11/ Why is this important? Replication at protein level confirms that inferences from gene expression are retained upon translation; in addition, pQTLs in other tissue (such as plasma) can provide very useful biomarkers for disease risk. Example with GPNMB COLOCs below

10/ Because these associations are expected to be mediated by a gene’s protein product, we repeated MR analysis using both UKB-PPP pQTLs and brain pQTLs (Robins et al.,). We replicated 11 in plasma and 20 in brain, including GPNMB, EGFR and CR1.

9/ For example, several genes were found to be putatively causal for PD risk; importantly, MR infers a directionality of effect (up or down) that can inform therapeutic targeting modelling. Example; increased expression of TMEM163 in microglia -> increased PD risk

8/ Adhering to STROBE-MR guidelines, we selected robust independent eQTLs as instruments (F-statistic > 15) for MR, and found 140 putatively causal cell-type/gene/trait associations, many of which in Alzheimer’s Disease, Multiple Sclerosis and Parkinson’s Disease.

6/ Many interaction-QTLs also influence colocalization; Notably, 23.6% of colocalizations showed disease dependency—e.g., TP53INP1 in the full cohort vs. controls-only.

5/ To uncover shared genetic regulation between gene expression and phenotypic risk, we employed genetic colocalization across 41 brain-related traits. We identified 501 COLOCs (PP.H4 > 0.8), with 74.4% specific to a single cell type.

4/ Combining disease and control tissue is common practice to maximize discovery power; however, we show that up to 41% of eQTLs interact with disease even after correcting for disease status. These relationships are important to clarify for downstream causal inference.

3/ An example below with GPNMB; this eQTL is specific to glia, where its strongest associations lie with astrocytes and OPCs but is absent from neurons, endothelial cells and pericytes. Why is this important? CT-specific eQTLs -> CT-specific causal inferences!

2/ Using single-nuclei RNA sequencing, we analyzed 2.3M cells from 391 brains (183 controls, 208 with CNS diseases). Combined with genotypes, we discovered eQTLs for 13,939 across 8 brain cell-types, many of which (39.1%) were cell-type specific.

Very excited to share our latest publication in @NatureGenet! Our manuscript shows how Mendelian Randomization (MR) isolates putatively causal links between cell-type specific gene expression & brain phenotypes.
www.nature.com/articles/s41...

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