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 analysis between brain cortical folding loci and the largest cortical expression quantitative trait locus (eQTL) summary dataset generated to date, Metabrain .

Techniques: Variant Assay, Derivative Assay

Journal: Nature Genetics

Article Title: Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

doi: 10.1038/s41588-023-01300-6

Figure Lengend Snippet: a , Number of conditional cis- eQTLs per eQTL dataset. b , Comparison of characteristics between primary and non-primary eQTLs. For the mean expression and pLI score values, each row compares the eQTL genes for that rank with eQTL genes from the previous rank (for example, for tertiary eQTLs, the non-significant (grey) distribution is from genes that have secondary but lack tertiary eQTLs). P values were calculated using a Wilcoxon test between significant and non-significant genes. Differences in mean gene expression levels (left), the distance between the most significant SNP–gene combination and the TSS (middle), and pLI scores (right) are shown. For primary, secondary and quaternary eQTLs, non-significant eQTLs have higher pLI scores. Vertical dotted lines indicate median of the distribution for the current rank (coloured) versus the previous rank (black). c , d , Number of overlapping eQTLs along with the R b and standard error (s.e.) values of primary cis -eQTLs between the cortex eQTLs of different ancestries ( c ) and the different brain regions for the EUR datasets ( d ). n , sample size of the dataset. e , Correlation of effect sizes and standard error of primary cis -eQTLs of Cortex-EUR (discovery, excluding GTEx) in all of the GTEx tissues (replication). Each dot is a different GTEx tissue; the x axis indicates the number of eQTLs that are significant in both discovery and replication.

Article Snippet: We combined 14 eQTL datasets into the ‘MetaBrain’ resource to maximize statistical power to detect eQTLs and create a brain-specific gene co-regulation network (Fig. , Supplementary Figs. – and Supplementary Table ).

Techniques: Comparison, Expressing, Gene Expression

Journal: Nature Genetics

Article Title: Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

doi: 10.1038/s41588-023-01300-6

Figure Lengend Snippet: a , Pearson’s correlations between the seven predicted cell-count proportions within cortex samples. b , Predicted cell-type proportions compared with cell-type proportions measured using IHC for 42 ROSMAP samples. Pearson’s correlation coefficients are provided. The cell-count predictions for most cell types closely approximate actual IHC cell counts. Shaded areas around regression lines indicate 95% confidence interval. c , Number of cell-type ieQTLs for Cortex-EUR deconvoluted cell types. The first 20 intersections with the highest overlap are shown. Oligodendrocytes have the most interactions, followed by astrocytes and other neurons. Notably, most interactions are unique for one cell type in 87.1% of the cases. d – f , Replication of cell-type ieQTLs for STMN4 ( d ), FAM221A ( e ) and CD38 ( f ), consisting of the scatterplot of the cell-type ieQTL in MetaBrain Cortex-EUR bulk RNA-seq (left) and a forest plot for the eQTL effect in the ROSMAP snRNA-seq data (right). Each dot in the scatterplots (left) represents a sample; colors indicate SNP genotype, with yellow being the minor allele; values under the genotypes are the Pearson’s correlation coefficients; interaction P values were determined using a one-sided F -test; eQTL P values were derived using the standard normal distribution from meta-analyzed z -scores. Forest plot (right): eQTL β values (dots) and standard error (error bars) with effect direction relative to the minor allele when replicating the eQTL effect in ROSMAP single-nucleus data ( n = 38); each row denotes a cell type-specific dataset; cell types highlighted in bold reflect the equivalent to the cell type used in the ieQTL. Vertical dashed lines indicate an eQTL beta of 0. TMM, trimmed mean of M-values; AST, astrocytes; END, endothelial cells; EX, excitatory neurons; IN, inhibitory neurons; MIC, microglia; OPC, oligodendrocyte precursor cells; OLI, oligodendrocytes; NEU, other neuron; PER, pericytes.

Article Snippet: We combined 14 eQTL datasets into the ‘MetaBrain’ resource to maximize statistical power to detect eQTLs and create a brain-specific gene co-regulation network (Fig. , Supplementary Figs. – and Supplementary Table ).

Techniques: Cell Counting, RNA Sequencing, Derivative Assay

Journal: Nature Genetics

Article Title: Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

doi: 10.1038/s41588-023-01300-6

Figure Lengend Snippet: a , Location of the identified trans -eQTLs (SNP and gene positions) in the genome. The size of the dots indicates the P value of the trans -eQTL (larger is more significant). b , Two examples of convergent effects, where multiple independent SNPs affect the same genes in trans . Trans- eQTLs of rs1427407 and rs4895441 on HBG2 (top). Trans- eQTL of rs1150668 and rs106871 on ZNF31 and S100A5 (bottom). Both panels are derived from Supplementary Table .

Article Snippet: We combined 14 eQTL datasets into the ‘MetaBrain’ resource to maximize statistical power to detect eQTLs and create a brain-specific gene co-regulation network (Fig. , Supplementary Figs. – and Supplementary Table ).

Techniques: Derivative Assay

Journal: Genome Biology

Article Title: PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

doi: 10.1186/s13059-023-03151-0

Figure Lengend Snippet: Graphical overview of the PICALO method. A PICALO takes eQTL data (i.e., gene expression and genotype dosage values) as input. B Map interactions with a starting position representing an initial guess of biological/technical context. C The starting position is optimized by maximizing the joint log-likelihood on a per-sample basis over multiple ieQTLs. D Mapping of the interactions and the subsequent optimization are repeated until convergence. The influence of the resulting principal interacting component (PIC) is regressed out from the gene expression data, and the process is repeated until no additional PICs and ieQTLs are identified. The resulting PICs capture technical and biological contexts such as cell type proportions. The illustrations shown in A, B and C are generated using dummy data

Article Snippet: We made use of the bulk RNA-seq eQTL datasets collected by BIOS [ ] (peripheral blood; n = 3997) and MetaBrain [ ] (multiple brain regions; n = 8727).

Techniques: Gene Expression, Generated

Journal: Genome Biology

Article Title: PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

doi: 10.1186/s13059-023-03151-0

Figure Lengend Snippet: A Pearson correlation heatmap correlating PICs to measured cell type proportions in the blood. The correlation p -values are corrected for multiple testing with Benjamini-Hochberg, and only correlations with an FDR < 0.05 are shown. B Regression plot showing the correlation between PIC2 and myeloid lineage cell proportions (granulocyte + monocyte) in the blood. C Simplified overview of the blood cell type lineage with annotations of PICs describing distinct (groups of) cell types using measured cell type proportions, gene set enrichments, and single-cell expression enrichment. Positive and negative signs indicate the direction of the effect. Only the first 10 PICs are considered. An image of the top layer cell type is created with BioRender.com . D Negatively correlating eQTL genes interacting with PIC10 showed enrichment for type II interferon signaling as annotated by the Interferome Database Annotation

Article Snippet: We made use of the bulk RNA-seq eQTL datasets collected by BIOS [ ] (peripheral blood; n = 3997) and MetaBrain [ ] (multiple brain regions; n = 8727).

Techniques: Expressing