Marazita/Weinberg. 3D Facial Norms Database

Although ample evidence exists that facial appearance and structure are highly heritable, there is a dearth of information regarding how variation in specific genes relates to the diversity of facial forms evident in our species. With the advent of affordable, non-invasive 3D surface imaging technology, it is now possible to capture detailed quantitative information about the face in a large number of individuals. By coupling state- of-the-art 3D imaging with advances in high-throughput genotyping, an unparalleled opportunity exists to map the genetic determinants of normal facial variation. An improved understanding of the relationship between genotype and facial phenotype may help illuminate the factors influencing liability to common craniofacial anomalies, particularly orofacial clefts, which are among the most prevalent birth defects in humans. This proposal has two major goals: (1) to construct a nonnative repository of 3D facial and genetic data and (2) to utilize this data repository to identify genes that influence normal midfacial variation. The first goal will focus on data generation and resource development and will involve the collection of 3D facial surface images and DNA samples on 3500 healthy Caucasian individuals (age 5-40) drawn from the general population. Quantitative facial measures will be extracted from the 3D images and all DNA samples will be genotyped for genome-wide SNP markers. Working in conjunction with the FaceBase hub, our intent is to create a scalable. Interactive and minable data resource available to outside investigators, which will contain facial measures, 3D images and genotypes. Ultimately, it is hoped that such a database will facilitate novel research initiatives. To illustrate this potential, the second goal of this proposal will focus on identifying SNPs associated with variation in midfacial morphology, including those facial features relevant to orofacial cleft predisposition. Salient measures of midfacial morphology will be derived from 3D facial surface images, and a genome-wide association approach will then be employed to identify polymorphisms that influence quantitative variation in the facial features of interest.