iiinova — where your digital self keeps evolving

Andrew Marderstein @amarderstein.bsky.social · 21/08/2026

Excited and honored to have been elected last week to the ASHG Board of Directors! @geneticssociety.bsky.social I’m excited to continue to support the next generation of geneticists, and help shape the future of human genetics. Thank you to everyone who supported me.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

17/ We're excited to share FLARE, >3 billion variant effect predictions across 132 adult and fetal brain and heart contexts, and all associated code and resources with the community.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

16/ Finally, FLARE informed common variant architecture. As a schizophrenia heritability annotation in S-LDSC, FLARE-fetal brain beat the strongest region-based annotations.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

15/ The paired RNA-seq also helped interpret FLARE variants. For example, two SNPs are only 78 bp apart but affect different genes: - one breaks an NFIL3 motif (lowering SENP3), - the other creates a ZEB/SNAI repressor motif (lowering TNFSF13).

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

14/ One of the other applications of FLARE is to identify mutations that underlie outlier gene expression. In 791 adult brain samples with paired WGS and brain RNA-seq, top-scoring FLARE-brain variants were enriched near genes showing outlier underexpression.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

13/ We then trained FLARE on heart data and applied it to de novo mutations in congenital heart disease, where most top scoring mutations were found in cases.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

12/ Applying FLARE-fetal brain to de novo mutations in ~2,000 autism families, 14 of the 16 highest-scoring mutations near syndromic autism genes occurred in probands rather than unaffected siblings, with several hits near CNTNAP2.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

11/ Leveraging these observations, we built FLARE (Functional Lasso Analysis of Regulatory Evolution). FLARE: - predicts conservation (PhyloP), - uses regulatory and genomic features, - can be trained on any specific context.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

10/ This is particularly striking in fetal neurons, which fits Medawar's theory of aging: selection wanes after reproduction, so fetal variants get purged while later ones escape.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

9/ Accounting for gene distance and constraint, ultra-rare variants had larger predicted effects than common ones while affecting a broader number of cellular and developmental contexts.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

8/ These models were powerful for studying ultra-rare variants (MAF<0.1%), where methods like GWAS are underpowered. This allowed us to ask about the regulatory properties that differ between common and rare variants.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

7/ We then used CRISPRi to knockdown the candidate enhancer in iPSC-derived microglia, which supported RASGEF1C as the target gene. This demonstrates how computational predictions can directly guide experimental validation!

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

6/ Fine-mapping + ATAC peak + ChromBPNet helped narrow down GWAS loci to the causal variant, TF motif, and cell type. For example, our approach identified a single variant in an Alzheimer’s locus, which creates a new ZEB/SNAI repressor motif to lower chromatin accessibility.

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

5/These predictions captured the effects of common, fine-mapped disease variants, which had larger effects in relevant contexts (e.g. predicted brain effects for fine-mapped GTEx brain eQTLs, or heart effects for CAD loci).

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

4/ To address this, we used: - single-cell ATAC-seq across adult and fetal brain and heart (132 contexts in all), - trained deep learning-based DNA sequence models of chromatin accessibility (ChromBPNet), - and made over 3 billion variant effect predictions

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

3/ Whole genome sequencing has led to a huge catalog of variants, and GWAS has linked many of the common ones to traits and disease. But a large fraction are rare or de novo, and we have few tools to assess functional impact (especially in the non-coding genome!)

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Andrew Marderstein @amarderstein.bsky.social · 15/06/2026

2/ This was first released as a preprint last year (biorxiv.org/content/10.1...), and we're thrilled to now see it published, which brings together machine learning, statistical genetics, evolutionary bio, & gene regulation to better understand functional impacts of non-coding variation.

biorxiv.org

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal...

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Reposted by Andrew Marderstein

Anshul Kundaje @anshulkundaje.bsky.social · 19/08/2025

@jengreitz.bsky.social l & my lab want to co-hire a computational biologist/biostatistician with project management expertise to help map the regulatory code of the human genome and discover genetic mechanisms of disease. Details below careersearch.stanford.edu/jobs/computa... Plz RT

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Greg Findlay @gregfindlay.bsky.social · 18/08/2025

Our latest research is out today on ‪@medrxivpreprint.bsky.social: www.medrxiv.org/content/10.1... Saturation genome editing of BRCA1 across cell types accurately resolves cancer risk. Led by the amazing Phoebe Dace. This one’s packed full of data, so check out the paper. Quick highlights… 🧵 1/n

medrxiv.org

Saturation genome editing of BRCA1 across cell types accurately resolves cancer risk

Germline pathogenic BRCA1 variants predispose women to breast and ovarian cancer. Despite accumulation of functional evidence for variants in BRCA1 , over half of reported single-nucleotide variants (...

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Caleb Lareau @caleblareau.bsky.social · 22/07/2025

Excited to share a new preprint from the lab with @ryandhindsa.bsky.social ! www.biorxiv.org/content/10.1... Led by @sherrynyeo.bsky.social, @erinmayc.bsky.social, and friends, we continue our journey to find viral DNA in our favorite place-- the overlooked and discarded reads in existing data! 1/

the treasure trove of all sequencing datasets

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Tobi Alegbe @tobioinformatics.bsky.social · 08/07/2025

🚨New preprint just dropped 🚨 medrxiv.org/content/10.1101/2025.06.24.25330216 The main output from my PhD is finally public and we’re SUPER excited about the findings! If you’re interested in what we learnt about IBD with a massive 700+ sample sc-eQTL dataset of the gut, read on!

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Michael Montgomery @michaeltmont.bsky.social · 11/06/2025

Had a lot of fun writing this “tools of the trade” highlight for our Variant-EFFECTS technology. Check it out! 🛠️

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Kate (Kathryn) Lawrence @itskatelawrence.bsky.social · 08/06/2025

Excited to share my first PhD paper in the @sbmontgom.bsky.social lab with @tamigj.bsky.social (www.biorxiv.org/content/10.1...)! Standard QTL methods treat each gene independently. But what if a single variant regulates multiple nearby genes at once - what we call “allelic proxitropy”? 🧵 ⬇️

Standard methods are equivalent to a flashlight, looking at each gene independently. We combine signals from multiple genes, turning a floodlight onto the genome.

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Jeremy Berg @jeremymberg.bsky.social · 22/02/2025

I have been trying to find the time to move away from the polical hellscape we find ourselves in to finish and share a bluetorial about science. This helps me remember what this is all about. Ironically, it is about the treatment of pain.

media.tenor.com

the word irony is written on a white background

ALT: the word irony is written on a white background

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Soumya Kundu @soumyakundu.bsky.social · 19/02/2025

Excited to see this out, and really thankful for @anshulkundaje.bsky.social @sbmontgom.bsky.social and everyone in both of their labs who contributed to this work to make it possible!

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Soumya Kundu @soumyakundu.bsky.social · 19/02/2025

This was a really fun collaboration with @amarderstein.bsky.social where we explored some of the interesting relationships between context-specific non-coding variant effects, disease, and evolution using deep learning models of chromatin accessibility in the brain and heart.

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Andrew Marderstein @amarderstein.bsky.social · 06/02/2025

Submissions for ASHG 2025’s Featured Symposium are due Feb 10 at 5:00 pm ET! Let me know if you have any questions. We're excited to see a wide range of proposals, ranging from AI/ML applications, clinical genetics, women's health, and more. www.ashg.org/meetings/202...

ashg.org

Featured Symposia

ASHG’s Featured Symposium are a part of the most premier science at the Annual Meeting, featuring 8,000 of the world’s leading geneticists. Questions: programs@ashg.org Looking to make your mark on th...

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Tim Coorens @timcoorens.bsky.social · 15/01/2025

Our new perspective article describing the human and non-human primate developmental GTEx projects is now out in @nature.com! We outline the scope, vision, opportunities and challenges of these projects here: www.nature.com/articles/s41...

nature.com

The human and non-human primate developmental GTEx projects - Nature

The developmental Genotype-Tissue Expression (dGTEx) projects will catalogue and integrate gene expression, regulation and genetics data across 120 human donors from birth to adulthood with developmen...

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Eric Topol @erictopol.bsky.social · 14/12/2024

A new study this week showed how the most common blood test performed-—the CBC, complete blood count—contains a treasure chest of information that we are missing in reporting out to patients and doctors. erictopol.substack.com/p/your-lab-t...

relationship of complete blood count setpoint metrics to 10-year mortality

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Eric Topol @erictopol.bsky.social · 11/12/2024

Our blood tests are interpreted by average reference values. That's missing a lot of rich information! Each person has their own tightly regulated setpoints. One healthy person's complete blood count setpoint can be differentiated from 98% of other healthy adults. www.nature.com/articles/s41...

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Andrew Marderstein @amarderstein.bsky.social · 02/12/2024

“our analyses suggest that less than 1% of blastocysts are fully euploid, and that many embryos possess low-level mosaic clones that are not captured during biopsy”

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Rosa Ma @rosaxma.bsky.social · 25/11/2024

What cell types drive congenital heart defects (CHD)? Some new answers in our latest preprint, where we explored: 1). Key cell types contributing to CHD genetics 2). Impact of noncoding variants on CHD risk www.medrxiv.org/content/10.1101/202…

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Christian Nefzger @nefzgerlab.bsky.social · 18/11/2024

Our recent study in Cell Metabolism provides compelling evidence that chromatin accessibility and transcription factor network remodeling in aging reflect the predictable degrading effects of a mechanism initially driving organismal maturation. Link: doi.org/10.1016/j.cmet.2024.06.006 Thread 🧵👇1/9

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