A disproportionate facial appearance often accompanies a severe Class III malocclusion and can result in a significant burden on the quality of life for those affected. Current therapies for this condition are aimed at treatment rather than prevention; thus, patients undergo years of orthodontic or orthopedic treatment, with many requiring surgical correction in adulthood. Studies since the 1970s have provided evidence that Class III skeletal characteristics have a strong genetic component. To elucidate preventive strategies and improve treatment modalities for these patients, studies identifying the genetic etiology of Class III malocclusion are warranted. However, detection of human susceptibility genes for Class III malocclusion is in its initial stages, since no etiologic mutations have yet been identified.

The few genetic mapping studies of Class III malocclusion thus far have found genetic linkages of mandibular prognathism to chromosome loci 1p22.1, 1p36, 3q26.2, 4p16, 6q25, 11q22, 12q13.13, 12q23, 14q 24.3, and 19p13.2 and have found positive association signals of mandibular height and prognathism to genes GHR , Matrilin-1 , EPB41 , TGFB3, LTBP , and MYO1H , indicating that molecular pathways implicated in bone ( TGFB3 , LTBP ) and cartilage ( GHR , Matrilin-1 ) development are plausible candidates for mandibular size discrepancies and should be considered in future research.

Although informative, the few genetic studies to date have limitations including modest sample sizes, exclusion of environmental effects, unknown generalization of results to other ancestries, and, finally and perhaps more importantly, limited phenotypes that cannot capture the complexities of Class III malocclusions. The success of genetic studies aimed at identifying causative genes for complex traits such as malocclusion depends greatly on a well-characterized phenotype to reduce heterogeneity. Studies of cross-sectional and retrospective longitudinal samples with conventional cephalometry or shape-analysis methods have attempted to characterize the dentoskeletal morphology in Class III children and adults of different ethnicities. In general, most of these studies have shown great variation in dentoskeletal morphology, yet the most common features in Class III subjects include a short anterior cranial base with an acute saddle angle, maxillary retrusion with a normal or protruded mandible, mandibular protrusion with a normal maxilla, and combinations of these anteroposterior discrepancies with a normal, excessive, or deficient vertical facial dimension along with protrusive maxillary incisors and retrusive mandibular incisors. Most of these components are present in the majority of Class III patients regardless of ethnic background, appear early in development, and tend to worsen with age.

Recently, studies using multivariable methods such as principal component analysis and cluster analysis applied to data from cephalometric radiographs have provided further insight into the characterization of Class III malocclusion phenotypes beyond traditional cephalometric methods. Principal component analysis essentially decomposes the correlations of a set of variables into orthogonal linear combinations of these variables (called components ). The information captured by the components decreases with the component order. Each component has scoring coefficients, or weights, for the included variables that allow for constructing a linear index that reflects a phenotypic axis of variation in the variables. In other words, principal component analysis accounts for the overall morphologic variation in the craniofacial complex. On the other hand, cluster analysis complements principal component analysis by identifying groups of subjects of similar phenotypes and allowing for traditional case-control comparisons.

Mackay et al studied morphologic variation in craniofacial forms using cluster analysis in 50 severe, nongrowing Class III patients requiring surgical correction and identified 5 subgroups. These findings provided good evidence that different forms of Class III malocclusion exist and can successfully be divided into groups based on similar phenotypes. Hong and Yi used cluster analysis to illustrate that different patterns of skeletal architecture—beyond the current simple classification based on the positions of the maxilla and the mandible, dentoalveolar units, and vertical relationships—contribute to the development of the Class III deformity. They identified 7 clusters in their Asian sample of 106 untreated Class III subjects with a mean age of 21 years (range, 16-32 years). Their clusters showed that in addition to the facial bones and dentition, the cranial base, cranial vault, and cervical spine were also involved in different but specific architectural patterns.

Abu Alhaija and Richardson studied 115 Class III children (aged 11.6-12.7 years) with cluster analysis and discriminant function analyses to differentiate favorable and unfavorable growers. They found 3 main clinical clusters according to long, short, and intermediate facial heights and determined that the power of discriminant function analysis to discriminate between favorable and unfavorable growers increases from 80% to 100% in some cases when cluster analysis is applied before discriminant function analysis.

The most recent article and the one most directly relevant to our study was by Bui et al in 2006; they characterized Class III malocclusion phenotypes using cluster analysis and principal component analysis of 67 cephalometric variables derived from 309 Class III subjects. Their sample included a wide age range (average, 19.1 years; range, 5.9-56.3 years) and was racially and ethnically diverse, consisting of 73% white subjects, 17% African Americans, 5% Asians, 3% Hispanics, and 2% another race or ethnicity. Subjects with previous orthodontic treatment, congenital abnormalities, trauma, or incomplete or low-quality cephalograms were excluded. Five clusters were identified, representing distinct subgroups of Class III malocclusion. In addition to the spectrum of phenotypic variation evidenced by the clusters, the investigators found that the first 5 principal components derived from the data explained 67% of the variation in the sample. Based on these combined findings, the authors suggested that different genes might be involved in controlling dimensions vs structures, and they questioned current treatment modalities that target the growth of the maxillary or mandibular skeletal structures. Although these data are informative, the sample included subjects who were still growing and did not have fully expressed phenotypes. In addition, the few ethnicities represented might not be large enough to be statistically meaningful, thus increasing phenotypic heterogeneity and limiting generalizability. Still, that study clearly demonstrated that Class III malocclusion exists in morphologically diverse patterns that can be classified into phenotypes using multivariable methods such as cluster analysis and principal component analysis.

Although previous studies have contributed to our understanding of the craniofacial components of the Class III malocclusion, there are limitations in sample sizes, sample selection criteria such as including growing subjects and not excluding other genetic or environmental traits such as missing or impacted teeth, heterogeneity due to race or ethnicity, and lack of or limited standardization of data with respect to key variables such as age and sex before applying the data reduction methods. Therefore, there is uncertainty regarding the extent to which the results from previous work, particularly from the most recent and methodologically advanced study of Bui et al, are generalizable to other samples and populations, and whether one can identify additional phenotypic variation in other samples.

In this study, we aimed at extracting phenotypes that could best capture the phenotypic variation in a large sample of white adults with Class III malocclusion using multivariate reduction methods. Using similar methods to those of Bui et al, one goal was to evaluate whether their phenotypes replicate in an ethnically homogeneous sample limited to postpubertal subjects. In light of the uncertainty about the generalizability of previous findings, replication studies are essential to evaluating the validity of this approach for phenotypic characterization. Another goal was to see whether we could explain meaningful additional variation in this sample. Such improvement in phenotypic variation can be important both clinically and for increasing the power of genetic studies. We applied rigorous sample inclusion criteria and carefully accounted for age and sex effects to increase the precision of the estimation. Our work builds on previous studies and provides a comprehensive set of Class III phenotypes that can be readily applied for phenotypic characterization of Class III subjects in other samples; this would facilitate large future collaborations of genetic studies.