Evaluation of the (hu)MANid program for sex and ancestry estimation in a diverse, contemporary CT scan-based sample

Human remains from forensic and bioarcheological contexts are often fragmentary, requiring methods for estimating a forensic profile that are based upon limited skeletal features. In 2017, Berg and Keryhercz created an online application, (hu)MANid, that provides sex and ancestry estimation from mandibular morphoscopic traits and linear measurements. In this study, we examine the utility of the (hu)MANid application in a diverse, urban US adult sample (aged 20-45; n = 143) derived from computed tomography (CT) scans. We secondarily conduct a preliminary analysis of the program's utility in a sample of adolescents (aged 15-17; n = 40). Six morphoscopic, and eleven morphometric traits were recorded as directed by the literature associated with the (hu)MANid program. Percent correct classification and posterior predictive values were calculated for the sex and ancestry estimations output by the program; chi-squared tests were employed to compare self-reported and predicted ancestry. In the adult sample, sex was accurately predicted for 75.52% of the sample. Ancestry prediction, however, was less favorable ranging from 19.3% to 50% correct. For the adolescent sample, correct sex estimation (45%) did not surpass what could occur by chance alone, though ancestry prediction fared better than in the larger adult sample (percent correct prediction overall average: 47.5%, range 35.71%-71.43%). The (hu)MANid application shows utility for use with CT scan-derived adult samples for sex estimation, but caution is warranted for ancestry estimation and use with samples that may not have reached full adult maturity.

Evaluating population affinity estimates in forensic anthropology: Insights from the forensic anthropology database for assessing methods accuracy (FADAMA)

Using a sample of anonymized U.S. forensic anthropology cases (n = 251) from the FADAMA database, we assess the degree of concordance between decedents' social identifiers and anthropologists' continental-based classifications. We report high success rates (>90%) that generally support previous findings, yet we acknowledge the limitations of assessing "ancestry" accuracy based on resolved cases and draw attention to situations in which our methods fail. For example, forensic anthropologists achieve just 20% accuracy when classifying individuals as "other" or "mixed"-problematic categories that we argue should be rejected. Leveraging our findings, we ask: what are we really estimating when we perform a skeletal assessment of "ancestry" in the US context? We argue that the "ancestry estimates" historically and routinely produced in forensic anthropology instead give information on population affinity: a measure of how similar a given case is to one among several socially relevant groups of interest. Distancing forensic anthropology from genetics and other disciplines that estimate ancestry, the approach of population affinity assesses similarities to both social and biological groupings, potentially at a fine-grained level, attempting to account for the complex histories, shared biologies, and wide ranges of diversity that characterize our communities and our casework. Population affinity is a flexible and inclusive approach that more accurately describes current forensic anthropological analyses of human variation. Going forward, we must acknowledge and build on the contributions of previous scholars as we work together toward our shared goal of theoretically grounded analyses of human variation that accurately and equitably serve all casework decedents.