iiinova — where your digital self keeps evolving

We have released summary stats here zenodo.org/records/1840... and code here github.com/yazhengdi/ED.... Feedback/criticism/comments welcome! 19/19

github.com

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Both Yazheng and I have learnt a lot from working on this project. We thank the participants of UKB and other cohorts for enabling our work, and many friends for giving us valuable feedback. Yazheng will be presenting this work at #recomb26 @recombconf.bsky.social 18/n

We hope this work provides a proof of concept and blueprint for improving the specificity and interpretability of EHR-based genetic studies, as well as their downstream utility in drug target identification and risk stratification. 17/n

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Overall, our EDGAR framework enables the prediction of disease-specific liabilities from EHR events, disease-specific measures and deep phenotype labels, and the separation of genetic effects on disease-specific liability from heritable biases that influence EHR events. 16/n

When we did that, we find that high rGs between external EHR GWAS with confounding traits disappeared. This implies that when we have well-predicted disease liabilities, enabled by EDGAR, we can identify biases in one EHR, and remove it from existing GWAS on another. 15/n

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Finally, we ask if the Common Bias identified in UKB is likely generalizable between EHRs: consistent with our hypothesis, we find that it has high rGs with external EHR GWAS, but not external deep phenotype GWAS. This makes us attempt removing it from external EHR GWAS. 14/n

We then identify a Common Bias factor across diseases with genomic SEM, and find that it has high rGs with socioeconomic and behavioral traits, many previously shown to affect UKB participation www.nature.com/articles/s41... 13/n

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This drives us to first isolate the disease-specific heritable confounder affecting EHR codes of each disease, using GWAS-by-subtraction, and find that genetic effects contributing to these biases in different diseases have high rGs (i.e. they are shared) with each other. 12/n

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This is corroborated by our cross-disease rG analysis in UKB: while deep phenotypes and EDGAR liabilities show similar cross-disease rGs, raw EHR codes show highly inflated rGs. This is likely due to a common heritable confounder www.nature.com/articles/s41... 11/n

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In contrast, raw UKB EHR codes have lower rGs with external deep phenotype GWAS, but higher rGs and hits replication in external EHR GWAS - this shows replicability between EHRs goes beyond etiological nature of diseases - it is likely an EHR-systemic factor drives this. 10/n

We validate our findings in external GWAS: compared to EHR phenotypes, EDGAR liabilities show higher GWAS hit replication and rGs with external deep phenotype GWAS - this suggests EDGAR liabilities capture replicable, disease-specific genetic effects. 9/n

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We then perform and compare GWAS on raw EHR codes, deep disease labels and EDGAR predicted liabilities: for most diseases, EDGAR predictions give more GWAS hits, higher rGs with deep phenotype labels, and higher PRS predictability and specificity. 8/n

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We first show that raw EHR codes have low correlations with deep disease labels for nine diseases in UKB, and EDGAR liability predictions do significantly better, especially when incorporating disease-relevant measures as inputs. 7/n

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We augment EDGAR with an active learning model that prioritizes individuals for obtaining deep disease labels, which reduces the N labels needed by >50% - substantially reducing costs for obtaining disease labels through patient recall in realistic EHR settings. 6/n

EDGAR predicts lifetime disease liabilities through aligning counts of EHR events with disease-specific measures realistically available in EHRs (e.g. blood biochemistry or spirometry) and independently ascertained, clinically validated disease labels (“deep phenotypes”). 5/n

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In our new paper, we propose EDGAR (EHR Disease liability prediction for Genetic Architecture Recovery), a prediction framework that combines the scale of EHR with the disease-relevance of deep phenotypes to break this circularity of bias. 4/n

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These influences, many of which hertiable, are difficult to disentangle from disease-specific genetics. They may also be replicable across EHRs, making confounded genetic findings look robust. Finally, these biases are likely to propagate in next-event prediction models. 3/n

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Harnessing EHR data for health research - Nature Medicine

First some background: using EHR codes in GWAS drives increase in statistical power in GWAS meta-analyses. Its potential, however, is undermined by systemic factors like coding practices and healthcare-access disparities between demographics: www.nature.com/articles/s41... 2/n

nature.com

Electronic health records hold immense potential for providing clinically useful insights for populations and individuals; this Review summarizes the opportunities and challenges, with an emphasis on ...

Learning lifetime disease liability reveals and removes genetic confounding in electronic health records

Our new preprint “Learning lifetime disease liability reveals and removes genetic confounding in electronic health records” is now online! Link to paper: This work is led by my postdoc Yazheng Di and it’s our first project at @bsse.ethz.ch :) medrxiv.org/cgi/content/... Thread 1/n

medrxiv.org

Electronic health records (EHRs) have become the cornerstone of population-scale genetic studies1, but factors including patterns of healthcare use shape which and how diagnoses are recorded, leading ...

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Na Cai @caina89.bsky.social · 16/02/2026

My department @bsse.ethz.ch is inviting applications for a new assistant professor (tenure-track) in computational immunology, interested candidates please see advert and apply! Deadline 15 April 2026 ethz.ch/en/the-eth-z...

ethz.ch

Assistant Professor (Tenure Track) of Computational Immunology

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Jonathan Pritchard @jkpritch.bsky.social · 06/01/2026

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 👇

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Na Cai @caina89.bsky.social · 20/12/2025

Importantly in this paper we derive a new metric PRS pleiotropy which we use to show shallow phenotypes indeed give non specific gwas signal that leads to non specific PRS predictions

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Sasha Gusev @sashagusevposts.bsky.social · 13/12/2025

I wrote about the bizarre case of Herasight, the embryo selection company going all in on eugenics.

open.substack.com

Embryo selection company Herasight goes all in on eugenics

...

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Anna Docherty @annadocherty.bsky.social · 19/10/2025

The PGC Suicide Working Group will provide a symposium at #WCPG2025 🇲🇽 covering our latest multi-ancestry GWAS and CNV meta-analyses, sex-specific meta-analyses, and GxEHR analyses. 🥳 @lcstoshio.bsky.social @andreyshabalin.bsky.social @sarahcolbert.bsky.social @caina89.bsky.social

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Yun S. Song @yun-s-song.bsky.social · 22/09/2025

We are excited to share GPN-Star, a cost-effective, biologically grounded genomic language modeling framework that achieves state-of-the-art performance across a wide range of variant effect prediction tasks relevant to human genetics. www.biorxiv.org/content/10.1... (1/n)

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Rayan Chikhi @rayanchikhi.bsky.social · 03/09/2025

🌎👩‍🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. doi.org/10.1101/2024...

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Peter Sudmant @psudmant.bsky.social · 13/08/2025

The Sudmant lab at UC Berkeley is seeking a postdoc to work on a fully funded NIH project to understand differences in DNA repair and somatic mutation across the primate tree of life. Please spread widely to those who may be interested aprecruit.berkeley.edu/JPF05052

aprecruit.berkeley.edu

Postdoctoral Scholar – Genomics, Aging, Somatic Mutation, Structural Variation, Evolution , Cancer – Integrative Biology

University of California, Berkeley is hiring. Apply now!

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Yun S. Song @yun-s-song.bsky.social · 06/06/2025

The 2026 Probabilistic Modeling in Genomics (ProbGen) meeting will be held at UC Berkeley, March 25-28, 2026. We have an amazing list of keynote speakers and session chairs: probgen2026.github.io Please help spread the news.

probgen2026.github.io

Home - ProbGen 2026

Your Site Description

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Na Cai @caina89.bsky.social · 26/07/2025

Congratulations, very exciting! 🎉

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Jolien Rietkerk @jolienrietkerk.bsky.social · 24/07/2025

‘Genetic Risk Effects on Psychiatric Disorders Act in Sets’, the title of our new preprint on @medrxivpreprint.bsky.social! This huge collaborative effort advances our understanding of psychiatric genetic architecture and emphasizes the importance of looking beyond additive effects. 🧵1/n; 🧪👩🏽‍🔬🧬

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This work would not have been possible if not for the persistent efforts of @jolienrietkerk.bsky.social Andy Dahl, Jonathan Flint, Andrew Schork and many others, as well as the data from participants in @ukbiobank.bsky.social and IPSYCH. We hope it will be informative to the field. 12/n

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Though our investigations and findings are centered on psychiatric disorders, the implications are generalizable to all complex traits and diseases, especially those with heterogeneous architectures and unclear diagnostic boundaries. 11/n

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Overall, our work provides a novel metric, the CE test, for informing diagnostic boundaries. Our results show that genetic effects on psych disorders act in sets, and calls for a re-evaluation of current approaches and assumptions. 10/n

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Finally, we find that common genetic effects across all five psych disorders, expected to capture common etiological axes among them, forms a cross-order set most plausibly explained by common confounders external to each disorder’s etiology. 9/n

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We further show that disorder-specific sets lead to comorbidity between two disorders, refuting the assumption that comorbidity is the result of additive effects of genetic effects on both, exemplified by the expectation that high rGs would lead to high comorbidity. 8/n

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These findings imply that rGs are insufficient to inform nosology, and that the CE test provides a way to determine whether putative disorders or subtypes should be merged or remain separate diagnostically. 7/n

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We also show that sets of genetic effects are disorder-specific, despite high genetic correlations (rG) between them. This opposes a well-established conjecture, based on high rG, that clinical boundaries do not reflect their pathogenic processes. 6/n

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In our paper, we test for CE in five psychiatric disorders in the @ukbiobank.bsky.social and the iPSYCH dataset using both polygenic risk scores (PRS) and family-based genetic risk scores (FGRS). We find negative CE for 3 out of 5 disorders, demonstrating there are unrecognized subtypes. 5/n

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A model and test for coordinated polygenic epistasis in complex traits | PNAS

The existence of these sets induces a structured form of statistical interactions called coordinated epistasis (CE), detailed in previous publications by Andy Dahl, Noah Zaitlen www.pnas.org/doi/10.1073/...; negative CE is seen when there are different sets of genetic effects. 4/n

pnas.org

Interactions between genetic variants—epistasis—is pervasive in model systems and can profoundly impact evolutionary adaption, population disease d...

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