Novel risk loci for COVID-19 hospitalization among admixed American populations

  1. Silvia Diz-de Almeida
  2. Raquel Cruz
  3. Andre D Luchessi
  4. José M Lorenzo-Salazar
  5. Miguel López de Heredia
  6. Inés Quintela
  7. Rafaela González-Montelongo
  8. Vivian Nogueira Silbiger
  9. Marta Sevilla Porras
  10. Jair Antonio Tenorio Castaño
  11. Julian Nevado
  12. Jose María Aguado
  13. Carlos Aguilar
  14. Sergio Aguilera-Albesa
  15. Virginia Almadana
  16. Berta Almoguera
  17. Nuria Alvarez
  18. Álvaro Andreu-Bernabeu
  19. Eunate Arana-Arri
  20. Celso Arango
  21. María J Arranz
  22. Maria-Jesus Artiga
  23. Raúl C Baptista-Rosas
  24. María Barreda- Sánchez
  25. Moncef Belhassen-Garcia
  26. Joao F Bezerra
  27. Marcos AC Bezerra
  28. Lucía Boix-Palop
  29. María Brion
  30. Ramón Brugada
  31. Matilde Bustos
  32. Enrique J Calderón
  33. Cristina Carbonell
  34. Luis Castano
  35. Jose E Castelao
  36. Rosa Conde-Vicente
  37. M Lourdes Cordero-Lorenzana
  38. Jose L Cortes-Sanchez
  39. Marta Corton
  40. M Teresa Darnaude
  41. Alba De Martino-Rodríguez
  42. Victor del Campo-Pérez
  43. Aranzazu Diaz de Bustamante
  44. Elena Domínguez-Garrido
  45. Rocío Eirós
  46. María Carmen Fariñas
  47. María J Fernandez-Nestosa
  48. Uxía Fernández-Robelo
  49. Amanda Fernández-Rodríguez
  50. Tania Fernández-Villa
  51. Manuela Gago-Dominguez
  52. Belén Gil-Fournier
  53. Javier Gómez-Arrue
  54. Beatriz González Álvarez
  55. Fernan Gonzalez Bernaldo de Quirós
  56. Anna González-Neira
  57. Javier González-Peñas
  58. Juan F Gutiérrez-Bautista
  59. María José Herrero
  60. Antonio Herrero-Gonzalez
  61. María A Jimenez-Sousa
  62. María Claudia Lattig
  63. Anabel Liger Borja
  64. Rosario Lopez-Rodriguez
  65. Esther Mancebo
  66. Caridad Martín-López
  67. Vicente Martín
  68. Oscar Martinez-Nieto
  69. Iciar Martinez-Lopez
  70. Michel F Martinez-Resendez
  71. Angel Martinez-Perez
  72. Juliana F Mazzeu
  73. Eleuterio Merayo Macías
  74. Pablo Minguez
  75. Victor Moreno Cuerda
  76. Silviene F Oliveira
  77. Eva Ortega-Paino
  78. Mara Parellada
  79. Estela Paz-Artal
  80. Ney PC Santos
  81. Patricia Pérez-Matute
  82. Patricia Perez
  83. M Elena Pérez-Tomás
  84. Teresa Perucho
  85. Mellina Pinsach-Abuin
  86. Guillermo Pita
  87. Ericka N Pompa-Mera
  88. Gloria L Porras-Hurtado
  89. Aurora Pujol
  90. Soraya Ramiro León
  91. Salvador Resino
  92. Marianne R Fernandes
  93. Emilio Rodríguez-Ruiz
  94. Fernando Rodriguez-Artalejo
  95. José A Rodriguez-Garcia
  96. Francisco Ruiz-Cabello
  97. Javier Ruiz-Hornillos
  98. Pablo Ryan
  99. José Manuel Soria
  100. Juan Carlos Souto
  101. Eduardo Tamayo
  102. Alvaro Tamayo-Velasco
  103. Juan Carlos Taracido-Fernandez
  104. Alejandro Teper
  105. Lilian Torres-Tobar
  106. Miguel Urioste
  107. Juan Valencia-Ramos
  108. Zuleima Yáñez
  109. Ruth Zarate
  110. Itziar de Rojas
  111. Agustín Ruiz
  112. Pascual Sánchez
  113. Luis Miguel Real
  114. SCOURGE Cohort Group
  115. Encarna Guillen-Navarro
  116. Carmen Ayuso
  117. Esteban Parra
  118. José A Riancho
  119. Augusto Rojas-Martinez
  120. Carlos Flores  Is a corresponding author
  121. Pablo Lapunzina
  122. Ángel Carracedo  Is a corresponding author
  1. ERN-ITHACA-European Reference Network, Spain
  2. Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Spain
  3. CIBERER, ISCIII, Spain
  4. Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Spain
  5. Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Brazil
  6. Genomics Division, Instituto Tecnológico y de Energías Renovables, Spain
  7. Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Spain
  8. Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Spain
  9. Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Spain
  10. Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, Spain
  11. CIBERINFEC, ISCIII, Spain
  12. Hospital General Santa Bárbara de Soria, Spain
  13. Navarra Health Service, NavarraBioMed Research Group, Spain
  14. Hospital Universitario Virgen Macarena, Neumología, Spain
  15. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Spain
  16. Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Spain
  17. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Spain
  18. School of Medicine, Universidad Complutense, Spain
  19. Biocruces Bizkai HRI, Spain
  20. Cruces University Hospital, Osakidetza, Spain
  21. Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Spain
  22. Fundació Docència I Recerca Mutua Terrassa, Spain
  23. Spanish National Cancer Research Centre, CNIO Biobank, Spain
  24. Hospital General de Occidente, Mexico
  25. Centro Universitario de Tonalá, Universidad de Guadalajara, Mexico
  26. Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Mexico
  27. Universidad Católica San Antonio de Murcia (UCAM), Spain
  28. Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Spain
  29. Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, Spain
  30. Escola Tecnica de Saúde, Laboratorio de Vigilancia Molecular Aplicada, Brazil
  31. Federal University of Pernambuco, Genetics Postgraduate Program, Brazil
  32. Hospital Universitario Mutua Terrassa, Spain
  33. Instituto de Investigación Sanitaria de Santiago (IDIS), Xenética Cardiovascular, Spain
  34. CIBERCV, ISCIII, Spain
  35. Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), Spain
  36. Medical Science Department, School of Medicine, University of Girona, Spain
  37. Hospital Josep Trueta, Cardiology Service, Spain
  38. Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, Spain
  39. Departamento de Medicina, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Spain
  40. CIBERESP, ISCIII, Spain
  41. Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna, Spain
  42. Universidad de Salamanca, Spain
  43. Osakidetza, Cruces University Hospital, Spain
  44. Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, Spain
  45. University of Pais Vasco, UPV/EHU, Spain
  46. Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Spain
  47. Hospital Universitario Río Hortega, Spain
  48. Servicio de Medicina intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), Spain
  49. Tecnológico de Monterrey, Mexico
  50. Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, Germany
  51. Hospital Universitario Mostoles, Unidad de Genética, Spain
  52. Instituto Aragonés de Ciencias de la Salud (IACS), Spain
  53. Instituto Investigación Sanitaria Aragón (IIS-Aragon), Spain
  54. Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Spain
  55. Unidad Diagnóstico Molecular, Fundación Rioja Salud, Spain
  56. Hospital Universitario de Salamanca-IBSAL, Servicio de Cardiología, Spain
  57. IDIVAL, Spain
  58. Hospital U M Valdecilla, Spain
  59. Universidad de Cantabria, Spain
  60. Universidad Nacional de Asunción, Facultad de Politécnica, United States
  61. Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), Spain
  62. Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Spain
  63. Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, Spain
  64. IDIS, Republic of Korea
  65. Hospital Universitario de Getafe, Servicio de Genética, Spain
  66. Ministerio de Salud Ciudad de Buenos Aires, Argentina
  67. Hospital Universitario Virgen de las Nieves, Servicio de Análisis Clínicos e Inmunología, Spain
  68. IIS La Fe, Plataforma de Farmacogenética, Spain
  69. Universidad de Valencia, Departamento de Farmacología, Spain
  70. Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Spain
  71. Universidad de los Andes, Facultad de Ciencias, Colombia
  72. SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Colombia
  73. Hospital General de Segovia, Medicina Intensiva, Spain
  74. Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Spain
  75. Hospital Universitario 12 de Octubre, Department of Immunology, Spain
  76. Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Spain
  77. Fundación Santa Fe de Bogota, Departamento Patologia y Laboratorios, Colombia
  78. Unidad de Genética y Genómica Islas Baleares, Spain
  79. Hospital Universitario Son Espases, Unidad de Diagnóstico Molecular y Genética Clínica, Spain
  80. Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Spain
  81. Universidade de Brasília, Faculdade de Medicina, Brazil
  82. Programa de Pós-Graduação em Ciências Médicas (UnB), Brazil
  83. Programa de Pós-Graduação em Ciencias da Saude (UnB), Brazil
  84. Hospital El Bierzo, Unidad Cuidados Intensivos, Spain
  85. Hospital Universitario Mostoles, Medicina Interna, Spain
  86. Universidad Francisco de Vitoria, Spain
  87. Departamento de Genética e Morfologia, Instituto de Ciências Biológicas, Universidade de Brasília, Brazil
  88. Programa de Pós-Graduação em Biologia Animal (UnB), Brazil
  89. Programa de Pós-Graduação Profissional em Ensino de Biologia (UnB), Brazil
  90. Universidad Complutense de Madrid, Department of Immunology, Ophthalmology and ENT, Spain
  91. Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Brazil
  92. Infectious Diseases, Microbiota and Metabolism Unit, CSIC Associated Unit, Center for Biomedical Research of La Rioja (CIBIR), Spain
  93. Inditex, A Coruña, Spain
  94. GENYCA, Spain
  95. Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Mexico
  96. Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional La Raza, Hospital de Infectología, Mexico
  97. Clinica Comfamiliar Risaralda, Colombia
  98. Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, L’Hospitalet de Llobregat, Spain
  99. Catalan Institution of Research and Advanced Studies (ICREA), Spain
  100. Hospital Ophir Loyola, Departamento de Ensino e Pesquisa, Brazil
  101. Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Spain
  102. Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Spain
  103. IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Spain
  104. IMDEA-Food Institute, CEI UAM+CSIC, Spain
  105. Complejo Asistencial Universitario de León, Spain
  106. Instituto de Investigación Biosanitaria de Granada (ibs GRANADA), Spain
  107. Universidad de Granada, Departamento Bioquímica, Biología Molecular e Inmunología III, Spain
  108. Hospital Infanta Elena, Allergy Unit, Valdemoro, Spain
  109. Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Spain
  110. Faculty of Medicine, Universidad Francisco de Vitoria, Spain
  111. Hospital Universitario Infanta Leonor, Spain
  112. Complutense University of Madrid, Spain
  113. Gregorio Marañón Health Research Institute (IiSGM), Spain
  114. Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Spain
  115. Hospital Clinico Universitario de Valladolid, Servicio de Anestesiologia y Reanimación, Spain
  116. Universidad de Valladolid, Departamento de Cirugía, Spain
  117. Hospital Clinico Universitario de Valladolid, Servicio de Hematologia y Hemoterapia, Spain
  118. Hospital de Niños Ricardo Gutierrez, Argentina
  119. Fundación Universitaria de Ciencias de la Salud, Colombia
  120. Spanish National Cancer Research Centre, Familial Cancer Clinical Unit, Spain
  121. University Hospital of Burgos, Spain
  122. Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Colombia
  123. Centro para el Desarrollo de la Investigación Científica, Paraguay
  124. Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Spain
  125. Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
  126. CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Spain
  127. Hospital Universitario de Valme, Unidad Clínica de Enfermedades Infecciosas y Microbiología, Spain
  128. Sección Genética Médica - Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, Spain
  129. Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), Spain
  130. Grupo Clínico Vinculado, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Spain
  131. Department of Anthropology, University of Toronto at Mississauga, Canada
  132. Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Mexico
  133. Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Instituto de Investigación Sanitaria de Canarias, Spain
  134. Department of Clinical Sciences, University Fernando Pessoa Canarias, Spain
  135. Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Spain

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 conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were 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. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.

eLife assessment

The authors conducted 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 solid. The work will be of interest to scientists studying the genetic basis of COVID pathogenesis.

https://doi.org/10.7554/eLife.93666.3.sa0

Introduction

To date, more than 50 loci associated with COVID-19 susceptibility, hospitalization, and severity have been identified using genome-wide association studies (GWAS) (Kanai et al., 2023; Pairo-Castineira et al., 2023). The COVID-19 Host Genetics Initiative (HGI) has made significant efforts (Niemi et al., 2021) 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 studies (Popejoy and Fullerton, 2016; Sirugo et al., 2019). In addition, while trans-ancestry meta-analyses are a powerful approach for discovering shared genetic risk variants with similar effects across populations (Li and Keating, 2014), 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 environmental exposures (Rosenberg et al., 2010). Their action is particularly important for infectious diseases due to the selective constraints that are imposed by host‒pathogen interactions (Karlsson et al., 2014; Kwok et al., 2021). Literature examples of this in COVID-19 severity include a DOCK2 gene variant in East Asians (Namkoong et al., 2022) and frequent loss-of-function variants in IFNAR1 and IFNAR2 genes in Polynesian and Inuit populations, respectively (Bastard et al., 2022; Duncan et al., 2022).

Including diverse populations in case‒control GWAS with unrelated participants usually requires a prior classification of individuals in genetically homogeneous groups, which are typically analyzed separately to control the population stratification effects (Peterson et al., 2019). Populations with recent admixture impose an additional challenge to GWASs 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 false positives due to population structure (Rosenberg et al., 2010). In fact, there are benefits in study power from modeling the admixed ancestries either locally, at the 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 strata (Mester et al., 2023). Despite the development of novel methods specifically tailored for the analysis of admixed populations (Atkinson et al., 2021), the lack of a standardized analysis framework and the difficulties in confidently clustering admixed individuals into particular genetic groups often lead 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 genetic factors (Cruz et al., 2022). Here, we present the findings of a GWAS meta-analysis in admixed Latin American (AMR) populations, comprising individuals from the SCOURGE Latin American cohort and the HGI studies, which allowed us to identify two novel severe COVID-19 loci, BAZ2B and DDIAS. Further analyses modeling 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 (PGS) 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 1608 hospitalized cases and 1887 controls (not hospitalized COVID-19 patients) from Latin American countries and from recruitments of individuals of Latin American descent conducted in Spain (Supplementary file 1). Quality control details and estimation of global genetic inferred ancestry (GIA) (Figure 1—figure supplement 1) are described in ‘Materials and 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 (‘Materials and 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).

Figure 1 with 1 supplement see all
Flow chart of this study.

Stage I of the study involved a meta-analysis of the Latin American genome-wide association studies (GWAS) from SCOURGE and the COVID-19 Host Genetics Initiative. The resulting meta-analysis was leveraged to prioritize genes by using a transcriptome-wide association study (TWAS), Bayesian fine-mapping and functional annotations, and to assess the generalizability of polygenic risk score (PGS) cross-population models in Latin Americans. Stage II involved two additional cross-population GWAS meta-analyses to further investigate the replicability of findings.

Table 1
Demographic characteristics of the SCOURGE Latin American cohort.
VariableNon-hospitalized
(N = 1887)
Hospitalized(N = 1625)
Age, mean years ±SD39.1 ± 11.954.1 ± 14.5
Sex, N (%)
Female (%)1253 (66.4)668 (41.1)
Global genetic inferred ancestry, % mean ± SD
European54.4 ± 16.239.4 ± 20.7
African15.3 ± 12.79.1 ± 11.6
Native American30.3 ± 19.851.3 ± 26.5
Comorbidities, N (%)
Vascular/endocrinological488 (25.9)888 (64.5)
Cardiac60 (3.2)151 (9.3)
Nervous15 (0.8)61 (3.8)
Digestive14 (0.7)33 (2.0)
Onco-hematological21 (1.1)48 (3.00)
Respiratory76 (4.0)118 (7.3)

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 4702 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; a quantile‒quantile plot is shown in Figure 2—figure supplement 1), two of which (BAZ2B and DDIAS) were novel discoveries. A Meta-Analysis Model-based Assessment of replicability (MAMBA) approach to leverage the strength and consistency of associations across the contributing studies supported >90% likelihood for one of the novel loci to likely replicate in future studies (Table 3). Four lead variants were identified, linked to other 310 variants (Supplementary files 2 and 3). 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 file 4).

Table 2
Lead independent variants in the admixed AMR genome-wide association studies (GWAS) meta-analysis.
SNP rsIDchr:posEANEAOR (95% CI)p-ValueEAF casesEAF controlsNearest geneMamba PPR
rs130038352:159407982TC1.20 (1.12–1.27)3.66E-080.5630.429BAZ2B0.30
rs357319123:45848457TC1.65 (1.47–1.85)6.30E-170.0870.056LZTFL10.95
rs24778206:41535254AT0.84 (0.79–0.89)1.89E-080.4530.517FOXP4-AS10.18
rs7759993411:82906875GA2.27 (1.7–3.04)2.26E-080.0160.011DDIAS0.95
  1. EA: effect allele; NEA: noneffect allele; EAF: effect allele frequency in the SCOURGE study; PPR: posterior probability of replicability.

Figure 2 with 1 supplement see all
Manhattan plot for the admixed AMR genome-wide association studies (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 on chromosome 2 (within BAZ2B), chromosome 3 (within LZTFL1), chromosome 6 (within FOXP4), and chromosome 11 (within DDIAS).

Table 3
Novel variants in the SC-HGIALL and SC-HGI3POP meta-analyses (with respect to HGIv7).

Independent signals after LD clumping.

SNP rsIDchr:posEANEAOR (95% CI)p-ValueNearest geneAnalysis
rs7656417216:3892266TG1.31 (1.19–1.44)9.64E-09CREBBPSC-HGI3POP
rs6683374219:4063488TC0.94 (0.92–0.96)1.89E-08ZBTB7ASC-HGI3POP
rs6683374219:4063488TC0.94 (0.92–0.96)2.50E-08ZBTB7ASC-HGIALL
rs287603420:6492834AT0.95 (0.93–0.97)2.83E-08CASC20SC-HGIALL
  1. EA: effect allele; NEA: non-effect allele.

Located within the BAZ2B gene, the sentinel variant rs13003835 (Figure 3) 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 groups (Kanai et al., 2023).

Figure 3 with 1 supplement see all
New loci associated with COVID-19 hospitalization in Admixed american populations.

(A) Regional association plots for rs1003835 at chromosome 2 and rs77599934 at chromosome 11. (B) Allele frequency distribution across the 1000 Genomes Project populations for the lead variants rs1003835 and rs77599934. Retrieved from The Geography of Genetic Variants Web or GGV.

The other novel risk locus is led by the sentinel variant rs77599934 (Figure 3), a rare intronic variant located in chromosome 11 within DDIAS and associated with the risk of COVID-19 hospitalization (OR = 2.27, 95% CI = 1.70–3.04, p=2.26 × 10–8).

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 file 2), which has not yet been reported in any other GWAS of COVID-19 to date.

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 populations (Degenhardt et al., 2022), and it was mapped to LZTFL1. While rs2477820 is a novel risk variant within the FOXP4 gene, it has a moderate LD (r2 = 0.295) with rs2496644, which has been linked to COVID-19 hospitalization (Kousathanas et al., 2022). This is consistent with the effects of LD in tag-SNPs when conducting GWAS in diverse populations.

None of the lead variants was associated with the comorbidities included in Table 1.

Functional mapping of novel risk variants

Variants belonging to the lead loci were prioritized by positional and expression quantitative trait loci (eQTL) mapping with FUMA, resulting in 31 mapped genes (Supplementary file 5). Within the region surrounding the lead variant in chromosome 2, FUMA prioritized four genes in addition to BAZ2B (PLA2R1, LY75, WDSUB1, and CD302). rs13003835 (allele C) is an eQTL of LY75 in the esophagus mucosa (NES = 0.27) and of BAZ2B-AS in whole blood (NES = 0.27), while rs2884110 (R2 = 0.85) is an eQTL of LY75 in lung (NES = 0.22). As for the chromosome 11, rs77599934 (allele G) is in moderate-to-strong LD (r2 = 0.776) with rs60606421 (G deletion, allele -), which is an eQTL associated with a reduced expression of DDIAS in the lungs (NES = −0.49, allele -). The sentinel variant for the region in chromosome 16 is in perfect LD (r2 = 1) with rs601183, an eQTL of ZNF774 in the lung.

Bayesian fine mapping

We performed different approaches to narrow down the prioritized loci to a set of most likely genes driving the associations. First, we computed credible sets at the 95% confidence level for causal variants and annotated them with VEP and V2G aggregate scoring. The 95% confidence credible set from the region of chromosome 2 around rs13003835 included 76 variants, which can be found in Supplementary file 6 and a regional plot is shown in Figure 3—figure supplement 1 (VEP and V2G annotations are included in Supplementary files 7 and 8). TheV2G score prioritized BAZ2B as the most likely gene driving the association. However, the approach was unable to converge allocating variants in a 95% confidence credible set for the region in chromosome 11.

Transcriptome-wide association study (TWAS)

Five novel genes, namely, SLC25A37, SMARCC1, CAMP, TYW3, and S100A12 (Supplementary file 9), were found to be 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 the lungs, CCR9 was significantly associated in whole blood, and IFNAR2 and SLC25A37 were associated in lymphocytes.

Likewise, we carried out TWAS analyses using the models trained in the admixed populations. However, no significant gene pairs were detected in this case. The top 10 genes with the lowest p-values for each of the datasets (Puerto Ricans, Mexicans, African Americans, and pooled cohorts) are shown in Supplementary file 10. Although not significant, KCNC3 was repeated in the four analyses, whereas MAPKAPK3, NAPSA, and THAP5 were repeated in three out of four. Both NAPSA and KCNC3 are located in the chromosome 19 and were reported in the latest HGI meta-analysis (Kanai et al., 2023).

All mapped genes from analyses conducted in AMR populations are shown in Figure 4, and associations for the two novel variants with expression are shown in Figure 4—figure supplement 1.

Figure 4 with 1 supplement see all
Summary of the results from gene prioritization strategies used for genetic associations in AMR populations.

Genome-wide association studies (GWAS) catalog association for BAZ2B-AS was with FEV/FCV ratio. Literature-based evidence is further explored in ‘Discussion’.

Genetic architecture of COVID-19 hospitalization in AMR populations

Allele frequencies of rs13003835 and rs77599934 across ancestries

Neither rs13003835 (BAZ2B) nor 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 directions between SAS-AMR and EUR-AFR-EAS and that there was large heterogeneity among them (Figure 5). We queried SNPs within 50 kb windows of the lead variant in each of the other populations that had p-value <0.01. The variant with the lowest p-value in the EUR population was rs559179177 (p=1.72 × 10–4), which is in perfect LD (r2 = 1) in the 1KGP EUR population with our sentinel variant (rs13003835), and in moderate LD r2 = 0.4 in AMR populations. Since this variant was absent from the AMR analysis, probably due to its low frequency, it could not be meta-analyzed. Power calculations revealed that the EUR analysis was underpowered for this variant to achieve genome-wide significance (77.6%, assuming an effect size of 0.46, EAF = 0.0027, and number of cases/controls as shown in the HGI website for B2-EUR). In the cross-population meta-analysis (B2-ALL), rs559179177 obtained a p-value of 5.9 × 10–4.

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 are 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 the potential to be a 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 5). Examining the LAI, the G allele occurs at a 10.8% 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 5). 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 whether 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). However, LD calculations using the 1KGP phase 3 dataset indicated that rs77599934 and rs75684040 were poorly correlated (r2 = 0.11). As for AFR populations, the variant with the lowest p-value was rs138860115 (p=8.3 × 10–3), but it was not correlated with the lead SNP of this locus.

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 file 11). 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 release (Kanai et al., 2023), this study replicated 39, and the association was stronger than in the original study in 29 of those (Supplementary file 12). 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-analysis (Pairo-Castineira et al., 2023). 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-19 (Pairo-Castineira et al., 2023). In addition, 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 esophageal mucosa. This variant was previously associated with COVID-19 hospitalization (Pairo-Castineira et al., 2023).

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 file 13). 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 to a Bayesian fine mapping (Supplementary file 6). Eight variants were included in the credible set for the region in chromosome 16 (meta-analysis SC-HGI3POP).

Polygenic risk score models

Using the 49 variants associated with disease severity that are shared across populations according to the HGIv7, we constructed a PGS model to assess its generalizability in the admixed AMR (Supplementary file 14). 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 explaining 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 2.89-fold (95% CI = 2.37–3.54, p=1.29 × 10–7) greater risk compared to individuals in the deciles between 4 and 6 (corresponding to a score of the median distribution).

We also examined the distribution of PGS across a five-level severity scale to further determine if there was any correspondence between clinical severity and genetic risk. Median PGS were lower in the asymptomatic and mild groups, whereas higher median scores were observed in the moderate, severe, and critical patients (Figure 6). We fitted a multinomial model using the asymptomatic class as a reference and calculated the OR for each category (Supplementary file 15), 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.

Polygenic risk distribution for COVID-19 hospitalization.

(A) Polygenic risk stratified by polygenic risk score (PGS) deciles comparing each risk group against the lowest risk group (OR–95% CI). (B) Distribution of the PGS in each of the severity scale classes. 0, asymptomatic; 1, mild disease; 2, moderate disease; 3, severe disease; 4, critical disease.

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 that were 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-population meta-analyses conducted to date, remaining undetectable.

Fine mapping of the region harboring 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, but our sentinel variant was in strong LD with an eQTL that associated with reduced gene expression of DDIAS in the lung. DDIAS, known as damage-induced apoptosis suppressor gene, is itself a plausible candidate gene. It has been linked to DNA damage repair mechanisms: research has shown that depletion of DDIAS leads to an increase in 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 repair (Brunette et al., 2019). Interestingly, a study found that infection by SARS-CoV-2 also triggered the phosphorylation of ATM kinase and inhibited repair mechanisms, causing the accumulation of DNA damage (Gioia et al., 2023). This gene has also been proposed as a potential biomarker for lung cancer after finding that it interacts with STAT3 in lung cancer cells, regulating IL-6 (Im et al., 2020; Im et al., 2023) and thus mediating inflammatory processes, while another study determined that its blockade inhibited lung cancer cell growth (Won et al., 2014). Another prioritized gene from this region was PRCP, an angiotensinase that shares substrate specificity with ACE2 receptor. It has been positively linked to hypertension and some studies have raised hypotheses on its role in COVID-19 progression, particularly in relation to the development of pro-thrombotic events (Angeli et al., 2023; Silva-Aguiar et al., 2020).

The risk region found in chromosome 2 harbors more than one gene. The lead variant rs13003835 is located within BAZ2B, and it increases the expression of the antisense BAZ2B gene in whole blood. BAZ2B encodes one of the regulatory subunits of the Imitation switch (ISWI) chromatin remodelers (Li et al., 2021) constituting the BRF-1/BRF-5 complexes with SMARCA1 and SMARCA5, respectively. Interestingly, it was discovered that lnc-BAZ2B promotes macrophage activation through regulation of BAZ2B expression. Its overexpression resulted in pulmonary inflammation and elevated levels of MUC5AC in mice with asthma (Xia et al., 2021). This variant was also an eQTL for LY75 (encoding lymphocyte antigen 75) in the esophageal mucosa tissue. Lymphocyte antigen 75 is involved in immune processes through antigen presentation in dendritic cells and endocytosis (Mahnke et al., 2000) and has been associated with inflammatory diseases, representing a compelling candidate for the region. Increased expression of LY75 has been detected within hours after infection by SARS-CoV-2 (Mitchell et al., 2013; Sims et al., 2013). 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 on chromosome 15 between the IQGAP1 and ZNF774 genes, although it did not reach genome-wide significance.

Secondary analyses revealed five TWAS-associated genes, some of which have already been linked to severe COVID-19. In a comprehensive multitissue gene expression profiling study (Gómez-Carballa et al., 2022), decreased expression of CAMP and S100A8/S100A9 genes in patients with severe COVID-19 was observed, while another study detected the upregulation of SCL25A37 among patients with severe COVID-19 (Policard et al., 2021). SMARCC1 is a subunit of the SWI/SNF chromatin remodeling complex that has been identified as proviral for SARS-CoV-2 and other coronavirus strains through a genome-wide screen (Wei et al., 2021). This complex is crucial for ACE2 expression and viral entry into the cell (Wei et al., 2023). However, it should be noted that using eQTL mostly from European populations such as those in GTEx could result in reduced power to detect associations.

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 were not associated 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 admixture (Pereira et al., 2022). This lack of replication aligns with the initial hypothesis of that study suggesting that the risk haplotype was derived from European populations, as we 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 the EUR, AFR, and admixed AMR GIA groups. According to a recent study, elevated levels of the ZBTB7A gene promote a quasihomeostatic state between coronaviruses and host cells, preventing cell death by regulating oxidative stress pathways (Zhu et al., 2022). This gene is involved in several signaling pathways, such as B- and T-cell differentiation (Gupta et al., 2020). On a separate note, CREBBP encodes the CREB binding protein (CBP), which is involved in transcriptional activation and is known to positively regulate the type I interferon response through virus-induced phosphorylation of IRF-3 (Yoneyama et al., 1998). In addition, the CREBP/CBP interaction has been implicated in SARS-CoV-2 infection (Yang et al., 2023) 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 variants (Yang et al., 2023).

We developed a cross-population PGS model, which effectively stratified individuals based on their genetic risk and demonstrated consistency with the clinical severity classification of the patients. Only a few polygenic scores were derived from COVID-19 GWAS data. Horowitz et al., 2022 developed a score using 6 and 12 associated variants (PGS ID: PGP000302) and reported an associated OR (top 10% vs rest) of 1.38 for risk of hospitalization in European populations, whereas the OR for Latin American populations was 1.56. Since their sample size and the number of variants included in the PGS were lower, direct comparisons are not straightforward. Nevertheless, our analysis provides the first results for a PRS applied to a relatively large AMR cohort, being of value for future analyses regarding PRS transferability.

This study is subject to limitations, mostly concerning sample recruitment and composition. The SCOURGE Latino American sample size is small, and the GWAS is likely 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 socioenvironmental factors that could have affected COVID-19 spread and hospitalization rates, although genetic principal components are known to capture nongenetic factors. Finally, we must acknowledge the lack of a replication cohort. We 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 in the meta-analysis, both by involving diverse populations in study designs and 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 by 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

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Commercial assay or kitChemagic DNA Blood 100 kitPerkinElmer Chemagen Technologies GmbH
Software, algorithmAxiom Analysis SuiteThermo Fisher ScientificVersion 4.0.3.3
Software, algorithmPLINKPurcell et al., 2007; https://www.cog-genomics.org/plink/RRID:SCR_001757Version 1.9; v2
Software, algorithmTOPMed Imputation Serverhttps://imputation.biodatacatalyst.nhlbi.nih.gov/Version 2
Software, algorithmADMIXTUREAlexander et al., 2009; https://dalexander.github.io/admixture/RRID:SCR_001263Version 1.3.0
Software, algorithmSAIGEgdsZheng and Davis, 2021; https://www.bioconductor.org/packages/release/bioc/html/SAIGEgds.htmlVersion 1.10.0
Software, algorithmMETALWiller et al., 2010; https://csg.sph.umich.edu/abecasis/metal/RRID:SCR_002013Version 2011-03-25
Software, algorithmFUMAWatanabe et al., 2017; https://fuma.ctglab.nl/RRID:SCR_017521Version 1.5.2
Software, algorithmMAMBAMcGuire et al., 2021; https://github.com/dan11mcguire/mambaVersion 1
Software, algorithmS-PrediXcan; S-MultiXcanBarbeira et al., 2018; https://github.com/hakyimlab/MetaXcanRRID:SCR_016739Version 1
Software, algorithmGTEx v8 mashr prediction modelshttps://predictdb.org/post/2021/07/21/gtex-v8-models-on-eqtl-and-sqtl/
OtherGWAS Cataloghttps://www.ebi.ac.uk/gwas/RRID:SCR_012745Section ‘Definition of the genetic risk loci and putative functional impact’

GWAS in Latin Americans from SCOURGE

The SCOURGE Latin American cohort

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A total of 3729 COVID-19-positive cases were recruited across five countries from Latin America (Mexico, Brazil, Colombia, Paraguay, and Ecuador) by 13 participating centers (Supplementary file 1) from March 2020 to July 2021. In addition, we included 1082 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 ancestries (Cruz et al., 2022). We used hospitalization as a proxy for disease severity and defined COVID-19-positive patients who underwent hospitalization as a consequence of the infection as cases and those who did not need hospitalization due to COVID-19 as controls.

Samples and data were collected with informed consent after the approval of the Ethics and Scientific Committees from the participating centers and by the Galician Ethics Committee Ref 2020/197. Recruitment of patients from IMSS (in Mexico City) was approved by the National Committee 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 tool (Harris et al., 2009; Harris et al., 2019), 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 with 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

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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 Axiom Analysis Suite v4.0.3.3 software.

Quality control steps and variant imputation

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A quality control (QC) procedure using PLINK 1.9 (Purcell et al., 2007) 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 contamination. Samples missing >2% of the variants were filtered out. Subsequently, we kept the autosomal SNPs, removed high-LD regions, and conducted LD pruning (windows of 1000 SNPs, with a step size of 80 and an 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 4348 individuals and 10,671,028 genetic variants were included in the analyses.

Genetic admixture estimation

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Global GIA, referred to the genetic similarity to the used reference individuals, was estimated with ADMIXTURE (Alexander et al., 2009) 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) (Auton et al., 2015) and merged them with the 79 Native American (AMR) samples from Mao et al., 2007 keeping the biallelic SNPs. LD-pruned variants were selected from this merge using the same parameters as in the QC. We then ran 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, 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 supervised clustering and estimate 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 3512 admixed Latin American (AMR) subjects for downstream analyses.

Association analysis

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The results for the SCOURGE Latin American GWAS were obtained by 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 sufficient control of population structure (Wojcik et al., 2019). The SCOURGE cohort consisted of 3512 COVID-19-positive patients: cases (n = 1625) were defined as hospitalized COVID-19 patients, and controls (n = 1887) were defined as non-hospitalized COVID-19-positive patients.

Logistic mixed regression models were fitted using the SAIGEgds (Zheng and Davis, 2021) package in R, which implements the two-step mixed SAIGE (Zhou et al., 2018) model methodology and the SPA test. Baseline covariables included sex, age (continuous), and the first 10 PCs. To account for potential heterogeneity in the recruitment and hospitalization criteria across the participating countries, we adjusted the models by groups of the recruitment areas classified into six categories: Brazil, Colombia, Ecuador, Mexico, Paraguay, and Spain. This dataset has not been used in any previously published GWAS of COVID-19.

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 3077 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 on the association results.

The meta-analysis was performed using an inverse-variance weighting method in METAL (Willer et al., 2010). The 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 Cochran’s Q-test. The inflation of results was assessed based on a genomic control (lambda).

Replicability of associations

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The model-based method MAMBA (McGuire et al., 2021) was used to calculate the posterior probabilities of replication for each of the lead variant (PPR; PP that an SNP has a non-zero replicable effect). We defined PPR <0.1 as a low posterior probability of replication, following the original paper, whereas those with a PPR >90% were considered consistent and likely to replicate in future studies. Variants with p<1 × 10–05 were clumped and combined with random pruned variants from the 1KGP AMR reference panel. Then, MAMBA was applied to the set of significant and non-significant variants.

Each of the lead variants was also tested for association with the main comorbidities in the SCOURGE cohort with logistic regression models (adjusted by the same base covariables as the GWAS).

Definition of the genetic risk loci and putative functional impact

Definition of lead variant and novel loci

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To define the lead variants in the loci that were genome-wide significant, 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 the SCOURGE cohort genotype data as the 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 separately; (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

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Functional annotation was performed with FUMA (Watanabe et al., 2017) 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 FUMA (Watanabe et al., 2017). Gene-based analysis was also performed using MAGMA (de Leeuw et al., 2015) 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 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 esophageal 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.

Bayesian fine-mapping

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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 the 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 and V2G annotation

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We used the Variant-to-Gene (V2G) score to prioritize the genes that were most likely affected by the functional evidence based on eQTL, chromatin interactions, in silico functional predictions, and distance between the prioritized variants and transcription start site (TSS), based on data from the Open Targets Genetics portal (Ghoussaini et al., 2021). Details of the data integration and the weighting of each of the datasets are described in detail at https://genetics-docs.opentargets.org/our-approach/data-pipeline. V2G is a score for ranking the functional genomics evidence that supports the connection between variants and genes (the higher the score the more likely the variant to be functionally implicated on the assigned gene). We used VEP release 111 (https://www.ensembl.org/info/docs/tools/vep/index.html; accessed April 10, 2024; McLaren et al., 2016) to annotate the following: gene symbol, function (exonic, intronic, intergenic, non-coding RNA, etc.), impact, feature type, feature, and biotype.

We queried the GWAS catalog (date of accession: 1/07/2024) for evidence of association of each of the prioritized genes with traits related to lung diseases or phenotypes. Lastly, those which were linked to COVID-19, infection, or lung diseases in the revised literature were classified as ‘literature evidence’.

Transcription-wide association studies

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TWAS were conducted using the pretrained prediction models with MASHR-computed effect sizes on GTEx v8 datasets (Barbeira et al., 2019a; Barbeira et al., 2021). The results from the Latin American meta-analysis were harmonized and integrated with the prediction models through S-PrediXcan (Barbeira et al., 2018) for lung, whole blood, lymphocyte, and esophageal mucosal 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 ran a multitissue TWAS (S-MultiXcan) to improve the power for association, as demonstrated recently (Barbeira et al., 2019b). TWAS was also performed using recently published gene expression datasets derived from a cohort of African Americans, Puerto Ricans, and Mexican Americans (GALA II-SAGE) (Kachuri et al., 2023).

Cross-population meta-analyses

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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 analyzed within HGI (B2-ALL). Additionally, to evaluate the three GIA components within the SCOURGE Latin American cohort (Bryc et al., 2010), 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.

Cross-population polygenic risk score

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A PGS for critical COVID-19 was derived by 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 with either severe disease or hospitalization in the latest HGIv7 release (Kanai et al., 2023) (using the hospitalization weights) or (2) associated with severe disease in the latest GenOMICC meta-analysis (Pairo-Castineira et al., 2023) that were not reported in the latest HGI release. A total of 48 markers were used in the PGS model (see Supplementary file 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 patients who were hospitalized vs COVID-19-positive patients who 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-R2. We also divided the sample into 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. These 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 noninvasive).

Data availability

Summary statistics from the SCOURGE Latin American GWAS and the analysis scripts are available from the public repository https://github.com/CIBERER/Scourge-COVID19 (copy archived at CIBERER, 2024).

The following previously published data sets were used

References

    1. Cruz R
    2. Diz-de Almeida S
    3. López de Heredia M
    4. Quintela I
    5. Ceballos FC
    6. Pita G
    7. Lorenzo-Salazar JM
    8. González-Montelongo R
    9. Gago-Domínguez M
    10. Sevilla Porras M
    11. Tenorio Castaño JA
    12. Nevado J
    13. Aguado JM
    14. Aguilar C
    15. Aguilera-Albesa S
    16. Almadana V
    17. Almoguera B
    18. Alvarez N
    19. Andreu-Bernabeu Á
    20. Arana-Arri E
    21. Arango C
    22. Arranz MJ
    23. Artiga M-J
    24. Baptista-Rosas RC
    25. Barreda-Sánchez M
    26. Belhassen-Garcia M
    27. Bezerra JF
    28. Bezerra MAC
    29. Boix-Palop L
    30. Brion M
    31. Brugada R
    32. Bustos M
    33. Calderón EJ
    34. Carbonell C
    35. Castano L
    36. Castelao JE
    37. Conde-Vicente R
    38. Cordero-Lorenzana ML
    39. Cortes-Sanchez JL
    40. Corton M
    41. Darnaude MT
    42. De Martino-Rodríguez A
    43. Del Campo-Pérez V
    44. Diaz de Bustamante A
    45. Domínguez-Garrido E
    46. Luchessi AD
    47. Eiros R
    48. Estigarribia Sanabria GM
    49. Carmen Fariñas M
    50. Fernández-Robelo U
    51. Fernández-Rodríguez A
    52. Fernández-Villa T
    53. Gil-Fournier B
    54. Gómez-Arrue J
    55. González Álvarez B
    56. Gonzalez Bernaldo de Quirós F
    57. González-Peñas J
    58. Gutiérrez-Bautista JF
    59. Herrero MJ
    60. Herrero-Gonzalez A
    61. Jimenez-Sousa MA
    62. Lattig MC
    63. Liger Borja A
    64. Lopez-Rodriguez R
    65. Mancebo E
    66. Martín-López C
    67. Martín V
    68. Martinez-Nieto O
    69. Martinez-Lopez I
    70. Martinez-Resendez MF
    71. Martinez-Perez A
    72. Mazzeu JF
    73. Merayo Macías E
    74. Minguez P
    75. Moreno Cuerda V
    76. Silbiger VN
    77. Oliveira SF
    78. Ortega-Paino E
    79. Parellada M
    80. Paz-Artal E
    81. Santos NPC
    82. Pérez-Matute P
    83. Perez P
    84. Pérez-Tomás ME
    85. Perucho T
    86. Pinsach-Abuin ML
    87. Pompa-Mera EN
    88. Porras-Hurtado GL
    89. Pujol A
    90. Ramiro León S
    91. Resino S
    92. Fernandes MR
    93. Rodríguez-Ruiz E
    94. Rodriguez-Artalejo F
    95. Rodriguez-Garcia JA
    96. Ruiz Cabello F
    97. Ruiz-Hornillos J
    98. Ryan P
    99. Soria JM
    100. Souto JC
    101. Tamayo E
    102. Tamayo-Velasco A
    103. Taracido-Fernandez JC
    104. Teper A
    105. Torres-Tobar L
    106. Urioste M
    107. Valencia-Ramos J
    108. Yáñez Z
    109. Zarate R
    110. Nakanishi T
    111. Pigazzini S
    112. Degenhardt F
    113. Butler-Laporte G
    114. Maya-Miles D
    115. Bujanda L
    116. Bouysran Y
    117. Palom A
    118. Ellinghaus D
    119. Martínez-Bueno M
    120. Rolker S
    121. Amitrano S
    122. Roade L
    123. Fava F
    124. Spinner CD
    125. Prati D
    126. Bernardo D
    127. Garcia F
    128. Darcis G
    129. Fernández-Cadenas I
    130. Holter JC
    131. Banales JM
    132. Frithiof R
    133. Duga S
    134. Asselta R
    135. Pereira AC
    136. Romero-Gómez M
    137. Nafría-Jiménez B
    138. Hov JR
    139. Migeotte I
    140. Renieri A
    141. Planas AM
    142. Ludwig KU
    143. Buti M
    144. Rahmouni S
    145. Alarcón-Riquelme ME
    146. Schulte EC
    147. Franke A
    148. Karlsen TH
    149. Valenti L
    150. Zeberg H
    151. Richards B
    152. Ganna A
    153. Boada M
    154. de Rojas I
    155. Ruiz A
    156. Sánchez-Juan P
    157. Real LM
    158. SCOURGE Cohort Group
    159. HOSTAGE Cohort Group
    160. GRA@CE Cohort Group
    161. Guillen-Navarro E
    162. Ayuso C
    163. González-Neira A
    164. Riancho JA
    165. Rojas-Martinez A
    166. Flores C
    167. Lapunzina P
    168. Carracedo A
    (2022) Novel genes and sex differences in COVID-19 severity
    Human Molecular Genetics 31:3789–3806.
    https://doi.org/10.1093/hmg/ddac132
    1. Degenhardt F
    2. Ellinghaus D
    3. Juzenas S
    4. Lerga-Jaso J
    5. Wendorff M
    6. Maya-Miles D
    7. Uellendahl-Werth F
    8. ElAbd H
    9. Rühlemann MC
    10. Arora J
    11. Özer O
    12. Lenning OB
    13. Myhre R
    14. Vadla MS
    15. Wacker EM
    16. Wienbrandt L
    17. Blandino Ortiz A
    18. de Salazar A
    19. Garrido Chercoles A
    20. Palom A
    21. Ruiz A
    22. Garcia-Fernandez A-E
    23. Blanco-Grau A
    24. Mantovani A
    25. Zanella A
    26. Holten AR
    27. Mayer A
    28. Bandera A
    29. Cherubini A
    30. Protti A
    31. Aghemo A
    32. Gerussi A
    33. Ramirez A
    34. Braun A
    35. Nebel A
    36. Barreira A
    37. Lleo A
    38. Teles A
    39. Kildal AB
    40. Biondi A
    41. Caballero-Garralda A
    42. Ganna A
    43. Gori A
    44. Glück A
    45. Lind A
    46. Tanck A
    47. Hinney A
    48. Carreras Nolla A
    49. Fracanzani AL
    50. Peschuck A
    51. Cavallero A
    52. Dyrhol-Riise AM
    53. Ruello A
    54. Julià A
    55. Muscatello A
    56. Pesenti A
    57. Voza A
    58. Rando-Segura A
    59. Solier A
    60. Schmidt A
    61. Cortes B
    62. Mateos B
    63. Nafria-Jimenez B
    64. Schaefer B
    65. Jensen B
    66. Bellinghausen C
    67. Maj C
    68. Ferrando C
    69. de la Horra C
    70. Quereda C
    71. Skurk C
    72. Thibeault C
    73. Scollo C
    74. Herr C
    75. Spinner CD
    76. Gassner C
    77. Lange C
    78. Hu C
    79. Paccapelo C
    80. Lehmann C
    81. Angelini C
    82. Cappadona C
    83. Azuure C
    84. COVICAT study group, Aachen Study (COVAS)
    85. Bianco C
    86. Cea C
    87. Sancho C
    88. Hoff DAL
    89. Galimberti D
    90. Prati D
    91. Haschka D
    92. Jiménez D
    93. Pestaña D
    94. Toapanta D
    95. Muñiz-Diaz E
    96. Azzolini E
    97. Sandoval E
    98. Binatti E
    99. Scarpini E
    100. Helbig ET
    101. Casalone E
    102. Urrechaga E
    103. Paraboschi EM
    104. Pontali E
    105. Reverter E
    106. Calderón EJ
    107. Navas E
    108. Solligård E
    109. Contro E
    110. Arana-Arri E
    111. Aziz F
    112. Garcia F
    113. García Sánchez F
    114. Ceriotti F
    115. Martinelli-Boneschi F
    116. Peyvandi F
    117. Kurth F
    118. Blasi F
    119. Malvestiti F
    120. Medrano FJ
    121. Mesonero F
    122. Rodriguez-Frias F
    123. Hanses F
    124. Müller F
    125. Hemmrich-Stanisak G
    126. Bellani G
    127. Grasselli G
    128. Pezzoli G
    129. Costantino G
    130. Albano G
    131. Cardamone G
    132. Bellelli G
    133. Citerio G
    134. Foti G
    135. Lamorte G
    136. Matullo G
    137. Baselli G
    138. Kurihara H
    139. Neb H
    140. My I
    141. Kurth I
    142. Hernández I
    143. Pink I
    144. de Rojas I
    145. Galván-Femenia I
    146. Holter JC
    147. Afset JE
    148. Heyckendorf J
    149. Kässens J
    150. Damås JK
    151. Rybniker J
    152. Altmüller J
    153. Ampuero J
    154. Martín J
    155. Erdmann J
    156. Banales JM
    157. Badia JR
    158. Dopazo J
    159. Schneider J
    160. Bergan J
    161. Barretina J
    162. Walter J
    163. Hernández Quero J
    164. Goikoetxea J
    165. Delgado J
    166. Guerrero JM
    167. Fazaal J
    168. Kraft J
    169. Schröder J
    170. Risnes K
    171. Banasik K
    172. Müller KE
    173. Gaede KI
    174. Garcia-Etxebarria K
    175. Tonby K
    176. Heggelund L
    177. Izquierdo-Sanchez L
    178. Bettini LR
    179. Sumoy L
    180. Sander LE
    181. Lippert LJ
    182. Terranova L
    183. Nkambule L
    184. Knopp L
    185. Gustad LT
    186. Garbarino L
    187. Santoro L
    188. Téllez L
    189. Roade L
    190. Ostadreza M
    191. Intxausti M
    192. Kogevinas M
    193. Riveiro-Barciela M
    194. Berger MM
    195. Schaefer M
    196. Niemi MEK
    197. Gutiérrez-Stampa MA
    198. Carrabba M
    199. Figuera Basso ME
    200. Valsecchi MG
    201. Hernandez-Tejero M
    202. Vehreschild MJGT
    203. Manunta M
    204. Acosta-Herrera M
    205. D’Angiò M
    206. Baldini M
    207. Cazzaniga M
    208. Grimsrud MM
    209. Cornberg M
    210. Nöthen MM
    211. Marquié M
    212. Castoldi M
    213. Cordioli M
    214. Cecconi M
    215. D’Amato M
    216. Augustin M
    217. Tomasi M
    218. Boada M
    219. Dreher M
    220. Seilmaier MJ
    221. Joannidis M
    222. Wittig M
    223. Mazzocco M
    224. Ciccarelli M
    225. Rodríguez-Gandía M
    226. Bocciolone M
    227. Miozzo M
    228. Imaz Ayo N
    229. Blay N
    230. Chueca N
    231. Montano N
    232. Braun N
    233. Ludwig N
    234. Marx N
    235. Martínez N
    236. Norwegian SARS-CoV-2 Study group
    237. Cornely OA
    238. Witzke O
    239. Palmieri O
    240. Pa Study Group
    241. Faverio P
    242. Preatoni P
    243. Bonfanti P
    244. Omodei P
    245. Tentorio P
    246. Castro P
    247. Rodrigues PM
    248. España PP
    249. Hoffmann P
    250. Rosenstiel P
    251. Schommers P
    252. Suwalski P
    253. de Pablo R
    254. Ferrer R
    255. Bals R
    256. Gualtierotti R
    257. Gallego-Durán R
    258. Nieto R
    259. Carpani R
    260. Morilla R
    261. Badalamenti S
    262. Haider S
    263. Ciesek S
    264. May S
    265. Bombace S
    266. Marsal S
    267. Pigazzini S
    268. Klein S
    269. Pelusi S
    270. Wilfling S
    271. Bosari S
    272. Volland S
    273. Brunak S
    274. Raychaudhuri S
    275. Schreiber S
    276. Heilmann-Heimbach S
    277. Aliberti S
    278. Ripke S
    279. Dudman S
    280. Wesse T
    281. Zheng T
    282. STORM Study group, The Humanitas Task Force, The Humanitas Gavazzeni Task Force
    283. Bahmer T
    284. Eggermann T
    285. Illig T
    286. Brenner T
    287. Pumarola T
    288. Feldt T
    289. Folseraas T
    290. Gonzalez Cejudo T
    291. Landmesser U
    292. Protzer U
    293. Hehr U
    294. Rimoldi V
    295. Monzani V
    296. Skogen V
    297. Keitel V
    298. Kopfnagel V
    299. Friaza V
    300. Andrade V
    301. Moreno V
    302. Albrecht W
    303. Peter W
    304. Poller W
    305. Farre X
    306. Yi X
    307. Wang X
    308. Khodamoradi Y
    309. Karadeniz Z
    310. Latiano A
    311. Goerg S
    312. Bacher P
    313. Koehler P
    314. Tran F
    315. Zoller H
    316. Schulte EC
    317. Heidecker B
    318. Ludwig KU
    319. Fernández J
    320. Romero-Gómez M
    321. Albillos A
    322. Invernizzi P
    323. Buti M
    324. Duga S
    325. Bujanda L
    326. Hov JR
    327. Lenz TL
    328. Asselta R
    329. de Cid R
    330. Valenti L
    331. Karlsen TH
    332. Cáceres M
    333. Franke A
    (2022) Detailed stratified GWAS analysis for severe COVID-19 in four European populations
    Human Molecular Genetics 31:3945–3966.
    https://doi.org/10.1093/hmg/ddac158
    1. Namkoong H
    2. Edahiro R
    3. Takano T
    4. Nishihara H
    5. Shirai Y
    6. Sonehara K
    7. Tanaka H
    8. Azekawa S
    9. Mikami Y
    10. Lee H
    11. Hasegawa T
    12. Okudela K
    13. Okuzaki D
    14. Motooka D
    15. Kanai M
    16. Naito T
    17. Yamamoto K
    18. Wang QS
    19. Saiki R
    20. Ishihara R
    21. Matsubara Y
    22. Hamamoto J
    23. Hayashi H
    24. Yoshimura Y
    25. Tachikawa N
    26. Yanagita E
    27. Hyugaji T
    28. Shimizu E
    29. Katayama K
    30. Kato Y
    31. Morita T
    32. Takahashi K
    33. Harada N
    34. Naito T
    35. Hiki M
    36. Matsushita Y
    37. Takagi H
    38. Aoki R
    39. Nakamura A
    40. Harada S
    41. Sasano H
    42. Kabata H
    43. Masaki K
    44. Kamata H
    45. Ikemura S
    46. Chubachi S
    47. Okamori S
    48. Terai H
    49. Morita A
    50. Asakura T
    51. Sasaki J
    52. Morisaki H
    53. Uwamino Y
    54. Nanki K
    55. Uchida S
    56. Uno S
    57. Nishimura T
    58. Ishiguro T
    59. Isono T
    60. Shibata S
    61. Matsui Y
    62. Hosoda C
    63. Takano K
    64. Nishida T
    65. Kobayashi Y
    66. Takaku Y
    67. Takayanagi N
    68. Ueda S
    69. Tada A
    70. Miyawaki M
    71. Yamamoto M
    72. Yoshida E
    73. Hayashi R
    74. Nagasaka T
    75. Arai S
    76. Kaneko Y
    77. Sasaki K
    78. Tagaya E
    79. Kawana M
    80. Arimura K
    81. Takahashi K
    82. Anzai T
    83. Ito S
    84. Endo A
    85. Uchimura Y
    86. Miyazaki Y
    87. Honda T
    88. Tateishi T
    89. Tohda S
    90. Ichimura N
    91. Sonobe K
    92. Sassa CT
    93. Nakajima J
    94. Nakano Y
    95. Nakajima Y
    96. Anan R
    97. Arai R
    98. Kurihara Y
    99. Harada Y
    100. Nishio K
    101. Ueda T
    102. Azuma M
    103. Saito R
    104. Sado T
    105. Miyazaki Y
    106. Sato R
    107. Haruta Y
    108. Nagasaki T
    109. Yasui Y
    110. Hasegawa Y
    111. Mutoh Y
    112. Kimura T
    113. Sato T
    114. Takei R
    115. Hagimoto S
    116. Noguchi Y
    117. Yamano Y
    118. Sasano H
    119. Ota S
    120. Nakamori Y
    121. Yoshiya K
    122. Saito F
    123. Yoshihara T
    124. Wada D
    125. Iwamura H
    126. Kanayama S
    127. Maruyama S
    128. Yoshiyama T
    129. Ohta K
    130. Kokuto H
    131. Ogata H
    132. Tanaka Y
    133. Arakawa K
    134. Shimoda M
    135. Osawa T
    136. Tateno H
    137. Hase I
    138. Yoshida S
    139. Suzuki S
    140. Kawada M
    141. Horinouchi H
    142. Saito F
    143. Mitamura K
    144. Hagihara M
    145. Ochi J
    146. Uchida T
    147. Baba R
    148. Arai D
    149. Ogura T
    150. Takahashi H
    151. Hagiwara S
    152. Nagao G
    153. Konishi S
    154. Nakachi I
    155. Murakami K
    156. Yamada M
    157. Sugiura H
    158. Sano H
    159. Matsumoto S
    160. Kimura N
    161. Ono Y
    162. Baba H
    163. Suzuki Y
    164. Nakayama S
    165. Masuzawa K
    166. Namba S
    167. Suzuki K
    168. Naito Y
    169. Liu Y-C
    170. Takuwa A
    171. Sugihara F
    172. Wing JB
    173. Sakakibara S
    174. Hizawa N
    175. Shiroyama T
    176. Miyawaki S
    177. Kawamura Y
    178. Nakayama A
    179. Matsuo H
    180. Maeda Y
    181. Nii T
    182. Noda Y
    183. Niitsu T
    184. Adachi Y
    185. Enomoto T
    186. Amiya S
    187. Hara R
    188. Yamaguchi Y
    189. Murakami T
    190. Kuge T
    191. Matsumoto K
    192. Yamamoto Y
    193. Yamamoto M
    194. Yoneda M
    195. Kishikawa T
    196. Yamada S
    197. Kawabata S
    198. Kijima N
    199. Takagaki M
    200. Sasa N
    201. Ueno Y
    202. Suzuki M
    203. Takemoto N
    204. Eguchi H
    205. Fukusumi T
    206. Imai T
    207. Fukushima M
    208. Kishima H
    209. Inohara H
    210. Tomono K
    211. Kato K
    212. Takahashi M
    213. Matsuda F
    214. Hirata H
    215. Takeda Y
    216. Koh H
    217. Manabe T
    218. Funatsu Y
    219. Ito F
    220. Fukui T
    221. Shinozuka K
    222. Kohashi S
    223. Miyazaki M
    224. Shoko T
    225. Kojima M
    226. Adachi T
    227. Ishikawa M
    228. Takahashi K
    229. Inoue T
    230. Hirano T
    231. Kobayashi K
    232. Takaoka H
    233. Watanabe K
    234. Miyazawa N
    235. Kimura Y
    236. Sado R
    237. Sugimoto H
    238. Kamiya A
    239. Kuwahara N
    240. Fujiwara A
    241. Matsunaga T
    242. Sato Y
    243. Okada T
    244. Hirai Y
    245. Kawashima H
    246. Narita A
    247. Niwa K
    248. Sekikawa Y
    249. Nishi K
    250. Nishitsuji M
    251. Tani M
    252. Suzuki J
    253. Nakatsumi H
    254. Ogura T
    255. Kitamura H
    256. Hagiwara E
    257. Murohashi K
    258. Okabayashi H
    259. Mochimaru T
    260. Nukaga S
    261. Satomi R
    262. Oyamada Y
    263. Mori N
    264. Baba T
    265. Fukui Y
    266. Odate M
    267. Mashimo S
    268. Makino Y
    269. Yagi K
    270. Hashiguchi M
    271. Kagyo J
    272. Shiomi T
    273. Fuke S
    274. Saito H
    275. Tsuchida T
    276. Fujitani S
    277. Takita M
    278. Morikawa D
    279. Yoshida T
    280. Izumo T
    281. Inomata M
    282. Kuse N
    283. Awano N
    284. Tone M
    285. Ito A
    286. Nakamura Y
    287. Hoshino K
    288. Maruyama J
    289. Ishikura H
    290. Takata T
    291. Odani T
    292. Amishima M
    293. Hattori T
    294. Shichinohe Y
    295. Kagaya T
    296. Kita T
    297. Ohta K
    298. Sakagami S
    299. Koshida K
    300. Hayashi K
    301. Shimizu T
    302. Kozu Y
    303. Hiranuma H
    304. Gon Y
    305. Izumi N
    306. Nagata K
    307. Ueda K
    308. Taki R
    309. Hanada S
    310. Kawamura K
    311. Ichikado K
    312. Nishiyama K
    313. Muranaka H
    314. Nakamura K
    315. Hashimoto N
    316. Wakahara K
    317. Sakamoto K
    318. Omote N
    319. Ando A
    320. Kodama N
    321. Kaneyama Y
    322. Maeda S
    323. Kuraki T
    324. Matsumoto T
    325. Yokote K
    326. Nakada T-A
    327. Abe R
    328. Oshima T
    329. Shimada T
    330. Harada M
    331. Takahashi T
    332. Ono H
    333. Sakurai T
    334. Shibusawa T
    335. Kimizuka Y
    336. Kawana A
    337. Sano T
    338. Watanabe C
    339. Suematsu R
    340. Sageshima H
    341. Yoshifuji A
    342. Ito K
    343. Takahashi S
    344. Ishioka K
    345. Nakamura M
    346. Masuda M
    347. Wakabayashi A
    348. Watanabe H
    349. Ueda S
    350. Nishikawa M
    351. Chihara Y
    352. Takeuchi M
    353. Onoi K
    354. Shinozuka J
    355. Sueyoshi A
    356. Nagasaki Y
    357. Okamoto M
    358. Ishihara S
    359. Shimo M
    360. Tokunaga Y
    361. Kusaka Y
    362. Ohba T
    363. Isogai S
    364. Ogawa A
    365. Inoue T
    366. Fukuyama S
    367. Eriguchi Y
    368. Yonekawa A
    369. Kan-O K
    370. Matsumoto K
    371. Kanaoka K
    372. Ihara S
    373. Komuta K
    374. Inoue Y
    375. Chiba S
    376. Yamagata K
    377. Hiramatsu Y
    378. Kai H
    379. Asano K
    380. Oguma T
    381. Ito Y
    382. Hashimoto S
    383. Yamasaki M
    384. Kasamatsu Y
    385. Komase Y
    386. Hida N
    387. Tsuburai T
    388. Oyama B
    389. Takada M
    390. Kanda H
    391. Kitagawa Y
    392. Fukuta T
    393. Miyake T
    394. Yoshida S
    395. Ogura S
    396. Abe S
    397. Kono Y
    398. Togashi Y
    399. Takoi H
    400. Kikuchi R
    401. Ogawa S
    402. Ogata T
    403. Ishihara S
    404. Kanehiro A
    405. Ozaki S
    406. Fuchimoto Y
    407. Wada S
    408. Fujimoto N
    409. Nishiyama K
    410. Terashima M
    411. Beppu S
    412. Yoshida K
    413. Narumoto O
    414. Nagai H
    415. Ooshima N
    416. Motegi M
    417. Umeda A
    418. Miyagawa K
    419. Shimada H
    420. Endo M
    421. Ohira Y
    422. Watanabe M
    423. Inoue S
    424. Igarashi A
    425. Sato M
    426. Sagara H
    427. Tanaka A
    428. Ohta S
    429. Kimura T
    430. Shibata Y
    431. Tanino Y
    432. Nikaido T
    433. Minemura H
    434. Sato Y
    435. Yamada Y
    436. Hashino T
    437. Shinoki M
    438. Iwagoe H
    439. Takahashi H
    440. Fujii K
    441. Kishi H
    442. Kanai M
    443. Imamura T
    444. Yamashita T
    445. Yatomi M
    446. Maeno T
    447. Hayashi S
    448. Takahashi M
    449. Kuramochi M
    450. Kamimaki I
    451. Tominaga Y
    452. Ishii T
    453. Utsugi M
    454. Ono A
    455. Tanaka T
    456. Kashiwada T
    457. Fujita K
    458. Saito Y
    459. Seike M
    460. Watanabe H
    461. Matsuse H
    462. Kodaka N
    463. Nakano C
    464. Oshio T
    465. Hirouchi T
    466. Makino S
    467. Egi M
    468. Biobank Japan Project
    469. Omae Y
    470. Nannya Y
    471. Ueno T
    472. Katayama K
    473. Ai M
    474. Fukui Y
    475. Kumanogoh A
    476. Sato T
    477. Hasegawa N
    478. Tokunaga K
    479. Ishii M
    480. Koike R
    481. Kitagawa Y
    482. Kimura A
    483. Imoto S
    484. Miyano S
    485. Ogawa S
    486. Kanai T
    487. Fukunaga K
    488. Okada Y
    (2022) DOCK2 is involved in the host genetics and biology of severe COVID-19
    Nature 609:754–760.
    https://doi.org/10.1038/s41586-022-05163-5

Article and author information

Author details

  1. Silvia Diz-de Almeida

    1. ERN-ITHACA-European Reference Network, Soria, Spain
    2. Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
    3. CIBERER, ISCIII, Madrid, Spain
    4. Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Raquel Cruz
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2813-8928
  2. Raquel Cruz

    1. ERN-ITHACA-European Reference Network, Soria, Spain
    2. Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
    3. CIBERER, ISCIII, Madrid, Spain
    4. Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Silvia Diz-de Almeida
    Competing interests
    No competing interests declared
  3. Andre D Luchessi

    Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Natal, Brazil
    Contribution
    Formal analysis, Validation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  4. José M Lorenzo-Salazar

    Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Miguel López de Heredia

    CIBERER, ISCIII, Madrid, Spain
    Contribution
    Data curation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Inés Quintela

    Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
    Contribution
    Data curation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Rafaela González-Montelongo

    Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Vivian Nogueira Silbiger

    Universidade Federal do Rio Grande do Norte, Departamento de Analises Clinicas e Toxicologicas, Natal, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9252-0278
  9. Marta Sevilla Porras

    1. CIBERER, ISCIII, Madrid, Spain
    2. Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Jair Antonio Tenorio Castaño

    1. ERN-ITHACA-European Reference Network, Soria, Spain
    2. CIBERER, ISCIII, Madrid, Spain
    3. Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Julian Nevado

    1. ERN-ITHACA-European Reference Network, Soria, Spain
    2. CIBERER, ISCIII, Madrid, Spain
    3. Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Jose María Aguado

    1. Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain
    2. Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, Madrid, Spain
    3. CIBERINFEC, ISCIII, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Carlos Aguilar

    Hospital General Santa Bárbara de Soria, Soria, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Sergio Aguilera-Albesa

    1. Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain
    2. Navarra Health Service, NavarraBioMed Research Group, Pamplona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Virginia Almadana

    Hospital Universitario Virgen Macarena, Neumología, Sevilla, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Berta Almoguera

    1. CIBERER, ISCIII, Madrid, Spain
    2. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Nuria Alvarez

    Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Álvaro Andreu-Bernabeu

    1. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
    2. School of Medicine, Universidad Complutense, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Eunate Arana-Arri

    1. Biocruces Bizkai HRI, Bizkaia, Spain
    2. Cruces University Hospital, Osakidetza, Bizkaia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  20. Celso Arango

    1. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
    2. School of Medicine, Universidad Complutense, Madrid, Spain
    3. Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  21. María J Arranz

    Fundació Docència I Recerca Mutua Terrassa, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  22. Maria-Jesus Artiga

    Spanish National Cancer Research Centre, CNIO Biobank, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  23. Raúl C Baptista-Rosas

    1. Hospital General de Occidente, Zapopan Jalisco, Mexico
    2. Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá Jalisco, Mexico
    3. Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Tonalá Jalisco, Mexico
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0273-4740
  24. María Barreda- Sánchez

    1. Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
    2. Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  25. Moncef Belhassen-Garcia

    Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, Salamanca, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  26. Joao F Bezerra

    Escola Tecnica de Saúde, Laboratorio de Vigilancia Molecular Aplicada, Brasilia, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  27. Marcos AC Bezerra

    Federal University of Pernambuco, Genetics Postgraduate Program, Recife, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  28. Lucía Boix-Palop

    Hospital Universitario Mutua Terrassa, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  29. María Brion

    1. Instituto de Investigación Sanitaria de Santiago (IDIS), Xenética Cardiovascular, Santiago de Compostela, Spain
    2. CIBERCV, ISCIII, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  30. Ramón Brugada

    1. CIBERCV, ISCIII, Madrid, Spain
    2. Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), Girona, Spain
    3. Medical Science Department, School of Medicine, University of Girona, Girona, Spain
    4. Hospital Josep Trueta, Cardiology Service, Girona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  31. Matilde Bustos

    Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, Seville, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  32. Enrique J Calderón

    1. Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC)- University of Seville- Virgen del Rocio University Hospital, Seville, Spain
    2. Departamento de Medicina, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
    3. CIBERESP, ISCIII, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  33. Cristina Carbonell

    1. Hospital Universitario de Salamanca-IBSAL, Servicio de Medicina Interna, Salamanca, Spain
    2. Universidad de Salamanca, Salamanca, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  34. Luis Castano

    1. CIBERER, ISCIII, Madrid, Spain
    2. Biocruces Bizkai HRI, Bizkaia, Spain
    3. Osakidetza, Cruces University Hospital, Bizkaia, Spain
    4. Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
    5. University of Pais Vasco, UPV/EHU, Bizkaia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  35. Jose E Castelao

    Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  36. Rosa Conde-Vicente

    Hospital Universitario Río Hortega, Valladolid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  37. M Lourdes Cordero-Lorenzana

    Servicio de Medicina intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  38. Jose L Cortes-Sanchez

    1. Tecnológico de Monterrey, Monterrey, Mexico
    2. Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, Magdeburg, Germany
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  39. Marta Corton

    1. CIBERER, ISCIII, Madrid, Spain
    2. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  40. M Teresa Darnaude

    Hospital Universitario Mostoles, Unidad de Genética, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  41. Alba De Martino-Rodríguez

    1. Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
    2. Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2436-9852
  42. Victor del Campo-Pérez

    Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  43. Aranzazu Diaz de Bustamante

    Hospital Universitario Mostoles, Unidad de Genética, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  44. Elena Domínguez-Garrido

    Unidad Diagnóstico Molecular, Fundación Rioja Salud, La Rioja, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  45. Rocío Eirós

    Hospital Universitario de Salamanca-IBSAL, Servicio de Cardiología, Salamanca, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  46. María Carmen Fariñas

    1. IDIVAL, Cantabria, Spain
    2. Hospital U M Valdecilla, Cantabria, Spain
    3. Universidad de Cantabria, Cantabria, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  47. María J Fernandez-Nestosa

    Universidad Nacional de Asunción, Facultad de Politécnica, Paraguay, United States
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7764-7791
  48. Uxía Fernández-Robelo

    Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  49. Amanda Fernández-Rodríguez

    1. CIBERINFEC, ISCIII, Madrid, Spain
    2. Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  50. Tania Fernández-Villa

    1. CIBERESP, ISCIII, Madrid, Spain
    2. Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  51. Manuela Gago-Dominguez

    1. Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
    2. IDIS, Seongnam, Republic of Korea
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  52. Belén Gil-Fournier

    Hospital Universitario de Getafe, Servicio de Genética, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  53. Javier Gómez-Arrue

    1. Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
    2. Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  54. Beatriz González Álvarez

    1. Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain
    2. Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  55. Fernan Gonzalez Bernaldo de Quirós

    Ministerio de Salud Ciudad de Buenos Aires, Buenos Aires, Argentina
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  56. Anna González-Neira

    Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  57. Javier González-Peñas

    1. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
    2. School of Medicine, Universidad Complutense, Madrid, Spain
    3. Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  58. Juan F Gutiérrez-Bautista

    Hospital Universitario Virgen de las Nieves, Servicio de Análisis Clínicos e Inmunología, Granada, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  59. María José Herrero

    1. IIS La Fe, Plataforma de Farmacogenética, Valencia, Spain
    2. Universidad de Valencia, Departamento de Farmacología, Valencia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  60. Antonio Herrero-Gonzalez

    Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  61. María A Jimenez-Sousa

    1. CIBERINFEC, ISCIII, Madrid, Spain
    2. Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  62. María Claudia Lattig

    1. Universidad de los Andes, Facultad de Ciencias, Bogotá, Colombia
    2. SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá, Colombia
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2113-9266
  63. Anabel Liger Borja

    Hospital General de Segovia, Medicina Intensiva, Segovia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  64. Rosario Lopez-Rodriguez

    1. CIBERER, ISCIII, Madrid, Spain
    2. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    3. Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  65. Esther Mancebo

    1. Hospital Universitario 12 de Octubre, Department of Immunology, Madrid, Spain
    2. Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  66. Caridad Martín-López

    Hospital General de Segovia, Medicina Intensiva, Segovia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  67. Vicente Martín

    1. CIBERESP, ISCIII, Madrid, Spain
    2. Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS) - Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  68. Oscar Martinez-Nieto

    1. SIGEN Alianza Universidad de los Andes - Fundación Santa Fe de Bogotá, Bogotá, Colombia
    2. Fundación Santa Fe de Bogota, Departamento Patologia y Laboratorios, Bogotá, Colombia
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  69. Iciar Martinez-Lopez

    1. Unidad de Genética y Genómica Islas Baleares, Islas Baleares, Spain
    2. Hospital Universitario Son Espases, Unidad de Diagnóstico Molecular y Genética Clínica, Islas Baleares, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  70. Michel F Martinez-Resendez

    Tecnológico de Monterrey, Monterrey, Mexico
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  71. Angel Martinez-Perez

    Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5934-1454
  72. Juliana F Mazzeu

    1. Universidade de Brasília, Faculdade de Medicina, Brasília, Brazil
    2. Programa de Pós-Graduação em Ciências Médicas (UnB), Brasília, Brazil
    3. Programa de Pós-Graduação em Ciencias da Saude (UnB), Brazila, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  73. Eleuterio Merayo Macías

    Hospital El Bierzo, Unidad Cuidados Intensivos, León, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  74. Pablo Minguez

    1. CIBERER, ISCIII, Madrid, Spain
    2. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  75. Victor Moreno Cuerda

    1. Hospital Universitario Mostoles, Medicina Interna, Madrid, Spai, Spain
    2. Universidad Francisco de Vitoria, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  76. Silviene F Oliveira

    1. Programa de Pós-Graduação em Ciencias da Saude (UnB), Brazila, Brazil
    2. Departamento de Genética e Morfologia, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, Brazil
    3. Programa de Pós-Graduação em Biologia Animal (UnB), Brasília, Brazil
    4. Programa de Pós-Graduação Profissional em Ensino de Biologia (UnB), Brasília, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7741-0257
  77. Eva Ortega-Paino

    Spanish National Cancer Research Centre, CNIO Biobank, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  78. Mara Parellada

    1. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain
    2. School of Medicine, Universidad Complutense, Madrid, Spain
    3. Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  79. Estela Paz-Artal

    1. Hospital Universitario 12 de Octubre, Department of Immunology, Madrid, Spain
    2. Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Transplant Immunology and Immunodeficiencies Group, Madrid, Spain
    3. Universidad Complutense de Madrid, Department of Immunology, Ophthalmology and ENT, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  80. Ney PC Santos

    Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  81. Patricia Pérez-Matute

    Infectious Diseases, Microbiota and Metabolism Unit, CSIC Associated Unit, Center for Biomedical Research of La Rioja (CIBIR), Logroño, Spain
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  82. Patricia Perez

    Inditex, A Coruña, Corunna, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  83. M Elena Pérez-Tomás

    Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5429-3414
  84. Teresa Perucho

    GENYCA, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  85. Mellina Pinsach-Abuin

    1. CIBERCV, ISCIII, Madrid, Spain
    2. Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), Girona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  86. Guillermo Pita

    Spanish National Cancer Research Centre, Human Genotyping-CEGEN Unit, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  87. Ericka N Pompa-Mera

    1. Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Mexico City, Mexico
    2. Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional La Raza, Hospital de Infectología, Mexico City, Mexico
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  88. Gloria L Porras-Hurtado

    Clinica Comfamiliar Risaralda, Pereira, Colombia
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  89. Aurora Pujol

    1. CIBERER, ISCIII, Madrid, Spain
    2. Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, L’Hospitalet de Llobregat, Barcelona, Spain
    3. Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9606-0600
  90. Soraya Ramiro León

    Hospital Universitario de Getafe, Servicio de Genética, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  91. Salvador Resino

    1. CIBERINFEC, ISCIII, Madrid, Spain
    2. Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  92. Marianne R Fernandes

    1. Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Belém, Brazil
    2. Hospital Ophir Loyola, Departamento de Ensino e Pesquisa, Belém, Brazil
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  93. Emilio Rodríguez-Ruiz

    1. IDIS, Seongnam, Republic of Korea
    2. Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  94. Fernando Rodriguez-Artalejo

    1. CIBERESP, ISCIII, Madrid, Spain
    2. Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
    3. IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Madrid, Spain
    4. IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  95. José A Rodriguez-Garcia

    Complejo Asistencial Universitario de León, León, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  96. Francisco Ruiz-Cabello

    1. IDIS, Seongnam, Republic of Korea
    2. Instituto de Investigación Biosanitaria de Granada (ibs GRANADA), Granada, Spain
    3. Universidad de Granada, Departamento Bioquímica, Biología Molecular e Inmunología III, Granada, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  97. Javier Ruiz-Hornillos

    1. Hospital Infanta Elena, Allergy Unit, Valdemoro, Madrid, Spain
    2. Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    3. Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  98. Pablo Ryan

    1. CIBERINFEC, ISCIII, Madrid, Spain
    2. Hospital Universitario Infanta Leonor, Madrid, Spain
    3. Complutense University of Madrid, Madrid, Spain
    4. Gregorio Marañón Health Research Institute (IiSGM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4212-7419
  99. José Manuel Soria

    Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  100. Juan Carlos Souto

    Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  101. Eduardo Tamayo

    1. Hospital Clinico Universitario de Valladolid, Servicio de Anestesiologia y Reanimación, Valladolid, Spain
    2. Universidad de Valladolid, Departamento de Cirugía, Valladolid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  102. Alvaro Tamayo-Velasco

    Hospital Clinico Universitario de Valladolid, Servicio de Hematologia y Hemoterapia, Valladolid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  103. Juan Carlos Taracido-Fernandez

    Data Analysis Department, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  104. Alejandro Teper

    Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  105. Lilian Torres-Tobar

    Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  106. Miguel Urioste

    Spanish National Cancer Research Centre, Familial Cancer Clinical Unit, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  107. Juan Valencia-Ramos

    University Hospital of Burgos, Burgos, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  108. Zuleima Yáñez

    Universidad Simón Bolívar, Facultad de Ciencias de la Salud, Barranquilla, Colombia
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  109. Ruth Zarate

    Centro para el Desarrollo de la Investigación Científica, Asunción, Paraguay
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  110. Itziar de Rojas

    1. Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
    2. Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  111. Agustín Ruiz

    1. Centre for Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
    2. Research Center and Memory clinic, ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  112. Pascual Sánchez

    CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  113. Luis Miguel Real

    Hospital Universitario de Valme, Unidad Clínica de Enfermedades Infecciosas y Microbiología, Sevilla, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4932-7429
  114. SCOURGE Cohort Group

    Contribution
    Data curation, Methodology, Resources
    Competing interests
    No competing interests declared
  115. Encarna Guillen-Navarro

    1. Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain
    2. Sección Genética Médica - Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, Murcia, Spain
    3. Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), Murcia, Spain
    4. Grupo Clínico Vinculado, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  116. Carmen Ayuso

    1. CIBERER, ISCIII, Madrid, Spain
    2. Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital - Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  117. Esteban Parra

    Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2057-8577
  118. José A Riancho

    1. CIBERER, ISCIII, Madrid, Spain
    2. IDIVAL, Cantabria, Spain
    3. Hospital U M Valdecilla, Cantabria, Spain
    4. Universidad de Cantabria, Cantabria, Spain
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0691-8755
  119. Augusto Rojas-Martinez

    Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3765-6778
  120. Carlos Flores

    1. Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
    2. Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Instituto de Investigación Sanitaria de Canarias, Santa Cruz de Tenerife, Spain
    3. Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
    4. Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    cflores@ull.edu.es
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5352-069X
  121. Pablo Lapunzina

    1. ERN-ITHACA-European Reference Network, Soria, Spain
    2. CIBERER, ISCIII, Madrid, Spain
    3. Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz IDIPAZ, Madrid, Spain
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  122. Ángel Carracedo

    1. CIBERER, ISCIII, Madrid, Spain
    2. Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
    3. Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain
    4. IDIS, Seongnam, Republic of Korea
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    angel.carracedo@usc.es
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1085-8986

Funding

Instituto de Salud Carlos III (COV20_00622)

  • Ángel Carracedo

Instituto de Salud Carlos III (COV20/00792)

  • Matilde Bustos

Instituto de Salud Carlos III (COV20_00181)

  • Carmen Ayuso

Instituto de Salud Carlos III (COV20_1144)

  • Amanda Fernández-Rodríguez
  • María A Jimenez-Sousa

Instituto de Salud Carlos III (PI20/00876)

  • Carlos Flores

European Regional Development Fund (A way of making Europe)

  • Ángel Carracedo

Fundación Amancio Ortega

  • Ángel Carracedo

Banco Santander

  • Ángel Carracedo

Estrella de Levante S.A.

  • Encarna Guillen-Navarro

Colabora Mujer Association

  • Encarna Guillen-Navarro

Obra Social La Caixa

  • Raúl C Baptista-Rosas

Agencia Estatal de Investigación (RTC-2017-6471-1)

  • Carlos Flores

Cabildo Insular de Tenerife (CGIEU0000219140)

  • Carlos Flores

Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57)

  • Carlos Flores

Xunta de Galicia

  • Silvia Diz-de Almeida

Axencia GAIN

  • Silvia Diz-de Almeida

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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. The 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. Instituto de Salud Carlos III (COV20_00622 to AC, COV20/00792 to MB, COV20_00181 to CA, COV20_1144 to MAJS and AFR, PI20/00876 to CF); European Union (ERDF) ‘A way of making Europe’. Fundación Amancio Ortega, Banco de Santander (to AC), Estrella de Levante SA and Colabora Mujer Association (to EGN) and Obra Social La Caixa (to RB); Agencia Estatal de Investigación (RTC-2017-6471-1 to CF), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to CF) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to CF). SD-DA was supported by a Xunta de Galicia predoctoral fellowship.

Ethics

Human subjects: Ethics and Scientific Committees from the participating centres and by the Galician Ethics Committee Ref 2020/197 gave ethical approval for this work. Recruitment of patients from IMSS (in Mexico City), was approved by of the National Committee of Clinical Research, Instituto Mexicano del Seguro Social, Mexico (protocol R-2020-785-082).

Version history

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You can cite all versions using the DOI https://doi.org/10.7554/eLife.93666. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2024, Diz-de Almeida, Cruz et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Silvia Diz-de Almeida
  2. Raquel Cruz
  3. Andre D Luchessi
  4. José M Lorenzo-Salazar
  5. Miguel López de Heredia
  6. Inés Quintela
  7. Rafaela González-Montelongo
  8. Vivian Nogueira Silbiger
  9. Marta Sevilla Porras
  10. Jair Antonio Tenorio Castaño
  11. Julian Nevado
  12. Jose María Aguado
  13. Carlos Aguilar
  14. Sergio Aguilera-Albesa
  15. Virginia Almadana
  16. Berta Almoguera
  17. Nuria Alvarez
  18. Álvaro Andreu-Bernabeu
  19. Eunate Arana-Arri
  20. Celso Arango
  21. María J Arranz
  22. Maria-Jesus Artiga
  23. Raúl C Baptista-Rosas
  24. María Barreda- Sánchez
  25. Moncef Belhassen-Garcia
  26. Joao F Bezerra
  27. Marcos AC Bezerra
  28. Lucía Boix-Palop
  29. María Brion
  30. Ramón Brugada
  31. Matilde Bustos
  32. Enrique J Calderón
  33. Cristina Carbonell
  34. Luis Castano
  35. Jose E Castelao
  36. Rosa Conde-Vicente
  37. M Lourdes Cordero-Lorenzana
  38. Jose L Cortes-Sanchez
  39. Marta Corton
  40. M Teresa Darnaude
  41. Alba De Martino-Rodríguez
  42. Victor del Campo-Pérez
  43. Aranzazu Diaz de Bustamante
  44. Elena Domínguez-Garrido
  45. Rocío Eirós
  46. María Carmen Fariñas
  47. María J Fernandez-Nestosa
  48. Uxía Fernández-Robelo
  49. Amanda Fernández-Rodríguez
  50. Tania Fernández-Villa
  51. Manuela Gago-Dominguez
  52. Belén Gil-Fournier
  53. Javier Gómez-Arrue
  54. Beatriz González Álvarez
  55. Fernan Gonzalez Bernaldo de Quirós
  56. Anna González-Neira
  57. Javier González-Peñas
  58. Juan F Gutiérrez-Bautista
  59. María José Herrero
  60. Antonio Herrero-Gonzalez
  61. María A Jimenez-Sousa
  62. María Claudia Lattig
  63. Anabel Liger Borja
  64. Rosario Lopez-Rodriguez
  65. Esther Mancebo
  66. Caridad Martín-López
  67. Vicente Martín
  68. Oscar Martinez-Nieto
  69. Iciar Martinez-Lopez
  70. Michel F Martinez-Resendez
  71. Angel Martinez-Perez
  72. Juliana F Mazzeu
  73. Eleuterio Merayo Macías
  74. Pablo Minguez
  75. Victor Moreno Cuerda
  76. Silviene F Oliveira
  77. Eva Ortega-Paino
  78. Mara Parellada
  79. Estela Paz-Artal
  80. Ney PC Santos
  81. Patricia Pérez-Matute
  82. Patricia Perez
  83. M Elena Pérez-Tomás
  84. Teresa Perucho
  85. Mellina Pinsach-Abuin
  86. Guillermo Pita
  87. Ericka N Pompa-Mera
  88. Gloria L Porras-Hurtado
  89. Aurora Pujol
  90. Soraya Ramiro León
  91. Salvador Resino
  92. Marianne R Fernandes
  93. Emilio Rodríguez-Ruiz
  94. Fernando Rodriguez-Artalejo
  95. José A Rodriguez-Garcia
  96. Francisco Ruiz-Cabello
  97. Javier Ruiz-Hornillos
  98. Pablo Ryan
  99. José Manuel Soria
  100. Juan Carlos Souto
  101. Eduardo Tamayo
  102. Alvaro Tamayo-Velasco
  103. Juan Carlos Taracido-Fernandez
  104. Alejandro Teper
  105. Lilian Torres-Tobar
  106. Miguel Urioste
  107. Juan Valencia-Ramos
  108. Zuleima Yáñez
  109. Ruth Zarate
  110. Itziar de Rojas
  111. Agustín Ruiz
  112. Pascual Sánchez
  113. Luis Miguel Real
  114. SCOURGE Cohort Group
  115. Encarna Guillen-Navarro
  116. Carmen Ayuso
  117. Esteban Parra
  118. José A Riancho
  119. Augusto Rojas-Martinez
  120. Carlos Flores
  121. Pablo Lapunzina
  122. Ángel Carracedo
(2024)
Novel risk loci for COVID-19 hospitalization among admixed American populations
eLife 13:RP93666.
https://doi.org/10.7554/eLife.93666.3

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https://doi.org/10.7554/eLife.93666

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