Analysis of the Accuracy of AncesTrees Software in Ancestry Estimation in Brazilian Identified Sample

Abstract

In the present study a software tool for craniometric ancestry estimation, AncesTrees, was evaluated in an identified Brazilian skeletal sample with known self-reported ancestry. Twenty-three craniometric measures were obtained from each skull and analyzed using AncesTrees software, with two classification strategies—tournamentForest and ancestralForest algorithm. The tournamentForest (53.54%) and ancestralForest algorithms with three ancestry groups (50.96%) were more accurate to classify Europeans, while the ancestralForest algorithm with six (50.00%) and two (67.64%) groups were more accurate to estimate the ancestry of African descents. Admixed ancestry specimens were classified predominantly as European descent. The use of the ancestralForest algorithm considering only European and African origin (58.42%) was the most accurate setup for ancestry estimation in Brazilian skulls. Supervised classification algorithms and tools such as the AncesTrees work based on data analysis and pattern matching, and there is no Brazilian sample in its database, the software showed a low accuracy Brazilian samples. The incorporation of representative craniometric data obtained from Brazilian skulls into the software database may significantly increase the accuracy of ancestry estimates.