Ron Do

🧵1/ Delighted to announce our new study: Machine learning-based penetrance of genetic variants by @IainSForrest et al. published in @ScienceMagazine! Paper: science.org/doi/10.1126/sc… Press release: mountsinai.org/about/newsroom… @AIHealthMtSinai @SinaiGenetics

Delighted to have our work on 🧬 resilience to 🩸cancer led by @GauravA_UK & amazing collaborators, including @KharasLab, published in @ScienceMagazine: science.org/doi/10.1126/sc… 🧵

Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. I'm excited to share our new work combining genetic associations and Perturb-seq to build interpretable causal graphs, out today in @Nature:

Successful drug development depends on identifying effective targets and optimizing drug design and clinical trial strategies. The authors review computational strategies, including ML, for integrating genetics into pharmaceutical pipelines go.nature.com/43VZslH

A nice weekend read on methods for prioritizing drug targets for complex diseases using human genomic data👇

We are pleased to announce that our new study explaining the missing heritability of many phenotypes using WGS data from ~347,000 UK Biobank participants has just been published in @Nature. Please check out our manuscript here: nature.com/articles/s4158….

New online! Genomics of drug target prioritization for complex diseases bit.ly/3JKy9DU

Genetic analyses of eight complex diseases using predicted continuous representations of disease. A study by @DoGenetics @IcahnMountSinai & others @CellRepMethods #ASHG25 hubs.li/Q03P9XMj0

Delighted to share our new research in @NatureComms. @Aine_duffy94 et al. (2025): nature.com/articles/s4146… “Human genetics can predict drug side effects.” Our SE-GPS framework quantifies this risk and helps inform drug target safety from the start. #Genomics #DrugDiscovery

🚨“Pathogenic” genetic variant does NOT = guaranteed disease 🚨 Yet, doctors and patients are still left to decide what to do with the label… In our new @NatureGenet perspective, we explain why this is and how we can improve genetic risk interpretation🧵

It took a village! Forever grateful to @DoGenetics Lab @IcahnMountSinai including @DrDanielJordan @OmegaPetrazzini and collaborators @girish_nadkarni @JudyCho7 @WendyKChung. No better way to kick off internal medicine residency @BayviewMedicine @HopkinsBayview @JohnsHopkins 💪

👇🏾 Excellent tweetorial by lead author @iainForrest of our recent @ScienceMagazine paper on machine learning–based penetrance.

Grateful for the shoutout @EricTopol - Thank you for spotlighting our work!

AI + routine labs help clarify genetic risk. @SinaiGenetics modeled 1,600 variants across 10 diseases using 1M+ health records, moving past binary labels. mountsinai.org/about/newsroom… #precisionmedicine #genetics #AIinHealthcare #AIinMedicine @IainSForrest @DoGenetics

Insightful perspective on our ML penetrance paper on penetrance and variant consequences, in @ScienceMagazine nicely written by Harper Raiken and @_amelie_rocks science.org/doi/10.1126/sc…

11/ This approach is scalable and can be applied to many different types of variants and diseases. It demonstrates how integrating ML + EHR + genomics can move us from categorical pathogenicity labels toward more data-driven and quantitative penetrance estimates. /end

10/ Applications include: - improving variant classification - guiding clinical genetics & counseling - prioritizing variants for research - personalized disease risk stratification.

🧵1/ Delighted to announce our new study: Machine learning-based penetrance of genetic variants by @IainSForrest et al. published in @ScienceMagazine! Paper: science.org/doi/10.1126/sc… Press release: mountsinai.org/about/newsroom… @AIHealthMtSinai @SinaiGenetics