For example, we found distinct eQTLs OSM in different cell types, with the NK cells-specific effect (only 👀) colocalizing with a risk locus for IBD (7/n)

We found over 30,000 colocalization events between our eQTLs and GWAS loci from 14 disease phenotypes and 44 blood traits, displaying remarkable cell type specificity (43% disease loci colocalize with an eQTL in only one cell type!) 🤩 (6/n)

These samples were sequenced deeply, with ~3,000 cells per individual across 28 cell types, giving us power to find common eQTLs for 83% of genes and rare variant signal for 47%, with variants often beautifully overlapping with functional annotation + in-house scATAC-seq 😍(5/n)

We leverage WGS to call common *and rare* variants, and use SAIGE-QTL to model single-cell counts, to identify >150,000 common eQTLs and >30,000 rare variant gene-level effects (via Burden + SKAT tests) (4/n)

📢 new preprint alert: So so excited to share our analysis on the impact of common and rare variants on single-cell gene expression in blood, using WGS and scRNA-seq data from nearly 2,000 individuals and 5.4m cells as part of TenK10K phase 1 🧬 www.medrxiv.org/content/10.1...
🧵👇 (1/n)

Sad to be missing #ASHG23, but check out the talk by the brilliant Wei Zhou talk on Saturday on our new scalable & efficient method for single-cell eQTL mapping!