Date of Degree
MS (Master of Science)
Moreno Uribe, Lina M.
First Committee Member
Second Committee Member
Third Committee Member
Purpose: The purpose of this study is to characterize class II malocclusion phenotypes from orthodontic photographs in an effort to understand variation in the vertical and transverse dimension from the frontal view and also to identify distinct subgroups of homogenous phenotypes that could be correlated to genetic variation in an effort to identify the genetic causes of class II malocclusion phenotypes.
Materials and Methods: The study sample included adult class II patients who were seeking treatment at the University of Iowa Orthodontic Graduate Clinic, University of Iowa Hospital Dentistry Clinic or surrounding private practice orthodontic clinics. The sample consisted of 330 Caucasian adult subjects (79 male, 251 female; age range 16-60 years) who met our eligibility criteria. 2D pre-treatment intraoral and extraoral photographs of 330 Class II adults were imported into Dolphin Imaging, version 11.0 (Dolphin Imaging Systems, Chatsworth, Calif). Non-digital photographs were scanned and imported into Dolphin Imaging. A total of 36 measurements were made. Fifteen were made on the extraoral frontal repose photograph, 15 were made on the extraoral smile photograph, and 6 were made on the intraoral frontal photograph. After the measurements were recorded, ratios, or proportions, were calculated from these facial measurements. Data reduction methods, principal component analysis (PCA) and cluster analysis (CA), were used due to the large number of measurement variables. The goal of these statistical tests is to identify the most homogeneous groups of individuals representing distinct class II phenotypes in an effort to reduce genetic heterogeneity. PCA was used to derive quantitative phenotypes and CA to identify phenotypically homogenous groups of individuals. The next goal of this study was to examine how the derived principal components correlate with the intraoral esthetic measurements. Descriptive statistics were derived for the esthetic variables. Pearson and Spearman correlations were used to analyze the relationship between the principle components and the esthetic measures.
Results: The principal components analysis revealed that four principal components accounted for nearly 80% of the total variance in the data. The four principal components were used as the basis for the attempted formation of clusters defining subphenotypes of class II malocclusion in our study. The clustering process was repeated to assess cluster over a range for the number of clusters from 2 to 7 clusters. Each fit was examined using the pseudo F statistic, the cubic clustering criterion, and cluster visualization. Unfortunately, none of the clustering models were a good fit for our data based on the cubic clustering criterion and the relationship between the pseudo-F statistic and the cubic clustering criterion. This study shows that there is minimal correlation between the esthetic dental measurements and the phenotypic variables represented by the 4 principal components.
Conclusions: A well-characterized class II malocclusion phenotype is crucial to reduce the heterogeneity when trying to find the causative genes for this complex trait. There have been numerous studies identifying environmental and genetic factors that lead to malocclusion, but none have fully characterized the class II phenotype. This study along with past and ongoing studies at the University of Iowa College of Dentistry are committed to fully characterizing the class II malocclusion phenotype using lateral cephalometric measurements, photographic measurements, 3-D cast measurements, and cone beam radiographic measurements. This data, along with DNA and environmental data will be combined to identify the causative gene for developing a class II malocclusion.
vii, 93 pages
Includes bibliographical references (pages 86-93).
Copyright 2013 Alison Ray
Ray, Alison. "Phenotypic characterization of class II malocclusion using two dimensional photographic measurements." MS (Master of Science) thesis, University of Iowa, 2013.