15/ What’s next? Landmark!

The Landmark project is a public-private partnership involving GSK, Novartis, Roche and UCB with the hope to find novel targets for Parkinson’s Disease.

www.imperial.ac.uk/news/256397/...

Watch this space! 😄

14/ Summary statistics publicly available here zenodo.org/records/1334... and general code used for analyses here; github.com/johnsonlab-i....

13/ Big thank you to all co-authors including @VerenaZuber, @JulienBryois, and many many more including important contributions from @ukdri.bsky.social ; @FancyNurun, @jojacksonhere.bsky.social and @nottalexi.bsky.social to name a few. Study conceived by Michael Johnson @imperialbrains.bsky.social.

12/ Thank you for reading! Tons more not covered here in the publication!

www.nature.com/articles/s41....

If you are interested to hear more, I will be presenting this work virtually at #ADPD2025 if you feel like dropping in!

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.

7/ MR necessitates associations free from disease presence to avoid reverse causation. Using a controls-only subset (N=183), we repeated eQTL + colocalization analysis. Despite a smaller cohort, we discovered an additional 91 COLOCs such as with PEX13 in MS (PP.H4 = 0.87).

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.

1/ 95% of new drugs fail in development. MR leverages natural allele randomization to link genetic variation with traits, and can use expression Quantitative Trait Loci (eQTLs) to infer putative causal relationships between gene expression and brain outcomes.