Zoltán Kutalik

@zkutalik.bsky.social

Statistical Geneticist, Group leader at University of Lausanne/Unisante, father, climber, runner

SIB is organizing the next edition of #eccb2026 (Geneva, 31.08.2026 - 04.09.2026), and we are now inviting the community to submit their tutorial and workshop proposals. 👉 A great opportunity to share tools, methods and expertise with 1,000+ international participants. Deadline: 5 January 2026.

European Conference on Computational Biology@eccb-europe.bsky.social · 9mo ago

📣 The call for tutorials & workshops at #ECCB2026 is now open! Share your tools, methods or expertise with the community. 🗓️ Deadline: 5 January 2026 👉 Submit: tinyurl.com/tw-eccb26 💡 ECCB will take place on 31 Aug–4 Sept in Geneva, gathering 1,000+ scientists from academia, industry, & healthcare.

Excited to share our latest manuscript, "Exposure accumulation drives age-dependent disease architectures and polygenic risk scores," led by Xilin Jiang: www.medrxiv.org/content/10.1... I am attempting an explainer thread for the first time here: (I am usually too exhausted to post one)

Exposure accumulation drives age-dependent disease architectures and polygenic risk scores

Our understanding of the dependence of the genetic and environmental architecture of common diseases on age is incomplete. Here, we use longitudinal data to quantify age-dependent genetic and environm...

medrxiv.org

🚨New preprint is out! How do genetic effects on complex traits change with age? In this work, we compare different approaches to obtain age-varying genetic effects, and show how design and modeling choices can impact the conclusions we draw. shorturl.at/17snd A thread 🧵👇

Design and model choices shape inference of age-varying genetic effects on complex traits

Understanding how genetic influences on complex traits change with age is a fundamental question in genetic epidemiology. Both cross-sectional (between-subject) and longitudinal (within-subject) appro...

shorturl.at

This represents a real tour-de-force by @adriaan-vd-graaf.bsky.social. It has so many strong aspects: (i) establishment of large scale ground truth data for causal inference [metabolomics/transcriptomics]; (ii) sophisticated method and (iii) biological insights through real data application.

Adriaan@adriaan-vd-graaf.bsky.social · last yr.

Our paper, MR-link-2 has just been published! Offering pleiotropy robust Mendelian randomization from a single region! www.nature.com/articles/s41...

A network of metabolites and their potential causal relationships. Green edges are Detected by the statistical causal inference method MR-link-2