Our new preprint “Learning lifetime disease liability reveals and removes genetic confounding in electronic health records” is now online! Link to paper: This work is led by my postdoc Yazheng Di and it’s our first project at @bsse.ethz.ch :) medrxiv.org/cgi/content/...
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Title page of a Psychological Bulletin article titled “Positive and Negative Parenting Practices and Offspring Disruptive Behavior: A Meta-Analytic Review of Quasi-Experimental Evidence,” listing authors Lucy Karwatowska, Francesca Solmi, Jessie R. Baldwin, Sara R. Jaffee, Essi Viding, Jean-Baptiste Pingault, and Bianca Lucia De Stavola, with institutional affiliations and abstract text below.
🚨 Our new meta-analysis on the causal influence of parenting on disruptive behaviour (DBDs) is now published in Psychological Bulletin. Across 45 quasi-experimental (QE) studies (N ≈ 38,600), we find a small but causal effect of negative parenting on DBDs.
Can AI be used to evaluate open science practices? 🤖🔎
Join us to find out from Dr Daryl Lee at the next ReproducibiliTea! 🫖
📅 Jan 13th, 3pm GMT
www.eventbrite.co.uk/e/evaluating...
@tabeasch.bsky.social @uclopenscience.bsky.social @uclpals.bsky.social @reproducibilitea.org @ukrepro.bsky.social
How many preregistered studies end up in the file drawer? 🗂️
Join us to find out from Eline Ensinck at the next ReproducibiliTea!
📅Nov 25, 3pm GMT
www.eventbrite.co.uk/e/are-prereg...
@tabeasch.bsky.social @reproducibilitea.org @uclopenscience.bsky.social @ukrepro.bsky.social @lakens.bsky.social
Hi #ASHG2025, the Lausanne team made it to Boston ! Excited to present our latest discoveries — come say hi and learn more about our research! @samuelmoix.bsky.social @rjhfmstr.bsky.social
I'm hiring! ✨ Looking for a Research Fellow to study environmental factors that mitigate intergenerational transmission of mental health.
3-year post at UCL @uclbrainscience.bsky.social with great opportunities for training, collaboration & exciting science!
🔗 Apply: www.jobs.ac.uk/job/DOZ633/r...
When is it appropriate to deviate from a pre-registration, and how should this be done? 🧐
Join us to find out from Daniël Lakens @lakens.bsky.social at the next ReproducibiliTea!
October 28, 1pm GMT.
Sign up: www.eventbrite.co.uk/e/when-and-h...
@tabeasch.bsky.social @reproducibilitea.org
🚨 New preprint out!
We reconstructed parental haplotypes in >440k individuals (UK & Estonian biobanks) to estimate assortative mating directly in the parental generation.
This reveals intensified assortment in recent generations.
www.biorxiv.org/content/10.1...
Amazing work from my colleague and postdoc Dr. Isabelle Foote on the genetic architecture of frailty. Encourage you to check it out!!!!
🚨 Our parent-of-origin study is out in Nature! 🧬
Maternal and paternal alleles can have distinct — even opposite — effects on human traits, revealing a hidden layer of genetic architecture that standard GWAS miss.
🔗 www.nature.com/articles/s41...
Highlights below!
Happy to receive any feedback you may have! Very grateful to everyone involved in this work, huge thanks to Simon Wiegrebe, Thomas Winkler (@winkusch.bsky.social) & Zoltán Kutalik (@zkutalik.bsky.social ) 👏
9/ Finally, we examined the role of non-linear age-varying genetic effects. While such effects could contribute to discrepancies between the two designs, given the differing age ranges in cross-sectional and longitudinal samples, they explained little of the observed differences.
8/ Focusing on other factors, we found that selective participation also contributed to differences between the two designs. This may reflect distinct participation mechanisms, such as selective enrolment in cross-sectional samples versus survival/dropout in longitudinal samples.
7/ However, effect size estimates showed less agreement between the two designs (r = 0.74). Similar to the phenotypic findings, differences were primarily due to gene-by-cohort effects, where genetic associations vary across birth years, introducing bias into cross-sectional estimates.
6/ Among the identified SNPs, 86% showed consistent interpretation across designs regarding the direction of age-varying genetic effects. These included both attenuation with age (e.g., for obesogenic traits) and intensification over time (e.g., for disease burden and medication use).
5/ At the genetic level, we identified 57 SNPs with significant age-varying effects. Most were detected in the cross-sectional design, likely reflecting greater statistical power due to larger sample sizes and broader age ranges.
4/ We observed that this likely reflects confounding by year-of-birth effects (e.g., younger cohorts tend to smoke less), which can bias age estimates in cross-sectional analyses.
3/ RESULTS:
At the phenotypic level, cross-sectional and longitudinal age effects showed only moderate agreement. For several traits, especially lifestyle behaviours, effects differed in their direction: e.g., smoking appeared to increase with age cross-sectionally but declined longitudinally.
2/ Using data on 31 health-related traits from the UK Biobank, we focused on two questions:
🔹 Do the two designs lead to the same conclusions?
🔹 If not, what are the sources of bias that account for the observed discrepancies?
1/ We compare two common approaches to modeling age-varying genetic effects:
🔹 Cross-sectional: Comparing genetic associations across individuals of different ages.
🔹 Longitudinal: Estimating genetic effects on change over time within the same individuals.
🚨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 🧵👇
A network of metabolites and their potential causal relationships. Green edges are Detected by the statistical causal inference method MR-link-2
Our paper, MR-link-2 has just been published! Offering pleiotropy robust Mendelian randomization from a single region! www.nature.com/articles/s41...
READING RECOMMENDATION 📖 Genes, environment, and aging: What determines cognitive and physical decline?
Using newly available longitudinal aging data from the UK Biobank, @tabeasch.bsky.social and colleagues uncovered genetic and environmental factors influencing cognitive and physical decline.
🧵 THREAD: Preprint from @chrisrayner.bsky.social reveals how to fix selection bias in the Norwegian #MoBa study using population-wide registry data! For the first time, we can quantify and adjust for selection bias in this major epidemiological resource.
Spread the good news!
osf.io/preprints/os...
🧠🧬🧑🤝🧑 New CoDE Lab study: Disorder-specific genetic effects drive the associations between psychopathology and cognitive functioning. Link to preprint: www.medrxiv.org/content/10.1... Led by the brilliant Wangjingyi Liao 🌟
A short thread summarising the study👇
Extremely excited to share the first effort of the Revived Genomics of Personality Consortium: A highly-powered, comprehensive GWAS of the Big Five personality traits in 1.14 million participants from 46 cohorts. www.biorxiv.org/content/10.1...
PGI Repository v2.0 preprint out! A 🧵 on the main results and updates @robel-alemu.bsky.social @paturley.bsky.social @alextisyoung.bsky.social
All GWA summary statistics will be soon available @gwascatalog.bsky.social (accession codes GCST90565836-GCST90565865)! As always, wonderful teamwork with @zkutalik.bsky.social and Jean-Baptiste Pingault @atcmap.bsky.social 🙂
Further, we found little evidence of common risks shared by (cross-sectional) level of functioning and (longitudinal) decline in cognitive and physical outcomes (11/11)
Using Mendelian Randomization to identify risk factors involved in age-related decline, we found that most risks were specific to either cognitive decline (e.g., Alzheimer’s disease liability) or physical decline (e.g., shorter telomere length, higher bone mineral density) (10/)
