Tabea Schoeler

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

Are Preregistered Studies Publicly Shared? With Eline Ensinck Eline Ensinck will present research on the number of preregistered studies which are never published and the reasons behind this

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

Research Fellow in Mental Health at UCL Apply for the Research Fellow in Mental Health role on jobs.ac.uk, the top job board for academic positions in higher education. View details and apply now.

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 and How to Deviate From a Preregistration, with Prof Daniël Lakens Prof Daniël Lakens will share guidance on appropriate circumstances and methods for deviating from a pre-registration

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.

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...

🚨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...

Combining cross-sectional and longitudinal genomic approaches to identify determinants of cognitive and physical decline - Nature Communications The genetics of human aging remains poorly understood due to limited longitudinal data. Here, the authors show distinct genetic architectures for baseline function and decline in cognitive and physica...

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/)

In line with the simulation results, longitudinal genetic effects obtained from baseline-adjusted change falsely captured substantial parts of the baseline genetic effects. Our recommendations are therefore in line with previous discussions in discouraging the use of baseline-adjusted change (9/)