Philippe Jawinski

So altert das Gehirn In einer Studie der HU Berlin wurden genetische Ursachen und beeinflussbare Risiken für die Alterung des Gehirns untersucht. Grundlage der Analyse sind die Daten der Britischen UK Biobank. Für die Stu...

Exciting times: Ich durfte bei RBB Radioeins ein paar Minuten über unsere Studie zum biologischen Alter des Gehirns in über 56.000 Teilnehmer:innen der UK Biobank sprechen.

👉 Interview: www.radioeins.de/programm/sen...
👉 Studie: www.nature.com/articles/s43...

Big thanks to Nature Aging for the shoutout! Curious to dig deeper? We’ve posted a thread with more details and background on the work 👇

Thread: bsky.app/profile/pjaw...

Largest multi-ancestry genomic study of Alzheimer’s disease led by @euffelmann.bsky.social and @daniposthu.bsky.social et al. — big leap forward for polygenic risk prediction and potential drug target discovery.

Preprint: www.medrxiv.org/content/10.1...

New genetic insights show how modifiable risk factors influence brain aging - Nature Aging Using genome-wide analyses in over 56,000 individuals, we identify 59 genetic loci linked to brain aging, of which 39 are novel. This work also uncovers key biological pathways that connect brain aging to mental, metabolic, cardiovascular and lifestyle factors, and offers insights for promoting healthy aging and preventing neurodegenerative diseases.

Our study on the genetics of brain age with >56,000 participants is out in Nature Aging — and here comes a more digestible Research Briefing with behind-the-paper insights and editor/expert reflections.

🧠 Direct link: www.nature.com/articles/s43...
🔓 Open link: rdcu.be/eJ95n

Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects - Nature Genetics Genome-wide analyses for 7,266 traits leveraging data from several genetic ancestry groups in UK Biobank identify new associations and enhance resources for interpreting risk variants across diverse p...

A project many years in the process, we’re pleased to present our work on multi-ancestry meta-analysis across a boatload of traits in the UK Biobank: www.nature.com/articles/s41...

Great opportunity to reshare the Big Five facets correlation plot I made earlier, showing how the synthetic data from synthpop matches the original dataset (n = 468). Cheers! :-P

Thanks for the question! Indeed, our phenome scan (Table S2) revealed links with wrist-accelerometer traits — notably fraction acceleration ≤ 1 mg (inactive time) and no-wear bias–adjusted acceleration maximum (peak movement) over 7 days, plus self-report measures like "walking for pleasure". 🏃‍♀️

🙌 Thanks to everyone involved — especially colleagues at @humboldtuni.bsky.social, @mpicbs.bsky.social, and @unileipzig.bsky.social for their support and collaboration.

📚 Take-home:
Brain aging is a complex but quantifiable trait shaped by genes and environment. Our findings suggest that healthier lifestyles are linked to younger-looking brains. Key genes like MAPT and pathways in immunity and neurogenesis give us clues for keeping brains younger for longer.

Genetic effect-size distribution analysis of BAG. a, Results are shown for combined GM and WM BAG, with neuroticism and standing height included as reference traits; effect-size distributions of the underlying susceptibility variants are shown; wider tails indicate a greater proportion of large-effect variants. b, Predicted number of genome-wide significant loci as a function of sample size. c, Proportion of genetic variance explained by genome-wide significant loci as a function of sample size.

We estimate that ~9–11k common genetic variants contribute to BAG — a level of polygenicity similar to height, but much lower than neuroticism. That’s good news: discovery power scales well, and with ~1 M participants (phew — still quite a way to go!) we could capture most common genetic influences.

Results of the generalized summary-data-based Mendelian randomization (GSMR) results for 3 risk factors (exposures) and combined brain age gap (outcome). Each plot shows multiple genetic variants serving as instruments to test for causality between the exposure and outcome variable. Under a causal model, variant effects on the outcome (bzy; y-axis) are expected to be linearly proportional to the variant effects on the exposure variable (bzx; x-axis). The ratio between bzy and bzx provides an estimate of the mediation effect of x on y (bxy). Variants flagged for potential horizontal pleiotropy were excluded using the HEIDI-outlier method. s.e. standard error of the mediation effect; pxy P value of the mediation effect.

⚡ Mendelian randomization suggests causal effects:
Higher blood pressure and type 2 diabetes drive accelerated brain aging — each 1 SD increase in blood pressure corresponds to ~0.5 years older brain age.

Genetic correlations between BAG and a wide range of complex traits. a, Genetic correlation matrix between BAG (columns) and 38 selected phenotypes from different health domains (rows). *P < 0.05 (nominal significance). **FDR < 0.05 (level of significance after correction for multiple testing). b, Volcano plot showing the magnitude (x axis) and significance (y axis) of LDSC-based genetic correlations between GM BAG and 989 traits, whose summary statistics were provided in ref. 80. The dashed horizontal line indicates the FDR-adjusted level of significance. All P values are two-sided. c, Forest plot showing the genetic correlation coefficients and standard errors for a subset of 23 exemplary traits that showed significant genetic correlations with GM BAG.

🔗 Genetic correlations show broad overlap with lifestyle, mental, physical, and socioeconomic traits:
⬆️ blood pressure, diabetes, drinking, smoking, depressed mood
⬇️ lung function, cognition, longevity, education, income
🧠 Brain aging lies at the intersection of body, mind, and environment.

🔎 Pathway analyses show enrichment for:
• Immune and viral response genes
• Neurogenesis and axon development
• Small GTPase binding – key regulators of cellular timing mechanisms

Brain aging is not just “wear and tear” — it’s tied to immune and developmental biology.

🧬 A new polygenic score for BAG explains ~10% of variance — a big leap from previous ~2%. Still, prediction in non-European ancestries remains limited, highlighting the urgent need for more diverse genomic data.

Genome-wide association meta-analyses of BAG traits. Manhattan (a–c) and quantile–quantile (QQ) plots (d–f) showing the results of the European-ancestry GWAS meta-analyses for the three BAG traits (n = 54,890). The Manhattan plots show the P values (−log10 scale) of the tested genetic variants on the y axis and base-pair positions along the chromosomes on the x axis. P values were derived from two-sided linear regression models using PLINK, followed by meta-analysis using inverse-variance weighting in METAL. The solid horizontal line indicates the threshold of genome-wide significance (two-sided P = 5.0 × 10−8, accounting for multiple testing). Index variants are highlighted by the diamonds. The results of the pseudoautosomal variants have been added to chromosome X. Quantile–quantile plots show the observed P values from the association analysis versus the expected P values under the null hypothesis of no effect (−log10 scale). For illustrative reasons, the y axis has been truncated at P = 1.0 × 10−40. a,d, GM BAG (Manhattan and QQ). b,e, WM BAG (Manhattan and QQ). c,f, Combined GM and WM BAG (Manhattan and QQ).

💥 We identified 59 genomic loci, including 39 never linked before to brain aging. The strongest signal is at MAPT (tau) — a well-known Alzheimer’s gene — confirming its central role in brain structural aging.

Phenotypic characteristics and associations of combined BAG. a, The blue dots in the first three plots (left to right) show brain-predicted age estimates (combined GM and WM) plotted against chronological age in the UKB discovery sample (n = 32,634), UKB replication sample (n = 21,881, merged across ancestries) and the LIFE-Adult replication sample (n = 1,833). To facilitate comparisons, the results of the UKB discovery sample are also shown as gray dots in the background of the LIFE replication plot. At this stage, brain-predicted age estimates have not yet been bias-corrected for regression dilution, as indicated by the solid linear regression line crossing the dashed identity line. The fourth plot shows the test–retest reliabilities of combined BAG in a subset of the UKB discovery (gray dots, n = 3,751) and UKB replication sample (blue dots, n = 395). BAG was residualized for sex, age, age2, scanner site and total intracranial volume. b, Cross-trait association results between combined BAG and 7,088 UKB phenotypes across several health domains. Analyses were conducted using PHESANT, which applies data-type-specific regression models (linear, logistic, ordered logistic or multinomial logistic regression). All models included sex, age, age2, scanner site and total intracranial volume as covariates. The horizontal lines indicate the Bonferroni-adjusted (solid) and FDR-adjusted (dashed) two-sided level of significance. The top associations per category are annotated. c, Surface plots showing the correlations between combined BAG and 220 FreeSurfer brain structure variables. The colors reflect the strength and direction of partial product-moment correlations (sex, age, age2, scanner site and total intracranial volume served as covariates). ICC, intraclass correlation coefficient (C, 1); rho, product-moment correlation coefficient.

🧠 The brain age gap (BAG) = your brain’s predicted MRI age minus your actual age.

A higher BAG means your brain looks “older” than expected. We show BAG is substantially heritable (~23–29% due to common variants), meaning genes do play a big role — but environment and lifestyle matter too.

Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health - Nature Aging This genomic study of magnetic resonance imaging-based brain age in 56,348 people identifies 59 genetic loci, links brain aging to mental and physical health, and suggests high blood pressure and type 2 diabetes as causal factors of brain aging.

🧠 Why do some brains age faster than others?
🧬 Our new Nature Aging study of over 56,000 participants explores the genetics of the “brain age gap” — the difference between your brain’s biological and chronological age.
nature.com/articles/s43587-025-00962-7

Thread below 👇

She gently reminded us that every primate — just like us — has their own personality, emotions, relationships, and individuality. Because of her, we see our place more clearly among the other beings we share this world with.

The funnel plot suggests a formidable publication bias—excellent material for class discussions! You can find my R script on OSF (osf.io/u6hsn/) and adapt it for your own purposes. 2/2
#OpenScience #Reproducibility #BehavioralGenetics

A while ago, when I was in my first semester teaching behavioral genetics, I was eager to discuss candidate gene controversies. I created an interactive funnel plot based on the meta-analysis by Karg et al. (2011), who reported strong evidence for an effect of 5-HTTLPR × stress on depression. 1/2

Fantastic work on Personality Genetics—absolutely love it! 🚀 Curious if applying SBayesRC with 7M variants could push polygenic score performance even further 🤔

Welcome to the Bluesky account for Stand Up for Science 2025!

Keep an eye on this space for updates, event information, and ways to get involved. We can't wait to see everyone #standupforscience2025 on March 7th, both in DC and locations nationwide!

#scienceforall #sciencenotsilence

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.

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!

OHBM 2024 in Seoul - Impressions from the conference and the city This is "OHBM 2024 in Seoul - Impressions from the conference and the city" by Philippe on Vimeo, the home for high quality videos and the people who love…

#OHBM2024 in Seoul was an unforgettable experience! Met brilliant minds, engaged in heated discussions, and partied with incredible people. ⬇️ Impressions from the conference and the city! 🏙️ Massive thanks to the USC team for the stunning drone footage of the ENIGMA rooftop party. vimeo.com/980726262