Thrilled to share the second half of my PhD work here! We show how data on expression quantitative trait loci (eQTL) relates to the structure of gene regulatory networks (GRN). Much of the GRN / eQTL picture is unmapped, but what we do have says a lot… (1/) doi.org/10.1101/2025...

I'm excited to announce that I'll be starting a lab at UCSF in the @ihgatucsf.bsky.social and @ucsf-epibiostat.bsky.social in July. We'll work at the intersection of statistical genetics, population genetics, and machine learning.

I have an opportunity to hire a staff scientist for my lab. Looking for someone with outstanding skillset in ML/statistics, genomics applications; interest in mentoring, strong publication record, PD experience required. Email CV to me+cc my assistant (see 'contact' on my website). Ad to follow.

A really nice paper by @drghawkes.bsky.social et al. argues that rare and common genetic associations converge on the same genes. While this seems at odds with our recent work about how burden tests and GWAS prioritize different genes, our results agree (🧬🧪🧵 1/6) www.biorxiv.org/content/10.1...

Excited to share the peer-reviewed version of our paper on predicting the chromatin response to TF dosage using transfer learning www.cell.com/cell-genomic...

Disease diagnostics using machine learning of B cell and T cell receptor sequences www.science.org/doi/10.1126/... TL;DR: BCRs ARE ALL YOU NEED! (Well actually .... keep reading) 1/

Japan can be a science heavyweight once more — if it rethinks funding Research leaders call for an end to substantial underfunding of interdisciplinary research in Japan. On my current visit to 🇯🇵 I can see the country is ready for a change #japan #academicSky 🧪 www.nature.com/articles/d41...

I posted a couple days ago about our new paper on building causal graphs from genetic associations + Perturb-seq. Here I want to expand on the value of using DIRECTIONAL information contained in LoF burden tests.🧵 [work led by @minetoota.bsky.social ] bsky.app/profile/jkpr...

Beautifully elegant work on integrating LoF, GWAS & Perturb-seq data to build causal paths from regulators to genes / programs to phenotype. And it didn't require a foundational virtual cell model (well almost ... gene & protein embeddings r used in GeneBayes)! 😜

Great new study from @jkpritch.bsky.social’s lab, led by @minetoota.bsky.social, combining ‘quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types’ 👇

@minetoota.bsky.social set the groundwork for many ongoing projects in @jkpritch.bsky.social and Marson lab. Great to see this out!

Really nice work. And they chose one of my favorite traits to model: mean corpuscular hemoglobin. Allows me to reuse one of my figures from a few weeks ago on a gene as old as eukaryotes, mitoferrin, which is needed to move iron into mitochondria. @jkpritch.bsky.social

Thank you Jonathan for these fantastic threads about our recent work! We dove into how we can model the gene regulatory architecture of complex traits with 1) Gene effects from LoF burden tests and 2) Perturb-seq.

Why do association studies prioritize trait-specific variants??? A quick thread about the importance of thinking about all traits at once 👇 1/6 (🧪🧬)

What do GWAS and rare variant burden tests discover, and why? Do these studies find the most IMPORTANT genes? If not, how DO they rank genes? Here we present a surprising result: these studies actually test for SPECIFICITY! A 🧵on what this means... (🧪🧬) www.biorxiv.org/content/10.1...

Beautiful work led by Maya Arce from Marson lab reveals a fascinating story about rewiring of a critical gene regulatory circuit in different T cell types: T effectors and Tregs www.nature.com/articles/s41...