ct eqtls  (Decon Laboratories)

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Workflow of application of Decon2 to predict cell counts followed by deconvolution of whole blood eQTLs. Using whole blood expression and FACS data of 500FG samples, Decon-cell predicts cell proportions with selected marker genes of circulating immune cell subpopulations. Validations of Decon-cell were carried out on three independent cohorts for which measurements of neutrophils/granulocytes, lymphocytes and monocytes CD14+ were available along with expression profiles of whole blood. Benchmarking of Decon-cell was performed against CIBERSORT and xCell . Decon-cell was applied to an independent cohort (BIOS) to predict cell counts using whole blood RNA-seq. Decon-eQTL subsequently integrates genotype and tissue expression data together with predicted cell proportions for samples in BIOS to detect cell type eQTLs. We validated Decon-eQTL using multiple independent sources, including expression profiles of purified cell subpopulations, eQTLs and chromatin mark QTLs (cmQTLs) from purified neutrophils, monocytes CD14+ and CD4+ T cells , and single-cell eQTL results . Benchmarking of Decon-eQTL was carried out for comparison with a previously reported methods that detected cell type–eQTL effects using whole blood expression data, i.e. the Westra et al.

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Expressing, Marker, RNA Sequencing, Purification, Comparison

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Deconvolution of whole blood eQTLs into CTi eQTLs. Decon-eQTL detects CTi eQTLs by integrating proportions of cell subpopulations (predicted by Decon-cell), gene expression and genotype information. a Number of deconvoluted CTi eQTLs in each cell type using whole blood RNA-seq data of 3189 samples in BIOS cohort. b Distribution of Spearman correlation coefficients between expression levels of CTi eQTL genes and cell counts for each cell subpopulation. The CTi eQTL genes show positive and statistically higher correlation (Spearman) with the relevant cell type proportions as compared to the rest (T-test p -value < 0.05) in an independent cohort (500FG)

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Gene Expression, RNA Sequencing, Expressing

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Validation of CTi eQTLs. a The expression of CTi eQTL genes in purified cell subpopulations from BLUEPRINT are significantly higher in the relevant cell subpopulation when compared to other available cell subtypes (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). b Genes differentially expressed (Adjusted p-value ≤0.5) between CD4+ T cells and NK cells are significantly enriched for CT eQTLs effects on CD4+ T cells (dots in purple, Fisher exact P = 1.8 × 10 17 ) and NK Cells (dots in yellow, Fisher exact P = 2.3 × 10 18 ), respectively. c CTi-eQTLs (FDR ≤ 0.05) show significantly larger effect sizes in the purified cell eQTL data compared to the rest of the whole blood eQTLs for which we do not detect a cell type effect, as shown for deconvoluted granulocyte eQTLs in neutrophil-derived eQTLs (green),monocytes (orange) and CD4+ T cells (purple)

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Biomarker Discovery, Expressing, Purification, Derivative Assay

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Allelic concordance of CTi eQTLs with eQTLs from purified cells. CTi eQTLs show high allelic concordance compared to eQTLs from purified cell subpopulations9. ( a ) for granulocyte eQTLs (green), CTi eQTLs achieved an allelic concordance of 99% compared to eQTLs from purified neutrophils. Similarly, the allelic concordances were 96 and 99% for CD14+ monocytes and CD4+ T cells, respectively. Except for monocytes, these values are higher than those observed for whole blood eQTLs when comparing to eQTLs from purified subpopulations, as shown in panel ( b )

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Purification