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.

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)