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Natsuhiko Kumasaka @natsuhiko83.bsky.social · 2d

Excited to share my poster at #ASHG2025 (Board 9173F)! Can’t wait to see everyone next week!

Reposted by Natsuhiko Kumasaka

Kaur Alasoo @kauralasoo.bsky.social · 12d

Perhaps the biggest change is that we have added some practical guidelines for performing MR with proxy exposures. These are summarised on this nice figure prepared by Ida Rahu:

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Reposted by Natsuhiko Kumasaka

Open Targets @opentargets.org · Aug 26

Out now on bioRxiv! 🧬🖥️

A team from the Cowley, Bassett, and @gosiatrynka.bsky.social labs integrate pooled iPSC phenotyping, transcriptomics, and CRISPR perturbations to identify genetic drivers of molecular and cellular phenotypes in microglia

www.biorxiv.org/content/10.1...
#neuroskyence

Integrated QTL mapping and CRISPR screening in pooled iPSC-derived microglia reveals genetic drivers of neurodegenerative risk

Mounting evidence implicates microglia in neurodegeneration, but linking disease-associated genetic variants to target genes and cellular phenotypes is hindered by the inaccessibility of these cells. ...

www.biorxiv.org

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Reposted by Natsuhiko Kumasaka

Adriaan @adriaan-vd-graaf.bsky.social · Jul 4

Our paper, MR-link-2 has just been published! Offering pleiotropy robust Mendelian randomization from a single region! www.nature.com/articles/s41...

A network of metabolites and their potential causal relationships. Green edges are Detected by the statistical causal inference method MR-link-2

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Reposted by Natsuhiko Kumasaka

Zixuan (Eleanor) Zhang @elezzx.bsky.social · Jan 27

Excited to present our work on developing jaxQTL, a fast single-cell eQTL mapping tool that improves power and robustness in identifying sc-eQTLs using count-based models. See details in threads 🧵
www.medrxiv.org/content/10.1...

Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping

Population-scale single-cell transcriptomic technologies (scRNA-seq) enable characterizing variant effects on gene regulation at the cellular level (e.g., single-cell eQTLs; sc-eQTLs). However, existi...

www.medrxiv.org

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Reposted by Natsuhiko Kumasaka

Open Targets @opentargets.org · Jun 26

Preprint out today!

A team led by @tobioinformatics.bsky.social and Bradley Harris in @carlanderson.bsky.social ‘s lab has created the largest single-cell atlas of IBD tissues to date

www.medrxiv.org/content/10.1...

UMAP of the 9 populations and 86 cell types identified after quality control and clustering

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Reposted by Natsuhiko Kumasaka

Yoav Gilad @ygilad.bsky.social · Jun 9

Why do so many disease risk variants show no regulatory effects in GTEx and/or other standard eQTL studies?

What do we mean when we say a regulatory effect is "context-specific" and how do we define the context?

We explored these questions using brain organoids and oxygen stress.

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Reposted by Natsuhiko Kumasaka

Robin Hofmeister @rjhfmstr.bsky.social · Jan 21

🚨 Preprint update!
We expanded our study on parent-of-origin effects with new findings from the MoBa cohort, now leveraging up to 265,000 individuals!

Discover fresh insights into the genetic architecture of early growth!

Updated preprint: www.medrxiv.org/content/10.1...

Robin Hofmeister @rjhfmstr.bsky.social · Dec 6

🚨 Our preprint on parent-of-origin effects (POEs) is out!

With our novel method, we inferred the parental origin of alleles for >220,000 individuals, uncovering novel insights into genetics, evolution, and health.

👉 Read here: www.medrxiv.org/content/10.1...

👇 Highlights below!

Parent-of-Origin inference and its role in the genetic architecture of complex traits: evidence from ~220,000 individuals

Parent-of-origin effects (POEs) occur when the impact of a genetic variant depends on its parental origin. Traditionally linked to genomic imprinting, these effects are believed to have evolved from p...

www.medrxiv.org

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Reposted by Natsuhiko Kumasaka

Haky Im @hakyim.bsky.social · May 14

Check out our scPrediXcan paper
www.cell.com/cell-genomic...
Led by the talented @Charles_Zhou12 and supervised by @MengjieChen6
and me, with thanks to many contributors.

scPrediXcan integrates deep learning and single cell expression data into a powerful cell type specific TWAS framework.

scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework

Zhou et al. introduce scPrediXcan, a novel transcriptome-wide association study framework that integrates the deep learning-based model ctPred for cell-type-specific expression prediction. Applied to ...

www.cell.com

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Reposted by Natsuhiko Kumasaka

Mike Inouye @mikeinouye.bsky.social · May 7

📣📣 Thrilled to see this cool work finally out in the wild! "Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology & prediction" www.nature.com/articles/s41...

Loads of cool findings including MR for alcohol usage -> increased variance in BC traits

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Reposted by Natsuhiko Kumasaka

Sasha Gusev @sashagusevposts.bsky.social · May 5

Nice! Environmentally responsive eQTLs are enriched for being more distal and for constrained genes relative to conventional QTLs.

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Reposted by Natsuhiko Kumasaka

Pradeep Natarajan @pnatarajanmd.bsky.social · Apr 23

Very excited to share our preprint led by M. Levin @skoyama.bsky.social J. Woerner & with S. Damrauer assessing genome-wide pleiotropy of >1,000 clinical traits across ~1.7M individuals with nearly 30K locus-trait associations!
www.medrxiv.org/content/10.1... @medrxivpreprint.bsky.social

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Reposted by Natsuhiko Kumasaka

Satoshi Koyama @skoyama.bsky.social · Apr 18

I proudly advocate for 2002 as the year GWAS began 🙂
www.nature.com/articles/ng1...

Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction - Nature Genetics

Nature Genetics - Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction

www.nature.com

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Reposted by Natsuhiko Kumasaka

Anna Cuomo @annasecuomo.bsky.social · Mar 24

📢 new preprint alert: So so excited to share our analysis on the impact of common and rare variants on single-cell gene expression in blood, using WGS and scRNA-seq data from nearly 2,000 individuals and 5.4m cells as part of TenK10K phase 1 🧬 www.medrxiv.org/content/10.1...
🧵👇 (1/n)

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Reposted by Natsuhiko Kumasaka

Shai Carmi @shaicarmi.bsky.social · Mar 10

Looks like a nice resource:
* Array genetic data for 80,638 Japanese children
* 1,163 child health and developmental traits (e.g. food allergy, anthropometric, developmental)
* Parental environmental exposures

www.medrxiv.org/content/10.1...

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Natsuhiko Kumasaka @natsuhiko83.bsky.social · Mar 20

Thrilled to share that our paper has been accepted to #ESHG2025 as an oral presentation!

bsky.app/profile/nats...

Natsuhiko Kumasaka @natsuhiko83.bsky.social · Feb 26

I'm delighted to present our nationwide effort to identify genetic determinants of child health and development in Japan.

Preprint: www.medrxiv.org/content/10.1...

Reposted by Natsuhiko Kumasaka

Mike Inouye @mikeinouye.bsky.social · Mar 4

📣 New from the lab: The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes www.nature.com/articles/s41...

Check out the INTERVAL RNAseq portal www.intervalrna.org.uk

Led by @alextokolyi.bsky.social & Elodie Persyn!

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Reposted by Natsuhiko Kumasaka

Seppe De Winter @seppedewinter.bsky.social · Feb 28

We wrote a review article on modelling and design of transcriptional enhancers using sequence-to-function models.

From conventional machine learning methods to CNNs and using models as oracles/generative AI for synthetic enhancer design!

@natrevbioeng.bsky.social

www.nature.com/articles/s44...

Modelling and design of transcriptional enhancers - Nature Reviews Bioengineering

Enhancers are genomic elements critical for regulating gene expression. In this Review, the authors discuss how sequence-to-function models can be used to unravel the rules underlying enhancer activit...

www.nature.com

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Reposted by Natsuhiko Kumasaka

Yoav Gilad @ygilad.bsky.social · Feb 26

studies of regulatory variation and GxE interactions in the heart. here's the preprint: www.biorxiv.org/content/10.1...

Guided Differentiation of Pluripotent Stem Cells into Heterogeneously Differentiating Cultures of Cardiac Cells

In principle, induced pluripotent stem cells (iPSCs) can differentiate into any cell type in the body. The challenge is to find a way to rapidly expand the dimensionality of cell types and cell states...

www.biorxiv.org

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Reposted by Natsuhiko Kumasaka

Jonathan Pritchard @jkpritch.bsky.social · Jan 26

Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. What key cellular functions link genetic variation to disease?

I'm very excited to present our new work combining associations and Perturb-seq to build interpretable causal graphs! A 🧵

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Reposted by Natsuhiko Kumasaka

Linda Kachuri @lindakachuri.bsky.social · Feb 25

📢 Thrilled to see this out, an atlas of e/sQTL across multiple tissues and diverse ancestries in 14,324 TOPMed participants: www.medrxiv.org/content/10.1...

Grateful to the #TOPMed #Omics WG for giving me an opportunity to learn and contribute to this important work 🧬

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expre...

www.medrxiv.org

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Natsuhiko Kumasaka @natsuhiko83.bsky.social · Feb 26

There's much more to explore in JECS and we'd love to hear your feedback!

All the 1,163 summary statistics are now available upon request!

forms.office.com/pages/respon...

Microsoft Forms

forms.office.com

Natsuhiko Kumasaka @natsuhiko83.bsky.social · Feb 26

In addition, genetic correlation analysis suggested some evidence that maternal environmental exposures during pregnancy influence child characteristics at birth.

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Natsuhiko Kumasaka @natsuhiko83.bsky.social · Feb 26

Additional longitudinal GWAS of BMI using Gaussian process regression identified 66 novel dynamic genetic associations along child development.

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Natsuhiko Kumasaka @natsuhiko83.bsky.social · Feb 26

We then performed GWAS on 1,163 child health and developmental traits and parental environmental exposures, identifying 4,985 common genomic loci, of which 25% represent novel associations not previously reported.

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