Abstract
The genetic basis of severe COVID-19 has been thoroughly studied and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we have conducted the largest GWAS meta-analysis for COVID-19 hospitalization in admixed Americans, comprising a total of 4,702 hospitalized cases recruited by SCOURGE and other seven participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and first discovered in Latin-American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort.
eLife assessment
The authors conduct a valuable GWAS meta-analysis for COVID-19 hospitalization in admixed American populations and prioritized risk variants and genes. The evidence supporting the claims of the authors is incomplete. The work will be of interest to scientists studying the genetic basis of COVID pathogenesis.
Introduction
To date, more than 50 loci associated to COVID-19 susceptibility, hospitalization, and severity have been identified using genome-wide association studies (GWAS)1,2. The COVID-19 Host Genetics Initiative (HGI) has made significant efforts3 to augment the power to identify disease loci by recruiting individuals from diverse populations and conducting a trans-ancestry meta-analysis. Despite this, the lack of genetic diversity and a focus on cases of European ancestries still predominate in the studies4,5. Besides, while trans-ancestry meta-analyses are a powerful approach for discovering shared genetic risk variants with similar effects across populations6, they may fail to identify risk variants that have larger effects on particular underrepresented populations. Genetic disease risk has been shaped by the particular evolutionary history of populations and the environmental exposures7. Their action is particularly important for infectious diseases due to the selective constrains that are imposed by the host-pathogen interactions8,9. Literature examples of this in COVID-19 severity includes a DOCK2 gene variant in East Asians10, and frequent loss of function variants in IFNAR1 and IFNAR2 genes in Polynesian and Inuit populations, respectively11,12.
Including diverse populations in case-control GWAS studies with unrelated participants usually require a prior classification of individuals in genetically homogeneous groups, which are typically analysed separately to control the population stratification effects13. Populations with recent admixture impose an additional challenge to the GWAS due to their complex genetic diversity and linkage disequilibrium (LD) patterns, requiring the development of alternative approaches and a careful inspection of results to reduce the false positives due to population structure7. In fact, there are benefits in study power from modelling the admixed ancestries either locally, at regional scale in the chromosomes, or globally, across the genome, depending on factors such as the heterogeneity of the risk variant in frequencies or the effects among the ancestry strata14. Despite the development of novel methods specifically tailored for the analysis of admixed populations15, the lack of a standardized analysis framework and the difficulties to confidently cluster the admixed individuals into particular genetic groups often leads to their exclusion from GWAS.
The Spanish Coalition to Unlock Research on Host Genetics on COVID-19 (SCOURGE) recruited COVID-19 patients between March and December 2020 from hospitals across Spain and from March 2020 to July 2021 in Latin-America (https://www.scourge-covid.org). A first GWAS of COVID-19 severity among Spanish patients of European descent revealed novel disease loci and explored age and sex varying effects of the genetic factors16. Here we present the findings of a GWAS meta-analysis in admixed American (AMR) populations, comprising individuals from the SCOURGE Latin-American cohort and the HGI studies, which allowed to identify two novel severe COVID-19 loci, BAZ2B and DDIAS. Further analyses modelling the admixture from three genetic ancestral components and performing a trans-ethnic meta-analysis led to the identification of an additional risk locus near CREBBP. We finally assessed a cross-ancestry polygenic risk score model with variants associated with critical COVID-19.
Results
Meta-analysis of COVID-19 hospitalization in admixed Americans
Study cohorts
Within the SCOURGE consortium, we included 1,608 hospitalized cases and 1,887 controls (not hospitalized COVID-19 patients) from Latin-American countries and from recruitments of individuals of Latin-American descend conducted in Spain (Supplementary Table 1). Quality control details and estimation of global genetic inferred ancestry (GIA) (supplementary Figure 1) are described in Methods, whereas clinical and demographic characteristics of patients included in the analysis are shown in Table 1. Summary statistics from the SCOURGE cohort were obtained under a logistic mixed model with the SAIGE model (Methods). Another seven studies participating in the COVID-19 HGI consortium were included in the meta-analysis of COVID-19 hospitalization in admixed Americans (Figure 1).
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_fig1.tif/full/max/0/default.jpg)
Flow chart of this study.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_tbl1.tif/full/max/0/default.jpg)
Demographic characteristics of the SCOURGE Latin-American cohort.
GWAS meta-analysis
We performed a fixed-effects GWAS meta-analysis using the inverse of the variance as weights for the overlapping markers. The combined GWAS sample size consisted of 4,702 admixed AMR hospitalized cases and 68,573 controls.
This GWAS meta-analysis revealed genome-wide significant associations at four risk loci (Table 2, Figure 2), two of which (BAZ2B and DDIAS) were novel discoveries. Variants of these loci were prioritized by positional and expression quantitative trait loci (eQTL) mapping with FUMA, identifying four lead variants linked to other 310 variants and 31 genes (Supplementary Tables 2-4). A gene-based association test revealed a significant association in BAZ2B and in previously known COVID-19 risk loci: LZTFL1, XCR1, FYCO1, CCR9, and IFNAR2 (Supplementary Table 5).
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_fig2.tif/full/max/0/default.jpg)
A) Manhattan plot for the admixed AMR GWAS meta-analysis. Probability thresholds at p=5×10-8 and p=5×10-5 are indicated by the horizontal lines. Genome-wide significant associations with COVID-19 hospitalizations were found in chromosome 2 (within BAZ2B), chromosome 3 (within LZTFL1), chromosome 6 (within FOXP4), and chromosome 11 (within DDIAS). A Quantile-Quantile plot is shown in supplementary Figure 2. B) Regional association plots for rs1003835 at chromosome 2 and rs77599934 at chromosome 11; C) Allele frequency distribution across The 1000 Genomes Project populations for the lead variants rs1003835 and rs77599934.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_tbl2.tif/full/max/0/default.jpg)
Lead independent variants in the admixed AMR GWAS meta-analysis.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_tbl3.tif/full/max/0/default.jpg)
Novel variants in the SC-HGIALL and SC-HGI3POP meta-analyses (with respect to HGIv7). Independent signals after LD clumping.
Located within the gene BAZ2B, the sentinel variant rs13003835 is an intronic variant associated with an increased risk of COVID-19 hospitalization (Odds Ratio [OR]=1.20, 95% Confidence Interval [CI]=1.12-1.27, p=3.66×10-8). This association was not previously reported in any GWAS of COVID-19 published to date. Interestingly, rs13003835 did not reach significance (p=0.972) in the COVID-19 HGI trans-ancestry meta-analysis including the five population groups1. Based on our mapping strategy (see Methods), we also prioritized PLA2R1, LY75, WDSUB1, and CD302 in this locus.
The other novel risk locus is led by the sentinel variant rs77599934, a rare intronic variant located in chromosome 11 within DDIAS and associated with risk of COVID-19 hospitalization (OR=2.27, 95%CI=1.70-3.04, p=2.26×10-8). The PRCP gene was an additional prioritized gene at this locus.
We also observed a suggestive association with rs2601183 in chromosome 15, which is located between ZNF774 and IQGAP1 (allele-G OR=1.20, 95%CI=1.12-1.29, p=6.11×10-8, see Supplementary Table 2), which has not yet been reported in any other GWAS of COVID-19 to date. This sentinel variant is in perfect LD (r2=1) with rs601183, an eQTL of ZNF774 in the lung.
The GWAS meta-analysis also pinpointed two significant variants at known loci, LZTFL1 and FOXP4. The SNP rs35731912 was previously associated with COVID-19 severity in EUR populations17, and it was mapped to LZTFL1. As for rs2477820, while it is a novel risk variant within gene FOXP4, it has a moderate LD (r2=0.295) with rs2496644, which has been linked to COVID-19 hospitalization18. This is consistent with the effects of LD in tag-SNPs when conducting GWAS in diverse populations.
Functional mapping of novel risk variants
Bayesian fine mapping
We performed different approaches to narrow down the prioritized loci to a set of most probable genes driving the associations. First, we computed credible sets at the 95% confidence for causal variants and annotated them with VEP and the V2G aggregate scoring (Supplementary Table 6, Supplementary Figure 3). The 95% confidence credible set from the region of chromosome 2 around rs13003835 included 76 variants. However, the approach was unable to converge allocating variants in a 95% confidence credible set for the region in chromosome 11.
Colocalization of eQTLs
To determine if the novel genetic risk loci were associated with gene expression in relevant tissues (whole blood, lung, lymphocytes, and oesophagus mucosa), we computed the posterior probabilities (PP) of colocalization for overlapping variants allocated to the 95% confidence credible set. We used the GTEx v8 tissues as the main expression dataset, although it is important to consider that the eQTL associations were carried out mainly on individuals of EUR ancestries. To confirm the colocalization in other ancestries, we also performed analyses on three expression datasets computed on admixed AMR, leveraging data from individuals with high African GIA, high Native-American ancestry, and from a pooled cohort (Methods). Results are shown in the supplementary Table 7.
Five genes (LY75, BAZ2B, CD302, WDSUB1, and PLA2R1) were the candidates for eQTL colocalization in the associated region in chromosome 2. However, LY75 emerged as the most likely causal gene for this locus since the colocalization in whole blood was supported with a PP for H4 (PPH4) of 0.941 and with robust results (supplementary Figure 4). Moreover, this also allowed to prioritize rs12692550 as the most probable causal variant for both traits at this locus with a PP_SNP_H4 of 0.74. Colocalization with gene expression data from admixed AMR validated this finding. LY75 also had evidence of colocalization in lungs (PPH4=0.887) and the esophagus mucosa (PPH4=0.758). However, we could not prioritize a single causal variant in these two other tissues and sensitivity analyses revealed a weak support.
CD302 and BAZ2B were the second and third most likely genes that could drive the association, respectively, according to the colocalization evidence. CD302 was the most probable according to the high AFR genetic ancestries dataset (supplementary Figure 5).
Despite the chromosome 11 region failing to colocalize with gene expression associations for any of the tissues, the lead variant rs77599934 is in moderate-to-strong LD (r2=0.776) with rs60606421, which is an eQTL associated to a reduced expression of DDIAS in the lungs (supplementary Figure 6). The highest PPH4 for DDIAS was in the high AFR genetic ancestry expression dataset (0.71).
Transcriptome-wide association study (TWAS)
Five novel genes, namely SLC25A37, SMARCC1, CAMP, TYW3, and S100A12 (supplementary Table 8) were found significantly associated in the cross-tissue TWAS. To our knowledge, these genes have not been reported previously in any COVID-19 TWAS or GWAS analyses published to date. In the single tissue analyses, ATP5O and CXCR6 were significantly associated in lungs, CCR9 was significantly associated in whole blood, and IFNAR2 and SLC25A37 were associated in lymphocytes.
Likewise, we carried out the TWAS analyses using the models trained in the admixed populations. However, no significant gene-pairs were detected in this case. The 50 genes with the lowest p-values are shown in the supplementary Table 9.
Genetic architecture of COVID-19 hospitalization in AMR populations
Allele frequencies of rs13003835 and rs77599934 across ancestries
Neither rs13003835 (BAZ2B) or rs77599934 (DDIAS) were significantly associated in the COVID-19 HGI B2 cross-population or population-specific meta-analyses. Thus, we investigated their allele frequencies (AF) across populations and compared their effect sizes.
According to gnomAD v3.1.2, the T allele at rs13003835 (BAZ2B) has an AF of 43% in admixed AMR groups while AF is lower in the EUR populations (16%) and in the global sample (29%). Local ancestry inference (LAI) reported by gnomAD shows that within the Native-American component, the risk allele T is the major allele, whereas it is the minor allele within the African and European LAI components. These large differences in AF might be the reason underlying the association found in AMR populations. However, when comparing effect sizes between populations, we found that they were in opposite direction between SAS-AMR and EUR-AFR-EAS and that there was a large heterogeneity among them (Figure 3).
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_fig3.tif/full/max/0/default.jpg)
Forest plot showing effect sizes and the corresponding confidence intervals for the sentinel variants identified in the AMR meta-analysis across populations. All beta values with their corresponding CIs were retrieved from the B2 population-specific meta-analysis from the HGI v7 release, except for AMR, for which the beta value and IC from the HGIAMR-SCOURGE meta-analysis is represented.
rs77599934 (DDIAS) had an AF of 1.1% for the G allele in the non-hospitalized controls (Table 2), in line with the recorded gnomAD AF of 1% in admixed AMR groups. This variant has potential to be population-specific variant, given the allele frequencies in other population groups such as EUR (0% in Finnish, 0.025% in non-Finnish), EAS (0%) and SAS (0.042%) and its greater effect size over AFR populations (Figure 3). Examining the LAI, the G allele occurs at 1.1% frequency in the African component while it is almost absent in the Native-American and European. Due to its low MAF, rs77599934 was not analyzed in the COVID-19 HGI B2 cross-population meta-analysis and was only present in the HGI B2 AFR population-specific meta-analysis, precluding the comparison (Figure 3). For this reason, we retrieved the variant with the lowest p-value within a 50 kb region around rs77599934 in the COVID-19 HGI cross-population analysis to investigate if it was in moderate-to-strong LD with our sentinel variant. The variant with the smallest p-value was rs75684040 (OR=1.07, 95%CI=1.03-1.12, p=1.84×10-3). Yet, LD calculations using the 1KGP phase 3 dataset indicated that rs77599934 and rs75684040 were poorly correlated (r2=0.11).
Cross-population meta-analyses
We carried out two cross-ancestry inverse variance-weighted fixed-effects meta-analyses with the admixed AMR GWAS meta-analysis results to evaluate whether the discovered risk loci replicated when considering other population groups. In doing so, we also identified novel cross-population COVID-19 hospitalization risk loci.
First, we combined the SCOURGE Latin American GWAS results with the HGI B2 ALL analysis (supplementary Table 10). We refer to this analysis as the SC-HGIALL meta-analysis. Out of the 40 genome-wide significant loci associated with COVID-19 hospitalization in the last HGI release1, this study replicated 39 and the association was stronger than in the original study in 29 of those (supplementary Table 11). However, the variant rs13003835 located in BAZ2B did not replicate (OR=1.00, 95%CI=0.98-1.03, p=0.644).
In this cross-ancestry meta-analysis, we replicated two associations that were not found in HGIv7 albeit they were sentinel variants in the latest GenOMICC meta-analysis2. We found an association at the CASC20 locus led by the variant rs2876034 (OR=0.95, 95%CI=0.93-0.97, p=2.83×10-8). This variant is in strong LD with the sentinel variant of that study (rs2326788, r2=0.92), which was associated with critical COVID-192. Besides, this meta-analysis identified the variant rs66833742 near ZBTB7A associated with COVID-19 hospitalization (OR=0.94, 95%CI=0.92-0.96, p=2.50×10-8). Notably, rs66833742 or its perfect proxy rs67602344 (r2=1) are also associated with upregulation of ZBTB7A in whole blood and in esophagus mucosa. This variant was previously associated with COVID-19 hospitalization2.
In a second analysis, we also explored the associations across the defined admixed AMR, EUR, and AFR ancestral sources by combining through meta-analysis the SCOURGE Latin American GWAS results with the HGI studies in EUR, AFR, and admixed AMR, and excluding those from EAS and SAS (Supplementary Table 12). We refer to this as the SC-HGI3POP meta-analysis. The association at rs13003835 (BAZ2B, OR=1.01, 95%CI=0.98-1.03, p=0.605) was not replicated and rs77599934 near DDIAS could not be assessed, although the association at the ZBTB7A locus was confirmed (rs66833742, OR=0.94, 95%CI=0.92-0.96, p=1.89×10-8). The variant rs76564172 located near CREBBP also reached statistical significance (OR=1.31, 95% CI=1.25-1.38, p=9.64×10-9). The sentinel variant of the region linked to CREBBP (in the trans-ancestry meta-analysis) was also subjected a Bayesian fine mapping (supplementary Table 6) and colocalization with eQTLs under the GTEx v8 MASHR models in lungs, esophagus mucosa, whole blood, and transformed lymphocytes. Eight variants were included in the credible set for the region in chromosome 16 (meta-analysis SC-HGI3POP), although CREBBP did not colocalize in any of the tissues.
Polygenic risk score models
Using the 49 variants associated with disease severity that are shared across populations according to the HGIv7, we constructed a polygenic risk score (PGS) model to assess its generalizability in the admixed AMR (Supplementary Table 13). First, we calculated the PGS for the SCOURGE Latin Americans and explored the association with COVID-19 hospitalization under a logistic regression model. The PGS model was associated with a 1.48-fold increase in COVID-19 hospitalization risk per every PGS standard deviation. It also contributed to explain a slightly larger variance (R2=1.07%) than the baseline model.
Subsequently, we divided the individuals into PGS deciles and percentiles to assess their risk stratification. The median percentile among controls was 40, while in cases it was 63. Those in the top PGS decile exhibited a 5.90-fold (95% CI=3.29-10.60, p=2.79×10-9) greater risk compared to individuals in the lowest decile, whereas the effects for the rest of the comparisons were much milder.
We also examined the distribution of PGS scores across a 5-level severity scale to further determine if there was any correspondence between clinical severity and genetic risk. Median PGS scores were lower in the asymptomatic and mild groups, whereas higher median scores were observed in the moderate, severe, and critical patients (Figure 4). We fitted a multinomial model using the asymptomatic class as reference and calculated the OR for each category (Supplementary Table 13), observing that the disease genetic risk was similar among asymptomatic, mild, and moderate patients. Given that the PGS was built with variants associated with critical disease and/or hospitalization and that the categories severe and critical correspond to hospitalized patients, these results underscore the ability of cross-ancestry PGS for risk stratification even in an admixed population.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_fig4.tif/full/max/0/default.jpg)
(A) Polygenic risk stratified by PGS deciles comparing each risk group against the lowest risk group (OR-95%CI); (B) Distribution of the PGS scores in each of the severity scale classes (0-Asymptomatic, 1-Mild disease, 2-Moderate disease, 3-Severe disease, 4-Critical disease).
Finally, we incorporated the novel lead SNPs from our AMR meta-analysis (rs13003835, rs2477820, and rs77599934) into the PGS model. Their inclusion in the model contributed to explain a larger variance (R2=1.74%) than the model without them. This result, however, should be taken with caution given the risk of overfitting due to the use of the same subjects both for the derivation and testing of the variants.
Discussion
We have conducted the largest GWAS meta-analysis of COVID-19 hospitalization in admixed AMR to date. While the genetic risk basis discovered for COVID-19 is largely shared among populations, trans-ancestry meta-analyses on this disease have primarily included EUR samples. This dominance of GWAS in Europeans, and the subsequent bias in sample sizes, can mask population-specific genetic risks (i.e., variants that are monomorphic in some populations) or be less powered to detect risk variants having higher allele frequencies in population groups other than Europeans. In this sense, after combining data from admixed AMR patients, we found two risk loci which are first discovered in a GWAS of Latin-American populations. Interestingly, the sentinel variant rs77599934 in the DDIAS gene is a rare coding variant (∼1% for allele G) with a large effect on COVID-19 hospitalization that is nearly monomorphic in most of the other populations. This has likely led to its exclusion from the cross-populations meta-analyses conducted to date, remaining undetectable.
Fine mapping of the region harbouring DDIAS did not reveal further information about which gene could be the more prone to be causal, or about the functional consequences of the risk variant. However, DDIAS, known as damage-induced apoptosis suppressor gene, is itself a plausible candidate gene. It has been linked to DNA damage repair mechanisms: research showed that depletion of DDIAS led to an increase of ATM phosphorylation and the formation of p53-binding protein (53BP1) foci, a known biomarker of DNA double-strand breaks, suggesting a potential role in double-strand break repair19. Similarly, elevated levels of phosphorylated nuclear histone 2AXγ were detected after knocking down DDIAS, further emphasizing its role in DNA damage20. Interestingly, a study found that the infection by SARS-CoV-2 also triggered the phosphorylation of the ATM kinase and inhibited repair mechanisms, causing the accumulation of DNA damage21. This same study reported the activation of the pro-inflammatory pathway p38/MAPK by the virus, which was as well prompted after knocking-down DDIAS20.
Regarding lung function, the role of DDIAS in lung cancer has been widely studied. It has been proposed as a potential biomarker for lung cancer after finding that it interacts with STAT3 in lung cancer cells, regulating IL-622,23 and thus mediating inflammatory processes. Furthermore, another study determined that its blockade inhibited lung cancer cell growth20. The sentinel variant was in strong LD with an eQTL that reduced gene expression of DDIAS in lung, and our findings suggest that DDIAS gene may be indeed involved in viral response. Hence, one reasonable hypothesis is that reduced expression of DDIAS could potentially facilitate SARS-CoV-2 infection through the downregulation of pathways involved in DNA repairment and inflammation. Another prioritized gene from this region was PRCP, an angiotensinase that has been linked to hypertension and for which a hypothesis on its role on COVID-19 progression has been raised24,25.
The risk region found in chromosome 2 prioritized more than one gene. The lead variant rs13003835 is located within BAZ2B. BAZ2B encodes one of the regulatory subunits of the Imitation switch (ISWI) chromatin remodelers26 constituting the BRF-1/BRF-5 complexes with SMARCA1 and SMARCA5, respectively, and the association signal colocalized with eQTLs in whole blood. The gene LY75 (encoding the lymphocyte antigen 75) also colocalized with eQTLs in whole blood, esophagus mucosa, and lung tissues. Lymphocyte antigen 75 is involved in immune processes through antigen presentation in dendritic cells and endocytosis27, and has been associated with inflammatory diseases, representing also a compelling candidate for the region. Increased expression of LY75 has been detected within hours after the infection by SARS-CoV-228,29. Lastly, the signal of CD302 colocalized in individuals with high AFR ancestral admixture in whole blood. This gene is located in the vicinity of LY75 and both conform the readthrough LY75-CD302. It is worth noting that differences in AF for this variant suggest that analyses in AMR populations might be more powered to detect the association, supporting the necessity of population-specific studies.
A third novel risk region was observed in chromosome 15, between the genes IQGAP1 and ZNF774, although not reaching genome-wide significance.
Secondary analyses revealed five TWAS-associated genes, some of which have been already linked to severe COVID-19. In a comprehensive multi-tissue gene expression profiling study30, decreased expression of CAMP and S100A8/S100A9 genes in COVID-19 severe patients was observed, while another study detected the upregulation of SCL25A37 among severe COVID-19 patients31. SMARCC1 is a subunit of the SWI/SNF chromatin remodelling complex that has been identified as pro-viral for SARS-CoV-2 and other coronavirus strains through a genome-wide screen32. This complex is crucial for ACE2 expression and the viral entry in the cell33.
To explore the genetic architecture of the trait among admixed AMR populations, we performed two cross-ancestry meta-analyses including the SCOURGE Latin-American cohort GWAS findings. We found that the two novel risk variants did not associate with COVID-19 hospitalization outside the population-specific meta-analysis, highlighting the importance of complementing trans-ancestry meta-analyses with group-specific analyses. Notably, this analysis did not replicate the association at the DSTYK locus, which was associated with severe COVID-19 in Brazilian individuals with higher European admixture34. This lack of replication supports the initial hypothesis of that study suggesting that the risk haplotype derived from European populations, as we have reduced the weight of this ancestral contribution in our study by excluding those individuals.
Moreover, these cross-ancestry meta-analyses pointed to three loci that were not genome-wide significant in the HGIv7 ALL meta-analysis: a novel locus at CREBBP, and two loci at ZBTB7A and CASC20 that were reported in another meta-analysis. CREBBP and ZBTB7A achieved a stronger significance when considering only EUR, AFR, and admixed AMR GIA groups. According to a recent study, elevated levels of the ZBTB7A gene promote a quasi-homeostatic state between coronaviruses and host cells, preventing cell death by regulating oxidative stress pathways35. This gene is involved in several signalling pathways, such as B and T cell differentiation36. On a separate note, CREBBP encodes the CREB binding protein (CBP), involved in transcription activation, that is known to positively regulate the type I interferon response through virus-induced phosphorylation of IRF-337. Besides, the CREBP/CBP interaction has been implicated in SARS-CoV-2 infection38 via the cAMP/PKA pathway. In fact, cells with suppressed CREBBP gene expression exhibit reduced replication of the so called Delta and Omicron SARS-CoV-2 variants38.
The cross-population PGS model effectively stratified individuals based on their genetic risk and demonstrated consistency with the clinical severity classification of the patients. The inclusion of the new variants in the PGS model slightly improved the predictive value of the PGS. However, it is important to confirm this last finding in an external admixed AMR cohort to address potential overfitting arising from using the same individuals both for the discovery of the associations and for testing the model.
This study is subject to limitations, mostly concerning the sample recruitment and composition. The SCOURGE Latino-American sample size is small and the GWAS is underpowered. Another limitation is the difference in case-control recruitment across sampling regions that, yet controlled for, may reduce the ability to observe significant associations driven by different compositions of the populations. In this sense, the identified risk loci might not replicate in a cohort lacking any of the parental population sources from the three-way admixture. Likewise, we could not explicitly control for socio-environmental factors that could have affected COVID-19 spread and hospitalization rates, although genetic principal components are known to capture non-genetic factors. Finally, we must acknowledge the lack of a replication cohort. We have used all the available GWAS data for COVID-19 hospitalization in admixed AMR in this meta-analysis due to the low number of studies conducted. Therefore, we had no studies to replicate or validate the results. These concerns may be addressed in the future by including more AMR GWAS studies in the meta-analysis, both by involving diverse populations in study designs and by supporting research from countries in Latin-America.
This study provides novel insights into the genetic basis of COVID-19 severity, emphasizing the importance of considering host genetic factors through using non-European populations, especially of admixed sources. Such complementary efforts can pin down new variants and increase our knowledge on the host genetic factors of severe COVID-19.
Materials and methods
GWAS in Latin Americans from SCOURGE
The SCOURGE Latin American cohort
A total of 3,729 of COVID-19 positive cases were recruited across five countries from Latin America (Mexico, Brazil, Colombia, Paraguay, and Ecuador) by 13 participating centres (supplementary Table 1) from March 2020 to July 2021. In addition, we included 1,082 COVID-19 positive individuals recruited between March and December 2020 in Spain who either had evidence of origin from a Latin American country or showed inferred genetic admixture between AMR, EUR, and AFR (with < 0.05% SAS/EAS). These individuals were excluded from a previous SCOURGE study that focused on participants with European genetic ancestries16. We used hospitalization as a proxy for disease severity and defined as cases those COVID-19 positive patients that underwent hospitalization as a consequence of the infection and used as controls those that did not need hospitalization due to COVID-19.
Samples and data were collected with informed consent after the approval of the Ethics and Scientific Committees from the participating centres and by the Galician Ethics Committee Ref 2020/197. Recruitment of patients from IMSS (in Mexico, City), was approved by of the National Comitte of Clinical Research, from Instituto Mexicano del Seguro Social, Mexico (protocol R-2020-785-082).
Samples and data were processed following normalized procedures. The REDCap electronic data capture tool39,40, hosted at Centro de Investigación Biomédica en Red (CIBER) from the Instituto de Salud Carlos III (ISCIII), was used to collect and manage demographic, epidemiological, and clinical variables. Subjects were diagnosed for COVID-19 based on quantitative PCR tests (79.3%), or according to clinical (2.2%) or laboratory procedures (antibody tests: 16.3%; other microbiological tests: 2.2%).
SNP array genotyping
Genomic DNA was obtained from peripheral blood and isolated using the Chemagic DNA Blood 100 kit (PerkinElmer Chemagen Technologies GmbH), following the manufacturer’s recommendations.
Samples were genotyped with the Axiom Spain Biobank Array (Thermo Fisher Scientific) following the manufacturer’s instructions in the Santiago de Compostela Node of the National Genotyping Center (CeGen-ISCIII; http://www.usc.es/cegen). This array contains probes for genotyping a total of 757,836 SNPs. Clustering and genotype calling were performed using the Axiom Analysis Suite v4.0.3.3 software.
Quality control steps and variant imputation
A quality control (QC) procedure using PLINK 1.941 was applied to both samples and the genotyped SNPs. We excluded variants with a minor allele frequency (MAF) <1%, a call rate <98%, and markers strongly deviating from Hardy-Weinberg equilibrium expectations (p<1×10-6) with mid-p adjustment. We also explored the excess of heterozygosity to discard potential cross-sample contaminations. Samples missing >2% of the variants were filtered out. Subsequently, we kept the autosomal SNPs and removed high LD regions and conducted LD-pruning (windows of 1,000 SNPs, with step size of 80 and r2 threshold of 0.1) to assess kinship and estimate the global ancestral proportions. Kinship was evaluated based on IBD values, removing one individual from each pair with PI_HAT>0.25 that showed a Z0, Z1, and Z2 coherent pattern (according to the theoretical expected values for each relatedness level). Genetic principal components (PCs) were calculated with PLINK with the subset of LD pruned variants.
Genotypes were imputed with the TOPMed version r2 reference panel (GRCh38) using the TOPMed Imputation Server and variants with Rsq<0.3 or with MAF<1% were filtered out. A total of 4,348 individuals and 10,671,028 genetic variants were included in the analyses.
Genetic admixture estimation
Global genetic inferred ancestry (GIA), referred to the genetic similarity to the used reference individuals, was estimated with the ADMIXTURE42 v1.3 software following a two-step procedure. First, we randomly sampled 79 European (EUR) and 79 African (AFR) samples from The 1000 Genomes Project (1KGP)43 and merged them with the 79 Native American (AMR) samples from Mao et al.44 keeping the biallelic SNPs. LD-pruned variants were selected from this merge using the same parameters as in the QC. We then run an unsupervised analysis with K=3 to redefine and homogenize the clusters and to compose a refined reference for the analyses, by applying a threshold of ≥95% of belonging to a particular cluster. As a result of this, 20 AFR, 18 EUR, and 38 AMR individuals were removed. The same LD-pruned variants data from the remaining individuals were merged with the SCOURGE Latin American cohort to perform a supervised clustering and estimated admixture proportions. A total of 471 samples from the SCOURGE cohort with >80% estimated European GIA were removed to reduce the weight of the European ancestral component, leaving a total of 3,512 admixed American (AMR) subjects for downstream analyses.
Association analysis
Results for the SCOURGE Latin Americans GWAS were obtained testing for COVID-19 hospitalization as a surrogate of severity. To accommodate the continuum of GIA in the cohort, we opted for a joint testing of all the individuals as a single study using a mixed regression model, as this approach has demonstrated a greater power and to sufficiently control population structure45. The SCOURGE cohort consisted of 3,512 COVID-19 positive patients: cases (n=1,625) were defined as hospitalized COVID-19 patients and controls (n=1,887) as non-hospitalized COVID-19 positive patients.
Logistic mixed regression models were fitted using the SAIGEgds46 package in R, which implements the two-step mixed SAIGE47 model methodology and the SPA test. Baseline covariables included sex, age, and the first 10 PCs. To account for a potential heterogeneity in the recruitment and hospitalization criteria across the participating countries, we adjusted the models by groups of the recruitment areas classified in six categories: Brazil, Colombia, Ecuador, Mexico, Paraguay, and Spain. This dataset has not been used in any previously GWAS of COVID-19 published to date.
Meta-analysis of Latin-American populations
The results of the SCOURGE Latin American cohort were meta-analyzed with the AMR HGI-B2 data, conforming our primary analysis. Summary results from the HGI freeze 7 B2 analysis corresponding to the admixed AMR population were obtained from the public repository (April 8, 2022: https://www.covid19hg.org/results/r7/), summing up 3,077 cases and 66,686 controls from seven contributing studies. We selected the B2 phenotype definition because it offered more power and the presence of population controls not ascertained for COVID-19 does not have a drastic impact in the association results.
The meta-analysis was performed using an inverse-variance weighting method in METAL48. Average allele frequency was calculated and variants with low imputation quality (Rsq<0.3) were filtered out, leaving 10,121,172 variants for meta-analysis. Heterogeneity between studies was evaluated with the Cochran’s-Q test. The inflation of results was assessed based on a genomic control (lambda).
Definition of the genetic risk loci and putative functional impact
Definition of lead variant and novel loci
To define the lead variants in the loci that were genome-wide significant, an LD-clumping was performed on the meta-analysis data using a threshold p-value<5×10-8, clump distance=1500 kb, independence set at a threshold r2=0.1 and used the SCOURGE cohort genotype data as LD reference panel. Independent loci were deemed as a novel finding if they met the following criteria: 1) p-value<5×10-8 in the meta-analysis and p-value>5×10-8 in the HGI B2 ALL meta-analysis or in the HGI B2 AMR and AFR and EUR analyses when considered by separate; 2) Cochran’s Q-test for heterogeneity of effects is <0.05/Nloci, where Nloci is the number of independent variants with p<5×10-8; and 3) the nearest gene has not been previously described in the latest HGIv7 update.
Annotation and initial mapping
Functional annotation was done with FUMA49 for those variants with a p-value<5×10-8 or in moderate-to-strong LD (r2>0.6) with the lead variants, where the LD was calculated from the 1KGP AMR panel. Genetic risk loci were defined by collapsing LD-blocks within 250 kb. Then, genes, scaled CADD v1.4 scores, and RegulomeDB v1.1 scores were annotated for the resulting variants with ANNOVAR in FUMA49. Gene-based analysis was also performed using MAGMA50 as implemented in FUMA, under the SNP-wide mean model using the 1KGP AMR reference panel. Significance was set at a threshold p<2.66×10-6 (which assumes that variants can be mapped to a total of 18,817 genes).
FUMA allowed us to perform an initial gene mapping by two approaches: (1) positional mapping, which assigns variants to genes by physical distance using 10-kb windows; and (2) eQTL mapping based on GTEx v.8 data from whole blood, lungs, lymphocytes, and oesophagus mucosa tissues, establishing a False Discovery Rate (FDR) of 0.05 to declare significance for variant-gene pairs.
Subsequently, to assign the variants to the most likely gene driving the association, we refined the candidate genes by fine mapping the discovered regions and implementing functional mapping.
To conduct a Bayesian fine mapping, credible sets for the genetic loci considered novel findings were calculated on the results from each of the three meta-analyses to identify a subset of variants most likely containing the causal variant at 95% confidence level, assuming that there is a single causal variant and that it has been tested. We used corrcoverage (https://cran.rstudio.com/web/packages/corrcoverage/index.html) for R to calculate the posterior probabilities of the variant being causal for all variants with an r2>0.1 with the leading SNP and within 1 Mb except for the novel variant in chromosome 19, for which we used a window of 0.5 Mb. Variants were added to the credible set until the sum of the posterior probabilities was ≥0.95. VEP (https://www.ensembl.org/info/docs/tools/vep/index.html) and the V2G aggregate scoring from Open Targets Genetics (https://genetics.opentargets.org) were used to annotate the biological function of the variants contained in the fine-mapped credible sets.
Colocalization analysis
We also conducted colocalization analyses to identify the putative causal genes that could act through the regulation of gene expression. FUMA’s eQTL mapping enabled the identification of genes whose expression was associated with the variants in whole blood, lungs, lymphocytes, and oesophagus mucosa tissues. We combined this information with the VEP and V2G aggregate scoring to prioritize genes. For the fine-mapping regions, we included the variants within the calculated credible sets. In the cases where the fine mapping was unsuccessful, we considered variants within a 0.2 Mb window of the lead variant.
For each prioritized gene, we then run COLOC51 to assess the evidence of colocalization between association signals and the eQTLs in each tissue, when at least one variant overlapped between them. COLOC estimates the posterior probability of two traits sharing the same causal variant in a locus. Prior probabilities of a variant being associated to COVID-19 phenotype (p1) and gene expression (p2) were set at 1×10-4, while pp2 was set at 1×10-6 as they are robust thresholds52. The posterior probability of colocalization (PP4) > 0.75 and a ratio PP4/PP3>3 were used as the criteria to support evidence of colocalization. Additionally, a threshold of PP4.SNP >0.5 was chosen for causal variant prioritization. In cases were colocalization of a single variant failed, we computed the 95% credible sets. The eQTL data was retrieved from GTEx v8 and only significant variant-gene pairs were considered in the analyses.
Colocalization in whole-blood was also performed using the recent published gene expression datasets derived from a cohort of African Americans, Puerto Ricans, and Mexican Americans (GALA II-SAGE)53. We used the results from the pooled cohort for the three discovered loci, and from the AFRHp5 (African genetic ancestry>50%) and IAMHp5 (Native American genetic ancestry>50%) cohorts for the risk loci in chromosomes 2 and 11. Results are shown in the Supplementary Table 10.
Sensitivity plots are shown in supplementary Figures 4 and 5.
Transcription-wide association studies
Transcriptome-wide association studies (TWAS) were conducted using the pretrained prediction models with MASHR-computed effect sizes on GTEx v8 datasets54,55. Results from the Latin-American meta-analysis were harmonized and integrated with the prediction models through S-PrediXcan56 for lungs, whole blood, lymphocytes and oesophagus mucosa tissues. Statistical significance was set at p-value<0.05 divided by the number of genes that were tested for each tissue. Subsequently, we leveraged results for all 49 tissues and run a multi-tissue TWAS to improve power for association, as demonstrated recently57. TWAS was also conducted with the MASHR models for whole-blood in the pooled admixed AMR from the GALA and SAGE studies53.
Cross-population meta-analyses
We conducted two additional meta-analyses to investigate the ability of combining populations to replicate our discovered risk loci. This methodology enabled the comparison of effects and the significance of associations in the novel risk loci between the results from analyses that included or excluded other population groups.
The first meta-analysis comprised the five populations analysed within HGI (B2-ALL). Additionally, to evaluate the three GIA components within the SCOURGE Latin-American cohort58, we conducted a meta-analysis of the admixed AMR, EUR, and AFR cohorts (B2). All summary statistics were retrieved from the HGI repository. We applied the same meta-analysis methodology and filters as in the admixed AMR meta-analysis. Novel variants from these meta-analyses were fine-mapped and colocalized with gene expression.
Cross-population Polygenic Risk Score
A polygenic risk score (PGS) for critical COVID-19 was derived combining the variants associated with hospitalization or disease severity that have been discovered to date. We curated a list of lead variants that were: 1) associated to either severe disease or hospitalization in the latest HGIv7 release1 (using the hospitalization weights); or 2) associated to severe disease in the latest GenOMICC meta-analysis2 that were not reported in the latest HGI release. A total of 49 markers were used in the PGS model (see supplementary Table 13) since two variants were absent from our study.
Scores were calculated and normalized for the SCOURGE Latin-American cohort with PLINK 1.9. This cross-ancestry PGS was used as a predictor for hospitalization (COVID-19 positive that were hospitalized vs. COVID-19 positive that did not necessitate hospital admission) by fitting a logistic regression model. Prediction accuracy for the PGS was assessed by performing 500 bootstrap resamples of the increase in the pseudo-R-squared. We also divided the sample in deciles and percentiles to assess risk stratification. The models were fit for the dependent variable adjusting for sex, age, the first 10 PCs, and the sampling region (in the Admixed AMR cohort) with and without the PGS, and the partial pseudo-R2 was computed and averaged among the resamples.
A clinical severity scale was used in a multinomial regression model to further evaluate the power of this cross-ancestry PGS for risk stratification. This severity strata were defined as follows: 0) asymptomatic; 1) mild, that is, with symptoms, but without pulmonary infiltrates or need of oxygen therapy; 2) moderate, that is, with pulmonary infiltrates affecting <50% of the lungs or need of supplemental oxygen therapy; 3) severe disease, that is with hospital admission and PaO2<65 mmHg or SaO2<90%, PaO2/FiO2<300, SaO2/FiO2<440, dyspnea, respiratory frequency≥22 bpm, and infiltrates affecting >50% of the lungs; and 4) critical disease, that is with an admission to the ICU or need of mechanical ventilation (invasive or non-invasive). We also included the novel risk variants as predictors alongside the PRS to determine if they provided increased prediction ability.
Data availability
Summary statistics from the SCOURGE Latin-American GWAS will be available at https://github.com/CIBERER/Scourge-COVID19.
Funding
Instituto de Salud Carlos III (COV20_00622 to A.C., COV20/00792 to M.B., COV20_00181 to C.A., COV20_1144 to M.A.J.S. and A.F.R., PI20/00876 to C.F.); European Union (ERDF) ‘A way of making Europe’. Fundación Amancio Ortega, Banco de Santander (to A.C.), Estrella de Levante S.A. and Colabora Mujer Association (to E.G.-N.) and Obra Social La Caixa (to R.B.); Agencia Estatal de Investigación (RTC-2017-6471-1 to C.F.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.).
SD-DA was supported by a Xunta de Galicia predoctoral fellowship.
Author contributions
Study design: RC, AC, CF. Data collection: SCOURGE cohort group. Data analysis: SD-DA, RC, ADL, CF, JML-S. Interpretation: SD-DA, RC, ADL. Drafting of the manuscript: SD-DA, RC, ADL, CF, AR-M, AC. Critical revision of the manuscript: SD-DA, RC, ADL, AC, CF, JAR, AR-M, PL. Approval of the final version of the publication: all co-authors.
Acknowledgements
The contribution of the Centro National de Genotipado (CEGEN), and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures, is also acknowledged. Authors are also particularly grateful for the supply of material and the collaboration of patients, health professionals from participating centers and biobanks. Namely Biobanc-Mur, and biobancs of the Complexo Hospitalario Universitario de A Coruña, Complexo Hospitalario Universitario de Santiago, Hospital Clínico San Carlos, Hospital La Fe, Hospital Universitario Puerta de Hierro Majadahonda—Instituto de Investigación Sanitaria Puerta de Hierro—Segovia de Arana, Hospital Ramón y Cajal, IDIBGI, IdISBa, IIS Biocruces Bizkaia, IIS Galicia Sur. Also biobanks of the Sistema de Salud de Aragón, Sistema Sanitario Público de Andalucía, and Banco Nacional de ADN.
Supplementary Material for: Novel risk loci for COVID-19 hospitalization among admixed American populations
Supplementary Tables are provided in a separate excel file
Supplementary figures
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs1.tif/full/max/0/default.jpg)
Global Genetic Inferred Ancestry (GIA) composition in the SCOURGE Latin-American cohort.
European (EUR), African (AFR) and Native American (AMR) GIA was derived with ADMIXTURE from a reference panel composed of Aymaran, Mayan, Nahuan, and Quechuan individuals of Native-American genetic ancestry and randomly selected samples from the EUR and AFR 1KGP populations. The colours represent the different geographical sampling regions from which the admixed American individuals from SCOURGE were recruited.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs2.tif/full/max/0/default.jpg)
Quantile-Quantile plot for the AMR GWAS meta-analysis.
A lambda inflation factor of 1.015 was obtained.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs3.tif/full/max/0/default.jpg)
Regional association plots for the fine mapped loci in chromosomes 2 (upper panel) and 16 (lower panel).
Coloured in red, the variants allocated to the credible set at the 95% confidence according to the Bayesian fine mapping. In blue, the sentinel variant.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs4.tif/full/max/0/default.jpg)
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs4a.tif/full/max/0/default.jpg)
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs4b.tif/full/max/0/default.jpg)
Sensitivity plots from COLOC with expression data from GTEx v8.
The range of p12 values (probability that a SNP is associated with both traits) for which the rule H4>0.7 is supported is shown in green in the right plots for each analysis. Plots in the left represent the variants included in the risk region common to both traits along their individual association -log10(p-values) for each trait, whereas the shading shows the posterior probability that the SNP is causal given H4 is true. Trait 1 corresponds to COVID-19 hospitalization, while trait 2 corresponds to gene expression in each analysis.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs5.tif/full/max/0/default.jpg)
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs5a.tif/full/max/0/default.jpg)
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs5b.tif/full/max/0/default.jpg)
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs5c.tif/full/max/0/default.jpg)
Sensitivity plots from COLOC with whole blood expression data from the GALA and SAGE II studies in AMR individuals.
AFRhp5 corresponds to the expression dataset computed in individuals with high African ancestries; AMRhp5 corresponds to the expression dataset computed individuals with high AMR ancestries; pooled corresponds to the dataset computed with the total of individuals from the study. In the right, the plots show in green the range of p12 values (probability that a SNP is associated with both traits) for which the rule H4>0.7 is supported. Plots in the left represent the variants included in the risk region common to both traits along their individual association -log10(p-values) for each trait, whereas the shading shows the posterior probability that the SNP is causal given H4 is true. Trait 1 corresponds to COVID-19 hospitalization, while trait 2 corresponds to gene expression.
![](https://prod--epp.elifesciences.org/iiif/2/93666%2Fv1%2Fcontent%2F23293871v3_figs6.tif/full/max/0/default.jpg)
Gene-tissue pairs for which either rs1003835 or rs60606421 are significant eQTLs at FDR<0.05 (data retrieved from https://gtexportal.org/home/snp/).
rs1003835 (chromosome 2) maps to BAZ2B, LY75, and PLA2R genes. As for the lead variant of chromosome 11, rs77599934, since it was not an eQTL, we used an LD proxy variant (rs60606421). DDIAS and PRCP genes map closely to this variant. NES and p-values correspond to the normalized effect size (and direction) of eQTL-gene associations and the p-value for the tissue, respectively.
Scourge Cohort Group
Full list of cohort members and affiliations
Javier Abellan1,2; René Acosta-Isaac3; Jose María Aguado4,5,6,7; Carlos Aguilar8; Sergio Aguilera-Albesa9,10; Abdolah Ahmadi Sabbagh11; Jorge Alba12; Sergiu Albu13,14,15; Karla A.M. Alcalá-Gallardo16; Julia Alcoba-Florez17; Sergio Alcolea Batres18; Holmes Rafael Algarin-Lara19,20; Virginia Almadana21; Julia Almeida22,23,24,25; Berta Almoguera26,27; María R. Alonso28; Nuria Alvarez28; Rodolfo Alvarez-Sala Walther18; Mónica T. Andrade 29,30; Álvaro Andreu-Bernabeu31,6; Maria Rosa Antonijoan32; Eunate Arana-Arri33,34; Carlos Aranda35,36; Celso Arango31,37,6; Carolina Araque38,39; Nathalia K. Araujo40; Izabel M.T. Araujo41; Ana C. Arcanjo42,43,44; Ana Arnaiz45,46,47; Francisco Arnalich Fernández48; María J. Arranz49; José Ramon Arribas Lopez48; Maria-Jesus Artiga50; Yubelly Avello-Malaver51; Carmen Ayuso26,27; Ana Margarita Baldión51; Belén Ballina Martín11; Raúl C. Baptista-Rosas52,53,54; Andrea Barranco-Díaz20; María Barreda-Sánchez55,56; Viviana Barrera-Penagos51; Moncef Belhassen-Garcia57,58; Enrique Bernal55; David Bernal-Bello59; Joao F. Bezerra60; Marcos A.C. Bezerra61; Natalia Blanca-López62; Rafael Blancas63; Lucía Boix-Palop64; Alberto Borobia65; Elsa Bravo66; María Brion67,68; Óscar Brochado-Kith69,7; Ramón Brugada70,71,68,72; Matilde Bustos73; Alfonso Cabello74; Juan J. Caceres-Agra75; Esther Calbo76; Enrique J. Calderón77,78,79; Shirley Camacho80; Cristina Carbonell81,58; Servando Cardona-Huerta82; Antonio Augusto F. Carioca83; Maria Sanchez Carpintero35,36; Carlos Carpio Segura18; Thássia M.T. Carratto84; José Antonio Carrillo-Avila85; Maria C.C. Carvalho86; Carlos Casasnovas87,88,27; Luis Castano33,34,27,89,90; Carlos F. Castaño35,36; Jose E. Castelao91; Aranzazu Castellano Candalija92; María A. Castillo80; Yolanda Cañadas36; Francisco C. Ceballos27; Jessica G. Chaux39; Walter G. Chaves-Santiago93,39; Sylena Chiquillo-Gómez19,20; Marco A. Cid-Lopez16; Oscar Cienfuegos-Jimenez82; Rosa Conde-Vicente94; M. Lourdes Cordero-Lorenzana95; Dolores Corella96,97; Almudena Corrales98,99; Jose L. Cortes-Sanchez82,100; Marta Corton26,27; Tatiana X. Costa101; Raquel Cruz27,102; Marina S. Cruz40; Luisa Cuesta103; Gabriela C.R. Cunha104; David Dalmau105,76; Raquel C.S. Dantas-Komatsu40; M. Teresa Darnaude106; Alba De Martino-Rodríguez107,108; Juan De la Cruz Troca109,110,78; Juan Delgado-Cuesta111; Aranzazu Diaz de Bustamante106; Covadonga M. Diaz-Caneja31,37,6; Beatriz Dietl76; Silvia Diz-de Almeida27,102; Elena Domínguez-Garrido112; Alice M. Duarte41; Anderson Díaz-Pérez20; Jose Echave-Sustaeta113; Rocío Eiros114; César O. Enciso-Olivera38,39; Gabriela Escudero115; Pedro Pablo España116; Gladys Mercedes Estigarribia Sanabria117; María Carmen Fariñas45,46,47; Marianne R. Fernandes118,119; Lidia Fernandez-Caballero26,27; María J. Fernandez-Nestosa120; Ramón Fernández45,121; Silvia Fernández Ferrero11; Yolanda Fernández Martínez11; Ana Fernández-Cruz122; Uxía Fernández-Robelo123; Amanda Fernández-Rodríguez69,7; Marta Fernández-Sampedro45,47,46; Ruth Fernández-Sánchez26,27; Tania Fernández-Villa124,78; Carmen Fernéndez Capitán92; Patricia Flores-Pérez125; Vicente Friaza78,79; Lácides Fuenmayor-Hernández20; Marta Fuertes Núñez11; Victoria Fumadó126; Ignacio Gadea127; Lidia Gagliardi35,36; Manuela Gago-Domínguez128,129; Natalia Gallego130; Cristina Galoppo131; Carlos Garcia-Cerrada1,2,132; Josefina Garcia-García55; Inés García26,27; Mercedes García35,36; Leticia García35,36; María Carmen García Torrejón133,2; Irene García-García65; Carmen García-Ibarbia45,47,46; Andrés C. García-Montero134; Ana García-Soidán135; Elisa García-Vázquez55; Aitor García-de-Vicuña33,136; Emiliano Garza-Frias82; Jesus Gaytán-Martínez137, Angela Gentile131; Belén Gil-Fournier138; Fernan Gonzalez Bernaldo de Quirós139; Manuel Gonzalez-Sagrado94; Hugo Gonzalo Benito140; Beatriz González Álvarez107,108; Anna González-Neira28; Javier González-Peñas31,6,37; Oscar Gorgojo-Galindo141; Florencia Guaragna131; Genilson P. Guegel142; Beatriz Guillen-Guio98; Encarna Guillen-Navarro55,143,144,27; Pablo Guisado-Vasco113; Luz D. Gutierrez-Castañeda145,39; Juan F. Gutiérrez-Bautista146; Luis Gómez Carrera18; María Gómez García128; Ángela Gómez Sacristán147; Javier Gómez-Arrue107,108; Mario Gómez-Duque93,39; Miguel Górgolas74; Sarah Heili-Frades148; Estefania Hernandez149; Luis D. Hernandez-Ortega150,151; Cristina Hernández Moro11; Guillermo Hernández-Pérez81; Rebeca Hernández-Vaquero152; Belen Herraez28; M. Teresa Herranz55; María Herrera35,36; María José Herrero153,154; Antonio Herrero-Gonzalez155; Juan P. Horcajada156,157,14,158,7; Natale Imaz-Ayo33; Maider Intxausti-Urrutibeaskoa159; Rafael H. Jacomo160; Rubén Jara55; Perez Maria Jazmin131; María A. Jimenez-Sousa69,7; Ángel Jiménez35,36; Pilar Jiménez146; Ignacio Jiménez-Alfaro161; Iolanda Jordan162,163,78; Rocío Laguna-Goya164,165; Daniel Laorden18; María Lasa-Lazaro164,165; María Claudia Lattig80,166; Ailen Lauriente131; Anabel Liger Borja167; Lucía Llanos169; Esther Lopez-Garcia109,110,78,170; Rosario Lopez-Rodriguez26,27; Leonardo Lorente171; José E. Lozano172; María Lozano-Espinosa167; Andre D. Luchessi173; Eduardo López Granados174,175,27; Amparo López-Bernús81,58; Miguel A. López-Ruz176,177,178; Aluísio X. Magalhães-Brasil 179;Ignacio Mahillo180,181,99; Esther Mancebo164,165; Carmen Mar116; Cristina Marcelo Calvo92; Miguel Marcos81,58; Alba Marcos-Delgado124; Pablo Mariscal Aguilar18; Marta Martin-Fernandez182; Laura Martin-Pedraza62; Amalia Martinez183; Iciar Martinez-Lopez184,185; Oscar Martinez-Nieto51,166; Pedro Martinez-Paz140; Angel Martinez-Perez186; Michel F. Martinez-Resendez82; María M. Martín187; María Dolores Martín188; Vicente Martín124,78; Caridad Martín-López167; José-Ángel Martín-Oterino81,58; María Martín-Vicente69; Ricardo Martínez149; Juan José Martínez88,27; Silvia Martínez45,47; Violeta Martínez Robles11; Eleno Martínez-Aquino189; Óscar Martínez-González190; Andrea Martínez-Ramas26,27; Laura Marzal26,27; Alicia Marín Candon65; Jose Antonio Mata-Marin,137 Juliana F. Mazzeu179,191,192; Jeane F.P. Medeiros40; Francisco J. Medrano77,78,79; Xose M. Meijome193,194; Natalia Mejuto-Montero195; Celso T. Mendes-Junior 84,196,197; Humberto Mendoza Charris66,20; Eleuterio Merayo Macías198; Fátima Mercadillo199; Arieh R. Mercado-Sesma150,151; Pablo Minguez26,27; Antonio J J. Molina124,78; Elena Molina-Roldán200; Juan José Montoya149; Patricia Moreira-Escriche201; Xenia Morelos-Arnedo66,20; Victor Moreno Cuerda1,2; Alberto Moreno Fernández92; Antonio Moreno-Docón55; Junior Moreno-Escalante20; Rubén Morilla79,202; Patricia Muñoz García203,99,6; Ana Méndez-Echevarria204; Pablo Neira131; Julian Nevado27,131,205; Israel Nieto-Gañán135; Joana F.R. Nunes42; Rocio Nuñez-Torres28; Antònia Obrador-Hevia206,207; J. Gonzalo Ocejo-Vinyals45,47; Virginia Olivar131; Silviene F. Oliveira179,208,208,210,211; Lorena Ondo26,27; Alberto Orfao22,23,24,25; Luis Ortega212; Eva Ortega-Paino50; Fernando Ortiz-Flores45,47; Rocio Ortiz-Lopez213,82; José A. Oteo12,214; Harry Pachajoa215,216; Manuel Pacheco149; Fredy Javier Pacheco-Miranda20; Irene Padilla Conejo11; Sonia Panadero-Fajardo85; Mara Parellada31,37,6; Roberto Pariente-Rodríguez131; Estela Paz-Artal164,165,217; Germán Peces-Barba218,99; Miguel S. Pedromingo Kus219; Celia Perales127; Patricia Perez220; Gustavo Perez-de-Nanclares33,221; Teresa Perucho222; Aline Pic-Taylor 42,208,209,211, Lisbeth A. Pichardo11; Mel·lina Pinsach-Abuin70,68; Luz Adriana Pinzón93,39; Guillermo Pita30; Francesc Pla-Junca223,27; Laura Planas-Serra88,27; Ericka N. Pompa-Mera224,137; Gloria L. Porras-Hurtado149; Aurora Pujol88,27,225; César Pérez226; Felipe Pérez-García227,228; Patricia Pérez-Matute214; Alexandra Pérez-Serra70,68; M. Elena Pérez-Tomás55; María Eugenia Quevedo Chávez19,20; Maria Angeles Quijada30,229; Inés Quintela128; Diana Ramirez-Montaño230; Soraya Ramiro León131; Pedro Rascado Sedes231; Delia Recalde107,108; Emma Recio-Fernández214; Salvador Resino69,7; Adriana P. Ribeiro29,30,232; Carlos S. Rivadeneira-Chamorro39; Diana Roa-Agudelo51; Montserrat Robelo Pardo231; Marilyn Johanna Rodriguez39; German Ezequiel Rodriguez Novoa131; Fernando Rodriguez-Artalejo109,110,78,170; Carlos Rodriguez-Gallego233,234; José A. Rodriguez-Garcia11; María A. Rodriguez-Hernandez73; Antonio Rodriguez-Nicolas146; Agustí Rodriguez-Palmero235,88; Paula A. Rodriguez-Urrego51; Belén Rodríguez Maya1; Marena Rodríguez-Ferrer20; Emilio Rodríguez-Ruiz231,129; Federico Rojo236,25; Andrea Romero-Coronado20; Filomeno Rondón García11; Lidia S. Rosa236; Antonio Rosales-Castillo237; Cladelis Rubio238,239; María Rubio Olivera35,36; Montserrat Ruiz88,27; Francisco Ruiz-Cabello146,177,240; Eva Ruiz-Casares222; Juan J. Ruiz-Cubillan45,47; Javier Ruiz-Hornillos241,36,242; Pablo Ryan243,244,245,7; Hector D. Salamanca38,39; Lorena Salazar-García80; Giorgina Gabriela Salgueiro Origlia 92; Cristina Sancho-Sainz159; Jorge Luis Sandoval-Ramírez,137 Anna Sangil64; Arnoldo Santos226; Ney P.C. Santos118; Amanda C.M. Saúde 30,246 Agatha Schlüter88,27; Sonia Segovia223,247,248; Alex Serra-Llovich249; Fernando Sevil Puras8; Marta Sevilla Porras27,130; Miguel A. Sicolo250,251; Vivian N. Silbiger173; Nayara S. Silva252; Fabiola T.C. Silva40; Cristina Silván Fuentes27; Jordi Solé-Violán253,99,254; José Manuel Soria186; Jose V. Sorlí96,97; Renata R. Sousa179; Juan Carlos Souto3; Karla S.C. Souza86; Vanessa S. Souza104; John J. Sprockel93,39; David A. Suarez-Zamora51; José Javier Suárez-Rama128; Pedro-Luis Sánchez114,58; Antonio J. Sánchez López255; María Concepción Sánchez Prados18; Javier Sánchez Real11; Jorge Sánchez Redondo1,256; Clara Sánchez-Pablo114; Olga Sánchez-Pernaute257; Xiana Taboada-Fraga195; Eduardo Tamayo258,140,7; Alvaro Tamayo-Velasco259; Juan Carlos Taracido-Fernandez155; Nathali A.C. Tavares260; Carlos Tellería107,108; Jair Antonio Tenorio Castaño27,130,205; Alejandro Teper131; Ronald P. Torres Gutiérrez221; Juan Torres-Macho261; Lilian Torres-Tobar39; Jesús Troya243; Miguel Urioste199; Juan Valencia-Ramos262; Agustín Valido21,263; Juan Pablo Vargas Gallo264,265; Belén Varón266; Romero H.T. Vasconcelos260; Tomas Vega267; Santiago Velasco-Quirce268; Julia Vidán Estévez11; Miriam Vieitez-Santiago45,47; Carlos Vilches269; Lavinia Villalobos11; Felipe Villar218; Judit Villar-Garcia270,271,272; Cristina Villaverde26,27; Pablo Villoslada-Blanco214; Ana Virseda-Berdices69; Valentina Vélez-Santamaría87,88; Virginia Víctor35,36; Zuleima Yáñez20; Antonio Zapatero-Gaviria273; Ruth Zarate274; Sandra Zazo236; Gabriela V. da Silva41; Raimundo de Andrés275; Jéssica N.G. de Araújo252; Carmen de Juan201; Julianna Lys de Sousa Alves Neri276; Carmen de la Horra79; Ana B. de la Hoz33; Victor del Campo-Pérez277; Manoella do Monte Alves278,279; Katiusse A. dos Santos86; Yady Álvarez-Benítez19,20; Felipe Álvarez-Navia81,58; María Íñiguez214; Miguel López de Heredia27; Ingrid Mendes27; Rocío Moreno27; Esther Sande27,129,102; Carlos Flores280,98,99,234; José A. Riancho45,46,47,27; Augusto Rojas-Martinez82; Pablo Lapunzina27,130,205; Angel Carracedo27,129,102,128
Scourge Cohort Group’s filiations
1 Hospital Universitario Mostoles, Medicina Interna, Madrid, Spain
2 Universidad Francisco de Vitoria, Madrid, Spain
3 Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
4 Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain
5 Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, Madrid, Spain
6 School of Medicine, Universidad Complutense, Madrid, Spain
7 Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
8 Hospital General Santa Bárbara de Soria, Soria, Spain
9 Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
10 Navarra Health Service, NavarraBioMed Research Group, Pamplona, Spain
11 Complejo Asistencial Universitario de León, León, Spain
12 Hospital Universitario San Pedro, Infectious Diseases Department, Logroño, Spain
13 Fundación Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Hospital de Neurorehabilitació, Barcelona, Spain
14 Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
15 Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain
16 Hospital General de Occidente, Guadalajara, Mexico
17 Microbiology Unit, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
18 Hospital Universitario La Paz-IDIPAZ, Servicio de Neumología, Madrid, Spain
19 Camino Universitario Adelita de Char, Mired IPS, Barranquilla, Colombia
20 Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Barranquilla, Colombia
21 Hospital Universitario Virgen Macarena, Neumología, Seville, Spain
22 Departamento de Medicina, Universidad de Salamanca, Salamanca, Spain
23 Centro de Investigación del Cáncer (IBMCC) Universidad de Salamanca - CSIC, Salamanca, Spain
24 Biomedical Research Institute of Salamanca (IBSAL) Salamanca, Spain
25 Centre for Biomedical Network Research on Cancer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
26 Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
27 Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
28 Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
29 Hospital das Forças Armadas, Brazil
30 Exército Brasileiro, Brazil
31 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
32 Clinical Pharmacology Service, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
33 Biocruces Bizkai HRI, Barakaldo, Bizkaia, Spain
34 Cruces University Hospital, Osakidetza, Barakaldo, Bizkaia, Spain
35 Hospital Infanta Elena, Valdemoro, Madrid, Spain
36 Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
37 Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
38 Fundación Hospital Infantil Universitario de San José, Bogotá, Colombia
39 Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia
40 Universidade Federal do Rio Grande do Norte, Programa de Pós-graduação em Ciências da Saúde, Natal, Brazil
41 Universidade Federal do Rio Grande do Norte, Departamento de Medicina Clínica, Natal, Brazil
42 Departamento de Genética e Morfologia, Instituto de Ciências Biológicas, Universidade de Brasília, Brasilia, Brazil
43 Colégio Marista de Brasilia, Brazil
44 Associação Brasileira de Educação e Cultura, Brazil
45 IDIVAL, Santander, Spain
46 Universidad de Cantabria, Santander, Spain
47 Hospital U M Valdecilla, Santander, Spain
48 Hospital Universitario La Paz-IDIPAZ, Servicio de Medicina Interna, Madrid, Spain
49 Fundació Docència I Recerca Mutua Terrassa, Barcelona, Spain
50 Spanish National Cancer Research Center, CNIO Biobank, Madrid, Spain
51 Fundación Santa Fe de Bogota, Departamento Patologia y Laboratorios, Bogotá, Colombia
52 Hospital General de Occidente, Zapopan, Jalisco, Mexico
53 Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá, Jalisco, Mexico
54 Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Tonalá, Jalisco, Mexico
55 Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
56 Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
57 Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, Salamanca, Spain
58 Universidad de Salamanca, Salamanca, Spain
59 Hospital Universitario de Fuenlabrada, Department of Internal Medicine, Madrid, Spain
60 Escola Tecnica de Saúde, Laboratorio de Vigilancia Molecular Aplicada, Pará, Brazil
61 Federal University of Pernambuco, Genetics Postgraduate Program, Recife, PE, Brazil
62 Hospital Universitario Infanta Leonor, Servicio de Alergia, Madrid, Spain
63 Hospital Universitario del Tajo, Servicio de Medicina Intensiva, Aranjuez, Spain
64 Hospital Universitario Mutua Terrassa, Barcelona, Spain
65 Hospital Universitario La Paz-IDIPAZ, Servicio de Farmacología, Madrid, Spain
66 Alcaldía de Barranquilla, Secretaría de Salud, Barranquilla, Colombia
67 Instituto de Investigación Sanitaria de Santiago (IDIS), Xenética Cardiovascular, Santiago de Compostela, Spain
68 Centre for Biomedical Network Research on Cardiovascular Diseases (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
69 Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
70 Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), Girona, Spain
71 Medical Science Department, School of Medicine, University of Girona, Girona, Spain
72 Hospital Josep Trueta, Cardiology Service, Girona, Spain
73 Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)-University of Seville-Virgen del Rocio University Hospital, Seville, Spain
74 Division of Infectious Diseases, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
75 Intensive Care Unit, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canaria, Spain
76 Hospital Universitario Mutua Terrassa, Terrassa, Spain
77 Departamento de Medicina, Hospital Universitario Virgen del Rocío,Universidad de Sevilla, Seville, Spain
78 Centre for Biomedical Network Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
79 Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)-University of Seville-Virgen del Rocio University Hospital, Seville, Spain
80 Universidad de los Andes, Facultad de Ciencias, Bogotá, Colombia
81 Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna, Salamanca, Spain
82 Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud and Hospital San Jose TecSalud, Monterrey, Mexico
83 University of Fortaleza (UNIFOR), Department of Nutrition. Fortaleza, Brazil
84 Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Brazil
85 Andalusian Public Health System Biobank, Granada, Spain
86 Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Ciências Farmacêuticas, Natal, Brazil
87 Neuromuscular Unit, Neurology Department, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat (Barcelona), Spain
88 Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, L’Hospitalet de Llobregat, Spain
89 Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
90 University of Pais Vasco, UPV/EHU, Bizkaia, Spain
91 Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
92 Hospital Universitario La Paz, Hospital Carlos III, Madrid, Spain
93 Hospital de San José, Sociedad de Cirugía de Bogota, Bogotá, Colombia
94 Hospital Universitario Río Hortega, Valladolid, Spain
95 Servicio de Medicina intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
96 Valencia University, Preventive Medicine Department, Valencia, Spain
97 Centre for Biomedical Network Research on Physiopatology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
98 Research Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain
99 Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
100 Otto von Guericke University, Departament of Microgravity and Translational Regenerative Medicine, Magdeburg, Germany
101 Maternidade Escola Janário Cicco, Natal, Brazil
102 Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
103 Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
104 Programa de Pós Graduação em Ciências da Saúde, Faculdade de Medicina, Universidade de Brasília, Brasilia, Brazil
105 Fundació Docència I Recerca Mutua Terrassa, Terrassa, Spain
106 Hospital Universitario Mostoles, Unidad de Genética, Madrid, Spain
107 Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
108 Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
109 Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
110 IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Madrid, Spain
111 Hospital Universitario Virgen del Rocío, Servicio de Medicina Interna, Seville, Spain
112 Unidad Diagnóstico Molecular. Fundación Rioja Salud, La Rioja, Spain
113 Hospital Universitario Quironsalud Madrid, Madrid, Spain
114 Hospital Universitario de Salamanca-IBSAL, Servicio de Cardiología, Salamanca, Spain
115 Hospital Universitario Puerta de Hierro, Servicio de Medicina Interna, Majadahonda, Spain
116 Biocruces Bizkaia Health Research Institute, Galdakao University Hospital, Osakidetza, Bizkaia, Spain
117 Instituto Regional de Investigación en Salud-Universidad Nacional de Caaguazú, Caaguazú, Paraguay
118 Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Pará, Brazil
119 Hospital Ophir Loyola, Departamento de Ensino e Pesquisa, Belém, Pará, Brazil
120 Universidad Nacional de Asunción, Facultad de Politécnica, Paraguay
121 Fundación Asilo San Jose, Santander, Spain
122 Unidad de Enfermedades Infecciosas, Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro, Instituto de Investigación Sanitaria Puerta de Hierro - Segovia de Arana, Madrid, Spain
123 Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
124 Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain
125 Hospital Universitario Niño Jesús, Pediatrics Department, Madrid, Spain
126 Unitat de Malalties Infeccioses i Importades, Servei de Pediatría, Infectious and Imported Diseases, Pediatric Unit, Hospital Universitari Sant Joan de Deú, Barcelona, Spain
127 Microbiology Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
128 Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS) Santiago de Compostela, Spain
129 Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain
130 Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz-IDIPAZ, Madrid, Spain
131 Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina
132 Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain Universidad Francisco de Vitoria, Madrid,Spain
133 Hospital Infanta Elena, Servicio de Medicina Intensiva, Valdemoro, Madrid, Spain
134 University of Salamanca, Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain
135 Department of Immunology, IRYCIS, Hospital Universitario Ramón y Cajal, Madrid, Spain
136 Osakidetza, Cruces University Hospital, Bizkaia, Spain
137 Instituto Mexicano del Seguro Social, IMSS. Centro Médico Nacional La Raza. Hospital de Infectología. Mexico City, Mexico.
138 Hospital Universitario de Getafe, Servicio de Genética, Madrid, Spain
139 Ministerio de Salud Ciudad de Buenos Aires, Buenos Aires, Argentina
140 Hospital Clinico Universitario de Valladolid, Unidad de Apoyo a la Investigación, Valladolid, Spain
141 Universidad de Valladolid, Departamento de Cirugía, Valladolid, Spain
142 Secretaria Municipal de Saude de Apodi, Natal, Brazil
143 Sección Genética Médica - Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, Murcia, Spain
144 Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), Murcia, Spain
145 Hospital Universitario Centro Dermatológico Federico Lleras Acosta, Bogotá, Colombia
146 Hospital Universitario Virgen de las Nieves, Servicio de Análisis Clínicos e Inmunología, Granada, Spain
147 Pneumology Department, Hospital General Universitario Gregorio Marañón (iiSGM), Madrid, Spain
148 Intermediate Respiratory Care Unit, Department of Pneumology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
149 Clinica Comfamiliar Risaralda, Pereira, Colombia
150 Centro Universitario de Tonalá, Universidad de Guadalajara, Guadalajara, Mexico
151 Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Guadalajara, Mexico
152 Unidad de Cuidados, Intensivos Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
153 IIS La Fe, Plataforma de Farmacogenética, Valencia, Spain
154 Universidad de Valencia, Departamento de Farmacología, Valencia, Spain
155 Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
156 Hospital del Mar, Infectious Diseases Service, Barcelona, Spain
157 Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Barcelona, Spain
158 CEXS-Universitat Pompeu Fabra, Spanish Network for Research in Infectious Diseases (REIPI), Barcelona, Spain
159 Biocruces Bizkaia Health Research Institute, Basurto University Hospital, Osakidetza, Bizkaia, Spain
160 Sabin Medicina Diagnóstica, Brazil
161 Opthalmology Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
162 Hospital Sant Joan de Deu,Pediatric Critical Care Unit, Barcelona, Spain
163 Paediatric Intensive Care Unit, Agrupación Hospitalaria Clínic-Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
164 Hospital Universitario 12 de Octubre, Department of Immunology, Madrid, Spain
165 Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Madrid, Spain
166 SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá, Colombia
167 Hospital General de Segovia, Medicina Intensiva, Segovia, Spain
168 Programa de Pós-Graduação em Biologia Animal, Universidade de Brasília, Brasília, Brazil
169 Clinical Trials Unit, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
170 IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain
171 Intensive Care Unit, Hospital Universitario de Canarias, La Laguna, Spain
172 Dirección General de Salud Pública, Consejería de Sanidad, Junta de Castilla y León, Valladolid, Spain
173 Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Natal, Brazil
174 Hospital Universitario La Paz-IDIPAZ, Servicio de Inmunología, Madrid, Spain
175 La Paz Institute for Health Research (IdiPAZ), Lymphocyte Pathophysiology in Immunodeficiencies Group, Madrid, Spain
176 Hospital Universitario Virgen de las Nieves, Servicio de Enfermedades Infecciosas, Granada, Spain
177 Instituto de Investigación Biosanitaria de Granada (ibs GRANADA), Granada, Spain
178 Universidad de Granada, Departamento de Medicina, Granada, Spain
179 Faculdade de Medicina, Universidade de Brasília, Brasilia, Brazil
180 Fundación Jiménez Díaz, Epidemiology, Madrid, Spain
181 Universidad Autónoma de Madrid, Department of Medicine, Madrid, Spain
182 Universidad de Valladolid, Departamento de Medicina, Valladolid, Spain
183 Hospital Universitario Infanta Leonor, Servicio de Medicina Intensiva, Madrid, Spain
184 Unidad de Genética y Genómica Islas Baleares, Islas Baleares, Spain
185 Hospital Universitario Son Espases, Unidad de Diagnóstico Molecular y Genética Clínica, Islas Baleares, Spain
186 Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
187 Intensive Care Unit, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
188 Preventive Medicine Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
189 Servicio de Medicina Interna, Sanatorio Franchin, Buenos Aires, Argentina
190 Hospital Universitario del Tajo, Servicio de Medicina Intensiva, Toledo, Spain
191 Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília, Brasilia, Brazil
192 Programa de Pós-Graduação em Ciências da Saúde, Universidade de Brasília, Brasilia, Brazil
193 Hospital El Bierzo, Gerencia de Asistencia Sanitaria del Bierzo (GASBI), Gerencia Regional de Salud (SACYL), Ponferrada, Spain
194 Grupo INVESTEN, Instituto de Salud Carlos III, Madrid, Spain
195 Unidad de Cuidados Intensivos, Complejo Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
196 Programa de Pós-Graduação em Genética da Faculdade de Medicina de Ribeirão Preto
197 Programa de Pós-Graduação em Química da Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto
198 Hospital El Bierzo, Unidad Cuidados Intensivos, León, Spain
199 Spanish National Cancer Research Centre, Familial Cancer Clinical Unit, Madrid, Spain
200 Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos (HCSC), Madrid, Spain
201 Hospital Universitario Severo Ochoa, Servicio de Medicina Interna, Madrid, Spain
202 Universidad de Sevilla, Departamento de Enfermería, Seville, Spain
203 Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain 204 Hospital Universitario La Paz-IDIPAZ, Servicio de Pediatría, Madrid, Spain 205 ERN-ITHACA-European Reference Network
206 Unidad de Genética y Genómica Islas Baleares, Unidad de Diagnóstico Molecular y Genética Clínica, Hospital Universitario Son Espases, Islas Baleares, Spain
207 Instituto de Investigación Sanitaria Islas Baleares (IdISBa), Islas Baleares, Spain
208 Programa de Pós-Graduação em Biologia Animal, Universidade de Brasília, Brasília, Brazil
209 Programa de Pós-Graduação em Ciências da Saúde, Universidade de Brasília, Brasília, Brazil
210 Programa de Pós-Graduação Profissional em Ensino de Biologia, Universidade de Brasília, Brasília, Brazil
211 Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília, Brasília, Brazil
212 Anatomía Patológica, Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos (HCSC), Madrid, Spain
213 Tecnológico de Monterrey, Monterrey, Mexico
214 Infectious Diseases, Microbiota and Metabolism Unit, CSIC Associated Unit, Center for Biomedical Research of La Rioja (CIBIR), Logroño, Spain
215 Centro de Investigación en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad Icesi
216 Departamento de Genetica, Fundación Valle del Lili
217 Universidad Complutense de Madrid, Department of Immunology, Ophthalmology and ENT, Madrid, Spain
218 Department of Neumology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
219 Hospital Nuestra Señora de Sonsoles, Ávila, Spain
220 Inditex, A Coruña, Spain
221 Osakidetza, Cruces University Hospital, Barakaldo, Bizkaia, Spain
222 GENYCA, Madrid, Spain
223 Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
224 Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Mexico City, Mexico
225 Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain
226 Intensive Care Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
227 Hospital Universitario Príncipe de Asturias, Servicio de Microbiología Clínica, Madrid, Spain
228 Universidad de Alcalá de Henares, Departamento de Biomedicina y Biotecnología, Facultad de Medicina y Ciencias de la Salud, Madrid, Spain
229 Drug Research Centre, Institut d’Investigació Biomèdica Sant Pau, IIB-Sant Pau, Barcelona, Spain
230 Departamento de Genetica, Clinica imbanaco
231 Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
232 Universidade de Brasília, Brasilia, Brazil
233 Department of Immunology, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
234 Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
235 University Hospital Germans Trias i Pujol, Pediatrics Department, Badalona, Spain
236 Department of Pathology, Biobank, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
237 Hospital Universitario Virgen de las Nieves, Servicio de Medicina Interna, Granada, Spain
238 Fundación Universitaria de Ciencias de la Salud, Grupo de Ciencias Básicas en Salud (CBS), Bogotá, Colombia
239 Sociedad de Cirugía de Bogotá, Hospital de San José, Bogotá, Colombia
240 Universidad de Granada, Departamento Bioquímica, Biología Molecular e Inmunología III, Granada, Spain
241 Hospital Infanta Elena, Allergy Unit, Valdemoro, Madrid, Spain
242 Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
243 Hospital Universitario Infanta Leonor, Madrid, Spain
244 Complutense University of Madrid, Madrid, Spain
245 Gregorio Marañón Health Research Institute (IiSGM), Madrid, Spain
246 Colégio Militar de Brasília
247 The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
248 Neuromuscular Unit, Neuropediatrics Department, Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu, Spain
249 Fundació Docència i Recerca Mutua Terrassa, Terrassa, Spain
250 Casa de Saúde São Lucas, Natal, Brazil
251 Hospital Rio Grande, Rio Grande do Norte, Natal, Brazil
252 Universidade Federal do Rio Grande do Norte, Pós-graduação em Biotecnologia - Rede de Biotecnologia do Nordeste (Renorbio), Natal, Brazil
253 Intensive Care Unit, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
254 Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
255 Biobank, Puerta de Hierro-Segovia de Arana Health Research Institute, Madrid, Spain
256 Universidad Rey Juan Carlos, Madrid, Spain
257 Reumathology Service, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
258 Hospital Clinico Universitario de Valladolid, Servicio de Anestesiologia y Reanimación, Valladolid, Spain
259 Hospital Clinico Universitario de Valladolid, Servicio de Hematologia y Hemoterapia, Valladolid, Spain
260 Hospital Universitario Lauro Wanderley, Brazil
261 Hospital Universitario Infanta Leonor, Servicio de Medicina Interna, Madrid, Spain
262 University Hospital of Burgos, Burgos, Spain
263 Universidad de Sevilla, Seville, Spain
264 Fundación Santa Fe de Bogota, Instituto de servicios medicos de Emergencia y trauma, Bogotá, Colombia
265 Universidad de los Andes, Bogotá, Colombia
266 Quironprevención, A Coruña, Spain
267 Junta de Castilla y León, Consejería de Sanidad, Valladolid, Spain
268 Gerencia Atención Primaria de Burgos, Burgos, Spain
269 Immunogenetics-Histocompatibility group, Servicio de Inmunología, Instituto de Investigación Sanitaria Puerta de Hierro - Segovia de Arana, Madrid, Spain
270 Hospital del Mar, Department of Infectious Diseases, Barcelona, Spain
271 IMIM (Hospital del Mar Medical Research Institute, Institut Hospital del Mar d’Investigacions Mediques), Barcelona, Spain
272 Universitat Autònoma de Barcelona, Department of Medicine, Spain
273 Consejería de Sanidad, Comunidad de Madrid, Madrid, Spain
274 Centro para el Desarrollo de la Investigación Científica, Asunción, Paraguay
275 Internal Medicine Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
276 Universidade Federal do Rio Grande do Norte, Programa de Pós Graduação em Nutrição, Natal, Brazil
277 Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
278 Universidade Federal do Rio Grande do Norte, Departamento de Infectologia, Natal, Brazil
279 Hospital de Doenças Infecciosas Giselda Trigueiro, Rio Grande do Norte, Natal, Brazil
280 Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
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