Florian Hartig Theoretical Ecology & Ecological Data Science

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A novel age-informative polygenic score improves predictive ability for kidney function and kidney function decline

Janina M. Herold, Simon Wiegrebe, Barbara Thorand, Thomas W. Winkler, Christian Gieger, Florian Hartig, Merle Behr, Annette Peters, Helmut Küchenhoff, Iris M. Heid

medRxiv (2026)
Cite this
@article{herold2026novel,
  author = {Janina M. Herold and Simon Wiegrebe and Barbara Thorand and Thomas W. Winkler and Christian Gieger and Florian Hartig and Merle Behr and Annette Peters and Helmut Küchenhoff and Iris M. Heid},
  title = {A novel age-informative polygenic score improves predictive ability for kidney function and kidney function decline},
  journal = {medRxiv},
  year = {2026},
}

Cited by, as of 07 September 2026

Preprint

Abstract

Polygenic scores (PGSs) are widely used to summarize the joint genetic effects for disease-related traits. However, while age-dependent genetic effects are increasingly recognized, their integration into PGSs remains underexplored. Kidney function, assessed by estimated glomerular filtration rate (eGFR), has strong age-related genetic effects, and prediction of kidney function decline is an unmet need. We develop an age-informative PGS for quantitative traits by generating age-specific weights via main and interaction effects and compare its performance to the age-agnostic PGS in theory and real data of eGFR. We test PGSs across 282 kidney function SNPs in cross-sectional and longitudinal data from UK Biobank (n=348,275, m=1,520,382) and independent population-based individuals aged 25 to 98 years (KORA and AugUR; n=9,057, m=16,804). In theory and real data, we illustrate that ignoring age mis-specifies genetic effects. The age-informative PGS has better performance than the age-agnostic PGS in young and old individuals (KORA and AugUR: 6.3% versus 5.9% of eGFR variance in 25- to 45-year-old, 2.3% versus 1.8% in 75- to 98-year-old). The PGS based on interaction effects explains more of the eGFR-decline variability than the age-agnostic PGS. The highest versus lowest PGS quintile predict eGFR-decline of -0.88 (95%-CI=[-0.93;-0.83]) versus -0.75 ml/min/1.73m2 (95%-CI=[-0.79;-0.71]) on the population-level, similar to strata by acquired risks like diabetes, obesity or albuminuria. Prediction of eGFR-decline on the individual level remains challenging by both, genetic or acquired risks. Overall, we provide a simple approach to an age-informative PGS for quantitative disease traits and illustrate its chances and challenges for predicting kidney function and kidney function decline.

What the paper shows and why it matters (AI-generated)

A methods collaboration outside Hartig's usual ecology focus, contributing statistical expertise to a genetic-epidemiology question: standard polygenic scores assume a gene's effect on a trait is constant across age, and this paper shows that assumption measurably mis-specifies kidney-function genetics in particular. Building age into the score via interaction effects improves prediction most exactly where the age-agnostic version is weakest — the young and the very old — though the paper is candid that predicting an individual's future kidney decline, as opposed to population-level patterns, remains hard regardless of which risk factors, genetic or acquired, are used.