ORCID Profile
0000-0002-7187-988X
Current Organisation
Queensland University of Technology
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Publisher: Springer Science and Business Media LLC
Date: 20-12-2017
DOI: 10.1007/S00414-016-1504-3
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 in idual 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 in iduals 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 in iduals 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.
Publisher: Informa UK Limited
Date: 11-01-2018
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.FSIGEN.2018.06.013
Abstract: 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) in iduals, but are not always appropriate for admixed in iduals. 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 in iduals with 1:1 and 3:1 ratios from AFR, EUR, EAS, and NAM reference populations. In iduals 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 s ling and input formatting procedure than is required by STRUCTURE. It can also be implemented in a spreadsheet without the need for long run times.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.FSIGEN.2019.102141
Abstract: The use of microhaplotypes (MHs) for ancestry inference has added to an increasing number of ancestry-informative markers (AIMs) for forensic application that includes autosomal single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). This study compares bi-allelic and tri-allelic SNPs as well as MH markers for their ability to differentiate African, European, South Asian, East Asian, and American population groups from the 1000 Genomes Phase 3 database. A range of well-established metrics were applied to rank each marker according to the population differentiation potential they measured. These comprised: absolute allele frequency differences (δ) Rosenberg's informativeness for (ancestry) assignment (I
No related grants have been discovered for Elaine Cheung.