The 89th Annual Meeting of the American Association of Physical Anthropologists (2020)


DNA methylation-based forensic age estimation in human bone

SHYAMALIKA GOPALAN1,2, JONATHAN GAIGE2 and BRENNA M. HENN2,3,4.

1Center for Genetic Epidemiology, University of Southern California, 2Department of Ecology and Evolution, Stony Brook University, 3UC Davis Genome Center, University of California, Davis, 4Department of Anthropology, University of California, Davis

April 18, 2020 , Diamond 8-9 Add to calendar

DNA methylation is an epigenetic modification of cytosine nucleotides that represents a promising suite of aging markers with broad potential applications. In particular, determining an individual’s age from their skeletal remains is an enduring problem in the field of forensic anthropology. All DNA methylation-based age prediction methods published so far focus on tissues other than bone. While high accuracy has been achieved for saliva, blood and sperm, which are easily accessible in living individuals, the highly tissue-specific nature of DNA methylation patterns means that age prediction models trained on these particular tissues may not be directly applicable to other tissues. Bone is a prime target for the development of DNA methylation-based forensic identification tools as skeletal remains are often recoverable for years post-mortem, and well after soft tissues have decomposed. In this study, we generate genome-wide DNA methylation data from 32 individual bone samples. We analyze this new dataset alongside published data from 133 additional bone donors, both living and deceased. We perform an epigenome-wide association study on this combined dataset to identify 108 sites of DNA methylation that show a significant relationship with age (FDR < 0.05). We also develop an age-prediction model using lasso regression that produces highly accurate estimates of age from bone spanning an age range of 49-112 years. Our study demonstrates that DNA methylation levels at specific CpG sites can serve as powerful markers of aging, and can yield more accurate predictions of chronological age in human adults than morphometric markers.

Funding was provided by the National Institute of Justice GRFP (2016-DN-BX-0011). JG was supported by a grant from the URECA summer program at Stony Brook University.


Slides/Poster (pdf)