Dysmorphology is the branch of pediatrics and clinical genetics concerned with structural birth defects and delineation of syndromes. More than 1500 syndromes that include orofacial dysmorphia have been described. Today, dysmorphology remains largely descriptive, with diagnoses based on subjective or semi-quantitative clinical impressions of facial and other anatomic features. Over the past decade, dramatic technological advances in imaging, quantification, and analysis of variation in complex three-dimensional (3D) shape have revolutionized the assessment of morphologic variation, permitting robust definition of quantitative morphometric phenotypes that can distinguish patients from controls in a variety of syndromes. The goal of this application is to develop systems that will enable diagnostic application of craniofacial 3D morphometrics in clinical practice.
We aim to define specific quantitative measures that characterize the aberrant facial shapes in a large number of human dysmorphic syndromes. Specifically, we aim to build a broad and deep 3D morphometric facial scan library of defined craniofacial dysmorphic syndromes, a resource that can be shared with approved investigators for research purposes via the NIDCR FaceBase Hub; to develop 3D geometric morphometric (GM) and dense surface modeling (DSM) analytical tools to systematically analyze and distinguish dysmorphic syndromes from unaffected individuals and from each other; and finally to develop a functional, automated, prototype clinical tool that is capable of simultaneously distinguishing a large number of syndromes, and that thereby can assist real-time diagnosis of syndromes in the clinical setting. We anticipate that 3D photomorphometric deep-phenotyping, in conjunction with the rapid advent of exome and genome sequencing in clinical medicine, will transform dysmorphology from a clinical art into a medical science.
We hypothesize that the aberrant facial shapes, or dysmorphology, of specific human syndromes can be specifically characterized using 3D photomorphometry to derive objective quantitative measures, and that these can be used to build a syndrome facial shape library. Ultimately, our work will lead to a prototype diagnostic too to accurately characterize and discriminate among dysmorphic syndromes. By developing quantitative 3D morphometry as a routine tool to assist clinical diagnosis, we aim to re-define dysmorphology in specific quantitative terms. This work will revolutionize diagnosis in the genetics clinic setting.
| Larson, Jacinda R; Manyama, Mange F; Cole, Joanne B et al. (2018) Body size and allometric variation in facial shape in children. Am J Phys Anthropol 165:327-342 |
| Li, Mao; Cole, Joanne B; Manyama, Mange et al. (2017) Rapid automated landmarking for morphometric analysis of three-dimensional facial scans. J Anat 230:607-618 |
| Percival, Christopher J; Liberton, Denise K; Pardo-Manuel de Villena, Fernando et al. (2016) Genetics of murine craniofacial morphology: diallel analysis of the eight founders of the Collaborative Cross. J Anat 228:96-112 |
| Brinkley, James F; Fisher, Shannon; Harris, Matthew P et al. (2016) The FaceBase Consortium: a comprehensive resource for craniofacial researchers. Development 143:2677-88 |
| Neben, Cynthia L; Roberts, Ryan R; Dipple, Katrina M et al. (2016) Modeling craniofacial and skeletal congenital birth defects to advance therapies. Hum Mol Genet 25:R86-R93 |
| Hallgrimsson, Benedikt; Percival, Christopher J; Green, Rebecca et al. (2015) Morphometrics, 3D Imaging, and Craniofacial Development. Curr Top Dev Biol 115:561-97 |
| Goodwin, Alice F; Kim, Rebecca; Bush, Jeffrey O et al. (2015) From Bench to Bedside and Back: Improving Diagnosis and Treatment of Craniofacial Malformations Utilizing Animal Models. Curr Top Dev Biol 115:459-92 |
