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.

NIH · biorxiv

Paper

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.

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Research summary

The paper claims that long-read epigenetic clocks can provide improved predictions of brain aging by utilizing diverse ancestry data. This research is relevant as it addresses biological aging through the development of more accurate aging clocks, which could contribute to understanding and potentially mitigating the effects of aging.

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