Elleke Tissink

Using structural equation models to estimate direct and indirect genetic effects and assortative mating in trio data - PROMENTA Guest lecture by Matt Keller

Two birds of a gene - catch Matt Keller's webinar at @uio.no on October 15th. Please share
www.sv.uio.no/promenta/eng...
@ispg.bsky.social @behaviorgenetic.bsky.social @unioslo-svfak.bsky.social @essgn.bsky.social @ibg.colorado.edu

05.10.2025 11:10 — 👍 16    🔁 11    💬 1    📌 0

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets Linear mixed-effects (LME) models are commonly used for analyzing longitudinal data. However, most applications of LME models rely on random intercepts or simple, e.g., stationary, covariance. Here, w...

Introducing FEMA-Long for high-dimensional large-scale mixed-effects modelling! Includes modelling unstructured covariance, non-linear effects using splines, time-dependent effects with spline interactions, and longitudinal GWAS with time-dependent genetic effects!
www.biorxiv.org/content/10.1...

16.05.2025 15:22 — 👍 12    🔁 10    💬 1    📌 1

My first postdoc was all about exploring the intertwined nature of insomnia, anxiety, and depression. Looking at this through the neuroimaging lens, @siemon.bsky.social published this huge effort in Nature Mental Health today 🧠 Stay tuned for the genetics counterpart... 🧬

02.05.2025 10:21 — 👍 3    🔁 0    💬 0    📌 0

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results - Nature Communications Here, the authors present the Network Correspondence Toolbox, which enables researchers to examine and report spatial correspondence between their neuroimaging results and widely used brain atlases.

It's finally here! Use the Network Correspondence Toolbox to help contextualize your neuroimaging findings 🧠

26.03.2025 01:54 — 👍 181    🔁 85    💬 11    📌 2

image showing a figure that illustrates the brain age prediction framework, whereby a machine learning algorithm is trained with multiple brain MRI images and then tested with new brain data. The new brain data is without an age tag, meaning the machine learning algorithm has to estimate age based on its training. The resulting brain predicted age (here 11) is compared to the actual age in the last panel (here 9), and a difference between them is calculated, known as the brain age gap (here 2).

New preprint! 🗞️

Lucy Whitmore and I discuss the many potential challenges in using the brain age prediction framework in children and adolescents, and make recommendations for future directions.

🔗: osf.io/preprints/ps...

14.03.2025 09:28 — 👍 15    🔁 8    💬 1    📌 1

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...

Incredibly proud to see our latest work out in Nature Genetics: www.nature.com/articles/s41...

Here we share our FLAMES framework, which predicts the effector genes in GWAS loci with state-of-the-art precision🔥

Special thanks to @daniposthu.bsky.social

A full thread describing findings below!

11.02.2025 09:58 — 👍 16    🔁 8    💬 1    📌 0

The ENIGMA-EEG working group of the ENIGMA consortium is looking for a PhD candidate to investigate the development and genetics of electrophysiological brain activity. Read all about it (and apply) here: werkenbij.amsterdamumc.org/en/vacatures...

11.02.2025 10:28 — 👍 1    🔁 1    💬 0    📌 0

𝐎𝐏𝐄𝐍 𝐂𝐀𝐋𝐋
We are excited to launch NeuroNarratives, an art-science residency bringing neuroscientists and artists together to create meaningful art based on science!

Sign up via de link in bio!

#opencall #artandscience #art #science #artscienceresidency #neuroscience

27.01.2025 15:07 — 👍 26    🔁 10    💬 1    📌 2