Thread by @lopeskp

We are thrilled to share our #preprint "Atlas of genetic effects in human #microglia transcriptome across brain regions, aging and #disease pathologies"!
Work led by @lopeskp @JackHumphrey_ #Gijsje and others from #deWitte and #Raj Lab (@towfiqueRaj)
t.ly/pZqQ 1/23

Here, we describe the Microglia Genomic Atlas (#MiGA) project. A genetic and transcriptomic resource of 255 primary human microglia isolated from 4 brain regions of 100 donors with neurodegenerative and psychiatric disease and controls. 2/23

We performed the following analysis: I) age-related (the age of donors range from 21 to 103 y.o! II) brain regional heterogeneity ; III) expression and splicing quantitative trait loci #eQTL & #sQTL. 3/23

Also, IV) #colocalization and V) #fine-mapping with 5 diseases: #Alzheimer's(AD), #Parkinson(PD), #Multiple Sclerosis(MS), #Bipolar disorder(BD) and #Schizophrenia(SVZ). 4/23

Microglia are very heterogeneous but, what are the sources of variation? We observed that donor-donor variation explained the most variance, followed by cause of death and region of the brain. Sex explained just a little bit. 5/23

Identified 4 distinct clusters of genes with specific expression patterns across brain regions. That included genes implicated in inflammatory processes (cluster 2), homeostatic functions (cluster 1), and hormonal signaling and interferon response (cluster 4) 6/23

We found out 1,693 genes associated with age (338 up and 1355 downregulated), including microglial-specific genes (P2RY12, TLR2, C2).
Interestingly, some of the upregulated genes are known to be in AD, and PD loci as MS4A6A, FCER1G, CR1, BST1, PTPN22, and TNFSF13. 7/23

We performed a meta-analysis across 4 regions (mashR) and we identified 3,611 eGenes and 4,614 sGenes. A large proportion of the eQTLs and sQTLs effects are shared between the regions, with some specific effects as well. 8/23

Examples of shared (CTSB) and specific effect (RNF40). 9/23

A large number of eQTLs in AD loci have shared effects between microglia and monocytes (e.g MS4A6A, RABEP1, CD33, FCER1G and ABAC7). However, there were eQTLs with specific microglial effects (e.g., BIN1, PICALM, USP6NL and GNGT2) 10/23

The CASS4 eQTL found highly significant in both microglia (#MiGA) and monocytes (#MyND) but with opposite directions of effect. 11/23

We used our meta-analyzed e/s-QTLs to explore whether disease-associated genetic variants may potentially act through microglia expression or splicing. 12/23

We found that AD and PD had the highest number of colocalizing loci in each QTL dataset with 10-30% of loci containing at least one colocalized gene. 13/23

We present coloc results in AD and PD. We have identified specific disease eQTLs in BIN1, USP6NL, and PICALM in AD, P2RY12 in PD and splicing QTLs for CD33, MS4A6A for AD and FAM49B, BST1 for PD and many others. 14/23

We next examined whether the microglia eQTLs that colocalized with disease GWAS loci were due to genetic variation within microglia-specific regulatory regions (enhancers). 15/23

But first, we performed fine-mapping (using a new tool in our lab, echoLocator, biorxiv.org/content/10.110…) and microglia-specific epigenomic data to prioritize genes and variants that influence disease susceptibility. 16/23

We found that 10 out of 17 genes colocalizing in AD, 8 out of 18 in PD, 4 out of 9 in SCZ, and 3 out of 17 in MS include fine-mapped SNPs that overlap with microglial enhancers. 17/23

In AD, we propose USP6NL to be the causal gene in the ECHDC3 locus, due to AD GWAS/ eQTL and the overlap of fine-mapped putative causal SNPs within a defined microglia enhancer which connects with the USP6NL promoter. 18/23

In PD, we propose P2RY12 in the MED12L locus through a similar mechanism. Fine-mapping revealed that the lead QTL SNP (in LD with GWAS SNP) is within microglia-specific enhancer. Lead QTL SNP rs3732765-A decreases PD risk and decreases P2RY12 expression. 19/23

Some limitations include: bulk RNA-seq not single cells (microglia are heterogeneous); sample size is still small (100 donors); pre- and post-mortem factors that have an impact on the microglial transcriptome 20/23

We have built the most comprehensive catalog to date of genetic effects on the #microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological diseases. 21/23

In the spirit of #openscience, all summary statistics are freely available without any restriction via Zenodo: doi.org/10.5281/zenodo…. ShinyApp is coming soon. 22/23

We thank the members of the #Raj (@towfiqueRaj) and #De Witte lab and all co-authors and most importantly, we thank the study participants for their generous gifts of brain donation. 23/23