Long-Read epigenetic clocks identify improved brain aging predictions.

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 multi-omics 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.

SUBMITTER: Grant SM 

PROVIDER: S-EPMC12621889 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature