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Tabea Schoeler

@tabeasch.bsky.social

200 followers 140 following 25 posts

Researcher at University of Lausanne | interested in genetic epidemiology, mental health & behaviour

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Tabea Schoeler @tabeasch.bsky.social · Jul 8

🚨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...

Reposted by Tabea Schoeler

Robin Hofmeister @rjhfmstr.bsky.social · 12d

🚨 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...

Reposted by Tabea Schoeler

Andrew Grotzinger @andrewgrotzinger.bsky.social · Aug 6

Amazing work from my colleague and postdoc Dr. Isabelle Foote on the genetic architecture of frailty. Encourage you to check it out!!!!

Institute for Behavioral Genetics (IBG) @ibg.colorado.edu · Aug 6

New work in Nature Genetics from IBG members lead by @isyfoote.bsky.social (along with @andrewgrotzinger.bsky.social) examining different measures of frailty using Genomic SEM. Findings reveal six genomic factors with unique biological pathways and clinical correlates. www.nature.com/articles/s41...

Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling - Nature Genetics

Multivariate genome-wide association analyses of the latent genetic architecture of frailty identify one general factor of genetic overlap across all frailty deficits and six factors indexing a shared...

Reposted by Tabea Schoeler

Robin Hofmeister @rjhfmstr.bsky.social · Aug 6

🚨 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!

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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 ) 👏

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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).

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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?

Tabea Schoeler @tabeasch.bsky.social · Jul 8

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.

Tabea Schoeler @tabeasch.bsky.social · Jul 8

🚨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...

Reposted by Tabea Schoeler

Adriaan @adriaan-vd-graaf.bsky.social · Jul 4

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

Reposted by Tabea Schoeler

Swiss Network for Dementia Research @swiss-dementia-network.ch · Jun 24

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.

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...

Reposted by Tabea Schoeler

Eivind Ystrom @eivindy.bsky.social · Jun 13

🧵 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...

Reposted by Tabea Schoeler

Margherita Malanchini @marghmalanchini.bsky.social · Jun 10

🧠🧬🧑‍🤝‍🧑 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👇

Reposted by Tabea Schoeler

Ted Schwaba @tedmond.bsky.social · May 20

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...

Reposted by Tabea Schoeler

Aysu Okbay @aysuo.bsky.social · May 20

PGI Repository v2.0 preprint out! A 🧵 on the main results and updates @robel-alemu.bsky.social @paturley.bsky.social @alextisyoung.bsky.social

bioRxivpreprint @biorxivpreprint.bsky.social · May 19

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference https://www.biorxiv.org/content/10.1101/2025.05.14.653986v1

Tabea Schoeler @tabeasch.bsky.social · May 19

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 🙂

Tabea Schoeler @tabeasch.bsky.social · May 19

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)

Tabea Schoeler @tabeasch.bsky.social · May 19

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/)

Tabea Schoeler @tabeasch.bsky.social · May 19

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/)

Tabea Schoeler @tabeasch.bsky.social · May 19

In total, 7 loci associated with longitudinal decline, implicating APOE and DUSP6 as the top genes associated with cognitive and physical decline, respectively. Overall, there was little overlap between the genetics of decline and physical/cognitive function (8/)