Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm
Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Current clocks are mostly based on linear models from hundreds of methylation sites, and are not suitable for sequencing-based data. We present GP-age, an epigenetic clock for blood, that uses a non-linear cohort-based model of 11,910 blood methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ~2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with far-reaching implications on aging research at the cellular level. By training three independent clocks, we show consistent deviations between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with future applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/524874v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@14eb626org.highwire.dtl.DTLVardef@98aecdorg.highwire.dtl.DTLVardef@1fc2ca1org.highwire.dtl.DTLVardef@d6138f_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIMachine learning analysis of a large cohort (~12K) of DNA methylomes from blood C_LIO_LIA 30-CpG regression model achieves a 2.1-year median error in predicting age C_LIO_LIImproved accuracy ([≥]1.75 years) from sequencing data, using neighboring CpGs C_LIO_LIPaves the way for easy and accurate age prediction from blood, using NGS data C_LI MotivationEpigenetic clocks that predict age from DNA methylation are a valuable tool in the research of human aging, with additional applications in forensic profiling, disease monitoring, and lifespan prediction. Most existing epigenetic clocks are based on linear models and require hundreds of methylation sites. Here, we present a compact epigenetic clock for blood, which outperforms state-of-the-art models using only 30 CpG sites. Finally, we demonstrate the applicability of our clock to sequencing-based data, with far reaching implications for a better understanding of epigenetic aging.