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Peer-reviewedCC BYNature aging

Design and model choices shape inference of age-varying genetic effects on complex traits.

  • Tabea Schoeler1,2,3,4
  • Simon Wiegrebe5,6
  • Thomas W Winkler5
  • Zoltán Kutalik7,8,9
9 affiliations
  1. 1Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. [email protected].
  2. 2Department of Clinical, Educational and Health Psychology, University College London, London, UK. [email protected].
  3. 3Swiss Institute of Bioinformatics, Lausanne, Switzerland. [email protected].
  4. 4University Center for Primary Care and Public Health, Lausanne, Switzerland. [email protected].
  5. 5Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
  6. 6Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany.
  7. 7Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. [email protected].
  8. 8Swiss Institute of Bioinformatics, Lausanne, Switzerland. [email protected].
  9. 9University Center for Primary Care and Public Health, Lausanne, Switzerland. [email protected].
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Abstract

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) approaches can contribute to answering this question but come with distinct strengths and limitations. Here we show that age-varying genetic effects obtained from the two designs were highly concordant in direction (84.21% of the 57 identified variants) but showed only moderate agreement in effect-size magnitude (Pearson's r= 0.51). Confounding by gene-by-birth year effects accounted for the largest proportion of variance in effect-size differences across single-nucleotide polymorphisms (SNPs) with age-varying effects between designs (70.8%). Participation bias accounted for an additional 11.6%, whereas unmodeled nonlinear age trajectories contributed minimally to these differences (4.2%). Overall, our results demonstrate that both cross-sectional and longitudinal designs can yield different estimates of age-varying genetic effects, principally due to cohort confounding and participation bias. As neither approach is immune to design-specific limitations, we recommend integrating both designs for robust inference, to help improve interpretability and more accurately characterize how genetic effects on complex traits change over the lifespan.

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Cite

BibTeXRIS
BibTeX
@article{schoeler2026design,
  title = {{Design and model choices shape inference of age-varying genetic effects on complex traits.}},
  author = {Tabea Schoeler and Simon Wiegrebe and Thomas W Winkler and Zoltán Kutalik},
  journal = {Nature aging},
  year = {2026},
  month = oct,
  doi = {10.1038/s43587-026-01232-w},
  url = {https://europepmc.org/article/MED/42839093},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Design and model choices shape inference of age-varying genetic effects on complex traits.
AU  - Tabea Schoeler
AU  - Simon Wiegrebe
AU  - Thomas W Winkler
AU  - Zoltán Kutalik
PY  - 2026
DA  - 2026/10/06
JO  - Nature aging
DO  - 10.1038/s43587-026-01232-w
UR  - https://europepmc.org/article/MED/42839093
AB  - 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) approaches can contribute to answering this question but come with distinct strengths and limitations. Here we show that age-varying genetic effects obtained from the two designs were highly concordant in direction (84.21% of the 57 identified variants) but showed only moderate agreement in effect-size magnitude (Pearson's r= 0.51). Confounding by gene-by-birth year effects accounted for the largest proportion of variance in effect-size differences across single-nucleotide polymorphisms (SNPs) with age-varying effects between designs (70.8%). Participation bias accounted for an additional 11.6%, whereas unmodeled nonlinear age trajectories contributed minimally to these differences (4.2%). Overall, our results demonstrate that both cross-sectional and longitudinal designs can yield different estimates of age-varying genetic effects, principally due to cohort confounding and participation bias. As neither approach is immune to design-specific limitations, we recommend integrating both designs for robust inference, to help improve interpretability and more accurately characterize how genetic effects on complex traits change over the lifespan.
SN  - 2662-8465
ER  -