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Morphoscopic ancestry estimates in Filipino crania using multivariate probit regression models.

Matthew C Go1,2, Joseph T Hefner3

  • 1Department of Anthropology, University of Illinois at Urbana-Champaign, 109 Davenport Hall, 607 South Mathews Avenue, Urbana, Illinois.

American Journal of Physical Anthropology
|January 17, 2020
PubMed
Summary

Probit analysis shows moderate success for estimating ancestry in forensic anthropology, particularly for understudied Filipino populations. This method effectively handles missing data and highlights the need for diverse reference samples in ancestry estimation.

Keywords:
Philippinesancestry estimationforensic anthropologynonmetric traitsordinal categorical data

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Area of Science:

  • Forensic anthropology
  • Human osteology
  • Population genetics

Background:

  • Probit analysis is a novel statistical method for ancestry estimation in forensic anthropology.
  • Understanding human cranial variation is crucial for accurate ancestral classification.

Purpose of the Study:

  • To evaluate probit analysis for ancestry estimation using ordinal data.
  • To assess Filipino cranial variation within a forensic context.

Main Methods:

  • Multivariate probit models were applied to morphoscopic traits of Filipino crania.
  • Ancestral reference groups included Africa, Asia, Europe, and Hispanics.
  • Admixture proportions were determined by posterior probabilities; missing data was accommodated.

Main Results:

  • Overall correct classification rates were 72.1% (three-group) and 68.6% (four-group).
  • Filipinos were classified as Asian (52.9% and 48.6%) but also showed African affiliations.
  • Classification accuracy was consistent between complete and incomplete crania.

Conclusions:

  • Probit analysis performs well with adequate population representation and can manage missing data.
  • Filipino ancestry classification showed moderate success, with phenotypic similarities to African samples.
  • Including Filipinos in reference samples can enhance the Asian category's diversity and improve ancestry estimation methods.