title: Prediction of biogeographical ancestry from genotype: a comparison of classifiers.
abstract: DNA can provide forensic intelligence regarding a donor's biogeographical ancestry (BGA) and other externally visible characteristics (EVCs). A number of algorithms have been proposed to assign individual human genotypes to a BGA using ancestry informative marker (AIM) panels. This study compares the BGA assignment accuracy of the population clustering program STRUCTURE and three generic classification approaches including a Bayesian algorithm, genetic distance, and multinomial logistic regression (MLR). A selection of 142 ancestry informative single nucleotide polymorphisms (SNPs) were chosen from existing marker panels (SNPforID 34-plex, Eurasiaplex, Seldin, and Kidd's AIM panels) to assess BGA classification at the continental level for Africans, Europeans, East Asians, and Amerindians. A training set of 1093 individuals with self-declared BGA from the 1000 Genomes phase 1 database was used by each classifier to predict BGA in a test set of 516 individuals from the HGDP-CEPH (Stanford) cell line panel. Tests were repeated with 0, 10, 50, 70, and 90% of the genotypes missing. Comparison of the area under the receiver operating characteristic curves (AUROCs) showed high accuracy in STRUCTURE and the generic Bayesian approach. The latter algorithm offers a computationally simpler alternative to STRUCTURE with little loss in accuracy and is suitable for phenotype prediction while STRUCTURE is not.
authors.affiliation: National Centre for Forensic Studies, Faculty of Education, Science, Technology & Mathematics (ESTeM), University of Canberra, Bruce, ACT 2601, Australia. Elaine.Cheung@canberra.edu.au.
authors.forename: Elaine Y Y
authors.initials: EYY
authors.lastname: Cheung
authors.affiliation: National Centre for Forensic Studies, Faculty of Education, Science, Technology & Mathematics (ESTeM), University of Canberra, Bruce, ACT 2601, Australia.
authors.forename: Michelle Elizabeth
authors.initials: ME
authors.lastname: Gahan
authors.affiliation: National Centre for Forensic Studies, Faculty of Education, Science, Technology & Mathematics (ESTeM), University of Canberra, Bruce, ACT 2601, Australia.
authors.forename: Dennis
authors.initials: D
authors.lastname: McNevin
mesh_terms: D000465:Algorithms; D005787:Gene Frequency; D005789:Genealogy and Heraldry; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D020641:Polymorphism, Single Nucleotide; D044469:Racial Groups / Q000235:genetics*
chemical_list: D005819:Genetic Markers
keywords: Bayesian; Biogeographical ancestry (BGA); Genetic distance; Multinomial logistic regression; Phenotype prediction; STRUCTURE