Integrating developmental morphogenetic theory and dental biodistance practices

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Differences in the size and shape of teeth are used in a variety of ways, from reconstructing evolutionary relationships between species to identifying individuals in modern forensics cases. As such, it is critical that we understand how tooth size and shape are inherited by studying the dentition in family trees. The Albert Dahlberg collection of dental casts is one of the largest in the world and consists of 7500 dentitions representing 3000 individuals. Most individuals are situated in known family trees thus allowing the researchers to explore the genetics of how dental characteristics are passed between generations. Previous work on the collection has contributed to our understanding of dental genetics, development, and evolution; however, the full potential of this archive has never been realized. Recent advances in developmental biology, genetic statistical tools and software, and 3D scanning technology now permit an exploration of tooth genetics in ways not previously possible, while scanning will also preserve this fragile resource for use by future generations. The project will record a comprehensive set of metric and nonmetric data from the collection. Using these data, project staff will generate heritability estimates for a large number of dental features using large sample sizes and complex statistical models. The project will also explore how dental features infer evolutionary relationships and how they identify closely related individuals based on their dental similarities. The dissemination of highly accurate digital models of the casts will provide researchers with the needed data to test a number of assumptions about how tooth size and shape are inherited and modified by evolutionary mechanisms. The project will benefit anthropology, developmental biology, paleontology, clinical dentistry and forensic dentistry. The larger significance is making this collection widely available to remote researchers, who will be able to mine and analyze the data in currently unknown ways for generations to come.