New method from our group for identifying disease-mediating genes using perturb-seq, eQTL, and GWAS data. Check out the thread:

Feel free to read the manuscript and try the software! Comments, suggestions, and questions are very welcome 😊 (n/n)

Huge thanks to my mentors @sashagusevposts.bsky.social and @nmancuso.bsky.social, and collaborators Yi Ding, Nathan LaPierre, Lili Wang, and Douglas Yao. (13/n)

Overall, our results highlight the value of integrating experimental trans effects with population-scale GWAS and eQTL data to identify disease-relevant mediating genes beyond single loci. (12/n)

GitHub - gusevlab/mrpeg: The python software to run mpeg framework. The python software to run mpeg framework. Contribute to gusevlab/mrpeg development by creating an account on GitHub.

Mr. PEG is open-source command-line Python software, implemented in JAX with JIT compilation for fast inference: github.com/gusevlab/mrpeg (11/n)

We also identify cases where Mr. PEG uniquely implicates PTGS2 as a mediating gene for gout, suggesting potential opportunities for drug repurposing. (10/n)

Notably, Mr. PEG effects learned from common-variant GWAS are associated with rare coding burden effects, capturing disease mechanisms missed by cis-eQTL-only approaches. (9/n)

These mediating genes are more constrained than background genes and show distinct epigenetic features compared to genes prioritized by GWAS hits, MR-based methods, or burden tests. (8/n)

We applied Mr. PEG to 43 GWAS, integrating cis-eQTLs from eQTLGen and Perturb-seq gene-to-gene effects (459 upstream → 13,152 downstream genes), identifying 546 mediating genes. (7/n)

Through extensive simulations, we show that Mr. PEG is well-calibrated, highly powered, and robust to realistic parameter choices and model misspecification. (6/n)

Mr. PEG unifies MR-like tests with experimental data by modeling GWAS signals as a function of eQTL effects of perturbed genes × gene-to-gene effects learned from perturbational screens. (5/n)

Perturbational screens (e.g., Perturb-seq) combine CRISPR screens with scRNA-seq to estimate gene-to-gene effects, providing more direct evidence for causal trans regulation than association-based trans-eQTLs. (4/n)

Recent work shows systematic differences between cis-eQTL and GWAS signals, supporting models where upstream genes regulate downstream genes, ultimately affecting disease through complex regulatory cascades. (3/n)

Most eQTL–GWAS methods prioritize cis-mediated effects, while trans-acting genes remain largely underexplored, limiting our understanding of gene regulatory networks underlying complex disease. (2/n)

Integrating perturbational screens, eQTL, and GWAS data identifies mediating genes for complex traits Most current GWAS-eQTL approaches prioritize genes whose mediating effects on complex traits act through cis-regulation, while trans-acting genes remain largely underexplored. Recent perturbational sc...

Happy to share our new preprint from @sashagusevposts.bsky.social and @nmancuso.bsky.social labs! We introduce Mr. PEG, a framework integrating perturbational screens, eQTL, and GWAS data to identify mediating genes for complex traits. (1/n) www.medrxiv.org/content/10.6...

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.

Glad to see everyone!

Congrats!!!

📢OUT TODAY @natgenet.nature.com

📰Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.

By @zeyunlu.bsky.social, @nmancuso.bsky.social and colleagues.

⬇️

www.nature.com/articles/s41...

Super happy!!!!!

I'm become quite partial to cell-type-biased (population-biased) terminology rather than cell-type-specific (population-specific) when it comes to QTLs, because it reflects better that these are a result of power, rather than biological differences.

Congrats on the first PhD work!! Very nicely done! @alnahid.bsky.social

👋 Hi everyone! We're the Center for Genetic Epidemiology, here at Keck School of Medicine, USC! Please follow us for updates on our member's research, community engagement, and activities! Stay tuned for more content 🧬 🧪 💻 👨‍🔬 👩‍🔬

Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association s... Abstract. Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS)

Our paper on mutli-omics analysis of ischemic stroke is now online! Initiated and led by Dr. Junghyun Jung (now at Cedars) with wonderful support of @adamdesmith.bsky.social . 🧬 academic.oup.com/hmg/advance-...

Thank you so much!!!

Postdoctoral Fellow in Statistical Genetics (f/m/x)

Dear statgen friends at #ASHG2023 #ASHG23, my lab is #hiring #phds #postdocs to work on heterogeneity and trajectories of psychiatric disorders using EHR/cohorts across US/Europe/Asia (incl. data from PGC), lots of cool stuff to be done!

Please reach out by email!
Ad link: tinyurl.com/7tae8k

Thank you so much!

MancusoLab at #ASHG2023 😎 🧬

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance sh... bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

Meet Rosace, a robust deep mutational scanning analysis tool that incorporates positional information and mean-variance shrinkage. Check it out if you are running DMS experiments or handling DMS data! (1/n)
www.biorxiv.org/content/10.1...

I'm writing a molecular perspective on heritability, behavior, (and eventually) race/ancestry, group differences. The idea is to start with what we've learned from genetic data and then work backwards to what we used to know from classical studies. gusevlab.org/projects/hsq/