Profiling epigenetic age in single cells and with low-pass sequencing data

A method of estimating an epigenetic age for a low-pass sample, the method comprising:

• providing a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites,
• providing a filtered methylation profile of the low-pass sample comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the low-pass sample and the reference methylation probability dataset,
• calculating the likelihood of observing the filtered methylation profile of the low-pass sample for a plurality of ages, and
• determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the low-pass sample.

A method of estimating epigenetic age of single cells in any mammalian tissue comprising estimating the change in average methylation levels with age for each CpG site using a univariate linear model and training data from bulk RRBS or DNAm array profiling to create a reference methylation probability dataset,

• isolating common CpG sites between any given single-cell profile and the reference methylation probability dataset,
• selecting a defined number of CpGs that exhibit the greatest absolute Pearson correlation with age in the reference methylation probability dataset to create a filtered methylation profile of an individual cell,
• calculating the likelihood of observing this filtered methylation profile of an individual cell at any given age, and
• determining the age for which this likelihood is maximal, thereby creating an accurate epigenetic age metric in single cells with different and sparse methylome profiles.