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Emil Uffelmann @euffelmann

PhD student in complex trait genetics @VUamsterdam

Joined August 2013
  • Tweets 463
  • Following 718
  • Followers 287
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Emil Uffelmann@euffelmann
13 Oct 2025
🚨 The preprint for our GWAS of Alzheimer’s disease (AD) is out Preprint: medrxiv.org/content/10.1101/… 🧵

Genomic analyses reveal new insights into Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia, with global case numbers projected to reach 153 million in 2050[1][1]. AD is highly heritable, with twin-based heritability estimates of...

medrxiv.org
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🧬 FUMA v2.0.0 is out, updated by Tanya Phung @ CTG Lab New FLAMES module (effector gene prioritization), new QTLs Analysis module, and expanded xQTL datasets in SNP2GENE fuma.ctglab.nl/
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Jian Yang@jyang1981
May 20
SV-GWAS is highly accessible now! Our new paper in @NatureGenet shows how HiFi long-read assemblies let us repurpose SNP-based GWAS data to impute structural variants (SVs) to interrogate their role in human complex traits and diseases. @WeiyangBai nature.com/articles/s41588-0…

Genome-wide associations of structural variants with human traits through imputation from long-read...

Nature Genetics - This study identifies structural variants (SVs) from long-read assemblies, develops a new reference panel and web application to impute SVs from single-nucleotide...

nature.com
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Pradeep Natarajan
@pnatarajanmd
Apr 23
Unauthorized attempt to sell UK Biobank de-identified participant data takes UK Biobank research offline for now ukbiobank.ac.uk/news/a-messa…
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Sasha Gusev@SashaGusevPosts
Feb 9
Another Claude project: a static site that pulls in GWAS SNP data from ensemble, multiple public biobanks, open targets, gtex, eqtl catalog, and OMIM.
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Doug Speed@dougthespeed
Apr 2
StratGWAS is our new tool for more efficient GWAS of heterogeneous diseases. Instead of treating cases equal, it weights them based on relevant phenotypic information such as medication use, age of onset or recruitment strategy. Full details on MedrXIv medrxiv.org/content/10.64898….

Incorporating phenotype heterogeneity in disease GWAS improves power while maintaining specificity

Common complex diseases are clinically heterogeneous, yet most genome-wide association studies (GWAS) assume cases are genetically homogeneous. This challenge is compounded in large-scale biobanks,...

medrxiv.org
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Emil Uffelmann retweeted
Bernardo Maciel@bernardopmaciel
Apr 2
Hello! Our new study where we investigated how genetic variants shape the functional wiring of our brains using UKB was just published. 🧵 Here are our main takeaways! (1/5) 🧵
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RT @sapinker: Bombshell: Oliver Sacks (a humane man & a fine essayist) made up many of the details in his famous case studies, deluding neu…

Oliver Sacks Put Himself Into His Case Studies. What Was the Cost?

The scientist was famous for linking healing with storytelling. Sometimes that meant reshaping patients’ reality.

newyorker.com
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Emil Uffelmann retweeted
Hakhamanesh Mostafavi@Hakha_Most
7 Nov 2025
How do GWAS and rare variant burden tests rank gene signals? In new work @Nature with Jeff Spence, @jkpritch, and our wonderful coauthors we find the key factors are what we call Specificity, Length, and Luck! 🧬🧪🧵 nature.com/articles/s41586-0…

Specificity, length and luck drive gene rankings in association studies

Nature - Genetic association tests prioritize candidate genes based on different criteria.

nature.com
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Loïc Yengo@LoicYengo
12 Nov 2025
We are pleased 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. Please check out our manuscript here: nature.com/articles/s41586-0….

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 complex...

nature.com
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Emil Uffelmann@euffelmann
13 Oct 2025
🚨 The preprint for our GWAS of Alzheimer’s disease (AD) is out Preprint: medrxiv.org/content/10.1101/… 🧵

Genomic analyses reveal new insights into Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia, with global case numbers projected to reach 153 million in 2050[1][1]. AD is highly heritable, with twin-based heritability estimates of...

medrxiv.org
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Emil Uffelmann retweeted
Marios Georgakis
@MariosGeorgakis
14 Oct 2025
The largest GWAS for Alzheimer's disease is now out 👉109,479 cases, 74,141 proxy cases, 2,131,799 controls, 499,708 proxy controls 👉improved SNP-based heritability: 5% in previous studies --> 19% 👉127 loci, 48 novel with enrichment in microglia & neurons
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Nathan Bell@nathanybell
14 Oct 2025
When do machine learning models actually outperform standard polygenic scores? 🤔 In our new preprint, we benchmark how non-additive genetic effects (i.e, dominance deviations) shape polygenic prediction across simulated and UK Biobank traits. 👉 medrxiv.org/content/10.1101/… 🧵 1/6

Benchmarking non-additive genetic effects on polygenic prediction and machine learning-based...

Polygenic scores (PGSs) are widely used to translate genome-wide association study (GWAS) findings into tools for genetic risk prediction. Most current approaches assume additive effects, yet the...

medrxiv.org
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Danielle@DPosthu
14 Oct 2025
Proud that the third GWAS for Alzheimer's dementia from the PGC-ALZ working group was just posted online! Huge amounts of work, and what a great collaboration! Check out our exciting findings below 👇 @PGCgenetics #ctglab #alzheimer #dementia #GWAS
Emil Uffelmann@euffelmann
13 Oct 2025
🚨 The preprint for our GWAS of Alzheimer’s disease (AD) is out Preprint: medrxiv.org/content/10.1101/… 🧵
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🚨 The preprint for our GWAS of Alzheimer’s disease (AD) is out Preprint: medrxiv.org/content/10.1101/… 🧵

Genomic analyses reveal new insights into Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia, with global case numbers projected to reach 153 million in 2050[1][1]. AD is highly heritable, with twin-based heritability estimates of...

medrxiv.org
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more replies
This study was only possible thanks to a collaboration of >100 co-authors, biobanks, pharma partners, direct-to-consumer companies, and, most importantly, the nearly 3 million participants who shared their data. I am immensely grateful for their participation.
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Special thanks to @Dubhghlaso (shared first author), my PIs @DPosthu and Ole Andreassen, and the whole Alzheimer's disease working group of the Psychiatric Genomics Consortium
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