You are welcome to explore other TenK10K studies for different biological questions:
tinyurl.com/tenk10k-flag... led by
@annasecuomo.bsky.social
tinyurl.com/tenk10k-repeat led by
@htanudisastro.bsky.social
tinyurl.com/tenk10k-causal led by
@alberthenry.bsky.social & Anne Senabouth (14/n)

For more detail check out the preprint at medrxiv.org/content/10.1..., or get in touch! (12/12)

As always, teamwork makes the dream work, huge thanks to everyone involved: supervisors Joseph Powell and @dgmacarthur.bsky.social, ‪@htanudisastro.bsky.social‬, Ellie Spenceley, @blakebowen.bsky.social, @alberthenry.bsky.social, Hao Lawrence Huang, @anglixue.bsky.social and many others! 👏(11/n)

Other work covering different aspects of this dataset is coming, so stay tuned! Starting with @htanudisastro.bsky.social on the role of tandem repeats in the regulation of single-cell expression :) read more at bsky.app/profile/htan... (updated version coming soon!)(10/n)

In summary, deeply sequenced scRNA-seq from ~2,000 individuals and >5m cells and matched WGS, combined with a powerful sc-eQTL mapping tool allow us to decipher how genetic variants shape the immune landscape at unprecedented resolution 🚀(9/n)

Check out the preprint to read more about how we define a framework to quantify cell type specificity, identify eQTLs that vary dynamically along biologically-informed cell states, and map cell state abundance QTLs! ‼️ medrxiv.org/content/10.1... (8/n)

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)

Yet, most single-cell eQTL maps only test for the effect of common variants and use “pseudo-bulk” individual-level aggregated expression, rather than modelling single-cell profiles directly. Both are addressed by our recently introduced, SAIGE-QTL www.medrxiv.org/content/10.1... (3/n)

Population-scale single-cell studies, where matched scRNA-seq and genotype data are available for hundreds (now thousands!) of individuals can transform our understanding of the cell contexts underpinning key processes in human biology and disease www.nature.com/articles/s41... (2/n)

This work was driven by brilliant PhD student @htanudisastro.bsky.social as a close collaboration with Joseph Powell’s team, especially postdoc @annasecuomo.bsky.social. Both Anna and Hope will be presenting at #ASHG2024 on Wednesday - we’d welcome comments as we prep the final dataset of over 2K!

give us a couple more weeks!