Danielle

Out today from CTGlab as part of the EU funded REALMENT consortium, hear hear👇 #PRS #machinelearning #prediction #genetic

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.bsky.social #ctglab #alzheimer #dementia #GWAS

If you are preparing your bachelor statistics course and would like to add optional material for students to better understand statistics on a conceptual level (see topics in the screenshot) my free textbook provides a state of the art overview. lakens.github.io/statistical_...

Check out the latest from @euffelmann.bsky.social #cgtlab!

#ctglab is hiring! We have #vacancies! 2 PhD & 2 postdoc positions, in #statistical #genetics and/or #bioinformatics - if you like GWAS, method development and linking with biology - check these out 👇

werkenbij.vu.nl/vacatures/tw...

werkenbij.vu.nl/vacatures/po...

werkenbij.vu.nl/vacatures/po...

#ctglab is hiring! We have #vacancies! 2 PhD & 2 postdoc positions, in #statistical #genetics and/or #bioinformatics - if you like GWAS, method development and linking with biology - check these out 👇

werkenbij.vu.nl/vacatures/tw...

werkenbij.vu.nl/vacatures/po...

werkenbij.vu.nl/vacatures/po...

Current integrative prioritization methods use either functional data (L2G, cS2G) or network convergence of GWAS signal (PoPS).

FLAMES combines both, by combining XGBoost predictions using functional evidence in the GWAS locus with PoPS predictions.

Prioritizing effector genes at trait-associated loci using multimodal evidence Nature Genetics - FLAMES is a machine learning approach combining variant fine-mapping, SNP-to-gene annotations and convergence-based gene prioritization scores to identify candidate effector genes...

Just out from #ctglab by @mjschipper.bsky.social
- our new tool FLAMES 🔥🔥, to identify the most likely causal genes in GWAS loci, using machine learning techniques, thoroughly benchmarked and freely available
Paper: rdcu.be/d9iQP
Code: github.com/Marijn-Schip...

@mjschipper.bsky.social 🔥

GitHub - MichelNivard/awesome-complex-trait-genetics: A list of awesome tools for complex trait genetics. A list of awesome tools for complex trait genetics. - MichelNivard/awesome-complex-trait-genetics

🚨 This will become a curated list of awesome tools for complex trait genetics, **add yours**! it may become a review in which case those who contribute are invited as co-authors.

I've set 2 personal goals.

One is to identify the causal gene for every lead snp in the @gwascatalog.bsky.social

The other is to assign a GWAS trait to every human gene.

Unfortunately the gwas catalog is growing exponentially and I'm not.

I'm at 103,605 "solved" loci out of 563,097 lead snps

The bacteria in your gut depend on where you are in the social network around you.

The microbes within us treat our social networks as the extended environment in which they thrive. They can spread from person to person.

New #HNL work out today in @natureportfolio.bsky.social . 1/

So sorry to miss #wcpg2025 #stuckwithgrantwriting BUT #ctglab is well represented, please look out for Marijn Schipper, Emil Uffelmann, Nathan Bell, Bernardo Maciel, Cato Romero, Jeanne Savage & Zeli Chen - they will be presenting the latest outcomes from our lab this week!

#EEGManyLabs will replicate 20 of the highest impact findings in the field in at least three labs each. All studies will be planned and published as Registered Reports.

Paper: d1wqtxts1xzle7.cloudfront.net/95338626/1_s...

AI and science: what 1,600 researchers think A Nature survey finds that scientists are concerned, as well as excited, by the increasing use of artificial-intelligence tools in research. A Nature survey finds that scientists are concerned, as wel...

A 2023 Nature survey of 1,600 researchers found that more than 25% use AI to help them write manuscripts and that more than 15% use the technology to help them write grant proposals. 👀

Now what happens if we estimate direct/indirect heritability (using a within-family method) in the presence of assortative mating and shared environment? Direct estimates are reasonable/deflated, but indirect estimates are HUGELY inflated by AM. Indirect heritability is NOT "genetic nurture".

A negative correlation between bill length and bill depth

A positive correlation between bill depth and bill length when appropriate sub-populations are considered

#rstats
What are your favourite examples when teaching students to think critically about data/figures and interpretations?

For example, Simpson's Paradox and the importance of considering important subpopulations - is brilliantly displayed in the Palmer Penguins dataset.