New preprint alert: we use sign errors as a test of how well TWAS works. Very worryingly we find that TWAS gets the sign wrong around 1/3 of the time (compared to 50% for pure guessing). You can read more about our analysis here, and what we think is going on 👇

How well does TWAS estimate a gene’s direction of effect on a trait? We think of this as an important stress-test for the accuracy of TWAS. In a new pre-print, we find that TWAS gets the sign wrong around 20-30% of the time! doi.org/10.64898/202... 1/n

I'm just delighted to announce our new preprint on genome-scale perturb-seq in CD4+ T cells. We learned both general lessons about the power of perturb-seq, and specific lessons about T cell biology. Led by amazing postdocs Emma Dann and Ronghui Zhu, with my wonderful collaborator Alex Marson.

Together with @ronghuizhu.bsky.social, we are thrilled to present our new perturb-seq study of 22M primary CD4+ T cells, across donors and timepoints – the result of a decade-long collaboration between the Marson @marsonlab.bsky.social and Pritchard @jkpritch.bsky.social labs 🧵 tinyurl.com/gwt2025

High false sign rates in transcriptome-wide association studies https://www.biorxiv.org/content/10.64898/2025.12.19.695550v1

@hakha.bsky.social and I wrote a Research Briefing (with a lay summary + "behind the scenes") of our paper on how genes are prioritized by GWAS and rare variant burden tests. 🧬🧪 www.nature.com/articles/d41...

One of the coolest papers I’ve read. Changed the way I think about GWAS and burden testing.

In this Article, Miao et al. introduce PIGEON, a statistical framework for estimating gene-environment interactions for complex traits.