These effects can therefore only be captured by using scRNAseq. However, even at this scale, there are many cell-types for which we are relatively underpowered. Continued high resolution eQTL mapping in ever larger datasets will likely continue to help understand GWAS hits. 14/

However, because we find this substantial enrichment at higher resolutions, we believe effector gene dysregulation is largely contextually restricted. Such effects may therefore hide from selective pressures, and persist at the common frequencies often found by GWAS. 13/

So are those effects found at each resolution equally likely to drive disease❓ NO - Those eGenes found at the cell-type level were SUBSTANTIALLY enriched for disease effector genes - a whopping ~3.5-fold (2.68/0.75) more so than the ‘All Cells’ level. 11/

To see which of these underpin susceptibility, we colocalised these with IBD GWAS. Remarkably, we nominate effector genes at an enormous 74 (❗) loci where one has not previously been nominated in @OpenTargets. This therefore SUBSTANTIALLY improves on previous efforts. 10/

Something really cool! 🌟 While most genes have an eQTL at the ‘All Cells’ level (‘eGenes’), many eQTLs were found only at higher resolutions. So while rarely finding new eGenes, we find many new regulators. These are further from the TSS and more likely found in enhancers. 9/

🗺️ We then mapped eQTLs at several resolutions;
1) ‘All Cells’ (like bulk)
2) Major populations (coarse resolution)
3) Cell-types (high resolution)
Doing this within or across anatomical sites, we find >84k eQTLs (❗) in 251 different annotations. 8/

To answer this, we generated ‘IBDverse’ 💫 - The world’s LARGEST collection of scRNAseq data from the sites most relevant for IBD!
🤯 Across this gigantic set of 2.2M cells from 732 samples, we identified 9 major populations that comprised 86 cell-types. 7/

😥 Unfortunately these have had little success. Most work, however, relies on bulk RNAseq, which requires homogenising a sample much like you do to fruit in a smoothie. But what if disease-causing gene dysregulation is missed by doing this? 5/

⏩ This led to widespread efforts to identify regulatory variants, a.k.a ‘expression quantitative trait loci’ (eQTLs), that colocalise with GWAS signals, in the hope of pinpointing the effector genes, tissues and cell-types. 4/