Additional file 1 of Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Creators: name: Aguirre-Gamboa, Raúl
name: de Klein, Niek
name: di Tommaso, Jennifer
name: Claringbould, Annique
name: van der Wijst, Monique GP
name: de Vries, Dylan
name: Brugge, Harm
name: Oelen, Roy
name: Võsa, Urmo
name: Zorro, Maria M.
name: Chu, Xiaojin
name: Bakker, Olivier B.
name: Borek, Zuzanna
name: Ricaño-Ponce, Isis
name: Deelen, Patrick
name: Xu, Cheng-Jiang
name: Swertz, Morris
name: Jonkers, Iris
name: Withoff, Sebo
name: Joosten, Irma
name: Sanna, Serena
name: Kumar, Vinod
name: Koenen, Hans J. P. M.
name: Joosten, Leo A. B.
name: Netea, Mihai G.
name: Wijmenga, Cisca
name: Franke, Lude
name: Li, Yang
Publisher: name: figshare
Year: 2020
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1 : Supplementary Figure 1: Prediction performance of Decon-cell within 500FG: The Y-axis represents the 73 immune cell types quantified by FACS in the 500FG cohort. The bar plot on the left panel shows the mean Prediction Performance (Spearman correlation coefficient between predicted and measured cells across 100-fold cross validations). On the right panel, box plots represent the distribution of the Prediction Performance within 100 iterations of the cross validations. A cutoff of mean Prediction Performance ≥0.5 was applied to define predictable cell types (green). Supplementary Figure 2. Signature genes selected for prediction of cell proportions by Decon-cell: (A) Total number of marker genes (genes selected in ≥80% of all models in the 100 iterations) per predictable cell type. Different colors indicate different subpopulations. (B) The number of genes significantly correlated with cell counts (Spearman correlation, adjusted P ≤ 0.05) (y-axis) shows the total number of significantly correlated genes, while the x-axis shows the prediction performance (x-axis). (C) Distributions of the total number of “strongly” correlated genes (absolute Spearman correlation ≥0.3) between predictable and unpredictable cell subpopulations. Supplementary Figure 3. Comparison of prediction performance between Decon-cell and other existing methods. (A) Performance of Decon-cell: the measured (x axis) and predicted cell proportions (y-axis) were compared for neutrophils (given by granulocytes in 500FG), lymphocytes and monocytes CD14+ and granulocytes in three independent cohorts (shown by row, from top to bottom: LLDeep (n = 627); LLS (n = 660); RS (n = 773)). (B) Comparison of prediction performance for Decon-cell, CIBERSORT and xCell in three independent cohorts for a total of 4 major immune subpopulations. Supplementary Figure 4. Prediction performance of xCell and CIBERSORT in three independent Dutch populations (LLDeep, n = 627; LLS, n = 660; RS, n = 773). (A) Scatter plots showing the measured cell proportions of circulating immune cells on the x-axis and the xCell enrichment score on the y-axis. (B) Scatter plots showing the measured cell proportions of circulating immune cells on the x-axis and the predicted cell proportions given by CIBERSORT) on the y-axis. Supplementary Figure 5. Expression of marker genes selected by Decon-cell. Expression levels (scaled, log2(TPM + 1)) of signature genes in the data in three purified cell subpopulations: CD4+ T cells (A), neutrophils/granulocytes (B) and monocytes (C) in the data from BLUEPRINT. Cell subpopulations are indicated in different colors by columns. Correlation of each of the signature genes and the cell subpopulation percentage in the 500FG cohort is shown on by the green bar at the left-hand side of heatmap figure, i.e. darker green corresponds to higher correlations. Supplementary Figure 6. Many of the CTi eQTL are cell type exclusive. Colored bar plot on the left shows the total number of significant CTi eQTLs in whole blood eQTLs (as also shown in Fig. 2a). Gray bar plot shows the total number of eQTLs shared across the possible combinations of the six cell subpopulations under study. Supplementary Figure 7. Variation of gene expression across samples for deconvoluted cell-type eQTLs genes in whole blood. Granulocyte eQTL genes show significantly higher variance across the BIOS samples (F test p-value ≤0.05) compared to those from monocytes, CD4+ T cells, CD8+ T cells, B cells and NK cells. Supplementary Figure 8. Validation of CTi eQTLs using effect sizes of eQTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) from the BIOS cohort show a significantly bigger effect size in purified cell eQTLs [9] from their relevant cell subtype as compared to other whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subpopulation because the differences in effect sizes are non-significant in all but one case (CD4+ T cell eQTL genes in monocyte-derived eQTLs). Supplementary Figure 9. Validation of CTi eQTLs using effect sizes of K27AC QTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) show a significantly bigger effect size for K27AC QTLs that have peaks located in the promoter region of the eGenes from their relevant cell subtype compared to the rest of the significant whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subtype because the differences in effect sizes are non-significant in all but the comparisons across Neutrophils and Monocytes (CD14+). Supplementary Figure 10. Validation of CTi eQTLs using effect sizes of K4ME1 QTLs from purified cells. CTi eQTLs (FDR ≤ 0.05) show a significantly bigger effect size for K4ME1 QTLs (where the eGenes is the closest gene tagging the K4ME1 QTLs peak) from their relevant cell subtype compared to the rest of the significant whole blood eQTLs (diagonal boxed comparisons). The off-diagonal comparisons show that these eQTL genes are specific to a cell subtype because the differences in effect sizes are non-significant in all but the comparisons between neutrophils and monocytes (CD14+). Supplementary Figure 11. Validation of CTi eQTLs using allelic concordance with eQTLs results from purified cells. CTi eQTLs (FDR ≤ 0.05) show high allelic concordance with their respective purified cell eQTLs. Top row shows allelic concordance of deconvoluted granulocyte eQTLs (all in green) against neutrophils, monocytes and CD4+ T cells. Second row shows deconvoluted monocyte eQTLs against purified cell eQTLs in the same order as the top row. Bottom row shows the same comparisons as for deconvoluted CD4+ eQTLs. Allelic concordance of the off-diagonal (comparing CTi eQLTs with non-relevant cell types) show a consistent decrease in allelic concordance. P-values are Bonferroni-corrected Fisher exact tests between groups. Supplementary Figure 12. Validation of CTi eQTLs using allelic concordance with K27AC results from purified cells. CTi eQTLs (FDR ≤ 0.05) show a high allelic concordance in their respective purified cell K27AC QTLs. Top row shows allelic concordance of deconvoluted granulocyte eQTLs (all in green) against neutrophils, monocytes and CD4+ T cells derived from K27AC QTLs. Second row shows deconvoluted monocyte eQTLs (all in orange) against purified cell K27AC QTLs in the same order as top row. Bottom row shows the same comparisons as for deconvoluted CD4+ eQTLs (all in purple). Allelic concordance of the off-diagonal (comparing deconvoluted eQTLs with non-relevant cell types) show a consistent decrease in allelic concordance when compared to the relevant cell type comparisons. P-values are Bonferroni-corrected Fisher exact tests between groups. Supplementary Figure 13. Allelic concordance between whole blood eQTLs and K27AC QTLs for purified neutrophils, CD14+ monocytes and CD4+ T cells. Supplementary Figure 14. Comparison of whole blood eQTLs with eQTLs from single cell RNA-seq Whole blood eQTLs show 89% allelic concordance for significant eQTLs derived from scRNA-seq data, comprising monocytes CD14+, B cells, CD4+ T cells, CD8+ T cells and NK cells. Supplementary Figure 15 Validation of cell type eQTLs detected in the BIOS cohort using the Westra et al method: (A) Expression of eGenes in purified cell subpopulations from BLUEPRINT (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). (B) CT eQTLs detected by the Westra method show a significantly larger effect size in purified cell eQTLs [11] as compared to the rest of the whole blood eQTLs. Boxed-diagonal shows the comparisons with relevant cell types where the effect differences are stronger. Supplementary Figure 16 Allelic concordance rates of cell type eQTLs detected using the Westra et al method and eQTLs from purified cells. Top row shows allelic concordance of granulocyte CT eQTLs against neutrophils, monocytes and CD4+ T cells. Second row shows CT monocyte eQTLs against purified cell eQTLs in the same order as top row. Bottom row shows the same comparisons for CT CD4+ eQTLs. Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method. Overlap of CT eQTLs detected with Decon-eQTL and the Westra et al method and those found to be significant in purified cell subpopulations for granulocyte QTLs (A), CD4+ T cells (B), and monocytes (C). Supplementary Figure 18 Distribution and correlation among circulating cell proportions. (A) Scatter plots show the correlations between different cell subpopulations in 89 samples from 500FG. Blue line indicates a fitted linear model. Diagonal plots depict the overall density distribution per cell type. Upper right triangle shows the Pearson correlation coefficient for each pairwise comparison. (B) Correlations between different cell subpopulations in the BIOS cohort obtained by prediction using Decon-cell. Supplementary Figure 19.General overview of the Decon2 method. (A) Gene expression can be used to predict cell count percentages of cell counts that are already trained in the Decon-Cell model. Additionally, the model can be trained on different cell types if expression data and cell count proportions are available. (B) Decon-eQTL models the cell type dependent eQTL effect using expression, genotype, and measured cell count proportions or, if unavailable, predicted cell count proportions.