Rasudas
A New Web-Based Application for Estimating Ancestry from Tooth Morphology
Bibliographic Data
| ID | 5397272 |
|---|---|
| Authors | G R Scott (0000-0003-2328-8003, University of Nevada, Reno, corresponding author), M A Pilloud (0000-0002-1704-3707, University of Nevada, Reno), David Navega (0000-0003-4495-3762, University of Coimbra), João D’oliveira Coelho (0000-0003-0871-1926, University of Coimbra), João D''Oliveira Coelho, João D'Oliveira Coelho, E Cunha (0000-0003-2998-371X, University of Coimbra), Joel D Irish (0000-0001-7857-8847, Liverpool John Moores University) |
| Year | 2018 |
| Volume | 1 |
| Issue | 1 |
| Publication date | 2018-01-19 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Forensic Anthropology (JOURNAL) |
| Journal identifiers | ISSN: 2573-5020 • E-ISSN: 2573-5039 |
| Publisher | University Press of Florida (PUBLISHER) |
| DOI | 10.5744/fa.2018.0003 |
| OpenAlex | W2785282562 |
| Language | EN |
| Citations received | 23 |
| References cited | 23 |
The use of crown and root morphology to estimate population relationships has a long history in dental anthropology. Over the past two decades, methods employing dental morphology within forensic anthropology have been formalized with the incorporation of statistical models. This paper presents a new web-based application (rASUDAS) that estimates the ancestry of unknown individuals based on their suite of tooth crown and root traits. The application utilizes 21 independent traits that were scored following the Arizona State University Dental Anthropology System (ASUDAS). The reference sample represents approximately 30,000 individuals from seven biogeographic regions. A naive Bayes classifier algorithm was created in the R open source programming language to assign posterior probabilities for individual group assignment. To test the application, 150 individuals were selected from the C. G. Turner II database with the proviso that an individual had to be scored for a minimum of 12 of the 21 traits. In a seven-group analysis, the model correctly assigned individuals to groups 51.8% of the time. In a four-group analysis, classification accuracy improved to 66.7%. With three groups, accuracy was at 72.7%. It is still necessary to validate the program using forensic cases and to augment the reference sample with modern skeletal data. However, results from the beta version of rASUDAS are presented as proof of concept on the potential of dental morphology in ancestry estimation in forensic contexts.KEYWORDS: dental morphology, dental non-metrics, quantitative analysis, Arizona State University Dental Anthropology System, Bayesian statistics
Archaeology · Forensic anthropology · Geography · Machine learning · Naive Bayes classifier · Population · Sociology · Statistics · Computer Science · Demography · Forensic and Genetic Research · Forensic Anthropology and Bioarchaeology Studies · Mathematics · Artificial Intelligence
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Variation in dental morphology and inference of continental ancestry in admixed Latin Americans
Population history of southern Italy during Greek colonization inferred from dental remains
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Non-metric traits of dental crown morphologies in the gallo-roman collection from La Favorite’s necropolis (Lyon, France) using the Arizona State University dental Anthropology system
Intentional dental modification in Panamá
Non-metric variations in individuals who died during the perinatal period in past populations
Morphology of Denisovan molars
Jordanian migration and mobility in the Middle Bronze Age (ca. 2100-1550 BCE) at Pella
A Companion to Forensic Anthropology
The Changing Dentition of Man
Evidence for a secular trend in the Negro dentition
Morphological features of Jat dentition
Molar size and shape in the estimation of biological ancestry
Secular trend in tooth size in urban Chinese assessed from two‐generation family data
Anterior femoral curvature
Carabelli's trait and tooth size of human maxillary first molars
| Unique citing works | 23 |
|---|---|
| Citations per year | 3,29 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 21 |