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Additional file 1. Supplementary methods

Additional file 3: Fig. S1. UMP projections of cells in PBMCs from normal controls, mild, moderate, and severe COVID-19 patients by using the Seurat R package (dataset #1). Fig. S2. Heatmap showing levels of well-known marker genes specific for each cell type in PBMCs. Fig. S3. Single-cell transcriptomes of PBMCs from normal controls, mild, moderate, and severe COVID-19 patients. Fig. S4. Hierarchical clustering using the PCC of a normalized transcriptome between controls and patients in cell type resolution. Fig. S5. Boxplots showing percentages of each cell type for PBMCs in donors from healthy control and COVID-19 patients. Fig. S6. scCODA determines the compositional differences of each cell type in PBMCs among donors from healthy control and COVID-19 patients. Fig. S7. Regional association plots for severe COVID-19-associated genetic loci based on meta-GWAS summary data. Fig. S8. Regional association plots for severe COVID-19-associated genetic loci based on meta-GWAS summary data. Fig. S9. Circus plot showing the results of MAGMA-based gene-level association analysis. Fig. S10. The 19 biological pathways enriched from the MAGMA-based pathway enrichment analysis. Fig. S11. High consistence results between MAGMA and S-MultiXcan analysis. Fig. S12. In silico permutation analysis of 100,000 times of random selections. Fig. S13. Multiple independent approaches identify genetics-relevant risk genes associated with severe COVID-19. Fig. S14. Plot of gene-drug interaction analysis for 34 risk genes. Fig. S15. The 10 biological pathways significantly enriched by 34 risk genes based on the KEGG database. Fig. S16. Barplots showing the results of RolyPoly among COVID-19 patients stratified by patient���s age. Fig. S17. Barplots showing the results of RolyPoly among COVID-19 patients stratified by patient���s sex. Fig. S18. Barplots showing the results of RolyPoly among COVID-19 patients stratified by patient���s BMI. Fig. S19. Barplots showing the results of RolyPoly COVID-19 patients stratified by patient���s smoking status. Fig. S20. Cell-ID-based enrichment in GWAS-identified gene signatures (34 genes) of CD16+monocytes and memory CD8+ T cells using scRNA-seq dataset. Fig. S21. Genetics-risk genes influenced three immune cell subsets for severe COVID-19. Fig. S22. Dot plot showing the expressed percent of three risk genes of CXCR6, CCR1, and ABO in each peripheral cell type in PBMCs among severe patients based on two scRNA-seq dataset of #2 and #3. Fig. S23. CCR1+ CD16+monocytes showing higher risk to cytokine storms among COVID-19 patients. Fig. S24. Boxplots showing the difference of inflammatory cytokine score and pathway activation score of CD16+ monocytes among normal controls and COVID-19 groups. Fig. S25. ABO+ megakaryocytes contribute higher risk to cytokine storms among severe COVID-19 patients. Fig. S26. Boxplots showing the difference of inflammatory cytokine score and pathway activation score of and megakaryocytes among normal controls and COVID-19 groups. Fig. S27. Evidence showing the multi-functionality of CXCR6+ memory CD8+T cells for severe COVID-19. Fig. S28. Boxplots showing the different score of several immunological features between CXCR6+ and CXCR6- memory CD8+T cells among normal controls and COVID-19 patients. Fig. S29. Cell-ID-based enrichment in functional terms of CD16+monocytes, megakaryocyte, and memory CD8+ T cells using scRNA-seq dataset. Fig. S30. Differences in the number of predicated cell-to-cell interactions in PBMCs. Fig. S31. Predicted cellular interaction of both CCR1+ CD16+ monocytes and CXCR6+ memory CD8+T cells with other immune cells in PBMCs. Fig. S32. Prediction of cell-to-cell interactions of cells in BALFs.

Additional file 2: Table S1. Samples collected from four independent scRNA-seq datasets on COVID-19. Table S2. Selected well-known markers used to define cell types in PBMCs. Table S3. Significant SNPs associated with severe COVID-19 identified by meta-GWAS analysis. Table S4. Replication of these identified loci by using samples with very severe respiratory confirmed COVID-19. Table S5. Significant genes associated with severe COVID-19 identified by MAGMA gene-based association analysis. Table S6. Significant enriched pathways associated with severe COVID-19 identified from MAGMA-based pathway enrichment analysis. Table S7. The 16 significant genes associated with severe COVID-19 identified by S-MultiXcan analysis based on 49 tissues from GTEx consortium. Table S8. The eight significant genes associated with severe COVID-19 identified by S-PrediXcan analysis based on lung and blood tissues. Table S9. The biological pathways enriched by 34 risk genes associated with severe COVID-19. Table S10. The percentage of three severe COVID-19-risk genes expressed in all 13 distinct cell types in PBMCs. Table S11. Summary of inflammatory and cytokine-related genes and genes in two identified KEGG pathways. Table S12. Highly-expressed inflammatory and cytokine genes among CCR1+ CD16+monocytes. Table S13. Pathway enrichment analysis of 351 highly-expressed genes among CCR1+ CD16+monocytes. Table S14. Druggble proteins collected from the ChEMBL database. Table S15. Functional enrichment analysis of 190 up-DEGs associated with severe COVID-19 based on the Reactome database. Table S16. Disease-based enrichment analysis of 190 up-DEGs associated with severe COVID-19 among CCR1+ CD16+monocytes based on the GLAD4U database. Table S17. 190 up-DEGs associated with severe COVID-19 among CCR1+ CD16+monocytes matched in druggable gene categories based on the DGIdb resource. Table S18. Highly-expressed inflammatory and cytokine genes among ABO+ megakaryocytes. Table S19. Pathway enrichment analysis of 424 highly-expressed genes among ABO+ megakaryocytes. Table S20. Disease-term enrichment analysis of 35 up-DEGs associated with severe COVID-19 among ABO+ megakaryocytes based on the GLAD4U database. Table S21. 35 up-DEGs significantly associated with severe COVID-19 among ABO+ megakaryocytes matched in druggable gene categories. Table S22. Pathway enrichment analysis of 158 highly-expressed genes among CXCR6+ memory CD8+T cells. Table S23. GO-terms enrichment analysis of 108 up-DEGs associated with COVID-19 among CXCR6+ memory CD8+T cells.