Ancestry estimation in South Africa using craniometrics and geometric morphometrics

Abstract

Population history and positive assortative mating directs gene flow in such a way that biological differences are recognized among groups. In turn, forensic anthropologists quantify biological differences to estimate ancestry. Some anthropologists argue that highly admixed population groups, such as South African coloureds, cannot achieve acceptable accuracies because within group variance is too large. Whereas ancestry estimation in South Africa has been limited to craniometric data from South African blacks and whites, the current study integrates craniometric and geometric morphometric data from the three largest South African groups. Crania from 377 South African individuals (black=158, white=112, and coloured=107) comprised the sample. Standard measurements were collected and the coordinate data were subjected to Generalized Procrustes Analysis (GPA), which resulted in size-free shape variables (ProCoords). A principal component analysis was used to combine the shape variation captured in the ProCoords (ProCoords PC). Linear discriminant analysis (LDA), using equal priors, stepwise variable selection and leave-one-out cross-validation, was conducted on the ProCoords, the ProCoords PCs, and the traditional craniometric data. The LDA using 18 stepwise selected ProCoords resulted in the highest cross-validated accuracy (89%). Utilization of geometric morphometric data emphasized that the relative location of cranial landmarks was more discriminating than simple linear distances. Regardless of high levels of genetic admixture, South African coloureds are a homogeneous group and morphologically distinct from other contemporaneous South African populations. Furthermore, the present study demonstrated a correspondence between peer-reported race and morphological differences in the crania of black, white, and coloured South Africans.

Keywords: Craniometric variation; Forensic Anthropology Population Data; Generalized procrustes analysis; Human variation; Linear discriminant analysis.