Long-Read epigenetic clocks identify improved brain aging predictions
Grant, S. M.; Makarious, M. B.; Meredith, M.; Moller, A.; Grant-Peters, M.; Hicks, A.; Mandal, A.; Auluck, P.; Leonard, H.; Kuznetsov, N.; Weller, C.; Reed, X.; Jain, M.; Ferrucci, L.; Cookson, M. R.; Ryten, M.; Nalls, M. A.; Billingsley, K. J.
Show abstract
Epigenetic clocks are widely used to estimate biological aging, yet most are built from array-based data from peripheral tissues of predominantly European-ancestry individuals, limiting generalizability. Here, we present aging clocks trained using GenoML, an automated machine learning platform for clinical and multiomics data, on DNA methylation from Oxford Nanopore long-read sequencing. These models leverage over 28 million CpG sites across individuals of African and European ancestry. Our findings highlight the power of long-read methylation data for constructing accurate, ancestry-aware aging clocks and emphasize the importance of inclusive training datasets.
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