| Population name | COVID-19-HGI |
| Genome | GRCh38 |
| Consortium | COVID-19 Host Genetics Initiative |
| Super population | MID,S/EAS,AFR,AMR,EU |
| Population description | (1) Critically ill COVID-19 cases defined as patients who were hospitalized due to symptoms associated with laboratory-confirmed SARS-CoV-2 infection and who required respiratory support or whose cause of death was associated with COVID-19, (2) the hospitalized COVID-19 group included patients who were hospitalized due to symptoms associated with laboratory-confirmed SARS-CoV-2 infection, and (3) reported infection cases group included individuals with laboratory-confirmed SARS-CoV-2 infection o |
| Population origin | Study dependent |
| Case population size | 13641 |
| Control population size | 2070709 |
| Comorbidities | not specified-study dependent |
| Mean / median age | 55.3 years mean |
| Sex | not specified-study dependent |
| Severity | Severe |
| Sample source | Nasopharyngeal swab / Whole blood |
| Method | Case-control meta-analyses in three main categories of COVID-19 disease according to predefined and partially overlapping phenotypic criteria. Each individual study that contributed data to a particular analysis met a minimum threshold of 50 cases for statistical robustness. |
| Bioinformatics | Each contributing study genotyped the samples and performed quality controls, data imputation and analysis independently, but following consortium recommendations. GWAS analysis was run using Scalable and Accurate Implementation of GEneralized mixed model (SAIGE) 51 on chromosomes 1-22 and X or PLINK. Study-specific summary statistics were then processed for meta-analysis. Potential false positives, inflation, and deflation were examined for each submitted GWAS. Standard error values as a function of effective sample size was used to find studies which deviated from the expected trend. Summary statistics passing this manual quality control were included in the meta-analysis. Variants with allele frequency of >0.1% and imputation INFO>0.6 were carried forward from each study. Variants and alleles were lifted over to genome build GRCh38, if needed, and harmonized to gnomAD 3.0 genomes by finding matching variants by strand flipping or switching ordering of alleles. If multiple matching v |
| Imputation details | For genotype imputation, participants suggested to use own reference panel, existing imputation panels or use the TopMed imputation server or the Michigan imputation server when possible. |
| Limitations | Due to the participation of different studies those enriched with severe cases or studies with antibody-tested controls may disproportionately contribute to genetic discovery despite potentially smaller sample sizes. The differences in genomic profiling technology, imputation, and sample size across the constituent studies can have dramatic impacts on replication and downstream analyses (particularly fine-mapping where differential missing patterns in the reported results can muddy the signal). |