iiinova — where your digital self keeps evolving

We thank for all participants and all researchers working hard on constructing this essential data for many many years.

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We hope our study is going to be a useful resource for the community, including summary statistics and fine-mapped alleles for functional follow-up studies.🧬

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Polygenic risk scores also showed an improved trend for RA prediction 🔮 for underrepresented populations, while we still definitely need more data from these pops to get more statistically significant differences!

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93% of RA loci had no coding variant in credible sets - the action is in non-coding regulatory DNA. We integrated single-cell ATAC and RNA-seq from RA synovial tissue to find which cell types and genes matter. We identified key roles of T cells, B cells and myeloid cells.

We identified 152 genome-wide significant loci, including 31 novel, and dramatically improved fine-mapping. Credible causal variant sets narrowed down significantly compared to previous studies. 60 loci had <10 variants in their 95% CS; 10 were pinpointed to a single variant.

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We used KOMAP, an ML approach combining billing codes + clinical notes via NLP, achieving 93% PPV. We demonstrated that this accurate phenotyping resulted in more significant loci and stronger effect sizes.

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However, defining RA phenotype accurately in biobanks including MVP is not an easy task. I know this diagnostic challenge from my experience as a rheumatologist! ICD codes💴 alone is known to be susceptible to diagnostic errors with only ~64% positive predictive value.

The key advance of this study is the including of African American and Admixed American populations that were least represented in previous studies! Previously, ~99% of participants of RA genetic studies were of European or East Asian ancestry.

New preprint! 📣We performed the largest multi-ancestry GWAS of rheumatoid arthritis (RA), the most common autoimmune disease, by analyzing the VA Million Veteran Program (MVP) with international RA cohorts.

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Maitreya Dunham @maitreya.bsky.social · 24/11/2025

An important piece from my neighbor, colleague and friend Will DeWitt et al. @wsdewitt.github.io www.biorxiv.org/content/10.6...

biorxiv.org

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Reposted by Saori Sakaue

Open faculty position (Assistant or Associate) in UW Medical Genetics. As you might expect, faculty often end up also interacting with Genome Sciences, so we're hoping for some great prospects! Note the clinical requirements for the position. apply.interfolio.com/176466

apply.interfolio.com

Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio

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Reposted by Saori Sakaue

loic-yengo.bsky.social @loic-yengo.bsky.social · 12/11/2025

First time on Bsky and first big announcement! I am excited 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. Our manuscript is here: www.nature.com/articles/s41....

nature.com

Estimation and mapping of the missing heritability of human phenotypes - Nature

WGS data were used from 347,630 individuals with European ancestry in the UK Biobank to obtain high-precision estimates of coding and non-coding rare variant heritability for 34 co...

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Saori Sakaue @saorisakaue.bsky.social · 14/10/2025

I’ll be at #ashg2025 ! Please let me know if you want to chat with me about research, my lab at UW saorisakaue.github.io etc etc! I’ll also be standing at the poster session on Friday 5075F at 2:30-4p if you can swing by to chat👋

saorisakaue.github.io

Home - Sakaue Lab @ Genome Sciences, UW

Lab website for Dr. Saori Sakaue

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Reposted by Saori Sakaue

Nobu Hamazaki @nobuhamazaki.bsky.social · 26/09/2025

New paper from my lab and @jshendure.bsky.social lab! Led by the brilliant @zukailiu.bsky.social and @cxqiu.bsky.social. We tackled how anterior and posterior progenitor cells cooperate to self-organize into an embryonic structure (termed AP-gastruloid). (1/n) www.biorxiv.org/content/10.1...

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The project was super fun and unexpected scientific journey, expanding my curiosity outside of the nucleus and DNA in genetics studies😀 I really appreciate warm mentorship from @soumya-boston.bsky.social and invaluable inputs from genetics community in Boston! Hope you enjoy reading the preprint😉

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Back to the original problem🔍 We've found that >50% of colocalization of causal variants between eQTL and neuropsychiatric disorders were specific to either nuclear or cytosolic eQTL! Subcellular localization of RNA and eQTL matters in identifying disease GWAS mechanisms (11/n

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We've conventionally assumed that multiple causal variants in eQTL or GWAS is typically LD independent and working on different biological mechanisms (eg. enhancer AND promoter), but in our cases they can be LD *dependent* and work in concert to affect the same mechanism of RNA stability (10/n

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Such examples with different causal variants between nucleus (enhancer) and cytosol (transcript) showed that sometimes many variants in complete linkage in cytosolic eQTL create a risk haplotype in the transcript, possibly suggesting a novel concept in multiple-causal-variant fine-mapping.(9/n

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.. which makes cytosolic eQTL variants more asymmetric, downstream-skewed relative to the TSS as they localize within transcribed regions where RNA chemical modification can happen and affect RNA stability in the cytosol. (8/n

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Surprisingly, 33% of eGenes had distinct causal variants between nucleus and cytosol for the same gene! Nuclear early RNA was preferentially regulated by distal enhancers at the DNA transcription level, while cytosolic late RNA was regulated by variants within transcripts.. (7/n

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More specifically, we asked if nuclear and cellular eQTL share the same causal variant (1), and if they are different, which genomic annotation(s) has preferential localization of causal variants for nuclear or cellular eQTL (2)? (6/n

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We analyzed both nuclear and cellular (mostly cytosolic) RNA compartments and associated their RNA abundance with genotype in the brain and the kidney to achieve this goal! (5/n

So we asked distinct genetic regulatory mechanisms across entire RNA lifecycle by comparing eQTL between early RNA in the nucleus and late post-transcriptionally modified RNA in the cytosol. These molecular understanding will help us understand disease alleles precisely (4/n

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But the DNA transcription into RNA is just the very first step among long journey of RNA lifecycle. RNA undergoes many processing, first in the nucleus e.g. splicing and polyA, then in the cytosol e.g. chemical modifications and degradation essential for mature RNA abundance (3/n

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Problem: eQTL reveals disease alleles' function on gene expression, while it's been so puzzling🧐 that most #GWAS alleles do not colocalize with #eQTL. The traditional wisdom in the field is that eQTL regulate DNA transcription in the nucleus by altering regulatory sequences (2/n

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