tuuliel.bsky.social
Professor at KTH, NY Genome Center, SciLifeLab, working on functional genomics and human genetics.
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TO REITERATE
We don't have to convince them to back down. That would be great, but unlikely.
But without the record of numerous and thoughtful comments, the ability to litigate this subsequently will be hampered.
ADD YOUR VOICE
/fin
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Everyone loves describing their dissertation research to non-academics, right? Now try that on the witness stand in a second language on a Zoom connection from ICE custody!
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And if you're at Biology of Genomes at CSHL this week, don't miss Sam's talk about this work on Friday morning! #BOG25 #BOG2025
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Neither approach does a stellar job, but I'm an optimist: Our works points to future potential, with larger and more diverse studies. The genes detected in these 1st gen CRISPRi work look pretty similar to eQTL data from 15 years ago (I was there). There is much to discover!
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Overall, this provides important insights in cis-target gene discovery from eQTLs and CRISPRi. I think we're ultimately going to need both tools, as both have fundamental weaknesses: eQTL's bias against constrained genes, and difficulty of applying CRISPR to diverse cell types.
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The analysis of gold-standard genes in these GWAS loci shows how much you'd miss using only one approach, and how startlingly orthogonal the discoveries are. The patterns are highly variable between the loci. It depends on your downstream application if you prefer higher sensitivity vs specificity.
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Analysis of the loci where the gold-standard gene was missed looks pretty grim: eQTLs find other i.e. misleading genes, while CRISPRi often lacks power. "Wow they both suck, but in totally different ways" was my first reaction to this plot.
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The 💰 question: what about “true” causal gene discovery? We compiled a list of 421 gold-standard genes, of which 53 were discovered as eGenes and 23 as cGenes - and only 9 with both! A higher proportion of cGenes are gold-standard genes, compared to eGenes or Hi-C targets.
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eGenes and cGenes have different properties too: cGenes are fewer, proximal, more constrained and more buffered, while eQTL mapping (especially across many cell types) finds more genes per locus, including distal ones. This is consistent with previous empirical work and theoretical work on eQTLs.
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Target genes do overlap, but not so much. This is partly due to power: sc-eQTLs contribute little, eQTLs struggle with low-freq variants, CRISPRi with low expr genes. CRISPRi (but not eQTL) power is low in non-overlapping loci, indicating that bigger CRISPRi studies -> better convergence.
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Briefly, we analyzed blood trait GWAS, eQTLs from blood cell types, and CRE (enhancer) CRISPRi+scRNA-seq data from K562s, primarily from Gasperini et al. and our own Morris et al. (with @johnomix.bsky.social). We were then able to analyze the overlap of discoveries in 882 GWAS CREs.
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We've worked on eQTLs and CRISPR for 10+ years now, and 4 years ago I hypothesized about their orthogonal strengths and weaknesses, for a review paper with @dgmacarthur.bsky.social. It’s exciting to finally have enough empirical data for a systematic analysis.
www.science.org/doi/10.1126/...
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We're all on the losing side in the Trump regime war against science.
And great science has always existed outside of the US too; the US system has been great but not as exceptional as many think. Notably, European research funding is not under a single agency for an 🍎-🍎 comparison to NIH/NSF.
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In times like this, it's disheartening to read some of the conversations (not the quoted tweet), where Europeans are gleeful about gaining edge & recruitment opportunities, or Americans suggesting that the US is/was the only place with investment, infra and structures for high-quality science.
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Thanks Nick!
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In summary, we show spatial transcriptomics data provides and important layer of insights for functional interpretation of GWAS data. We hope to see broad use of the tools and resources we compiled, and look forward to even broader applications as the ST data sets improve in scope and resolution.
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Drug target genes for a given disease share enrichments with genetically implicated genes. We also show vignettes of genes that are genetically associated to a disease, expressed in disease-enriched structures, and targets for known drugs.
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But what about spatial vs cell type data? Analyzing sc/sn data from the brain suggests that some diseases could arise from dysfunction in a particular cell type that may or may not be spatially enriched, while others may derive from dysfunction of multiple cell types in a given tissue structure.
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The brain is particularly cool, with its well-defined substructure. We see the expected neuropsych enrichment in cortex, while Alzheimer’s genes are enriched in white matter, thalamus and choroid plexus. Spatial correlation shows subclusters of disease genes in distinct spatial domains.
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While genes often showed a broad enrichment across a tissue, in about 20% of disease-tissue pairs only a subset of tissue structures were enriched, highlighting the value of spatial data. Cool patterns across many diseases/tissues - and a big resource for domain specialists to explore!
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We processed 31 published 🐭 & 🙂 spatial datasets from 8 organs with genetically implicated (and drug target) genes in 32 human diseases from @opentargets.org. We then mapped the tissue structures where disease genes are higher expressed than the null, using our new approach STEAM.
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Enrichment of GWAS signal has provided crucial insights into tissues, cell types and pathways that underlie disease. But what about tissue structures identified from emerging spatial data sets? This is what we wanted to tackle, embedded a world-leading hub for spatial omics at @scilifelab.se and KTH