Journal: Nature Communications

Article Title: Genetic map of regional sulcal morphology in the human brain from UK biobank data

doi: 10.1038/s41467-022-33829-1

Figure Lengend Snippet: a Association of the lead rs1452628:T variant with reduced sulcal widths across the brain. (Grey colours indicate associations with p rep > 0.05). b Left: regional association plot of MetaBrain KCNK2 eQTLs for spinal cord, basal ganglia, hippocampus and cerebellum. Right: regional association plots and colocalization of cortex KCNK2 eQTL and different lead variants in the KCNK2 locus. A subset of associations shown for each different lead variant shown due to space constraints. P derived from regression-based tests.

Article Snippet: We performed colocalization analyses between brain eQTLs from MetaBrain and brain folding loci using the coloc R package.

Techniques: Variant Assay, Derivative Assay

Journal: Psychological Medicine

Article Title: Shared genetics and causal relationship between sociability and the brain’s default mode network

doi: 10.1017/S0033291725000832

Figure Lengend Snippet: Analysis workflow. A schematic of the workflow of our analyses. We utilized genetic correlations and bi-directional MR to assess the genetic overlap between rs-fMRI traits and sociability to prioritize selected rs-fMRI traits for the downstream gene prioritization strategy. First, the GWAS of the prioritized rs-fMRI traits and sociability were analyzed using FUMA to map associated genetic regions to genes. We then leveraged eQTLs of gene expression in five brain tissues in an MR framework to provide further putative causal evidence for the mapped genes. Genes from these mapping steps were included in a TieDIE network propagation analysis using the underlying STRING protein–protein interaction network. Separately, we also integrated a human brain transcriptomics atlas (snRNA-seq data) in a CELLECT framework with the rs-fMRI and sociability GWAS. This step allowed us to identify genes whose increased expression are specific to cell types, in specific brain regions, for our traits of interest. Our final list of prioritized genes consisted of those genes which were identified by FUMA and showed at least nominal evidence in both the eQTL MR and CELLECT analyses, for both sociability and at least one rs-fMRI trait. Finally, we used the TieDIE network propagation scores to rank the list of prioritized genes.

Article Snippet: We utilized eQTLs from the MetaBrain resource, a meta-analysis of brain-derived eQTLs across five different tissues (basal ganglia, cerebellum, cortex, hippocampus, and spinal cord) (de Klein et al., ).

Techniques: Gene Expression, 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: 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: 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.

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: 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.

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: 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.

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: 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.

Techniques: Purification

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 single cell RNAseq. a Comparison in allelic direction between CTi eQTLs and eQTLs from single cell RNAseq experiments in 6 cell types. b Comparison in allelic direction between Westra model eQTLs and single cell eQTLs. In both panels coloured diamonds are FDR < 0.05, grey circles are FDR > = 0.0 in the single cell data, and the size is the -log10(p-value) of the predicted cell type interacting eQTLs

Article Snippet: 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.

Techniques: Comparison

Journal: bioRxiv

Article Title: Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

doi: 10.1101/2023.03.13.532468

Figure Lengend Snippet: (A) Illustration of decon-eQTL mapping. (B) The number of decon-eQTLs identified in different cell types at FDR<0.05 in the permutation test. (C) Pi1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and (D) eQTLs from snRNAseq study (Bryois et al.). (E) Comparison of decon-eQTLs and bulk-tissue eQTLs. The top barplot shows the Pi1 values of decon-eQTLs in bulk-tissue eQTLs. The bottom plot shows the intersections between decon-eQTLs and bulk-tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.

Article Snippet: The cell-type eQTLs identified with deconvoluted gene expression data were named deconvolution eQTLs (decon-eQTL).

Techniques: Comparison

Journal: bioRxiv

Article Title: Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

doi: 10.1101/2023.03.13.532468

Figure Lengend Snippet: (A) Total SCZ GWAS heritability (h2) explained by eQTLs. (B) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h2/number of SNPs in each eQTL category.

Article Snippet: The cell-type eQTLs identified with deconvoluted gene expression data were named deconvolution eQTLs (decon-eQTL).

Techniques:

Journal: International Journal of Epidemiology

Article Title: Identifying factors contributing to increased susceptibility to COVID-19 risk: a systematic review of Mendelian randomization studies

doi: 10.1093/ije/dyac076

Figure Lengend Snippet: The characteristics of the included Mendelian randomization studies of factors on COVID-19 outcomes [(A) severity, (B) hospitalization, (C) susceptibility]

Article Snippet: Pairo-Castineira E 2021 , Two-sample , Druggable targets , eQTL of IFNAR2 , IFNAR1 , IL6R , JAK1 , CTSL , IFNGR2 , CSF3 (SD) , A , GenOMICC, COVID-19 HGI R2, 23andMe, UKB , European , European.

Techniques: Expressing, Activity Assay, Reflux, Cannabis, Cell Counting, Marker, Plasmid Preparation, Coagulation