Analyzing longitudinal trait trajectories using GWAS identifies genetic variants for kidney function decline

Simon Wiegrebe (), Mathias Gorski, Janina M. Herold, Klaus J. Stark, Barbara Thorand, Christian Gieger, Carsten A. Böger, Johannes Schödel, Florian Hartig, Han Chen, Thomas W. Winkler, Helmut Küchenhoff and Iris M. Heid ()
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Simon Wiegrebe: University of Regensburg
Mathias Gorski: University of Regensburg
Janina M. Herold: University of Regensburg
Klaus J. Stark: University of Regensburg
Barbara Thorand: Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH)
Christian Gieger: Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH)
Carsten A. Böger: University Hospital Regensburg
Johannes Schödel: Uniklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg
Florian Hartig: University of Regensburg
Han Chen: The University of Texas Health Science Center at Houston
Thomas W. Winkler: University of Regensburg
Helmut Küchenhoff: LMU Munich
Iris M. Heid: University of Regensburg

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Understanding the genetics of kidney function decline, or trait change in general, is hampered by scarce longitudinal data for GWAS (longGWAS) and uncertainty about how to analyze such data. We use longitudinal UK Biobank data for creatinine-based estimated glomerular filtration rate from 348,275 individuals to search for genetic variants associated with eGFR-decline. This search was performed both among 595 variants previously associated with eGFR in cross-sectional GWAS and genome-wide. We use seven statistical approaches to analyze the UK Biobank data and simulated data, finding that a linear mixed model is a powerful approach with unbiased effect estimates which is viable for longGWAS. The linear mixed model identifies 13 independent genetic variants associated with eGFR-decline, including 6 novel variants, and links them to age-dependent eGFR-genetics. We demonstrate that age-dependent and age-independent eGFR-genetics exhibit a differential pattern regarding clinical progression traits and kidney-specific gene expression regulation. Overall, our results provide insights into kidney aging and linear mixed model-based longGWAS generally.

Date: 2024
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DOI: 10.1038/s41467-024-54483-9

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