| Population name | Zhu |
| Genome | GRCh38 |
| Consortium | Universities, Hospitals and Institutes in China |
| Super population | EAS |
| Population description | COVID-19 respiratory disease and hospitalization cases (Wuhan Union Hospital) between January 15 and April 4, 2020 |
| Population origin | Chinese |
| Case population size | 466 |
| Control population size | 0 |
| Comorbidities | 288 individuals with at least one comorbidity including hypertension (N = 180, 38.63%), diabetes (N = 95, 20.38%), and coronary heart disease (N = 63, 13.52%) |
| Mean / median age | 23-97 years (20–39 (8.5%), 40–59 (31.1%), 60–79 (51.1%), and 80–99 (9.2%)) |
| Sex | 237 female; 229 male |
| Severity | Severe |
| Sample source | TBD |
| Method | Samples sequenced for genotyping using the DNBSEQ platform at a mean sequencing depth of 17.8x. GWAS aanlysis was performed for all laboratory traits to discover significant associations. The COVID-19 HGI round 5 meta-analysis results were used to study susceptibility and severity. One and two sample Mendelian randomization analyses was performed to determine causal effects between laboratory traits and diseases status. Gene set enrichment analysis was also performed. |
| Bioinformatics | For genotyping, samples with a call rate of <0.99, closely related individuals identified by identity-by-descent (IBD >0.1) calculated in KING, and (iii) outliers identified by principal component analysis based on three-sigma rules were excluded from further analysis. Standard quality control criteria for genetic variants was applied by removing those with a SNP call rate <0.99, minor allele frequency (MAF) < 0.01, and Hardy-Weinberg equilibrium p value < 1E-06. PLINK v2.0 was used to perform single-variant GWAS analyses using a linear regression model for the quantitative laboratory features under the assumption of additive allelic effects of the SNP dosage. Age, sex, and the top six principal components (PCs) of genetic ancestry were normalized and the resulting residuals applied using a Z-score normal transformation. The number of PCs was chosen by using EIGENSTRAT software. A genome-wide significance threshold of 5E-08 and a study-wide significance threshold of 6.41E-10 (=5E-08/78 |
| Imputation details | Imputation was performed with Beagle v4.0 taking GL as input in EAS population of 1,000 Genomes Project (1KGP) as reference panel. |
| Limitations | 1. Small sample size numbers and small genetic effect sizes resulted in no genome-wide signals associated with severe status, 2. Only one valid SNPs were associated with two genetic traits that cause disease eventhough these traits are known to ne polygeneic, 3. The genetic mechanisms that mediate COVID-19 traits require deeper investigation and were only briefly explored in this study, and 4. Further study into transcriptome and proteome- wide association should be included to uncover functiona |