁸³, Thogallan Rosane⁷⁸⁰, Laura Wild⁷⁸⁴, Elizabeth Allan⁷⁸⁵, Kate Darlington⁷⁸⁵, Fyfon Davies⁷⁸⁵, Jack Easton⁷⁸⁵, Sumit Kumar⁷⁸⁶, Richard Lean⁷⁸⁶, Daniel Menzies⁷⁸⁶, Richard Pugh⁷⁸⁶, Xinyi Qiu⁷⁸⁵, Línos Davies⁷⁸⁵, Hannah Bates⁷⁸⁵, Jeremy Scanlon⁷⁸⁵, Gwyneth Davies⁷⁸⁵, Callum Mackay⁷⁸⁵, Joanne Lewis⁷⁸⁵, Stephanie Rees⁷⁸⁵, Samantha Coetzee⁷⁸⁶, Alistair Gales⁷⁸⁶, Igor Otahal⁷⁸⁶, Meena Raj⁷⁸⁶, Craig Sell¹⁷⁶, Helen Langton¹⁷⁶, Rachel Prout¹⁷⁶, Michael Waters¹⁷⁸⁷, Catherine Novis¹⁷⁶⁷, Gill Arbane¹⁷⁸⁸, Aneta Bociek¹⁷⁸⁸, Sara Campos¹⁷⁸⁸, Neus Grau¹⁷⁸⁸, Man Owen Jones¹⁷⁸⁹, Rosario Lim¹⁷⁸⁸, Martina Marotti¹⁷⁸⁰, Marlies Ostermann¹⁷⁸⁰, Urban Shanker¹⁷⁸⁰, Christopher Whitten¹⁷⁸⁸, Anthony Barron¹⁷⁸⁰, Ciara Collins¹⁷⁸⁰, Sundeek Kauf¹⁷⁸⁹, Heather Passmore¹⁷⁸⁰, Claire Prendergast¹⁷⁸⁰, Anna Reed¹⁷⁸⁰, Paula Rogers¹⁷⁸⁰, Rajinder Shokkar¹⁷⁸⁰, Meriel Woodruff¹⁷⁸⁰, Hayley Middleton¹⁷⁸⁰, Oliver Polgar¹⁷⁸⁰, Claire Nolan¹⁷⁸⁰, Vicky Thwaites¹⁷⁸⁰, Kanta Mahay¹⁷⁸⁰, Chunda Sri-Chandana¹⁷⁹⁰, Joslan Scherewode¹⁷⁹⁰, Lorraine Stephenson¹⁷⁹⁰, Sarah Marsh¹⁷⁸⁰, Hollie Bancroft¹⁶⁸, Mary Bellamy¹⁶⁸⁰, Margaret Carmody¹⁶⁸⁰, Jacqueline Daglish¹⁶⁸⁰, Faye Moore¹⁶⁸⁰, Joanne Rhodes¹⁶⁸⁰, Mirriam Sangombe¹⁶⁸⁰, Salma Kadir¹⁶⁸⁰, James Scriven¹⁶⁸⁰, Amanda Ayers¹⁹², Wendy Harrison¹⁹², Julie North¹⁹², Anna Cavazzon¹⁶⁸⁰, Maeve Cockrell¹⁶⁸⁰, Eleanor Corcan¹⁶⁸⁰, Maria Depante¹⁶⁸⁰, Clare Finney¹⁶⁸⁰, Ellen Jerome¹⁶⁸⁰, Mark Phail¹⁶⁸⁰, Monalisa Nayak¹⁶⁸⁰, Harriet Noble¹⁶⁸⁰, Kevin O'Reilly¹⁶⁸⁰, Evita Pappa¹⁶⁸⁰, Rohit Saha¹⁶⁸⁰, San Saha¹⁶⁸⁰, John Smith¹⁶⁸⁰, Abigail Knighton¹⁶⁸⁰, Kevin Gill¹⁶⁸⁰, Paul Paul¹⁶⁸⁰, Vili Ratnam¹⁶⁸⁰, Sarah Shelton¹⁶⁸⁰, Insi Ynter¹⁶⁸⁰, David Tapista¹⁶⁸⁰, Rebecca Crowe¹⁶⁸⁰, Rita Fernandes¹⁶⁸⁰, Rosalina Herdman-Grant¹⁶⁸⁰, Anna Joseph¹⁶⁸⁰, India Lovéde¹⁶⁸⁰, India McKeney¹⁶⁸⁰, Eriko Morino¹⁶⁸⁰, Andres Naranjo¹⁶⁸⁰, RichardSIMPs¹⁶⁸⁰, Kathryn Sollela¹⁶⁸⁰, Andrew Swain¹⁶⁸⁰, Harish Venkatesh¹⁶⁸⁰, Jacyntha Khera¹⁶⁸⁰, Jonathan Fox¹⁶⁸⁰, Russell Barber¹⁶⁸⁰, Claire Hewitt¹⁶⁸⁰, Annette Hilddrith¹⁶⁸⁰, Karen Jackson-Lawrence¹⁶⁸⁰, Michael Shepard¹⁶⁸⁰, Natalie Keenan¹⁶⁸⁰, Marc Lyons¹⁶⁸⁰, Helen Wassall¹⁶⁸⁰, Chris Marsh¹⁶⁸⁰, Mervin Mahenthran¹⁶⁸⁰, Emma Carter¹⁶⁸⁰, Thomas Kong¹⁶⁸⁰, Oluronke Adanini¹⁶⁷⁰, Nikhil Bhatia¹⁶⁷⁰, Maines Msika¹⁶⁸⁰, Miranda Forsey¹⁶⁸⁰, Agilan Kaliappan¹⁶⁸⁰, Anne Nicholson¹⁶⁸⁰, Joanne Riches¹⁶⁸⁰, Mark Vertue¹⁶⁸⁰, Esther Mwaura¹⁶⁸⁰, Richard Stewart¹⁶⁸⁰, Felicity Williams¹⁶⁸⁰, Lynn Wren¹⁶⁸⁰, Sara-Beth Sutherland¹⁶⁸⁰, Ceri Battle¹⁶⁸⁰, Elaine Brinkworth¹⁶⁸⁰, Rachel Harford¹⁶⁸⁰, Carl Murphy¹⁶⁸⁰, Luke Newey¹⁶⁸⁰, Tabitha Rees¹⁶⁸⁰, Marie Williams¹⁶⁸⁰, Sophie Arnold¹⁶⁸⁰, David Brealey¹⁶⁸⁰, John Hardy¹⁶⁸⁰, Henry Houlden¹⁶⁸⁰, Eleanor Moncur¹⁶⁸⁰, Eamon Raith¹⁶⁸⁰, Ambreen Tariq¹⁶⁸⁰, Arianna Tucci¹⁶⁸⁰, Karen Convery¹⁶⁸⁰, Deirdre Fottrell-Gould¹⁶⁸⁰, Lisa Hudig¹⁶⁸⁰, Jocelyn Keshet-Price¹⁶⁸⁰, Georgina Randall¹⁶⁸⁰, Kate Pogson¹⁶⁸⁰, Marwa Abdelaziz¹⁶⁸⁰, Dhanalakshmi Bakhvatsalam¹⁶⁸⁰, Munzir Elhassan¹⁶⁸⁰, Arunkumar Ganesan¹⁶⁸⁰, Anne Haldeos¹⁶⁸⁰, Jeronimo Moreno-Cuesta¹⁶⁸⁰, Dharan Purohit¹⁶⁸⁰, Rachel Vincent¹⁶⁸⁰, Kugan Xavier¹⁶⁸⁰, Kumar Rohit¹⁶⁸⁰, Frater Alasdair¹⁶⁸⁰, Malik Saleem¹⁶⁸⁰, Carter David¹⁶⁸⁰, Samuel Jenkins¹⁶⁸⁰, Zoe Lamond¹⁶⁸⁰, Alanna Wall¹⁶⁸⁰, Bryan Yates¹⁶⁸⁰, Jessica Reynolds¹⁶⁸⁰, Helen Campbell¹⁶⁸⁰, Maria Thompson¹⁶⁸⁰, Steve Duffy¹⁶⁸⁰, Sophie Butcher¹⁶⁸⁰, Susie O'Sullivan¹⁶⁸⁰, Nicola Butterworth-Cowin¹⁶⁸⁰, Beth Deacon¹⁶⁸⁰, Meg Hibbert¹⁶⁸⁰, Carla Potchery¹⁶⁸⁰, Dariusz Tetla¹⁶⁸⁰, Christopher Woodford¹⁶⁸⁰, Lara Durga¹⁶⁸⁰, Gareth Kennard-Holden¹⁶⁸⁰, Laura Ortiz-Ruiz de Gordoa¹⁶⁸⁰, Emily Peasgood¹⁶⁸⁰, Claire Phillips¹⁶⁸⁰, Denise Skinner¹⁶⁸⁰, Jane Lyard¹⁶⁸⁰, Dee Mullan¹⁶⁸⁰, Julie Newman¹⁶⁸⁰, Ellie Davies¹⁶⁸¹, Lisa Roche¹⁶⁸¹, Sonia Sathel¹⁶⁸¹, Lutece Brimfield¹⁶⁸¹, Zoe Daly⁸⁸¹, David Pogson¹⁶⁸¹, Shei Davies¹⁶⁸¹, Amy Collins¹⁶⁸¹, Waqas Khalig¹⁶⁸¹,533, Estefania Treus Gude¹⁶⁸¹, Louise Allen¹⁶⁸⁰, Eva Beranova¹⁶⁸⁰, Nikki Crisp¹⁶⁸⁰, Joanne Deery¹⁶⁸⁰, Tracy Hazelton¹⁶⁸⁰, Alicia Knight¹⁶⁸⁰, Carly Price¹⁶⁸⁰, Sorrell Tilby¹⁶⁸⁰, Salah Turk¹⁶⁸⁰, Sharon Turney¹⁶⁸⁰, Julian Giles¹⁶⁸¹, Simon Booth¹⁶⁸¹, Gillian Bell¹⁶⁸¹, Katy English¹⁶⁸¹, Amro Katary¹⁶⁸¹, Louise Wilcox¹⁶⁸¹, Rachael Campbell¹⁶⁸¹, Noreen Clarke¹⁶⁸¹, Jonathan Whiteside¹⁶⁸¹, Mairi Mascarenhas¹⁶⁸¹, Avril Donaldson¹⁶⁸¹, Joanna Matheson¹⁶⁸¹, Fiona Barrett¹⁶⁸¹, Marianne O'Hara¹⁶⁸¹, Laura O'Keefe¹⁶⁸¹, Clare Bradley¹⁶⁸¹, Dawn Collier¹⁶⁸¹, Anil Hormis¹⁶⁸¹, Rachel Walker¹⁶⁸¹, Victoria Maynard¹⁶⁸¹, Tahara Patel¹⁶⁸¹, Matthew Smith¹⁶⁸¹, Srikanth Chukkambotta¹⁶⁸¹, Aayesha Kazi¹⁶⁸¹, Janice Hartley¹⁶⁸¹, Joseph Dykes¹⁶⁸¹, Muhammad Hijazi¹⁶⁸¹, Sarah Keith¹⁶⁸¹, Meherunnisa Khan¹⁶⁸¹, Janet Ryan-Smith¹⁶⁸¹, Philippa Springle¹⁶⁸¹, Jacqueline Thomas¹⁶⁸¹, Nick Trumper¹⁶⁸¹, Samuel Saad¹⁶⁸¹, Dabheoc Coleman¹⁶⁸¹, Christopher Fine¹⁶⁸¹, Roseanna Matt¹⁶⁸¹, Bethan Gay¹⁶⁸¹, Jack Dalziel¹⁶⁸¹, Syamlan Ali¹⁶⁸¹, Drew Goodchild¹⁶⁸¹, Rhianna Hartling¹⁶⁸¹, Ravi Bhattarjee¹⁶⁸¹, Wendy Goddard¹⁶⁸¹, Chloe Davison¹⁶⁸¹, Stephen Duberly¹⁶⁸¹, Jeanette Hargreaves¹⁶⁸¹, Rachel Bolton¹⁶⁸¹, Shondond Laha¹⁶⁸², Mark Verlander¹⁶⁸², Alexandra Williams¹⁶⁸², Helen Blackman¹⁶², Ben Creagh-Laha¹⁶⁸², Sinead Donlon¹⁶², Natalia Michalak-Glinska¹⁶², Sheila Mtuwa¹⁶², Veronika Pristopan¹⁶², Armorel Salberg¹⁶², Eleanor Smith¹⁶², Sarah Stone¹⁶², Charles Piercy¹⁶², Kevin Leibner¹⁶², Lynn Abel¹⁶²⁰, Michael Brett¹⁶²⁰, Brian Digby¹⁶²⁰, Lissa Turner¹⁶²⁰, Hannah Ellis¹⁶²⁰, Natalie Stroud¹⁶³¹, Shiney Cherian¹⁶³², Sean Cutler¹⁶³², Anne Emma Horsen¹⁶³², Anna Roynon-Reed¹⁶³², Tamas Szakmany¹⁶³², Gemma Williams¹⁶³², Owen Richards¹⁶³², Yusuf Cheema¹⁶³², Norfaizan Ahmad⁵⁶³,⁸³³, Joann Barker⁵⁶³,⁸³³, Kris Bauchmuller⁵⁶³,⁸³³, Sarah Bird⁵⁶³,⁸³³, Kay Cawthron⁵⁶³,⁸³³, Kate Harrington⁵⁶³,⁸³³, Yvonne Jackson⁵⁶³,⁸³³, Faith Kibutu⁵⁶³,⁸³³, Becky Lenagh⁵⁶³,⁸³³, Shamsido Masuko⁵⁶³,⁸³³, Gary H. Mills⁵⁶³,⁸³³, Ajay Raithatha⁵⁶³,⁸³³, Matthew Wiles⁵⁶³,⁸³³, Jayne Willson⁵⁶³,⁸³³, Helen Newell⁵⁶³,⁸³³, Alison Lye⁵⁶³,⁸³³, Lorenza Nwafora⁵⁶³,⁸³³, Claire Jarman⁵⁶³,⁸³³, Sarah Rowland-Jones⁵⁶³,⁸³³, David Foote⁵⁶³,⁸³³, Joby Cole⁵⁶³,⁸³³, Roger Thompson⁵⁶³,⁸³³, James Watson⁵⁶³,⁸³³
for Genomics (CCG), University of Cologne, Cologne, Germany. ²⁰³Department of
Anesthesiology and Intensive Care Medicine, University Hospital Essen, University
Duisburg-Essen, Essen, Germany. ²⁰⁴Department of Child and Adolescent Psychiatry,
University Hospital Essen, University of Duisburg-Essen, Essen, Germany. ²⁰⁵Department of
Infectious Diseases, University Hospital Essen, University Duisburg-Essen, Essen, Germany.

²⁰⁶Department of Pneumology, Allergology and Respiratory Medicine, University Hospital Saarland, Homburg/Saar, Germany. ²⁰⁷Center of Human and Molecular Biology, Department of Human Genetics, University Hospital Saarland, Homburg/Saar, Germany. ²⁰⁸Department of Genetics & Epigenetics, Saarland University, Saarbrücken, Germany. ²⁰⁹Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy. ²¹⁰University of Colorado Anschutz Medical Campus, Aurora, CO, USA. ²¹¹Department of Genetics and Development, Institute for Genomic Medicine, Columbia University, New York, NY, USA. ²¹²Department of Medicine, Institute for Genomic Medicine, Columbia University, New York, NY, USA. ²¹³Department of Biomedical Informatics, Columbia University, New York, NY, USA. ²¹⁴Department of Pediatrics, Columbia University, New York, NY, USA. ²¹⁵Department of Medicine, Columbia University, New York, NY, USA. ²¹⁶Institute for Genomic Medicine, Columbia University, New York, NY, USA. ²¹⁷Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA. ²¹⁸Department of Pathology and Cell Biology, Columbia University, New York, NY, USA. ²¹⁹Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²²⁰Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. ²²¹Division of Gastroenterology, Department of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²²²Department of Biochemistry, College of Medicine, Ehwa Womans University, Seoul, Republic of Korea. ²²³Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea. ²²⁴Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. ²²⁵Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea. ²²⁶Department of Physical & Rehabilitation Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²²⁷Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. ²²⁸Biomedical Institute for Convergence at SKKU, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea. ²²⁹Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. ²³⁰Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. ²³¹Research Environment Open NETwork, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea. ²³²Global Science Experimental Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea. ²³³Division of Infectious Diseases, Department of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²³⁴Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²³⁵Department of Occupational and Environmental Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. ²³⁶Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea. ²³⁷Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany. ²³⁸Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. ²³⁹Instituto de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain. ²⁴⁰ICREA, Barcelona, Spain. ²⁴¹Research Group for Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology, Plön, Germany. ²⁴²Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany. ²⁴³Department of Gastroenterology, Hospital Universitario Ramón y Cajal, University of Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain. ²⁴⁴Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. ²⁴⁵Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain. ²⁴⁶Charite Universitätsmedizin Berlin, Berlin, Germany. ²⁴⁷Hospital Universitario Clínico San Cecilio, Granada, Spain. ²⁴⁸Instituto de Investigación Ibs.Granada, Granada, Spain. ²⁴⁹Emergency Department, University Hospital Regensburg, Regensburg, Germany. ²⁵²Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Regensburg, Germany. ²⁵³Medical University of Innsbruck, Department of Medicine and Christian Doppler Laboratory on Iron and Phosphate Biology, Innsbruck, Austria. ²⁵⁴Institute of Clinical Medicine, University of Oslo, Oslo, Norway. ²⁵⁶Department of Microbiology, Oslo University Hospital, Oslo, Norway. ²⁵⁷Hospital Clinic, University of Barcelona and IDIBAPS, Barcelona, Spain. ²⁵⁸European Foundation for the Study of Chronic Liver Failure (EF-CLIF), Barcelona, Spain. ²⁵⁹Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAM), University of Cologne, Cologne, Germany. ²⁶⁰Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany. ²⁶¹Genomes for Life-GCAT labGermans Trias i Pujol Research Institute (IGTP), Badalona, Spain. ²⁶²IRCCS Humanitas Research Hospital, Milan, Italy. ²⁶³Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. ²⁶⁴Institute of Transfusionsmedicine, University Hospital Schleswig-Holstein (UKSH), Kiel, Germany. ²⁶⁵Klinik für Innere Medizin I, Universitätsklinikum Schleswig-Holstein, Kiel Campus, Kiel, Germany. ²⁶⁶Zentrum für Humangenetik Regensburg, Regensburg, Germany. ²⁶⁷University Hospital Schleswig-Holstein (UKSH), Kiel Campus, Kiel, Germany. ²⁶⁸Section for Gastroenterology, Department of Transplantation Medicine, Division for Cancer Medicine, Surgery and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway. ²⁶⁹Research Institute for Internal Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway. ²⁷⁰Norwegian PSC Research Center, Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway. ²⁷¹Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ²⁷²Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. ²⁷³Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA. ²⁷⁴Randaberg Municipality, Randaberg, Norway. ²⁷⁵Department of
University Hospital of North Norway, Tromsø, Norway. ³⁶²Faculty of Health Sciences, UIT The Arctic University of Norway, Tromsø, Norway. ³⁶³Catalan Institute of Oncology (ICO), Barcelona, Spain. ³⁶⁴Bellvitge Biomedical Research Institute (IDBELL), Barcelona, Spain. ³⁶⁵University de Barcelona (UB), Barcelona, Spain. ³⁶⁶deCODE genetics, Reykjavik, Iceland.
³⁶⁷Mucosal Immunology Lab, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Universidad de Valladolid-CISC, Valladolid, Spain. ³⁶⁸Centro de Investigaciones Biomédicas en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain. ³⁷⁰Valladolid University Hospital, Valladolid, Spain. ³⁷⁰Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. ³⁷²SYNLAB Estonia, University of Tartu, Tartu, Estonia. ³⁷³University of Tartu, Tartu, Estonia. ³⁷⁴Kuressaare Hospital, Kuressaare, Estonia. ³⁷⁵Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia. ³⁷⁷West Tallinn Central Hospital, Tallinn, Estonia. ³⁷⁸University of Tartu, Tartu University Hospital, Tartu, Estonia. ³⁷⁹Estonian Health Insurance Fund, Tallinn, Estonia. ³⁸⁰Tartu University Hospital, Tartu, Estonia. ³⁸¹FinnGen, Helsinki, Finland. ³⁸²Finnish Institute for Health and Welfare (THL), Helsinki, Finland. ³⁸³University of Helsinki, Faculty of Medicine, Clinical and Molecular Metabolism Research Program, Helsinki, Finland. ³⁸⁴Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland. ³⁸⁵Department of Ophthalmology, University of Basel, Basel, Switzerland. ³⁸⁶Infectious Diseases Service, Department of Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland. ³⁸⁶Centre for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland. ³⁸⁸Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St Gallen, St Gallen, Switzerland. ³⁹⁰Division of Intensive Care, Geneva University Hospitals and the University of Geneva Faculty of Medicine, Geneva, Switzerland. ³⁹¹Infectious Disease Service, Department of Internal Medicine, Geneva University Hospital, Geneva, Switzerland. ³⁹²Clinique de Médecine et spécialités, Infectiologie, HFR-Fribourg, Fribourg, Switzerland. ³⁹³Infectious Diseases Division, University Hospital Centre of the Canton of Vaud, Hospital of Valais, Sion, Switzerland. ³⁹⁴Functional Role Genomics of Infectious Diseases, University Hospital and University of Lausanne, Lausanne, Switzerland. ³⁹⁵Registry COVID, University Hospital and University of Lausanne, Lausanne, Switzerland. ³⁹⁶Pneumonia Prediction using Lung Ultrasound, University Hospital and University of Lausanne, Lausanne, Switzerland. ³⁹⁷Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland. ³⁹⁸COVID-19 Risk Prediction in Swiss ICUs-Trial, Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St Gallen, St Gallen, Switzerland. ³⁹⁸GCAT-Genomes for Life, Germans Trias I Pujol Health Sciences Research Institute (IGTP), Badalona, Spain. ⁴⁰⁰Catalan Institute of Oncology, Bellvitge Biomedical Research Institute, Consortium for Biomedical Research in Epidemiology and Public Health, University of Barcelona, Barcelona, Spain. ⁴⁰¹Barcelona Supercomputing Center, Centro Nacional de Supercomputación (BSC-CNS), Life & Medical Sciences, Barcelona, Spain. ⁴⁰²University of Siena, DIISM-SAILAB, Siena, Italy.
⁴⁰³Université Côte d'Azur, Irria, CNRS, I3S, Maasai, Nice, France. ⁴⁰⁴Division of Infectious Diseases and Immunology, Department of Medical Sciences and Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy. ⁴⁰⁵Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy. ⁴⁰⁶Department of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili Hospital, Brescia, Italy. ⁴⁰⁷Chirurgia Vascolare, Ospedale Maggiore di Crema, Crema, Italy. ⁴⁰⁸III Infectious Diseases Unit, ASST-FFB-Sacco, Milan, Italy. ⁴⁰⁹Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy. ⁴¹⁰Department of Specialized and Internal Medicine, Tropical and Infectious Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy. ⁴¹⁸Unit of Respiratory Diseases and Lung Transplantation, Department of Internal and Specialist Medicine, University of Siena, Siena, Italy. ⁴¹⁹Department of Emergency and Urgency, Medicine, Surgery and Neurosciences, Unit of Intensive Care Medicine, Siena University Hospital, Siena, Italy. ⁴¹⁰Department of Medical, Surgical and Neurosciences and Radiological Sciences, Unit of Diagnostic Imaging, University of Siena, Siena, Italy. ⁴¹⁸Rheumatology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, Siena, Italy. ⁴¹⁹Department of Specialized and Internal Medicine, Infectious Diseases Unit, San Donato Hospital, Arezzo, Arezzo, Italy. ⁴²⁰Department of Emergency, Anesthesia Unit, San Donato Hospital, Arezzo, Italy. ⁴²¹Department of Specialized and Internal Medicine, Pneumology Unit and UTIP, San Donato Hospital, Arezzo, Italy. ⁴²²Department of Emergency, Anesthesia Unit, Misericordia Hospital, Grosseto, Italy. ⁴²³Department of Specialized and Internal Medicine, Infectious Diseases Unit, Misericordia Hospital, Grosseto, Italy. ⁴²⁴Department of Preventive Medicine, Azienda USL Toscana Sud Est, Arezzo, Italy. ⁴²⁵Clinical Chemical Analysis Laboratory, Misericordia Hospital, Grosseto, Italy. ⁴²⁶Territorial Scientific Technician Department, Azienda USL Toscana Sud Est, Arezzo, Italy. ⁴²⁷Clinical Chemical Analysis Laboratory, San Donato Hospital, Arezzo, Italy. ⁴²⁸Department of Health Sciences, Clinic of Infectious Diseases, ASST Santà Paolo e Carlo, University of Milan, Milan, Italy.
⁴²⁹Department of Anesthesia and Intensive Care, University of Modena and Reggio Emilia, Modena, Italy. ⁴³⁰HIV/AIDS Department, National Institute for Infectious Diseases, IRCCS, Lazzaro Spallanzani, Rome, Italy. ⁴³¹Infectious Diseases Clinic, Department of Medicine, Azienda Ospedaliera di Perugia, Perugia, Italy. ⁴³²Infectious Diseases Clinic, Santa Maria Hospital, University of Perugia, Perugia, Italy. ⁴³³Department of Infectious Diseases, Treviso Hospital, Treviso, Italy. ⁴³⁴Clinical Infectious Diseases, Mestre Hospital, Venezia, Italy.
⁴³⁵Infectious Diseases Clinic, ULSS1, Belluno, Italy. ⁴³⁶Medical Genetics and Laboratory of Medical Genetics Unit, A.O.R.N "Antonio Cardarelli", Naples, Italy. ⁴³⁷Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy. ⁴³⁸CEINGE Biotecnologie Avanzate, Naples, Italy. ⁴³⁹IRCCS SDN, Naples, Italy. ⁴⁴⁰Unit of Respiratory Physiopathology, AORN dei Colli, Monaldi Hospital, Naples, Italy. ⁴⁴¹Division of Medical Genetics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy. ⁴⁴²Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy. ⁴⁴³Infectious Diseases Clinic, Policlinico San Martino Hospital, IRCCS for Cancer Research, Genova, Italy. ⁴⁴⁴Microbiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University of Medicine, Rome, Italy.
⁴⁴⁵Department of Laboratory Sciences and Infectious Diseases, Fondazione Policlinico Universitario AGemelli IRCCS, Rome, Italy. ⁴⁴⁶Department of Cardiovascular Diseases, University of Siena, Siena, Italy. ⁴⁴⁷Otolaryngology Unit, University of Siena, Siena, Italy.
⁴⁴⁸Department of Internal Medicine, ASST Valtellina e Alto Lario, Sondrio, Italy. ⁴⁴⁹First Aid Department, Luigi Curto Hospital, Polla, Italy. ⁴⁵⁰U.O.C. Laboratorio di Genetica Umana, Genova, Italy. ⁴⁵¹Infectious Diseases Clinics, University of Modena and Reggio Emilia, Modena, Italy. ⁴⁵²Department of Respiratory Diseases, Azienda Ospedaliera di Cremona, Cremona, Italy. ⁴⁵³U.O.C. Medicina, ASST Nord Milano, Ospedale Bassini, Milan, Italy. ⁴⁵⁴Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, San Luca Hospital, Milan, Italy. ⁴⁵⁶Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy. ⁴⁵⁷Center for Cardiac Arrhythmias of Genetic Origin, Istituto Auxologico Italiano, IRCCS, Milan, Italy. ⁴⁵⁸Laboratory of Cardiovascular Genetics, Istituto Auxologico Italiano, IRCCS, Milan, Italy. ⁴⁵⁹Unit of Infectious Diseases, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy. ⁴⁶⁰Department of Cardiology, Institute of Montescano, Istituti Clinici Scientifici Maugeri, IRCCS, Pavia, Italy. ⁴⁶¹Department of Cardiac Rehabilitation, Institute of Tradate (VA), Istituti Clinici Scientifici Maugeri, IRCCS, Pavia, Italy. ⁴⁶²Cardiac Rehabilitation Unit, Fondazione Salvatore Maugeri, IRCCS, Scientific Institute of Milan, Milan, Italy. ⁴⁶³IRCCS Mondino Foundation, Pavia, Italy. ⁴⁶⁴Medical Genetics Unit, Meyer Children's University Hospital, Florence, Italy. ⁴⁶⁵Department of Medicine, Pneumology Unit, Misericordia Hospital, Grosseto, Italy. ⁴⁶⁶Department of Preventive Medicine, Azienda USL Toscana Sud Est, Arezzo, Italy. ⁴⁶⁷Department of Anesthesia and Intensive Care Unit, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Polo Universitario, University of Milan, Milan, Italy. ⁴⁶⁸Health Management, Azienda USL Toscana Sudest, Arezzo, Italy. ⁴⁶⁹Department of Mathematics, University of Pavia, Pavia, Italy. ⁴⁷⁰Independent researcher, Milan, Italy. ⁴⁷⁰Scuola Normale Superiore, Pisa, Italy.
⁴⁷¹CNR-Consignio Nazionale delle Ricerche, Istituto di Biologia e Biotecnologia Agraria (IBBA), Milano, Italy. ⁴⁷³Veos Digital, Milan, Italy. ⁴⁷⁴Core Research Laboratory, ISPRO, Florence, Italy.
⁴⁷⁶Division of Infectious Diseases and Immunology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy. ⁴⁷⁶Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy. ⁴⁸⁰Clinical Chemistry Laboratory, Cytogenetics and Molecular Genetics Section, Diagnostic Department, ASST Spedali Civili di Brescia, Brescia, Italy. ⁴⁸¹Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy. ⁴⁸²Department of Molecular Medicine, University of Padova, Padua, Italy.
⁴⁸³Laboratory of Regulatory and Functional Genomics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy. ⁴⁸⁴Clinical Trial Office, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy. ⁴⁸⁵Department of Health Sciences, University of Genova, Genova, Italy. ⁴⁸⁶Oncologia Medica e Ufficio Flussi Sondrio, Sondrio, Italy. ⁴⁸⁷Local Health Unit, Pharmaceutical Department of Grosseto, Toscana Sud Est Local Health Unit, Grosseto, Italy. ⁴⁸⁸Independent researcher, Milan, Italy. ⁴⁸⁹Direzione Scientifica, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy. ⁴⁹⁰Fondazione per la ricerca Ospedale di Bergamo, Bergamo, Italy. ⁴⁹¹Allelica, New York, NY, USA. ⁴⁹²School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King's College London, London, UK. ⁴⁹⁴Medical and Population Genomics, Wellcome Sanger Institute, Hinxton, UK. ⁴⁹⁵Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service (NHS) Foundation Trust, Bradford, UK. ⁴⁹⁷Institute of Population Health Sciences, Queen Mary University of London, London, UK. ⁴⁹⁸Genes & Health, Blizard Institute, Queen Mary University of London, London, UK. ⁴⁹⁹Institute of Population Health Sciences, Queen Mary University of London, London, UK. ⁵⁰⁰Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. ⁵⁰¹Heart Institute (InCor), University of Sao Paulo Med School, São Paulo, Brazil.
⁵⁰²Genentech, San Francisco, CA, USA. ⁵⁰³DNA Link Inc., Seoul, Republic of Korea. ⁵⁰⁴Seoul National University Hospital Gangnam Center, Seoul, Republic of Korea. ⁵⁰⁵Division of Infectious Diseases, Department of Internal Medicine, Chungnam National University School of Medicine, Daejeon, Republic of Korea. ⁵⁰⁶East Kent Hospitals NHS Foundation Trust, Canterbury, UK. ⁵⁰⁷Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. ⁵⁰⁸Division of Infectious Diseases, Department of Internal Medicine, Incheon Medical Center, Incheon, Republic of Korea. ⁵⁰⁹Department of Infectious Diseases, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea. ⁵¹⁰Department of Internal Medicine, Pusan National University School of Medicine and Medical Research Institute, Pusan National University Hospital, Busan, Republic of Korea. ⁵¹¹Division of Infectious Diseases, Department of Internal Medicine, Myongji Hospital, Goyang, Republic of Korea. ⁵¹²Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Republic of Korea. ⁵¹³Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA. ⁵¹⁴Department of Medicine, Stanford University, Stanford, CA, USA. ⁵¹⁵Department of Genetics, Stanford University, Stanford, CA, USA. ⁵¹⁶Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. ⁵¹⁸Department of Pathology, Stanford University, Stanford, CA, USA. ⁵¹⁹Illumina, San Diego, CA, USA. ⁵²⁰Computational Biology, Drug Discovery Sciences, Takeda Pharmaceuticals, Boston, MA, USA. ⁵²¹Department of Computational Biology, Swiss Institute of Bioinformatics (SIB), University of Lausanne, Lausanne, Switzerland. ⁵²²Royal Victoria Hospital, Belfast, UK. ⁵²⁴Chelsea & Westminster NHS Foundation Trust, London, UK.
⁵²⁵Northampton General Hospital NHS Trust, Northampton, UK. ⁵²⁶Wrexham Maelor Hospital, Wrexham, UK. ⁵²⁷University College Dublin, St Vincent's University Hospital, Dublin, Ireland.
⁵²⁸University Hospitals Coventry & Warwickshire NHS Trust, Coventry, UK. ⁵²⁹Watford General Hospital, Watford, UK. ⁵³⁰NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK. ⁵³¹Queen Alexandra Hospital (Hampshire), Portsmouth Hospital Trust, Portsmouth, UK. ⁵³²Princess Royal Hospital, Brighton & Sussex Universities Hospitals NHS Trust, Brighton, UK. ⁵³³Bassettlaw Hospital, Doncaster and Bassettlaw, Worksop, UK.
⁵³⁴Darent Valley Hospital, Dartford & Gravesham NHS Trust, Dartford, UK. ⁵³⁵High Containment Laboratories, University of Birmingham, Birmingham, UK. ⁵³⁶Queen Elizabeth the Queen Mother Hospital, Margate, UK. ⁵³⁷John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. ⁵³⁸Royal Albert Edward Infirmary (Wigan), Wrightington, Wigan and Leigh, Wigan, UK. ⁵³⁹Manchester Royal Infirmary, Manchester University Hospitals NHS Foundation Trust, Manchester, UK. ⁵⁴⁰Furness General Hospital, Morecambe Bay NHS Foundation Trust, Barrow-in-Furness, UK. ⁵⁴¹Castle Hill Hospital, Hull University Teaching Hospital Trust, Hull, UK. ⁵⁴²Hillingdon Hospital, Hillingdon Hospital, London, UK. ⁵⁴³St Thomas Hospital, Guys and St Thomas Foundation Trust, London, UK. ⁵⁴⁴University Hospitals Coventry and Warwickshire, Coventry, UK. ⁵⁴⁵St Michaels Hospital (Bristol), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK. ⁵⁴⁶Stepping Hill Hospital, Stockport NHS Foundation Trust, Manchester, UK. ⁵⁴⁷Royal Liverpool Hospital, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK. ⁵⁴⁸Bristol Royal Hospital (Children's), University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK. ⁵⁴⁹Scarborough Hospital, York Teaching Hospitals NHS Foundation Trust, York, UK. ⁵⁵⁰Liverpool Heart & Chest Hospital, Liverpool Heart & Chest NHS Foundation Trust, Liverpool, UK. ⁵⁵¹James Paget University Hospitals NHS Foundation Trust, Great Yarmouth, UK. ⁵⁵²The
James Cook University Hospital, South Tees NHS Foundation Trust, Middlesbrough, UK.
⁵⁵³Aberdeen Royal Infirmary, Grampan, Aberdeen, UK.
⁵⁵⁴University of Edinburgh, Edinburgh, UK.
⁵⁵⁶Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
⁵⁵⁶Worcestershire Royal Hospital, Worcestershire Acute NHS Trust, Worcester, UK.
⁵⁵⁷Conquest Hospital, Hastings, East Sussex Healthcare NHS Trust, Seaford, UK.
⁵⁵⁸Dorset County Hospital, Dorset County Hospital NHS Foundation Trust, Dorchester, UK.
⁵⁵⁹Royal Bournemouth General Hospital, University Hospitals Dorset NHS Foundation Trust, Bournemouth, UK.
⁵⁶⁰Harrrogate Hospital, Harrogate and District NHS Foundation Trust, Harrogate, UK.
⁵⁶¹Burnley General Teaching Hospital, East Lancashire Hospitals NHS Hospitals, Burnley, UK.
⁵⁶²Torbay Hospital, Torbay & South Devon NHS Foundation Trust, Torbay, UK.
⁵⁶³Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
⁵⁶⁴St Georges Hospital (Tooting), St Georges University Hospitals NHS Foundation Trust, London, UK.
⁵⁶⁵Blackpool Victoria Hospital, Blackpool Teaching Hospitals NHS Foundation Trust, Blackpool, UK.
⁵⁶⁶The Royal London Hospital, Barts Health NHS Trust, London, UK.
⁵⁶⁷Salford Royal NHS Foundation Trust, Salford Royal NHS Foundation Trust, Manchester, UK.
⁵⁶⁸University Hospital of North Durham, County Durham and Darlington Foundation Trust, Durham, UK.
⁵⁶⁹Norfolk and Norwich University Hospital, Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich, UK.
⁵⁷⁰Fairfield General Hospital, Pennine Acute Hospitals NHS Trust, Manchester, UK.
⁵⁷⁰Hereford County Hospital, Wye Valley NHS Trust, Hereford, UK.
⁵⁷⁰Southampton General Hospital, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
⁵⁷⁰Northampton General Hospital, Northampton General Hospital NHS Trust, Northampton, UK.
⁵⁷⁰University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, UK.
⁵⁷¹University of Bristol, Bristol, UK.
⁵⁷²Leighton Hospital, Mid Cheshire Hospitals NHS Foundation Trust, Crewe, UK.
⁵⁷³Diana Princess of Wales Hospital (Grimsby), North Lincolnshire & Goole, Grimsby, UK.
⁵⁷⁴Manor Hospital, Walsall Healthcare NHS Trust, Walsall, UK.
⁵⁷⁵Addenbrookes Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.
⁵⁷⁶West Suffolk Hospital, West Suffolk Hospital NHS Foundation Trust, Bury St Edmunds, UK.
⁵⁷⁸Basingstoke and North Hampshire Hospital, Hampshire Hospitals NHS Foundation Trust, Basingstoke, UK.
⁵⁸⁰North Cumbria Integrated Care NHS Foundation Trust, Carlisle, UK.
⁵⁸⁰Warwick Hospital, South Warwickshire NHS Foundation Trust, Warwick, UK.
⁵⁸⁰Birmingham Women's and Children's Hospital, Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK.
⁵⁸⁰Nottingham City Hospital, Nottingham University Hospitals NHS Trust, Nottingham, UK.
⁵⁸⁰Glangwili Hospital Child Health Section, Hywel Dda University Health Board, Carmarthen, UK.
⁵⁸¹Alder Hey Children's Hospital, Alder Hey Children's NHS Foundation Trust, Liverpool, UK.
⁵⁸¹Brlonglais General Hospital, Hywel Dda University Health Board, Aberystwyth, Wales.
⁵⁸²Worthing Hospital, Western Sussex Hospitals NHS Foundation Trust, Worthing, UK.
⁵⁸²Rotheram District General Hospital, The Rotheram NHS Foundation Trust, Rotherham, UK.
⁵⁸³Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK.
⁵⁸⁴Hormerton Hospital, Hormer University Hospital NHS Foundation Trust, London, UK.
⁵⁸⁵Airedale Hospital, Airedale NHS Foundation Trust, Keighley, UK.
⁵⁸⁶Basildon Hospital, Basildon and Thurock University NHS Foundation Trust, Basildon, UK.
⁵⁸⁷The Christie NHS Foundation Trust, Manchester, UK.
⁵⁸⁷Queen Elizabeth Hospital (Greenwich), Lewisham and Greenwich NHS Trust, London, UK.
⁵⁸⁷The Whittington Hospital, Whittington Health NHS Trust, London, UK.
⁵⁸⁷Sheffield Children's Hospital, Sheffield Children's NHS Foundation Trust, Sheffield, UK.
⁵⁸⁷Royal United Hospital, Bath, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK.
⁵⁸⁸Western General Hospital, Edinburgh, UK.
⁵⁸⁸Mid and South Essex NHS Foundation Trust, Basildon, UK.
⁵⁸⁹Hinchingbrooke Hospital, North West Anglia NHS Foundation Trust, Peterborough, UK.
⁵⁸⁹Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK.
⁶⁰⁰University Hospital (Coventry), University Hospitals Coventry and Warwickshire, Coventry, UK.
⁶⁰¹The Walton Centre, The Walton Centre, Liverpool, UK.
⁶⁰¹Royl Royal Infirmary, Hull University Teaching Hospital Trust, Hull, UK.
⁶⁰¹Darlington Memorial Hospital, County Durham and Darlington Foundation Trust, Darlington, UK.
⁶⁰¹Queen Elizabeth Hospital (Gateshead), Gateshead NHS Foundation Trust, Newcastle, UK.
⁶⁰¹Warrington Hospital, Warrington & Halton Hospitals NHS Foundation Trust, Warrington, UK.
⁶⁰²University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
⁶⁰³St Mary's Hospital (Isle of Wight), Isle of Wight NHS Trust, Isle of Wight, UK.
⁶⁰⁴The Maidstone Hospital, Maidstone & Tunbridge Wells NHS Trust, Maidstone, UK.
⁶⁰⁴Huddersfield Royal, Calderdale and Huddersfield NHS Foundation Trust, Huddersfield, UK.
⁶⁰⁴Royal Surrey County Hospital, Guildford, UK.
⁶⁰⁴Countess of Chester Hospital, Countess of Chester Hospital NHS Foundation Trust, Chester, UK.
⁶⁰⁴Frimley Park Hospital, Frimley Health Foundation Trust, Frimley, UK.
⁶⁰⁵Leeds General Infirmary, Leeds Teaching Hospitals, Leeds, UK.
⁶⁰⁵North Middlesex Hospital, North Middlesex University Hospital NHS Trust, London, UK.
⁶⁰⁵Arrowe Park Hospital, Wirral University Teaching Hospital NHS Foundation Trust, Wirral, UK.
⁶⁰⁵Great Ormond Street Hospital, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
⁶⁰⁵Royal Shrewsbury Hospital, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, UK.
⁶⁰⁵East Surrey Hospital (Redhill), Surrey & Sussex Healthcare, Redhill, UK.
⁶⁰⁵Burton Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Burton-on-Trent, UK.
⁶⁰⁵Kent and Canterbury Hospital, East Kent Hospitals NHS Foundation Trust, Canterbury, UK.
⁶⁰⁵Weston Area General Trust, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
⁶⁰⁵Luton and Dunstable University Hospital, Luton, UK.
⁶⁰⁵Glasgow Royal Infirmary, Greater Glasgow and Clyde, Glasgow, UK.
⁶⁰⁵Derbyshire Healthcare, Derbyshire Healthcare NHS Foundation Trust, Derby, UK.
⁶⁰⁵Macclesfield General Hospital, East Cheshire NHS Foundation Trust, Macclesfield, UK.
⁶⁰⁵Chelsea and Westminster Hospital, Chelsea and Westminster NHS Trust, London, UK.
⁶⁰⁵Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
⁶⁰⁵Prince Philip Hospital, Hwel Dda University Health Board, Llanelli, UK.
⁶⁰⁵George Eliot Hospital - Acute Services, George Eliot Hospital, Nuneaton, UK.
⁶⁰⁵Kettering General Hospital, Kettering General Hospital NHS Foundation Trust, Kettering, UK.
⁶⁰⁵Birmingham Heartlands Hospital, Birmingham, UK.
⁶⁰⁵Russells Hall Hospital, The Dudley Group NHS Foundation Trust, Dudley, UK.
⁶⁰⁶Harefield Hospital, Royal Brompton & Harefield Trust, London, UK.
⁶⁰⁶Lister Hospital, East and North Hertfordshire NHS Trust, Stevenage, UK.
⁶⁰⁶Musgrove Park Hospital (Taunton & Somerset), Somerset NHS Foundation Trust, Taunton, UK.
⁶⁰⁶Queen's Hospital, Haverford (Romford), Barking, Havering and Redbridge University Hospitals NHS Trust, London, UK.
⁶⁰⁶Southport & Formby District General Hospital, Southport and Ormskirk Hospital NHS Trust, Southport, UK.
⁶⁰⁶New Cross Hospital, The Royal Wolverhampton NHS Trust, Wolverhampton, UK.
⁶⁰⁶King's College Hospital, London, UK.
⁶⁰⁶The Royal Victoria Infirmary, Newcastle Hospitals NHS Trust, Newcastle, UK.
⁶⁰⁶The Great Western Hospital,

Great Western Hospitals NHS Foundation Trust, Swindon, UK.
⁶⁰⁴Ninewells Hospital, Tayside, Dundee, UK.
⁶⁰⁵Poole Hospital NHS Trust, Poole, UK.
⁶⁰⁵Burton Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK.
⁶⁰⁵William Harvey Hospital, Ashford, East Kent Hospitals NHS Foundation Trust, Willesborough, UK.
⁶⁰⁵King's Mill Hospital, Sherwood Forest Hospitals NHS Foundation Trust, Sutton-in-Ashfield, UK.
⁶⁰⁵Liverpool Women's NHS Foundation Trust, Liverpool, UK.
⁶⁰⁵Dewsbury Hospital, Mid Yorkshire Hospitals NHS Trust, Dewsbury, UK.
⁶⁰⁵Northern Devon District Hospital, Northern Devon Healthcare NHS Trust, Barnstaple, UK.
⁶⁰⁵Tameside General Hospital, Tameside and Glossop Integrated Care NHS Foundation Trust, Manchester, UK.
⁶⁰⁵Sandwell General Hospital, Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK.
⁶⁰⁵Broomfield Hospital, Mid and South Essex University Hospitals Group, Broomfield, UK.
⁶⁰⁵Wycombe Hospital, Buckingham Healthcare NHS Trust, Wycombe, UK.
⁶⁰⁶University Hospital of North Tees, North Tees and Hartlepool NHS Trust, Stockton-on-Tees, UK.
⁶⁰⁶Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.
⁶⁰⁶Bedford Hospital, Bedford, UK.
⁶⁰⁶Colchester General Hospital, East Suffolk and North Essex Foundation Trust, Colchester, UK.
⁶⁰⁶Queen Elizabeth Hospital (Birmingham) and Heartlands, University Hospital Birmingham NHS Foundation Trust, Birmingham, UK.
⁶⁰⁶Chesterfield Royal Hospital, Chesterfield Royal Hospital NHS Foundation Trust, Chesterfield, UK.
⁶⁰⁶Princess Alexandra Hospital, The Princess Alexandra Hospital NHS Trust, Harlow, UK.
⁶⁰⁶Watford General Hospital, West Hertfordshire Hospitals NHS Trust, Watford, UK.
⁶⁰⁶Milton Keynes Hospital, Milton Keynes University Hospitals NHS Foundation Trust, Milton Keynes, UK.
⁶⁰⁶Royal Bolton General Hospital, Bolton Foundation Trust, Bolton, UK.
⁶⁰⁷Royal Gwent (Newport), Aneurin Bevan University Health Board, Newport, UK.
⁶⁰⁷The Royal Marsden Hospital (London), The Marsden NHS Foundation Trust, London, UK.
⁶⁰⁷Queen Elizabeth Hospital (East Grinstead), Queen Victoria Hospital NHS Foundation Trust, East Grinstead, UK.
⁶⁰⁷County Hospital (Stafford), University Hospitals of North Midlands NHS Trust, Stafford, UK.
⁶⁰⁷Whiston Hospital, St Helen's & Knowlsey Hospitals NHS Trust, Prescot, UK.
⁶⁰⁷Croydon University Hospital, London, UK.
⁶⁰⁷Gloucester Royal, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK.
⁶⁰⁷Medway Maritime Hospital, Medway Maritime NHS Trust, Gillingham, UK.
⁶⁰⁷Royal Papworth Hospital Everard, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.
⁶⁰⁸Derriford (Plymouth), University Hospital Plymouth NHS Trust, Plymouth, UK.
⁶⁰⁸St Helier Hospital, Epsom and St Helier University Hospital NHS Trust, London, UK.
⁶⁰⁸Royal Berkshire Hospital, Royal Berkshire Foundation Trust, London, UK.
⁶⁰⁸Bradford Royal Infirmary, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK.
⁶⁰⁸Northwick Park, London North West University Hospital Trust, London, UK.
⁶⁰⁸Ealing Hospital, London North West University Hospital Trust, London, UK.
⁶⁰⁸Royal Cornwall Hospital (Tresliske), Royal Cornwall NHS Trust, Truro, UK.
⁶⁰⁸Ashford Hospital, Ashford & St Peter's Hospital, Stanwell, UK.
⁶⁰⁸Leicester Royal Infirmary (includes Glenfield Site), University Hospitals of Leicester, UK.
⁶⁰⁸Grantham and District Hospital, United Lincolnshire Hospitals NHS Trust, Grantham, UK.
⁶⁰⁹University Hospital Aintree, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
⁶⁰⁹North Tyneside General Hospital, Northumbria Healthcare NHS Trust, North Shields, UK.
⁶⁰⁹Queen Elizabeth Hospital (King's Lynn), Queen Elizabeth Hospital King's Lynn NHS Foundation Trust, King's Lynn, UK.
⁶⁰⁹The Crick Institute, London, UK.
⁶⁰⁹William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
⁶⁰⁹Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.
⁶⁰⁹Intensive Care National Audit & Research Centre, London, UK.
⁶⁰⁹Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
⁶⁰⁹Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, UK.
⁶⁰⁹Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
⁶⁰⁹Biostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
⁶⁰⁹Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK.
⁶⁰⁹Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
⁶⁰⁹Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
⁶⁰⁹School of Life Sciences, Westlake University, Hangzhou, China.
⁶⁰⁹Westlake Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou, China.
⁶⁰⁹Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK.
⁶⁰⁹Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.
⁶⁰⁹Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
⁶⁰⁹MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
⁶⁰⁹School of Informatics, University of Edinburgh, Edinburgh, UK.
⁶⁰⁹Royal Hospital for Children, Glasgow, UK.
⁶⁰⁹MRC-University of Glasgow Centre for Virus Research, Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
⁶⁰⁹Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
⁶⁰⁹Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.
⁶⁰⁹Department of Critical Care Medicine, Queen's University and Kingston Health Sciences Centre, Kingston, Ontario, Canada.
⁶⁰⁹Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK.
⁶⁰⁹Department of Intensive Care Medicine, Royal Victoria Hospital, Belfast, UK.
⁶⁰⁹UCL Centre for Human Health and Performance, London, UK.
⁶⁰⁹Clinical Research Centre at St Vincent's University Hospital, University College Dublin, Dublin, Ireland.
⁶⁰⁹National Heart and Lung Institute, Imperial College London, London, UK.
⁶⁰⁹Imperial College Healthcare NHS Trust London, London, UK.
⁶⁰⁹NIHR Health Protection Research Unit for Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences University of Liverpool, Liverpool, UK.
⁶⁰⁹Respiratory Medicine, Alder Hey Children's Hospital, Institute in The Park, University of Liverpool, Alder Hey Children's Hospital, Liverpool, UK.
⁶⁰⁹Department of Intensive Care Medicine, Guy's and St Thomas NHS Foundation Trust, London, UK.
⁶⁰⁹Department of Medicine, University of Cambridge, Cambridge, UK.
⁶⁰⁹Airedale General Hospital, Keighley, UK.
⁶⁰⁹Barts Health NHS Trust, London, UK.
⁶⁰⁹Basildon Hospital, Basildon, UK.
⁶⁰⁹BHRT (Barking Havering) - Queens Hospital and King George Hospital, Romford, UK.
⁶⁰⁹Bradford Royal Infirmary, Bradford, UK.
⁶⁰⁹Brlonglais General Hospital, Aberystwyth, UK.
⁶⁰⁹Broomfield Hospital, Chelmsford, UK.
⁶⁰⁹Calderdale Royal Hospital, Halifax, UK.
⁶⁰⁹Charing Cross Hospital, St Mary's Hospital and Hammersmith Hospital, London, UK.
⁶⁰⁹Barnet Hospital, London, UK.
⁶⁰⁹Birmingham Children's Hospital, Birmingham, UK.
⁶⁰⁹St John's Hospital Livingston,
Livingston, UK. 748Aberdeen Royal Infirmary, Aberdeen, UK. 749 Addenbrooke's Hospital, Cambridge, UK. 750 Aintree University Hospital, Liverpool, UK. 752 Arrowe Park Hospital, Wirral, UK. 753 Ashford and St Peter's Hospital, Lyne, UK. 754 Basingstoke and North Hampshire Hospital, Basingstoke, UK. 756 Borders General Hospital, Melrose, UK. 756Chesterfield Royal Hospital Foundation Trust, Chesterfield, UK. 757 Eastbourne District General Hospital, East Sussex, UK and Conquest Hospital, Eastbourne, UK. 758 Barnsley Hospital, Barnsley, UK. 759 Blackpool Victoria Hospital, Blackpool, UK. 760 East Surrey Hospital, Redhill, UK. 761 Good Hope Hospital, Birmingham, UK. 762 Hereford County Hospital, Hereford, UK. 763 Hull Royal Infirmary, Hull, UK. 765Kent & Canterbury Hospital, Canterbury, UK. 766 Manchester Royal Infirmary, Manchester, UK. 767 Nottingham University Hospital, Nottingham, UK. 768 Pilgrim Hospital, Lincoln, UK. 769 Queen Elizabeth Hospital, Birmingham, UK. 770 Salford Royal Hospital, Manchester, UK. 771 Tameside General Hospital, Ashton-under-Lyne, UK. 772 The Tunbridge Wells Hospital and Maidstone Hospital, Maidstone, UK. 773 The Royal Oldham Hospital, Manchester, UK. 774 The Royal Papworth Hospital, Cambridge, UK. 775 University College Hospital, London, UK. 776 Withybush General Hospital, Haverfordwest, UK. 777 Wythenshawe Hospital, Manchester, UK. 778 Yeovil Hospital, Yeovil, UK. 779Cumberland Infirmary, Carlisle, UK. 780 Darent Valley Hospital, Dartford, UK. 781 Dumfries and Galloway Royal Infirmary, Dumfries, UK. 782 Ealing Hospital, London, UK. 783 Fairfield General Hospital, Bury, UK. 784 George Eliot Hospital NHS Trust, Nuneaton, UK. 785Glan Clwyd Hospital, Bodelwyddan, UK. 786 Glangwili General Hospital, Camarthen, UK. 787 The Great Western Hospital, Swindon, UK. 788Guys and St Thomas' Hospital, London, UK. 789Harefield Hospital, London, UK. 790 Harrogate and District NHS Foundation Trust, Harrogate, UK. 792 James Paget University Hospital NHS Trust, Great Yarmouth, UK. 794King's Mill Hospital, Nottingham, UK. 795Kingston Hospital, Kingston, UK. 796 Lincoln County Hospital, Lincoln, UK. 797 Liverpool Heart and Chest Hospital, Liverpool, UK. 798Macclesfield District General Hospital, Macclesfield, UK. 798Medway Maritime Hospital, Gillingham, UK. 800Milton Keynes University Hospital, Milton Keynes, UK. 801Morrison Hospital, Swanssea, UK. 802National Hospital for Neurology and Neurosurgery, London, UK. 803Norfolk and Norwich University hospital (NNUH), Norwich, UK. 804North Middlesex University Hospital NHS Trust, London, UK. 806Northumbria Healthcare NHS Foundation Trust, North Shields, UK. 807Peterborough City Hospital, Peterborough, UK. 808Prince Charles Hospital, Merthyr Tydfil, UK. 809Royal Sussex County Hospital, Brighton, UK. 810Princess Royal Hospital, Haywards Heath, UK. 811Princess of Wales Hospital, Llantrisant, UK. 812Queen Alexandra Hospital, Portsmouth, UK. 813Queen Elizabeth Hospital, London, UK. 814Queen Victoria Hospital, East Grinstead, UK. 816Queen's Hospital Burton, Burton-On-Trent, UK. 817Rigmore Hospital, Inverness, UK. 818Rotherham General Hospital, Rotherham, UK. 819Royal Blackburn Teaching Hospital, Blackburn, UK. 820Royal Preston Hospital, Preston, UK. 821Royal Surrey County Hospital, Guildford, UK. 822Royal Albert Edward Infirmary, Wigan, UK. 823The Royal Alexandra Children's Hospital, Brighton, UK. 824Royal Alexandra Hospital, Paistey, UK. 825Royal Bolton Hospital, Bolton, UK. 826University Hospitals Dorset NHS Foundation Trust, Dorchester, UK. 827Royal Brompton Hospital, London, UK. 828Imperial College London, London, UK. 829Royal Cornwall Hospital, Truro, UK. 830Royal Free Hospital, London, UK. 831Royal Glamorgan Hospital, Pontyclun, UK. 832Royal Gwent Hospital, Newport, UK. 833Northern General Hospital, Sheffield, UK. 834Royal Hampshire County Hospital, Winchester, UK. 835Royal Manchester Children's Hospital, Manchester, UK. 836Royal Stoke University Hospital, Stoke-on-Trent, UK. 837Salisbury District Hospital, Salisbury, UK. 838Sandwell General Hospital, Birmingham, UK. 839Scarborough General Hospital, Scarborough, UK. 840Scunthorpe General Hospital, Scunthorpe, UK. 841Southmead Hospital, Bristol, UK. 842St George's Hospital, London, UK. 843St Mary's Hospital, Newport, UK. 844Stoke Mandeville Hospital, Aylesbury, UK. 845Sunderland Royal Hospital, Sunderland, UK. 846Alexandra Hospital, Redditch and Westchester Royal Hospital, Worcester, UK. 847The Christie NHS Foundation Trust, Manchester, UK. 848The Queen Elizabeth Hospital, King's Lynn, UK. 849The Royal Liverpool University Hospital, Liverpool, UK. 850The Royal Marsden NHS Foundation Trust, London, UK. 851Torbay Hospital, Torquay, UK. 852University Hospital Monklands, Airdrie, UK. 853University Hospital Lewisham, London, UK. 854University Hospital North Durham, Darlington, UK. 855University Hospital of North Tees, Stockton-on-Tees, UK. 856University Hospital of Wales, Cardiff, UK. 857University Hospital Wishaw, Wishaw, UK. 858Victoria Hospital, Kirkcaldy, UK. 859Warrington General Hospital, Warrington, UK. 860West Cumberland Hospital, Whitehaven, UK. 860Western Sussex Hospitals, Chichester, UK. 862Whiston Hospital, Prescot, UK. 863York Hospital, York, UK. 864Ysbtyb Gwynedd, Bangor, UK. 865Countess of Chester Hospital, Cheshire, UK. 866Croydon University Hospital, Croydon, UK. 867Diana Princess of Wales Hospital, Grimsby, UK. 868Dorset County Hospital, Dorchester, UK. 869Forth Valley Royal Hospital, Falkirk, UK. 870Furness General Hospital, Barrow-in-Furness, UK. 871Alder Hey Children's Hospital, Liverpool, UK. 872Derriford Hospital, Plymouth, UK. 873Glasgow Royal Infirmary, Glasgow, UK. 874Glenfield Hospital, Leicester, UK. 875Gloucestershire Royal Hospital, Gloucester, UK. 876Golden Jubilee National Hospital, Clydebank, UK. 87 Great Ormond St Hospital and UCL Great Ormond St Institute of Child Health NIHR Biomedical Research Centre, London, UK. 876Homerton University Hospital Foundation NHS Trust, London, UK. 879James Cook University Hospital, Middlesbrough, UK. 880John Radcliffe Hospital, Oxford, UK. 881Leicester Royal Infirmary, Leicester, UK. 882Lister Hospital, Stevenage, UK. 883New Cross Hospital, Wolverhampton, UK. 884Royal Victoria Infirmary, Newcastle Upon Tyne, UK. 885Ninewells Hospital, Dundee, UK. 886North Devon District Hospital, Barnstaple, UK. 887North Manchester General Hospital, Manchester, UK. 888Northwick Park Hospital, London, UK. 889Prince Philip Hospital, Lianelli, UK. 890Pinderfields General Hospital, Wakefield, UK. 891Poole Hospital, Poole, UK. 892Royal Shrewsbury Hospital, Shrewsbury, UK. 893Princess Royal Hospital, Telford, UK. 894Queen Elizabeth Hospital Gateshead, Gateshead, UK. 900Queen Elizabeth University Hospital, Glasgow, UK. 901Royal Berkshire NHS Foundation Trust, Reading, UK. 902Royal Derby Hospital, Derby, UK. 903Royal Devon and Exeter Hospital, Exeter, UK. 904Royal Infirmary of Edinburgh, Edinburgh, UK. 905Royal Lancaster Infirmary, Lancaster, UK. 906Royal United Hospital, Bath, UK. 907Russells Hall Hospital, Dudley, UK. 908Sheffield Children's Hospital, Sheffield, UK. 909Southampton General Hospital, Southampton, UK. 910Southend University Hospital, Westcliff-on-Sea, UK. 910Southport and Formby District General Hospital, Ormskirk, UK. 912St James's University Hospital and Leeds General Infirmary, Leeds, UK. 913Bristol Royal Infirmary, Bristol, UK. 914Stepping Hill Hospital, Stockport, UK. 915The Princess Alexandra Hospital, Harlow, UK. 916University Hospital Crosshouse, Kilmarrock, UK. 917University Hospital Haimryes, East Kilbride, UK. 918Craigavon Area Hospital, Craigavon, UK. 919Warwick Hospital, Warwick, UK. 920West Middlesex Hospital, Isleworth, UK. 921Whittington Hospital, London, UK. 923William Harvey Hospital, Ashford, UK. 932Section of Molecular Virology, Imperial College London,

London, UK. 933Antimicrobial Resistance and Hospital Acquired Infection Department, Public Health England, London, UK. 934Department of Infectious Disease, Imperial College London, London, UK. 935National Infection Service, Public Health England, London, UK. 936MRC-University of Glasgow Centre for Virus Research, Glasgow, UK. 937 Liverpool School of Tropical Medicine, Liverpool, UK. 938Institute of Infection and Global Health, University of Liverpool, Liverpool, UK. 940Virology Reference Department, National Infection Service, Public Health England, London, UK. 941Department of Pharmacology, University of Liverpool, Liverpool, UK. 942Nuffield Department of Medicine, University of Oxford, Oxford, UK. 944 Nottingham University Hospitals NHS Trust, Nottingham, UK. 945Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK. 946ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. 947Division of Infection and Immunity, University College London, London, UK. 948Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK. 949Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK. 950Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. 951Department of Pediatrics and Virology, Imperial College London, London, UK. 952The Florey Institute for Host-Pathogen Interactions, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK. 953Institute for Global Health, University College London, UK. 954Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. 955Department of Child Life and Health, University of Edinburgh, Edinburgh, UK. 960Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. 961Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK. 962National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, UK. 963Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK. 964Institute of Translational Medicine, University of Liverpool, Liverpool, UK. 965Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK. 966University of Liverpool, Liverpool, UK. 967University of Glasgow, Glasgow, UK. 968Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh, UK. 969Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands. 971Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. 972Genotek, Moscow, Russia. 973Helix, San Mateo, CA, USA. 974Center for Genomic Medicine, Desert Research Institute, Reno, NV, USA. 977Renown Health, Reno, NV, USA. 97824Genetics, Boston, MA, USA. 979Hospital La Paz Institute for Health Research, Madrid, Spain. 980Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan. 981Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. 982Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. 983Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan. 984Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, The University of Tokyo, Tokyo, Japan. 985Laboratory of Viral Infection I, Department of Infection Control and Immunology, Ómura Satoshi Memorial Institute & Graduate School of Infection Control Sciences, Kitasato University, Tokyo, Japan. 986Department of Surgery, Keio University School of Medicine, Tokyo, Japan. 987Department of Organoid Medicine, Keio University School of Medicine, Tokyo, Japan. 988Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan. 989Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan. 990Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. 991Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan. 992Department of Insured Medical Care Management, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan. 993Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Chiba, Japan. 994Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan. 995M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan. 996Department of Pathology and Tumor Biology Institute for the Advanced Study of Human Biology (WPI-ASHBI), Kyoto University, Kyoto, Japan. 997Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden. 998Department of Emergency and Critical Care Medicine, Keio University School of Medicine, Tokyo, Japan. 1000Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan. 1001Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan. 1002Division of Infection Control and Prevention, Osaka University Hospital, Suita, Japan. 1003Department of Biomedical Ethics and Public Policy, Osaka University Graduate School of Medicine, Suita, Japan. 1004Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. 1005Department of Pulmonary Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan. 1006Department of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo, Japan. 1007Laboratory of Immune Regulation, Department of Microbiology and Immunology, Osaka University Graduate School of Medicine, Suita, Japan. 1008Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan. 1010Clinical Research Center, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan. 1011Department of Medical Informatics, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan. 1012Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan. 1013Clinical Laboratory, Tokyo Medical and Dental University of Medicine, Tokyo, Japan. 1014Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan. 1015Department of Respiratory Medicine, Graduate School of Medicine, Faculty of Medicine, Juntendo University, Tokyo, Japan. 1016Department of Emergency and Disaster Medicine, Graduate School of Medicine, Juntendo University, Tokyo, Japan. 1017Department of Cardiovascular Biology and Medicine, Graduate School of Medicine, Faculty of Medicine, Juntendo University, Tokyo, Japan. 1018Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan. 1019Department of General Medicine, Tokyo Women's Medical University, Tokyo, Japan. 1020Department of Respiratory
Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK. ¹²⁰²Public Health England, Field Service, Addenbrooke's Hospital, Cambridge, UK. ¹²⁰³Public Health England, Data and Analytical Services, National Infection Service, London, UK. ¹²⁰⁴Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA. ¹²⁰⁵Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA. ¹²⁰⁷Department of Clinical Research and Leadership, George Washington University, Washington, DC, USA.

¹²⁰⁸Department of Human Genetics, The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. ¹²⁰⁹Strangeways Research Laboratory, The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK. ¹³⁰⁰Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK. ¹³¹British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. ¹³¹⁰British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK. ¹³¹¹The National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK. ¹³¹⁴Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. ¹³¹⁵Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK. ¹³¹⁶Department of Epidemiology, Emory University Rollins School of Public Health, North Druid Hills, GA, USA. ¹³¹⁷Atlanta CA Health Care System, North Druid Hills, GA, USA. ¹³¹⁸Center for Population Genomics, MAVERIC, VA Boston Healthcare System, Boston, MA, USA. ¹³¹⁹MAVERIC, VA Boston Healthcare System, Boston, MA, USA.

¹³²⁰Stanford University, Stanford, CA, USA. ¹³²¹Palo Alto VA Healthcare System, Stanford, CA, USA. ¹³²²Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. ¹³²³Department of Haematology, Central Hospital of Bolzano (SABES-ASDAA), Bolzano, Italy. ¹³²⁴Laboratory of Clinical Pathology, Hospital of Bressanone (SABES-ASDAA), Bressanone, Italy. ¹³²⁵University of Alcalá, Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Madrid, Spain. ¹³²⁶Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA. ¹³²⁷Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. ¹³²⁸Faculty of Medicine, University of Iceland, Reykjavik, Iceland.

¹³²¹Infectious Disease Unit, Hospital of Massa, Massa, Italy. ¹³²²Department of Clinical Medicine, Public Health, Life and Environment Sciences, University of L'Aquila, L'Aquila, Italy. ¹³²³UOSD Laboratorio di Genetica Medica - ASL Viterbo, San Lorenzo, Italy. ¹³²⁴Unit of Infectious Diseases, S. M. Annunziata Hospital, Florence, Italy. ¹³²⁵Infectious Disease Unit, Hospital of Lucca, Lucca, Italy. ¹³²⁶Department of Clinical and Experimental Medicine, Infectious Diseases Unit, University of Pisa, Pisa, Italy. ¹³²⁷Clinic of Infectious Diseases, Catholic University of the Sacred Heart, Rome, Italy. ¹³²⁸Department of Diagnostic and Laboratory Medicine, Institute of Biochemistry and Clinical Biochemistry, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University of the Sacred Heart, Rome, Italy. ¹³²⁹Private University in the Principality of Liechtenstein, Triesen, Liechtenstein. ¹³³Digestive Diseases Unit, Virgen del Rocio University Hospital, Institute of Biomedicine of Seville, University of Seville, Seville, Spain. ¹³⁴Department of Biochemistry, University Hospital Vall d'Hebron, Barcelona, Spain.

¹²⁴³University of Sevilla, Sevilla, Spain. ¹²⁴⁴Instituto de Biomedicina de Sevilla, Sevilla, Spain.

¹²⁴⁵Hospital Universitario Virgen del Rocio de Sevilla, Sevilla, Spain. ¹²⁴⁶Consejo Superior de Investigaciones científicas, Madrid, Spain. ¹²⁴⁷Humanitas Clinical and Research Center, IRCCS, Milan, Italy. ¹²⁴⁸Immunohematology Department, Banc de Sang i Teixits, Autonomous University of Barcelona, Barcelona, Spain. ¹²⁴⁹August Pi i Sunyer Biomedical Research Institute, Hospital Clinic, University of Barcelona, Barcelona, Spain.

¹²⁵⁰Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy. ¹²⁵¹Internal Medicine Department, Virgen del Rocio University Hospital, Sevilla, Spain. ¹²⁵²Department of Biomedical Sciences, Humanitas University, Milan, Italy.

¹²⁵³Department Emergency, Anesthesia and Intensive Care, University Milano-Bicocca, Monza, Italy. ¹²⁵⁴Department of Medical Sciences, Università degli Studi di Torino, Turin, Italy. ¹²⁵⁵Department of Medical Microbiology, Clinic of Laboratory Medicine, St Olav's Hospital, Trondheim, Norway. ¹²⁵⁶Department of Infectious Diseases, St Olav's Hospital, Trondheim University Hospital, Trondheim, Norway. ¹²⁵⁷Department of Clinical and Molecular Medicine, NTNU, Trondheim, Norway. ¹²⁵⁸Department of Research, St Olav's Hospital, Trondheim University Hospital, Trondheim, Norway. ¹²⁵⁹Institute of Parasitology and Biomedicine Lopez-Neyra, Granada, Spain. ¹²⁶⁰Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany. ¹²⁶¹German Research Center for Cardiovascular Research, partner site Hamburg-Lübeck-Kiel, Lübeck, Germany. ¹²⁶²University Heart Center Lübeck, Lübeck, Germany. ¹²⁶³Department of Research, Ostfold Hospital Trust, Gralum, Norway.

¹²⁶⁴Pediatric Departement, Centro Tettamanti- European Reference Network (ERN) PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM/Ospedale San Gerardo, Milan, Italy. ¹²⁶⁵Geminicenter for Sepsis Research, Institute of Circulation and Medical Imaging (ISB), NTNU, Trondheim, Norway. ¹²⁶⁶Clinic of Anesthesia and Intensive Care, St Olav's Hospital, Trondheim University Hospital, Trondheim, Norway. ¹²⁶⁷Clinic of Medicine and Rehabilitation, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway. ¹²⁶⁸Stefan-Morsch-Stiftung, Birkenfeld, Germany. ¹²⁶⁹Center of Bioinformatics, Biostatistics, and Bioimaging, School of Medicine and Surgery, University of Milano Bicocca, Milan, Italy. ¹²⁷⁰Phase 1 Research Centre, ASST Monza, School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy. ¹²⁷¹Pneumologia ASST-Monza, University of Milano-Bicocca, Milano, Italy. ¹²⁷²School of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy. ¹²⁷³Infectious Diseases Unit, San Gerardo Hospital, Monza, Italy. ¹²⁷⁴SODIR-VHIR research group, Barcelona, Spain. ¹²⁷⁵Bioinformatics area, Fiundación progreso y Salud, Andalucía, Spain. ¹²⁷⁶Present address: Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ¹²⁷⁷Present address: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

¹²⁷⁸Present address: Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ¹²⁷⁹Present address: Harvard Medical School, Boston, MA, USA.

¹²⁸⁰These authors contributed equally: Mari E. K. Niemi, Juha Karjalainen, Benjamin M. Neale, Mark Daly, Andrea Ganna. ¹²⁸¹Unaffiliated: Sangyoon Im, Jason Kendall, Michael MacMahon, Mark Peters, Robert Thompson, Martin Williams. ¹²⁸²e-mail: bneale@broadinstitute.org; mark.daly@helsinki.fi; andrea.ganna@helsinki.fi
## Methods

### Contributing studies

All of the participants were recruited following protocols approved by local Institutional Review Boards; this information is collected in Supplementary Table 1 for all 46 studies. All protocols followed local ethics recommendations and informed consent was obtained when required. Information about sample numbers, sex and age from for each contributing study is given in Supplementary Table 1. In total, 16 studies contributed data to the analysis of critical illness due to COVID-19, 29 studies contributed data to hospitalized COVID-19 analysis and 44 studies contributed to the analysis of all cases of COVID-19. Each individual study that contributed data to a particular analysis met a minimum threshold of 50 cases, as defined by the phenotypic criteria, for statistical robustness. The effective sample sizes for each ancestry group shown in Fig. 1 were calculated for display using the formula: $((4 \times N_{case} \times N_{control})/(N_{case} + N_{control}))$. Details of contributing research groups are provided in Supplementary Table 1.

### Phenotype definitions

COVID-19 disease status (critical illness and hospitalization status) was assessed following the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia³⁸. The critically ill COVID-19 group included patients who were hospitalized owing to symptoms associated with laboratory-confirmed SARS-CoV-2 infection and who required respiratory support or whose cause of death was associated with COVID-19. The hospitalized COVID-19 group included patients who were hospitalized owing to symptoms associated with laboratory-confirmed SARS-CoV-2 infection.

The reported SARS-CoV-2 infection group included individuals with laboratory-confirmed SARS-CoV-2 infection or electronic health record, ICD coding or clinically confirmed COVID-19, or self-reported COVID-19 (for example, by questionnaire), with or without symptoms of any severity. Genetic-ancestry-matched control individuals for the three case definitions were sourced from population-based cohorts, including individuals whose exposure status to SARS-CoV-2 was either unknown or infection-negative for questionnaire/electronic-health-record-based cohorts. Additional information regarding individual studies contributing to the consortium are described in Supplementary Table 1.

### Genome-wide association studies and meta-analyses

Each contributing study genotyped the samples and performed quality controls, data imputation and analysis independently, but following the consortium recommendations (information is available at https://www.covid19hg.org/). We recommended that genome-wide association study (GWAS) analyses were run using Scalable and Accurate Implementation of G-Generalized mixed model (SAIGE)³⁹ on chromosomes 1–22 and X. The recommended analysis tool was SAIGE, but studies also used other software such as PLINK⁴⁰. The suggested covariates were age, age², sex, age × sex and the 20 first principal components. Any other study-specific covariates to account for known technical artefacts could be added. SAIGE automatically accounts for sample relatedness and case–control imbalances. Quality-control and analysis approaches for individual studies are reported in Supplementary Table 1.

Study-specific summary statistics were then processed for meta-analysis. Potential false positives, inflation and deflation were examined for each submitted GWAS. Allele frequency plots against gnomAD 3.0 genomes were manually inspected for each study. Standard error values as a function of the effective sample size were used to find studies that deviated from the expected trend. Summary statistics passing this manual quality control were included in the meta-analysis. Variants with an allele frequency of >0.1% and an imputation INFO score of >0.6 were carried forward from each study. Variants and alleles were lifted over to genome build GRCh38, if needed, and harmonized to

gnomAD 3.0 genomes⁴¹ by finding matching variants by strand flipping or switching the ordering of alleles. If multiple matching variants were included, the best match was chosen according to the minimum fold change in absolute allele frequency. Meta-analysis was performed using the inverse-variance-weighted (IVW) method on variants that were present in at least two-thirds of the studies contributing to the phenotype analysis. The method summarizes effect sizes across the multiple studies by computing the mean of the effect sizes weighted by the inverse variance in each individual study.

We report 13 meta-analysis variants that pass the genome-wide significance threshold after adjusting the threshold for multiple traits tested ($P < 5 \times 10^{-8}/3$). We report the unadjusted *P* values for each variant. We tested for heterogeneity between estimates from contributing studies using Cochran's Q-test⁴²,⁴³. This is calculated for each variant as the weighted sum of squared differences between the effect sizes and their meta-analysis effect, the weights being the inverse variance of the effect size. *Q* is distributed as a $\chi^2$ statistic with *k* (number of studies) minus one degrees of freedom. Two loci reached genome-wide significance but were excluded from the significant results in Supplementary Table 2 due to heterogeneity between estimates from contributing studies and missingness between studies at chr. 6: 31057940–31380334 and chr. 7: 54671568–54759789; however, these regions are not excluded from the corresponding summary statistics in data release 5 (COVID-19 HGI (https://www.covid19hg.org/results/r5/) and GWAS Catalog (study code GCST011074)). For each of the lead variants reported in Supplementary Table 2, we aimed to find loci specific to susceptibility or severity by testing whether there was heterogeneity between the effect sizes associated with hospitalized COVID-19 (progression to severe disease) and reported SARS-CoV-2 infection. We used the Cochran's Q measure⁴²,⁴³, calculated for each variant as the weighted sum of squared differences between the two analysis effect sizes and their meta-analysis effect with the weights being the inverse variance of the effect size. A significant *P* value of *P* < 0.004 ((0.05/13 loci) for multiple tests) indicates that the effect sizes for a particular variant are significantly different in the two analyses (Supplementary Table 2). For the nine loci, in which the lead variant effect size was significantly higher for hospitalized COVID-19, we carried out the same test again but comparing effect sizes from hospitalized COVID-19 with critically ill COVID-19 (Supplementary Table 4). Furthermore, we carried out the same test comparing meta-analysed hospitalized COVID-19 (population as controls) and hospitalized COVID-19 (SARS-CoV-2-positive but non-hospitalized as controls) (Supplementary Table 4). For these pairs of phenotype comparisons, we generated new meta-analysis summary statistics to use; including only those studies that could contribute data to both phenotypes that were under comparison.

### Principal component projection

To project every GWAS participant into the same principal component (PC) space, we used pre-computed PC loadings and reference allele frequencies. For reference, we used unrelated samples from the 1000 Genomes Project and the Human Genome Diversity Project and computed PC loadings and allele frequencies for the 117,221 single-nucleotide polymorphisms (SNPs) that (1) are available in every cohort; (2) have a minor allele frequency of >0.1% in the reference; and (3) are LD-pruned ($r^2 < 0.8$; 500-kb window). We then asked each cohort to project their samples using our automated script provided at https://github.com/covid19-hg/. It internally uses the PLINK²⁴⁻⁵ score function with the variance-standardize option and reference allele frequencies (--read-freq); so that each cohort-specific genotype/dosage matrix is mean-centred and variance-standardized with respect to reference allele frequencies, but not cohort-specific allele frequencies. We further normalized the projected PC scores by dividing the values by a square root of the number of variants used for projection to account for a subtle difference due to missing variants.
## Gene prioritization

To prioritize candidate causal genes reported in full in Supplementary Table 2, we used various gene prioritization approaches using both locus-based and similarity-based methods. Because we only describe the in silico gene prioritization results without characterizing the actual functional activity in vitro or in vivo, we aimed to provide a systematic approach to nominate potential causal genes in a locus using the following criteria.

(1) The closest gene: a gene that is closest to a lead variant by distance to the gene body.

(2) Genes in the LD region: genes that overlap with a genomic range containing any variants in LD ($r^2 > 0.6$) with a lead variant. For LD computation, we retrieved LD matrices provided by gnomAD v.2.1.1⁴¹ for each population analysed in this study (except for admixed American, Middle Eastern and South Asian genetic ancestry populations, for whom data are not available). We then constructed a weighted-average LD matrix by per-population sample sizes in each meta-analysis, which we used as a LD reference.

(3) Genes with coding variants: genes with at least one loss-of-function or missense variant (annotated by VEP⁴⁵ v.95 with GENCODE v.29) that is in LD with a lead variant ($r^2 > 0.6$).

(4) eGenes: genes with at least one fine-mapped cis-eQTL variant (PIP > 0.1) that is in LD with a lead variant ($r^2 > 0.6$) (Supplementary Table 5). We retrieved fine-mapped variants from the GTEx v.8²⁰ (https://www.finucanelab.org/) and eQTL catalogue⁴⁶. In addition, we looked up significant associations in the Lung eQTL Consortium²¹ ($n = 1,103$) to further support our findings in lung with a larger sample size (Supplementary Table 7). We note that, in contrast to the GTEx or eQTL catalogue, we only looked at associations and did not fine-map our data to the Lung eQTL Consortium data.

(5) V2G: a gene with the highest overall V2G score based on Open Targets Genetics (OTG)²⁶. For each variant, the overall V2G score aggregates differentially weighted evidence of variant-gene associations from several data sources, including molecular cis-QTL data (for example, cis-protein QTLs from ref. ⁴⁷, cis-eQTLs from GTEx v.7 and so on), interaction-based datasets (for example, promoter capture Hi-C), genomic distance and variant effect predictions (VEP) from Ensembl. A detailed description of the evidence sources and weights used is provided in the OTG documentation (https://genetics-docs.opentargets.org/our-approach/data-pipeline)²⁶.

## Phenome-wide association study

To investigate the evidence of shared effects of 15 index variants for COVID-19 and previously reported phenotypes, we performed a phenome-wide association study. We considered phenotypes in OTG obtained from the GWAS catalogue (this included studies with and without full summary statistics, *n* = 300 and 14,013, respectively)⁴⁸ and from the UK Biobank. Summary statistics for UK Biobank traits were extracted from SAIGE³⁹ for binary outcomes (*n* = 1,283 traits) and Neale v.2 (*n* = 2,139 traits) for both binary and quantitative traits (http://www.nealelab.is/uk-biobank/) and FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results). We report PheWAS results for phenotypes for which the lead variants were in high LD ($r^2 > 0.8$) with the 13 genome-wide significant lead variants from our main COVID-19 meta-analysis (Supplementary Table 6). This conservative approach allowed spurious signals primarily driven by proximity rather than actual colocalization to be removed (see Methods).

To remove plausible spurious associations, we retrieved phenotypes for GWAS lead variants that were in LD ($r^2 > 0.8$) with COVID-19 index variants.

## Heritability

LD score regression v.1.0.1⁴⁹ was used to estimate the SNP heritability of the phenotypes from the meta-analysis summary statistic files. As this

method depends on matching the LD structure of the analysis sample to a reference panel, the summary statistics of European ancestry only were used. Sample sizes were *n* = 5,101 critically ill cases of COVID-19 and *n* = 1,383,241 control participants, *n* = 9,986 hospitalized cases of COVID-19 and *n* = 1,877,672 control participants, and *n* = 38,984 cases and *n* = 1,644,784 control participants for the analysis of all cases— all including the 23andMe cohort. Pre-calculated LD scores from the 1000 Genomes European reference population were obtained online (https://data.broadinstitute.org/alkesgroup/LDSCORE/). Analyses were conducted using the standard program settings for variant filtering (removal of non-HapMap3 SNPs, the HLA region on chromosome 6, non-autosomal, $\chi^2 > 30$, minor allele frequency of <1%, or allele mismatch with reference). We additionally report SNP heritability estimates for the all-ancestries meta-analyses, calculated using European panel LD scores, in Supplementary Table 8.

## Partitioned heritability

We used partitioned LD score regression⁵⁰ to partition COVID-19 SNP heritability in cell types in our summary statistics for European ancestry only. We ran the analysis using the baseline model LD scores calculated for European populations and regression weights that are available online (https://github.com/bulik/ldsc). We used the COVID-19 summary statistics for European ancestry only for the analysis.

## Genome-wide association summary statistics

We obtained genome-wide association summary statistics for 43 complex-disease, neuropsychiatric, behavioural or biomarker phenotypes (Supplementary Table 10). These phenotypes were selected based on their putative relevance to COVID-19 susceptibility, severity or mortality, with 19 selected based on the Centers for Disease Control list of underlying medical conditions associated with COVID-19 severity¹¹ or traits reported to be associated with increased risk of COVID-19 mortality by Open Safely⁵². Summary statistics generated from GWAS using individuals of European ancestry were preferentially selected if available. These summary statistics were used in subsequent genetic correlation and Mendelian randomization analyses.

## Genetic correlation

LD score regression⁵⁰ was also used to estimate the genetic correlations between our COVID-19 meta-analysis phenotypes reported using samples of only European ancestry, and between these and the curated set of 38 summary statistics. Genetic correlations were estimated using the same LD score regression settings as for heritability calculations. Differences between the observed genetic correlations of SARS-CoV-2 infection and COVID-19 severity were compared using a z-score method⁵³.

## Mendelian randomization

Two-sample Mendelian randomization was used to evaluate the potential for causal association of the 38 traits on COVID-19 hospitalization, on COVID-19 severity and reported SARS-CoV-2 infection using samples of only European ancestry. Independent genome-wide significant SNPs robustly associated with the exposures of interest ($P < 5 \times 10^{-8}$) were selected as genetic instruments by performing LD clumping using PLINK⁴⁰. We used a strict $r^2$ threshold of 0.001, a 10-Mb clumping window, and the European reference panel from the 1000 Genomes Project⁵⁴ to discard SNPs in LD with another variant with a smaller *P*-value association. For genetic variants that were not present in the hospitalized COVID-19 analysis, PLINK was used to identify proxy variants that were in LD ($r^2 > 0.8$). Next, the exposure and outcome datasets were harmonized using the R package TwoSampleMR⁵⁵. Namely, we ensured that the effect of a variant on the exposure and outcome corresponded to the same allele, we inferred positive-strand alleles and dropped palindromes with ambiguous allele frequencies, as well as incompatible alleles. Supplementary Table 10 includes the harmonized datasets used in the analyses.
The global test from Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO)⁵⁶ software was used to investigate over-all horizontal pleiotropy. In brief, the standard IVW meta-analytic framework was used to calculate the average causal effect by excluding each genetic variant used to instrument the analysis. A global statistic was calculated by summing the observed residual sum of squares, that is, the difference between the effect predicted by the IVW slope exclud-ing the SNP, and the observed effect of the SNP on the outcome. Over-all horizontal pleiotropy was subsequently analysed by comparing the observed residual sum of squares, with the residual sum of squares expected under the null hypothesis of no pleiotropy. The MR-PRESSO global test was shown to perform well when the outcome and exposure GWASs are not disjoint (although the power to detect horizontal plei-tropy is slightly reduced by complete sample overlap). We also used the regression intercept in MR-Egger⁵⁷ to evaluate potential bias due to directional pleiotropic effects. This additional check was used in Mendelian randomization analyses with an I²_GX index surpassing the recommended threshold (I²_GX > 90%)⁵⁸. Contingent on the MR-PRESSO global test results we analysed the causal effect of each exposure on COVID-19 hospitalization by using a fixed-effect IVW meta-analysis as the primary analysis, or, if pleiotropy was present, the MR-PRESSO outlier-corrected test. The IVW approach estimates the causal effect by aggregating the single-SNP causal effects (obtained using the ratio of coefficients method—that is, the ratio of the effect of the SNP on the outcome over the effect of the SNP on the exposure) in a fixed-effects meta-analysis. The SNPs were assigned weights based on their inverse variance. The IVW method confers the greatest statistical power for estimating causal associations⁵⁹, but assumes that all variants are valid instruments and can produce biased estimates if the average pleio-tropic effect differs from zero. Alternatively, when horizontal pleio-tropy was present, we used the MR-PRESSO outlier-corrected method to correct the IVW test by removing outlier SNPs. We conducted further sensitivity analyses using alternative Mendelian randomization meth-ods that provide consistent estimates of the causal effect even when some instrumental variables are invalid, at the cost of reduced statisti-cal power including: (1) Weighted median estimator (WME); (2) weighted mode-based estimator (WMBE); and (3) MR-Egger regression. Robust causal estimates were defined as those that were significant at an FDR of 5% and either (1) showed no evidence of heterogeneity (MR-PRESSO global test *P* > 0.05) or horizontal pleiotropy (Egger inter-cept *P* > 0.05); or (2) in the presence of heterogeneity or horizontal pleiotropy, the WME-, WMBE-, MR-Egger- or MR-PRESSO-corrected estimates were significant (*P* < 0.05). All statistical analyses were con-ducted using R v.4.0.3. Mendelian randomization analysis was per-formed using the 'TwoSampleMR' v.0.5.5 package⁵⁵.

## Website and data distribution

In anticipation of the need to coordinate many international partners around a single meta-analysis effort, we created the COVID-19 HGI website (https://covid19hg.org). We were able to centralize informa-tion, recruit partner studies, rapidly distribute summary statistics and present preliminary interpretations of the results to the public. Open meetings are held on a monthly basis to discuss future plans and new results; video recordings and supporting documents are shared (https://covid19hg.org/meeting-archive). This centralized resource provides a conceptual and technological framework for organizing global academic and industry groups around a shared goal. The website source code and additional technical details are available at https://github.com/covid19-hg/covid19hg.

To recruit new international partner studies, we developed a work-flow in which new studies are registered and verified by a curation team (https://covid19hg.org/register). Users can explore the registered studies using a customized interface to find and contact studies with similar goals or approaches (https://covid19hg.org/partners). This helps to promote organic assembly around focused projects that are

adjacent to the centralized effort (https://covid19hg.org/projects). Visitors can query study information, including study design and research questions. Registered studies are visualized on a world map and are searchable by institutional affiliation, city and country.

To encourage data sharing and other forms of participation, we created a rolling acknowledgements page (https://covid19hg.org/acknowledgements) and directions on how to contribute data to the central meta-analysis effort (https://covid19hg.org/data-sharing). Upon the completion of each data freeze, we post summary statistics, plots and sample size breakdowns for each phenotype and contributing cohort (https://covid19hg.org/results). The results can be explored using an interactive web browser (https://app.covid19hg.org). Several computational research groups carry out follow-up analyses, which are made available for download (https://covid19hg.org/in-silico). To enhance scientific communication to the public, preliminary results are described in blog posts by the scientific communications team and shared on Twitter. The first post was translated to 30 languages with the help of 85 volunteer translators. We compile publications and pre-prints submitted by participating groups and summarize genome-wide significant findings from these publications (https://covid19hg.org/publications).

## Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

## Data availability

Summary statistics generated by the COVID-19 HGI are available at https://www.covid19hg.org/results/rS/ and are available in the GWAS Catalog (study code GCST011074). The analyses described here include the freeze-5 data. COVID-19 HGI continues to regularly release new data freezes. Summary statistics for non-European ancestry samples are not currently available due to the small individual sample sizes of these groups, but results for lead variants of 13 loci are reported in Supplementary Table 3. Individual level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale lab (http://www.nealelab.is/uk-biobank/), Finucane lab (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finnngen.fi/en/access_results) and the eQTL catalogue release 3 (http://www.ebi.ac.uk/eqtl/).

## Code availability

The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses are available on GitHub (https://github.com/covid19-hg/) and the code for the Mendelian randomization and genetic correlation pipeline is available on GitHub at https://github.com/marcoralab/MRcovid.

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**Acknowledgements** We thank the entire COVID-19 HGI community for their contributions and continued collaboration. The work of the contributing studies was supported by numerous grants from governmental and charitable bodies. Acknowledgements specific to contributing studies are provided in Supplementary Table 13. We thank G. Butler-Laporte, G. Wojcik, M.-G. Hollm-Delgado, C. Willer and G. Davey Smith for their extensive feedback and discussion.

**Author contributions** Author contributions are provided within the author list.

**Competing interests** A full list of competing interests is supplied as Supplementary Table 13.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41586-021-03767-x.

**Correspondence and requests for materials** should be addressed to Benjamin M. Neale, Mark Daly, Andrea Ganna, Benjamin M. Neale or Mark Daly.

**Peer review information** *Nature* thanks Samira Asgari, Paul McLaren and Neneh Sallah for their contribution to the peer review of this work. Peer reviewer reports are available.

**Reprints and permissions information** is available at http://www.nature.com/reprints.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

**Extended Data Fig. 1 | Analytical summary of the COVID-19 HGI meta-analysis.** Using the analytical plan set by the COVID-19 HGI, each individual study runs their analyses and uploads the results to the Initiative, who then runs the meta-analysis. There are three main analyses that each study can contribute summary statistics to: critically ill COVID-19, hospitalized COVID-19 and reported SARS-CoV-2 infection. The phenotypic criteria used to define cases are listed in the dark grey boxes, along with the numbers of cases

(N) included in the final all-ancestries meta-analysis. Controls were defined in the same way across all three analyses as everybody that is not a case—for example, population controls (light grey box). Sensitivity analyses—not reported in this extended data figure—also included mild and/or asymptomatic cases of COVID-19 as control individuals. Sample number (N) of control individuals differed between the analyses due to the difference in the number of studies contributing data to these.
<figure><img src="image_10.png" /><figcaption>**Extended Data Fig. 2** | Projection of contributing studies samples into the same PC space. We asked participating studies to perform a PC projection using the 1000 Genomes Project and Human Genome Diversity Project as a reference, with a common set of variants. For each panel (except for the reference), coloured points correspond to contributed samples from each cohort, whereas grey points correspond to the reference samples from the 1000 Genomes Project. Colour represents a genetic population that each</figcaption></figure>

cohort specified. As 23andMe, Genomics England 100,000 Genomes Project (GenomicsEngland100kgp), and Million Veterans Program (MVP) only submitted PCA images, we overlaid their submitted transparent images using the same coordinates, instead of directly plotting them. Populations are defined as African (AFR), admixed American (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID) and South Asian (SAS), Oceanian (OCE).
<figure><img src="image_11.png" /></figure>

**Extended Data Fig. 3** | Locus-zoom plots of the 3p21.31 region for reported SARS-CoV-2 infection. **a**, A standard plot without exclusion. Here, the severity lead variant rs10490770 (chr. 3: 45823240T:C) is shown as a lead variant.

**b**, Additional independent susceptibility signal(s) after excluding variants with $r^2 > 0.05$ with rs10490770. The susceptibility lead variant rs2271616 (chr. 3: 45796521G:T) is highlighted.
Critically ill COVID-19+ (N.cases=6,179, N controls=1,483,780)

<figure><img src="image_12.png" /></figure>

**Extended Data Fig. 4** | Genome-wide meta-analysis association results for critical illness due to COVID-19. The locus on chromosome 6 is the HLA locus, which was removed from the list of reported loci in Supplementary Table 2 due to the high heterogeneity in effect size estimated between studies included in

the analysis. The locus on chromosome 7 was also not reported in Supplementary Table 2 due to missingness across studies—that is, the high number of studies in the meta-analysis that did not report summary statistics for this region. There are two association peaks on chromosome 19.
<figure><img src="image_13.png" /></figure>

**Extended Data Fig. 5** | Sensitivity analyses for overlapping controls in genomiCC and UK Biobank. Comparison of the beta effect sizes (top) and unadjusted *P* values (bottom) of the 13 lead variants, using data from the COVID-19 critical illness meta-analysis in all the cohorts to leaving out genomiCC (cases, *n* = 4,354; controls, *n* = 1,474,655; total, *n* = 1,479,009), leaving out the UK Biobank (UKBB; cases, *n* = 5,870; controls, *n* = 1,155,203; total, *n* = 1,161,073) and leaving out both genomiCC and UK Biobank (cases, *n* = 4,045; controls, *n* = 1,146,078; total, *n* = 1,150,123) (from left to right, respectively). Top, dots and grey bars represent the beta effect size estimates ± standard

error from the corresponding GWAS meta-analysis. Bottom, dots represent two-sided *P* values from the corresponding GWAS meta-analysis. Filled dots indicate variants that showed genome-wide significance in the full meta-analysis of critical illness due to COVID-19, and empty dots represent variants that were not significant for critical illness but were significant for either hospitalization due to COVID-19 or reported SARS-CoV-2 infection. Red dots represent variants that showed genome-wide significance in the leave-one-out analysis for genomiCC, UK Biobank or genomiCC and UK Biobank.
<figure><img src="image_14.png" /><figcaption>a</figcaption></figure>

**Extended Data Fig. 6** | Comparison of $\chi^2$ statistics and $r^2$ values to the lead variant in the 3p21.31 region. a–c, Data are shown for critical illness (a), hospitalization (b) and reported SARS-CoV-2 infection (c). The left blue peak in

<figure><img src="image_15.png" /><figcaption>COVID-19 hospitalization (release 5)
Lead variant: chr3:45823240:T:C</figcaption></figure>

c, which is uncorrelated with the lead variants in the region, indicates that there are independent signals.

<figure><img src="image_16.png" /><figcaption>COVID-19 reported infection (release 5)
Lead variant: chr3:45823240:T:C</figcaption></figure>
<figure><img src="image_17.png" /></figure>

**Extended Data Fig. 7 | Comparison of the effect sizes of lead variants between pairs of COVID-19 meta-analyses.** Comparison of effect sizes for the nine variants associated with severity of COVID-19 disease. a, Comparing hospitalized cases of COVID-19 versus population controls (n = 10,428 cases and n = 1,483,270 controls) and critically ill cases of COVID-19 versus population controls (n = 6,179 cases and n = 1,483,780 controls). b, Hospitalized cases of COVID-19 versus population controls (n = 5,806 cases and n = 1,144,263 controls) and hospitalized cases of COVID-19 versus non-hospitalized cases of

COVID-19 (n = 5,773 cases and n = 15,497 controls). Sample sizes for hospitalized cases of COVID-19 versus population controls differ between a and b due to differences in the sampling of studies selected for the analysis. This selection included all studies that were able to contribute data to the respective analyses that the data were compared to (shown on the y axis) in each panel. Dots represent the effect size beta estimates, bars represent the 95% confidence interval of the estimates. Effect size estimates and *P* values for heterogeneity tests (Cochran's Q, two-tailed test) are reported in Supplementary Table 3.
<figure><img src="image_18.png" /></figure>

**Extended Data Fig. 8** | PheWAS for genome-wide significant lead variants.

Selected phenotypes associated with genome-wide significant COVID-19 variants (see Supplementary Table 6 for a complete list). We report those associations for which a lead variant from a previous GWAS result was in high LD ($r^2 > 0.8$) with the index COVID-19 variants. The colour represents the z-scores of correlated risk increasing alleles for the trait. The total number of associations for each COVID-19 variant is highlighted in the grey box.
<figure><img src="image_19.png" /></figure>

**Extended Data Fig. 9** | Genetic correlation with COVID-19 phenotypes. Each column shows the genetic correlation results for the three COVID-19 phenotypes (European-ancestry analyses only): critical illness, hospitalization and reported SARS-CoV-2 infection. The traits that the genetic correlation is run against are listed on the left. Significant correlations (FDR < 0.05) are

shown with their 95% confidence intervals in red, nominally significant correlations (P < 0.05) are in black and non-significant correlations are in grey. Two-sided P values were calculated using LDSC for genetic correlations and exact estimates, unadjusted standard errors and two-sided P values are available in Supplementary Table 11.
<figure><img src="image_20.png" /><figcaption>**Extended Data Fig. 10** | Mendelian randomization sensitivity analyses.
Genetic correlations and Forest plots displaying the causal estimates for each of the sensitivity analyses used in the Mendelian randomization analysis for</figcaption></figure>

trait pairs that were significant at an FDR of 5%. Two-sided *P* values were estimated using IVW, WME, WMBE and MR-PRESSO analyses. RBC, red blood cell count.
# Reporting Summary

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For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

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Our web collection on [statistics for biologists](#) contains articles on many of the points above.

## Software and code

Policy information about [availability of computer code](#)

**Data collection** No code was used to collect data in the study.

**Data analysis** Each individual study that contributed genetic-phenotype association summary statistics to the consortium carried out their association analyses independently of the consortium (study-specific information outlined in Supplementary Table 1). However, the consortium did release phenotyping and analysis guidelines as a recommendation (https://www.covid19hg.org/). For quality control of genotype data we recommended using the Ricopili pipeline (PMID: 31393554). For genotype phasing and imputation we recommended the TopMed Imputation Server (PMID: 27571263) or Michigan Imputation Server (PMID: 27571263). For genome-wide association study (GWAS), we recommended SAIGE (PMID: 30104761), but some studies used PLINK (PMID: 17701901). Each study then submitted their GWAS summary statistics to the consortium for meta-analysis.

LD score regression v 1.0.1 [PMID: 25642630] was used for heritability and partitioned heritability analyses. Variants for Mendelian randomization instruments were selected using PLINK version 1.90b6.18 (PMID: 17701901). Exposure and outcome datasets were harmonized, and MR statistical analysis conducted using R version 4.0.3. with the R-package TwoSampleMR version 0.5.5 (PMID: 29846171) (which included Fixed-effects IVW analysis (PMID: 24114802), weighted median estimator (WME) (PMID: 27061298), weighted mode based estimator (WMBE) and MR Egger regression (PMID: 26050253)) and additionally MR-PRESSO version 1.0 (PMID: 29686387).

Code availability statement: The code for summary statistics liftover, projection PCA pipeline including precomputed loadings and meta-analysis are available at https://github.com/covid19-hg/ and the code for Mendelian randomization and genetic correlation pipeline at https://github.com/marcoralab/MRCovid.
# Data

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**Data availability statement:**

Summary statistics generated by COVID-19 HGI are available at https://www.covid19hg.org/results/r5/ and are available on GWAS Catalog (study code GCST011074). The analyses described here utilize the freeze 5 data. COVID-19 HGI continues to regularly release new data freezes. Summary statistics for non-European ancestry samples are not currently available due to the small individual sample sizes of these groups, but results for 13 loci lead variants are reported in Supplementary Table 3. Individual level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale lab (http://www.nealelab.is/uk-biobank/), Finucane lab (https://www.finucanelab.org), FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results), and eQTL catalogue release 3 (http://www.ebi.ac.uk/eqtl/).

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# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Sample size**

The consortium meta-analysed genome-wide association study (GWAS) summary statistics from any individual study that had included a minimum of n=50 cases and n=50 controls in their analysis. The cutoff at n=50 cases and n=50 controls was aimed at reducing noise to the meta-analysis, but also to be inclusive of studies that had not yet accumulated large numbers of COVID-19 patient data. No statistical calculation for adequate sample size was performed, but the results identifying multiple genomic regions at genome-wide significance threshold indicates adequate power for genetic discovery.

**Data exclusions**

Individual level phenotype and genotype data exclusions were performed by each individual study, following the consortium analysis plan recommendations (www.covid19hg.org). Possible reasons for sample exclusion included removing genetic ancestry outliers within a study (using principal components analysis), poor quality of genetic data or lack of phenotypic data for a sample.

The consortium manually examined GWAS summary statistics data submitted by each study (for each submitted analysis separately), including sample size used for analysis, allele frequency check against gnomad reference panel, and distribution of test statistics. After meta-analysis, the results were checked for heterogeneity variant effects between contributing studies, and Table 1 excludes two genome-wide significant loci that were deemed to have extremely heterogeneous effects, but these variants are reported in the released consortium summary statistics (with heterogeneity test values).

**Replication**

No replication was performed. The consortium meta-analysed GWAS summary statistics, bringing together as many studies as possible to achieve the largest possible sample size and statistical power for association. This meant that the consortium included most large studies of COVID-19 host genetics that have been performed to date, so it was not possible to perform replication analyses in external cohorts. Therefore we performed manual checks on each study contributing summary statistics before entering them into the meta-analysis. In addition, after meta-analysis, we performed a check for heterogeneity between variant association estimates across studies contributing data. This allowed us to better understand whether the variant effects differed much between individual studies.

**Randomization**

No randomization was performed because there was no allocation of samples to experimental groups.

**Blinding**

Blinding was not relevant to the study. The case status and severity of symptoms was evaluated for each sample by investigators from each study respectively. The consortium recommended using covariates to control for confounding: age + age2 + sex + age*sex + 20 principal components (obtained using genetic data) + study specific covariates (if any). The consortium meta-analysed summary statistics from these case/control studies, not individual level data. Details of which variables each study used and how the calculated PCs for their analysis are available in Supplementary Table 1.

# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.