New article in WIREs Forensic Science by members of MSU Forensic Anthropology Lab on ancestry estimation

Department of Anthropology PhD students Rhian Dunn, Micayla Spiros, Kelly Kamnikar, and Amber Plemons and Assistant Professor Dr. Joseph Hefner recently published an article in Wiley Interdisciplinary Reviews (WIREs): Forensic Science, titled “Ancestry estimation in forensic anthropology: A review”. The article discusses the methods and future of ancestry estimation in forensic anthropology.

Read the full article at: https://doi.org/10.1002/wfs2.1369

Abstract: “Forensic anthropologists assist law enforcement agencies and medical examiner’s offices with investigations involving human remains, providing insight into trauma analysis, the establishment of postmortem interval, and the estimation of biological profile data. Ancestry is considered one of the more difficult aspects of the biological profile, due in large part to the complicated relationship between skeletal morphology and social constructs. The methods used to estimate ancestry rely on the correlation between skeletal morphology, geographic origin, and an individual’s social race. While there is a well-documented contemptible history associated with the development of ancestry estimation methods in biological anthropology, many of the more traditional, now antiquated, methods are still used in some laboratories. The push to improve the framework within which ancestry data are analyzed requires validation and reassessment for each method in addition to the development of novel approaches utilizing modern technological advances. An array of software programs designed to aid in ancestry estimation is available. These advances do not signal the end of ancestry-related research. Indeed, several areas seemingly stagnated by tradition and time require further study through more than simply review and rarefaction. The future of ancestry estimation research centers on: (a) abandoning the trait list approach, (b) rejecting the three-group model, (c) establishing larger and more representative reference data, (d) assessing the utility of mixed method models, and (e) developing new statistical approaches and updating current software tools.”

03.31.20