DNA methylation has been increasingly recognised for its potential use in forensic age prediction. In this study, our prediction model based on three CpG sites in three methylation markers was able to predict age for liquid blood samples with a mean absolute deviation of 5 years. The overall prediction accuracy of our model was similar to a previously published model, reanalyzed in our study. Additionally, the present study examined age prediction from bloodstains recovered at Day 0 and Day 21 using reduced quantities of DNA.

Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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