PT  - JOURNAL ARTICLE
AU  - Lucas Paulo de Lima Camillo
AU  - Louis R Lapierre
AU  - Ritambhara Singh
TI  - AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning
AID  - 10.1101/2021.06.01.446559
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.01.446559
4099  - http://biorxiv.org/content/early/2021/06/01/2021.06.01.446559.1.short
4100  - http://biorxiv.org/content/early/2021/06/01/2021.06.01.446559.1.full
AB  - Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years. Their accuracy and potential for generalization vary widely based on the training data. Here, we gathered 143 publicly available data sets from several human tissues to develop AltumAge, a highly accurate and precise age predictor based on deep learning. Compared to Horvath’s 2013 model, AltumAge performs better across both normal and malignant tissues and is more generalizable to new data sets. Interestingly, it can predict gestational week from placental tissue with low error. Lastly, we used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observed that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. We studied the associated genes of these CpG sites and found literary evidence of their involvement in age-related gene regulation. Using chromatin annotations, we observed that the CpG sites with the highest contribution to the model predictions were related to heterochromatin and gene regulatory regions in the genome. We also found age-related KEGG pathways for genes containing these CpG sites. In general, neural networks are better predictors due to their ability to capture complex feature interactions compared to the typically used regularized linear regression. Altogether, our neural network approach provides significant improvement and flexibility to current epigenetic clocks without sacrificing model interpretability.Competing Interest StatementThe authors have declared no competing interest.