Prediction of biogeographical ancestry in admixed individuals

Estimation of ancestral affiliation for human genotypes is now possible for major geographic populations and has been employed for forensic casework. Prediction algorithms, such as the Snipper Bayesian classifier, have the ability to classify non-admixed BGA in African (AFR), European (EUR), East Asian (EAS), and most Amerindian (NAM) individuals, but are not always appropriate for admixed individuals. Artificial admixture was simulated for all possible admixture ratios (1:1, 3:1, 2:1:1, and 1:1:1:1) from four grandparents. The simulated genotypes were used to test the accuracy of various prediction algorithms, most successful of which were the population genetics program, STRUCTURE, and a novel genetic distance algorithm (GDA). STRUCTURE was ideal for admixed individuals with 1:1 and 3:1 ratios from AFR, EUR, EAS, and NAM reference populations. Individuals with 1:1:1:1 BGA proportions were more accurately predicted by GDA. The use of hypothetical root genotypes improved the accuracy of GDA predictions for 1:1 and 3:1 admixtures and STRUCTURE classification of 1:1:1:1 admixture. The GDA requires only allele or genotype frequency values from each reference population, which offers a simpler sampling and input formatting procedure than is required by STRUCTURE. It can also be implemented in a spreadsheet without the need for long run times.