SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis
- 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
Abstract
Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.
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BibTeX
@misc{wu2026sardine,
title = {{SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis}},
author = {Y. Wu and Y. Wang and R. Moreno and S. M. Merrill and V. B. Dwaraka and K. B. Seale and R. Smith and L. L. Schmitz and Q. Lu},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.05.763758},
url = {https://www.biorxiv.org/content/10.64898/2026.11.05.763758v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis
AU - Y. Wu
AU - Y. Wang
AU - R. Moreno
AU - S. M. Merrill
AU - V. B. Dwaraka
AU - K. B. Seale
AU - R. Smith
AU - L. L. Schmitz
AU - Q. Lu
PY - 2026
DA - 2026/11/05
PB - bioRxiv
DO - 10.64898/2026.11.05.763758
UR - https://www.biorxiv.org/content/10.64898/2026.11.05.763758v1
AB - Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.
ER -