Bioinformatician. Interested in post-GWAS analysis. PhD candidate Complex Trait Genetics at Posthuma Lab. Vrije Universiteit Amsterdam
Joined November 2021
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Marijn Schipper retweeted
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
Marijn Schipper retweeted
Our new preprint on "The genetic landscape of heterogeneity in human functional brain connectivity" is available on medRxiv. Check it out! @MJ_Schipper, Christiaan de Leeuw, @cato_romero, @khelwegen, @DPosthu, Jeanne Savage and Martijn van den Heuvel.
medrxiv.org/content/10.1101/…
Incredibly proud to see our latest work out in Nature Genetics: nature.com/articles/s41588-0…
Here we share our FLAMES framework, which predicts the effector genes in GWAS loci with state-of-the-art precision🔥
Special thanks to @DPosthu
Full thread describing findings below!
We use FLAMES to prioritize 180 schizophrenia risk genes. We find that these genes are highly enriched in synaptic functions.
Clustering these genes based on relative expression throughout the lifetime shows that ~one third of these genes are expressed strongest prenatally.
FLAMES is freely available from GitHub: github.com/Marijn-Schipper/F…
Enhanced PDF available here: rdcu.be/d9iQP
This is likely my last tweet, interested in more? Follow me on bsky.app/profile/mjschipper.…
Marijn Schipper retweeted
- T30: @MJ_Schipper "FLAMES: Accurate Causal Gene Prioritization in Psychiatric Gwas Loci " ECIP-Poster Finalist - FLAMES is the latest tool for our lab and was just accepted for publication!
Marijn Schipper retweeted
The 7th event is scheduled for November 7th! We're excited to welcome @ILibedinsky , PhD candidate from @VUamsterdam , who will be presenting their work on quantifying brain connectivity signatures by means of polyconnectomic scoring! translational-neuro.org/even…
Marijn Schipper retweeted
Tools that predict the causal genes in GWAS loci are too complex! Many use XGBoost models with >45 features (i.e. L2G, Ei).
CALDERA achieves better performance despite using LASSO regression and only 12 features. This makes it much easier to understand why a gene was chosen.
Marijn Schipper retweeted
And the winner is ... Bernardo Maciel who last week won the Poster Prize at the Dutch Neuroscience Meeting. Congratulations and well deserved!! @VUamsterdam
Marijn Schipper retweeted
Could not be more delighted to present our work investigating how over 220,000 complex and molecular trait-associated genetic variants affect transcriptional regulation using massively parallel reporter assays!
biorxiv.org/content/10.1101/…
See below for a 🧵. 1/n
Marijn Schipper retweeted
Super proud to see our human neurodevelopmental atlas of chromatin accessibility out in nature! Thank you to @slinnarsson @MJ_Schipper @DPosthu and @ReagorCaleb, as well as the KI hospital and Cambridge University for a very joyful collaboration. nature.com/articles/s41586-0…
Marijn Schipper retweeted
1/16 Our new preprint is out, in which we developed an approach to transform Polygenic (Risk) Scores (PGSs) to disorder probabilities (i.e. the absolute lifetime disorder risk). Here is a brief overview.
xitter.cf/medrxivpreprint/status…
Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach medrxiv.org/cgi/content/shor… #medRxiv
Very happy to announce that the preprint for FLAMES, our novel tool to predict the effector gene of complex trait loci is live:
medrxiv.org/content/10.1101/… (1/5)
Applying FLAMES on the fine-mapping of the latest PGC3 schizophrenia GWAS we prioritize 187 genes in different loci. We show that these genes separate into a pre-natal cluster enriched in neurodevelopmental processes and a post-natal cluster enriched in synaptic signalling. (5/5)
And before I forget, with special thanks to @DPosthu @StatGen_DeLeeuw @Dubhghlaso @HubersNikki, Dorret Boomsma and Mick O'Donovan