Download Phenotypic characterization of class CIII malocclusion

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Iowa Research Online
Theses and Dissertations
Spring 2012
Phenotypic characterization of class CIII
malocclusion
Kaci C. Vela
University of Iowa
Copyright 2012 Kaci Cerka Vela
This thesis is available at Iowa Research Online: http://ir.uiowa.edu/etd/3005
Recommended Citation
Vela, Kaci C.. "Phenotypic characterization of class CIII malocclusion." MS (Master of Science) thesis, University of Iowa, 2012.
http://ir.uiowa.edu/etd/3005.
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Part of the Orthodontics and Orthodontology Commons
PHENOTYPIC CHARACTERIZATION OF CLASS III MALOCCLUSION
by
Kaci C. Vela
A thesis submitted in partial fulfillment
of the requirements for the Master of
Science degree in Orthodontics
in the Graduate College of
The University of Iowa
May 2012
Thesis Supervisor: Professor Lina M. Moreno
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
_______________________
MASTER'S THESIS
_______________
This is to certify that the Master's thesis of
Kaci C. Vela
has been approved by the Examining Committee
for the thesis requirement for the Master of Science
degree in Orthodontics at the May 2012 graduation.
Thesis Committee:
Lina M. Moreno, Thesis Supervisor
Robert N. Staley
James S. Wefel
Deborah V. Dawson
To Joe and Kyndl
ii
ACKNOWLEDGMENTS
This project would not be possible without the considerable input and support
from my thesis committee members, Dr. Lina Moreno, Dr. Robert Staley, Dr. James
Wefel and Dr. Deborah Dawson. I truly appreciate your guidance and for allowing me to
contribute to orthodontic research. I also owe a debt of gratitude to Colleen Kummet and
Dr. Nathan Holton for their vast knowledge and input over the past three years. I cannot
express enough appreciation to Chika Takeuchi and Patricia Hancock for their endless
help with sample collection and also to Steven Miller, MaryBeth Hoppens and Skyler
Johnson for their assistance. Finally, I would like to thank all of the doctors and their
staff who contributed to my sample, Dr. Paul Hermanson, Dr. John Hermanson, Dr.
Clayton Parks, Dr. Jason Schmidt, Dr. Tom Stark, Dr. David Gehring, Dr. Carney Loucks
and Dr. Jenni Buren, Dr. Tom Southard, Dr. John Casko and Dr. Robert Staley.
I must express my sincere gratitude to Dr. Tom Southard, Dr. John Casko, Dr.
Robert Staley, Dr. David Jones, Dr. Lina Moreno, Dr. Andrew Lidral and the entire
faculty at the University of Iowa Department of Orthodontics for allowing me the
opportunity to realize my dream and pursue such a wonderful career. Thank you for
dedicating your lives to education; without your guidance, instruction and research
contributions, our field would not be where it is today.
Finally, I owe everything to my husband, Joe, for his countless hours of
encouragement and patience, and to my daughter Kyndl, who has been a part of my
education process her entire life. I would not be here without the support from my
parents, Frank and Toni Cerka, and my father- and mother-in-law, Janie and Joe Vela.
Most importantly, I owe everything to God for the wonderful blessings he has bestowed
upon me. Thank you all!
iii
TABLE OF CONTENTS
LIST OF TABLES ...............................................................................................................v
LIST OF FIGURES ........................................................................................................... vi
INTRODUCTION ...............................................................................................................1
LITERATURE REVIEW ....................................................................................................2
Malocclusion Demographics ............................................................................2
Genetic Review .................................................................................................4
Etiology of Malocclusion .................................................................................6
Class III Malocclusion Morphology ...............................................................10
MATERIALS AND METHODS .......................................................................................23
Sample ............................................................................................................23
Cephalometric Procedure................................................................................24
Method Error ..................................................................................................25
Intraclass Correlation...............................................................................25
Difference Testing ...................................................................................26
Statistical Analysis..........................................................................................27
Principal Component Analysis ................................................................28
Cluster Analysis.......................................................................................29
RESULTS ........................................................................................................................35
DISCUSSION ....................................................................................................................70
Limitations of the Study .................................................................................72
Clinical Application ........................................................................................73
Future Projects ................................................................................................73
SUMMARY AND CONCLUSIONS ................................................................................75
REFERENCES ..................................................................................................................76
iv
LIST OF TABLES
Table 1
Eligibility Criteria and Descriptive Statistics from Bui 2006 ...... 20
Table 2
Summary of Cluster Analysis from Bui 2006 ............................... 22
Table 3
Summary of Principal Component Analysis from Bui 2006 ......... 22
Table 4
Eligibility Criteria .......................................................................... 24
Table 5
63 Cephalometric Variables ........................................................... 32
Table 6
Cephalometric Landmark Definitions ........................................... 33
Table 7
Principal Component Eigenvalues ................................................ 39
Table 8
PC1 Cephalometric Variables ........................................................ 41
Table 9
PC2 Cephalometric Variables ........................................................ 43
Table 10
PC3 Cephalometric Variables ........................................................ 45
Table 11
PC4 Cephalometric Variables ........................................................ 47
Table 12
PC5 Cephalometric Variables ........................................................ 49
Table 13
PC6 Cephalometric Variables ........................................................ 51
Table 14
Summary of Principal Component Analysis ................................. 52
Table 15
Summary of Cluster Analysis ........................................................ 62
Table 16
Summary of Cluster Descriptions ................................................. 69
v
LIST OF FIGURES
Figure 1
Mean Shapes for each Cluster from Mackay 1992 ....................... 19
Figure 2
Representative Tracing from each Cluster from Bui 2006 ........... 21
Figure 3
Scree Plots ...................................................................................... 38
Figure 4
Component Scores 95% Prediction Ellipse .................................... 38
Figure 5
PC1 Extremes ................................................................................. 40
Figure 6
PC1 Quartiles ................................................................................. 40
Figure 7
PC2 Extremes ................................................................................. 42
Figure 8
PC2 Quartiles ................................................................................. 42
Figure 9
PC3 Extremes ................................................................................. 44
Figure 10 PC3 Quartiles ................................................................................. 44
Figure 11 PC4 Extremes ................................................................................. 46
Figure 12
PC4 Quartiles ................................................................................ 46
Figure 13
PC5 Extremes ................................................................................ 48
Figure 14
PC5 Quartiles ................................................................................ 48
Figure 15
PC6 Extremes ................................................................................ 50
Figure 16
PC6 Quartiles ................................................................................ 50
Figure 17 Cluster Validation Criteria ............................................................ 53
Figure 18 Plots of CIII Clusters – Centroids Labeled (Can 1 & 2) ............... 54
Figure 19 Plots of CIII Clusters – Centroids Labeled (Can 1 & 3) ............... 55
Figure 20 Plots of CIII Clusters – Centroids Labeled (Can 1 & 4) ............... 56
Figure 21 Plots of CIII Clusters – Centroids Labeled (Can 2 & 3) ............... 57
Figure 22 3-D Plot of 5 Clusters of CIII Malocclusion Subjects .................... 58
Figure 23 3-D Plot of 5 Clusters of CIII Malocclusion Subjects .................... 59
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Figure 24 3-D Plot of 5 Clusters of CIII Malocclusion Subjects .................... 60
Figure 25 3-D Plot of 5 Clusters of CIII Malocclusion Subjects .................... 61
Figure 26 Cluster Centroids ........................................................................... 63
Figure 27 Cluster 1 Centroid, Description and Core ..................................... 64
Figure 28 Cluster 2 Centroid, Description and Core ..................................... 65
Figure 29 Cluster 3 Centroid, Description and Core ..................................... 66
Figure 30 Cluster 4 Centroid, Description and Core ..................................... 67
Figure 31 Cluster 5 Centroid, Description and Core ..................................... 68
vii
1
INTRODUCTION
Severe malocclusion causes a distorted facial appearance and results in a
significant burden on the quality of life for those affected (Stenvik, Espeland et al. 2011).
Current therapies are aimed at symptoms rather than prevention; thus, patients undergo
years of orthodontic/orthopedic treatment with a great majority requiring surgical
correction in adulthood. In order to elucidate preventive strategies and improved
treatment modalities for these patients, studies identifying the genetic etiology of Class
III malocclusion are needed.
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 (Wilcox, Wyszynski et al. 2003). The purpose of this study is to
characterize Class III malocclusion phenotypes into distinct subgroups that could
potentially be related to genetic variation. Lateral cephalometric radiographs of
Caucasian adult subjects were measured to identify clusters of the most homogeneous
groups of Class III individuals. These data will provide the necessary infrastructure for
future genetic studies aimed at detection of the gene(s) responsible for unbalanced
craniofacial growth resulting in severe Class III malocclusion.
2
LITERATURE REVIEW
Malocclusion Demographics
Balanced facial growth is essential for the development of a well-proportioned
face and functional occlusion of the teeth. Normal occlusion occurs when the teeth are
well aligned and the maxilla and mandible are positioned in an ideal relationship to one
another. Data from the NHANES III show only 30% of American children present with
normal occlusion (Proffit, Fields et al. 1998), thus signifying the ubiquitous nature of
malocclusion in the United States. Malocclusion can result from dental problems alone
(crowding, early loss of a primary tooth) or in combination with discrepancies in the size
and shape of the various components of the craniofacial complex. CI malocclusion is
defined as a normal relationship of the molars (mesio-buccal cusp of the maxillary first
permanent molar occluding with the buccal groove of the lower first mandibular molar),
but the line of occlusion is incorrect due to malposed teeth, rotations or other causes; 50%
of America’s youth fall into this category. CII malocclusion occurs when the lower
molar is positioned distally relative to the upper molar and is prevalent in 15% of the
population. When skeletal in nature, CII malocclusion can be due to excessive growth of
the maxilla, deficient growth of the mandible, or a combination of the two. Conversely,
Class III malocclusion results when the lower molar is mesially positioned relative to the
upper molar. 75% of Class III malocclusion cases are caused by a skeletal imbalance
(Staudt and Kiliaridis 2009) due to either deficient maxillary growth (19-37% of adults),
excessive mandibular growth (19-40%) or a combination thereof (1.5%-34.5%) (Dietrich
1970; Jacobson, Evans et al. 1974; Ellis and McNamara 1984; Guyer, Ellis et al. 1986).
Overall, Class III malocclusion represents a very small proportion of the US population
(<5%) (Emrich, Brodie et al. 1965), but affects people in every country, especially the
Oriental population (El-Mangoury and Mostafa 1990; Yang 1990). While the functional
occlusion of the dentition is an important factor to consider, unbalanced craniofacial
3
growth leading to distorted facial appearance has enormous psychosocial impact
worldwide.
Individuals presenting with Class III malocclusion due to an underlying skeletal
discrepancy (maxillary deficiency and/or mandibular prognathism) experience more
challenging problems that result in decreased physical, social and psychological health
(Kiyak 2008). Severe Class III malocclusion can affect physical health in terms of pain
associated with temporomandibular disorders, interfere with speech and mastication,
negatively impact self-concept, and also influence social acceptance as perceived
attractiveness and intelligence by others (Zhang, McGrath et al. 2006). Due to the
negative consequences on quality of life for patients with malocclusion, many patients
seek orthodontic treatment. In fact, it is estimated that up to 50% of children and youths
receive orthodontic care in the United States and abroad (Yang 1990; Proffit, Fields et al.
1998). Current treatment modalities for Class III malocclusion include dental
extractions, dentofacial orthopedic therapy such as facemask/reverse pull headgear
(Delaire 1997; Ngan, Hagg et al. 1997) or chin cup (Sugawara and Mitani 1997),
miniplate supported treatment (De Clerck, Cornelis et al. 2009; Cha, Choi et al. 2011),
distraction osteogenesis (Dias, De Silva et al. 2011), and/or orthognathic surgery. While
treatment options have greatly improved over time and vastly enhance the lives of these
individuals (Zhou, Hagg et al. 2002), many undergo treatment for years, yet still require
orthognathic surgery in adulthood (Proffit and White 1991; Watanabe, Suda et al. 2005).
To date, about 4% of the US population have malocclusion deformities that necessitate
surgical orthodontic treatment (Proffit 2007). Thus, in order to improve treatment
efficiency and effectiveness for these patients, studies identifying the etiology of Class III
malocclusion are needed. Before delving into Class III malocclusion etiology, however,
an assessment of the current genetic methods used to elucidate malocclusion
susceptibility genes would be beneficial.
4
Genetic Review
A basic review of genetic principles and biomolecular techniques used to identify
specific disease-susceptibility genes is helpful in understanding the specific aim of this
study and relating it to the importance for future research. The human genome consists
of the total complement of inherited genetic information contained within 46
chromosomes in the nucleus of each cell (Graber, Vanarsdall et al. 2005). The smallest
physical and functional unit of inheritance is the human gene, which is located in a
specific region within a chromosome called the locus. An individual’s complete set of
genes (genotype) contains the entire DNA sequence necessary for the production of RNA
or proteins, which when expressed as observable characteristics, describe an individual’s
phenotype (Baltimore 2001). Specific phenotypic properties are depicted as traits and
can develop as a result of the influence of a single gene locus (monogenic) or due to the
influence of multiple genetic loci and environmental factors (polygenic/multifactorial)
(Mossey 1999). Genetic etiology for monogenic traits (i.e., cystic fibrosis) is easier to
discern than for polygenic traits such as malocclusion. In order to increase efficiency and
power for detecting the genes involved in this heterogeneous trait, a precise phenotype
definition must first be obtained (Ellsworth and Manolio 1999). Subsequently,
homogeneous groups of subjects with similar phenotypic characteristics can be targeted
for collection of DNA, increasing the success of genetic studies aimed at defining the
etiology of Class III malocclusion.
Identification of genetic variants contributing to disease can be carried out by
candidate gene or whole genome studies via linkage and association analyses. Linkage
analysis is a genetic mapping technique that pinpoints specific regions of chromosomes
that are likely to contain the risk gene responsible for the manifested trait (Ellsworth and
Manolio 1999). In linkage analysis, the initial identification of loci believed to be
involved requires examination of the shared genomic region from related subjects
5
(parent-offspring or sibling pairs) with similar phenotypes. Linkage analysis is more
powerful at detecting rare genetic variants with stronger effects on the phenotype.
Genome-wide association studies on the other hand usually test for associations
between small variations in the genome called single nucleotide polymorphisms (SNPs)
and the presence of the trait of interest. SNPs consist of single base changes in the DNA,
and particular alleles (i.e., alternative forms of a genomic sequence) of these SNPs can
occur at higher frequencies in individuals displaying a particular phenotype compared to
controls. Such SNPs can be located near a gene of interest or within a regulatory region
of the genome and their co-occurrence with the trait in question could indicate causality
or elevated risk of developing a particular disease (Manolio 2010). Once a gene in an
area of the genome is identified, perhaps because its gene products are involved in early
relevant developmental pathways for example, it becomes a candidate gene for specific
analysis of its structure to identify the relevant allele (Lusis 1988). Fine-mapping
techniques further characterize the candidate regions to locate the etiologic variant near
or within a gene of interest by genotyping additional markers or directly sequencing the
genomic region. Such data is then compared amongst family members for segregation
(inheritance) patterns in affected individuals. Individuals presenting with the disease will
demonstrate recognizable patterns of linkage while unaffected family members show a
lack of consistent patterning (Ellsworth and Manolio 1999). Alternatively, if cases and
controls were collected, successive association analyses with the additional data could be
performed until there is statistical evidence of the occurrence of the particular allele in
cases more frequently than in controls. Eventually, associated alleles are tested for
detrimental effects in vitro or in animal models expressing the phenotype of interest.
Both linkage and association analyses have their own advantages and disadvantages,
which are dependent on the underlying genetic architecture of the disease, the
characteristics of the samples/families that are available, and the realities of technology
and the economy. This research study focuses on the initial step of characterization of
6
Class III malocclusion phenotypes as required by genetic studies aimed at successfully
detecting the genetic etiology of Class III malocclusion.
Etiology of Malocclusion
Malocclusion is a manifestation of both environmental and genetic interaction on the
development of the craniofacial complex (Moyers and Krogman 1971; Nakata, Yu et al.
1973; Harris 1975; Mossey 1999; Jena, Duggal et al. 2005). Environmental factors
known to contribute to malocclusion include trauma, hormonal imbalances, muscle
dysfunction, poor nutrition, illness, pituitary gland diseases, mandibular posture habits,
caries experience, premature loss of primary teeth, history of prolonged sucking or
resting tongue habits, mouth breathing, enlarged tonsils, atypical swallowing, and low
socioeconomic status (Litton, Ackermann et al. 1970; Proffit 2007; Hebling, Cortellazzi
et al. 2008; Mtaya, Brudvik et al. 2009). Unfortunately, these factors contribute to less
than 5% of all malocclusion cases (Proffit 2007). Due to the frequent observation of
familial aggregation of malocclusion, it is likely that genetic factors play a substantial
role in the remaining 95% of cases; however, precise knowledge of these factors or their
interactions with the environment remains elusive.
A literature review carried out by Lauweryns in 1993 concluded that 40% of the
dental and skeletal variations that lead to malocclusion could be attributed to genetic
factors (Lauweryns, Carels et al. 1993). Studies evaluating the genetic contribution of
certain dental anomalies associated with malocclusion are present in the literature
(Townsend, Richards et al. 2006; Townsend, Hughes et al. 2009). Hughes and Townsend
quantified the extent of variation in different occlusal features such as interdental
spacing, overbite, overjet and arch dimensions of Austrailian twins and indicated a
moderate to relatively high genetic contribution to the observed variation (Hughes,
Thomas et al. 2001). More recently, Ting suggested an association for the genes EDA
and XEDAR in dental crowding present in Class I patients by identifying 5 SNPs that
7
were significantly different in a genotype or allele frequency distribution in the Hong
Kong Chinese case-control population (Ting, Wong et al. 2011). While these studies
provide evidence for the heritability of dental occlusal characteristics that contribute to
malocclusion, other studies have come to the opposite conclusion (Corruccini, Sharma et
al. 1986; Harris and Johnson 1991; King, Harris et al. 1993). For instance, Corruccini
could not demonstrate significant heritability for occlusal traits among Indian twins
suggesting that dental patterns are environmentally based. Harris and Johnson also noted
almost all of the occlusal variability within their sample of untreated subjects was
acquired rather than inherited. These conflicting data suggest that dental variation is
more dependent upon environmental factors whereas skeletal characteristics have a
stronger heritability estimate.
Long before research studies were carried out to support the heritability of
craniofacial form, however, it was evident that skeletal malocclusion traits had a strong
genetic etiology. The best-known example of familial inheritance is the Hasburg Jaw, in
which mandibular prognathism recurred over multiple generations in European royalty
(Hodge 1977; Chudley 1998). Studies carried out in 1965 of patients with genetically
linked congenital abnormalities such as Turner or Klinefelter syndrome further
implicated the genetic determination of dentofacial structures by demonstrating that X
chromosome anueploidy resulted in defects primarily associated with jaw growth (Gorlin,
Redman et al. 1965). Currently, familial segregation, parent-offspring similarity and twin
concordance studies are the best methods to determine the extent to which a skeletal
characteristic is inherited (Nakata, Yu et al. 1973; Schouboe and Hauge 1973; Jena,
Duggal et al. 2005).
Studies since the 1960s have provided evidence that Class III skeletal characteristics
are genetically transmitted (Litton, Ackermann et al. 1970; Markovic 1983; Mossey
1999). Nakasima illustrated a familial tendency in the development of Class III
malocclusion in a Japanese population via analyzing parent-offspring correlations using
8
lateral and frontal cephalometric radiographs (Nakasima, Ichinose et al. 1982). A similar
study using lateral cephalograms to estimate the heritability of craniofacial characteristics
showed that the position of the lower jaw, the anterior and posterior face heights, and the
cranial base dimensions were highly heritable between parents and offspring
(Johannsdottir, Thorarinsson et al. 2005). These findings mirrored a previous twin study
that also identified significant hereditary variations in the mandibular body length, the
total face height, the lower face height and the anterior cranial base. The upper face
height, however, was not found to contribute greatly to the genetic variability of the face
as a whole in this sample (Horowitz, Osborne et al. 1960). More specifically, the
mandibular prognathism component of Class III malocclusion has been shown to exhibit
high monozygotic concordance in the literature (Wolff, Wienker et al. 1993; Watanabe,
Suda et al. 2005). In fact, heritability estimates for mandibular prognathism in 1st degree
relatives ranged from 31.6%- 84.3% (Watanabe, Suda et al. 2005; Cruz, Krieger et al.
2007). Segregation analysis in families with mandibular prognathism favors an
autosomal dominant gene with incomplete penetrance (Thompson and Winter 1988; ElGheriani, Maher et al. 2003; Cruz, Krieger et al. 2008; Otero, Quintero et al. 2010);
however, a recessive and a polygenic model of transmission have also been considered
(Iwagaki, 1938, Litton et al., 1970, Nakasima et al., 1982). Overall, these studies confirm
the genetic influence on skeletal craniofacial dimensions contributing to Class III
malocclusion.
The size and shape of the mandible and maxilla in mice have also been shown to be
highly heritable. Linkage studies aimed at identifying the genes that regulate normal
maxillofacial development have mapped quantitative trait loci to mouse chromosomes 5,
10, 11 and 15 for the mandible (Dohmoto, Shimizu et al. 2002; Suto 2009) and
chromosome 12 for the maxilla (Oh, Wang et al. 2007). Likewise, mouse expression and
functional studies have provided insight on some of the molecular pathways that play a
role in facial development. Fibroblast growth factors (FGFs), bone morphogenic proteins
9
(BMPs), Hedgehogs, WNT genes, epidermal growth factors (EGFs) and other
transcription factors appeared to be involved controlling the patterning and development
of the face (Mina 2001). Comparison of mouse chromosomal location and signaling
molecule networks involved in maxillo-mandibular growth with their human counterparts
have facilitated studies aimed at the identification of human susceptibility genes for Class
III malocclusion.
Association studies of mandibular height and prognathism in humans have found
positive signals in cases of Asian descent for the growth horomone receptor gene (GHR),
erythrocyte membrane protein band 4.1 (EPB41), transforming growth factor beta 3
(TGFB3) and latent transforming growth factor beta binding protein 2 (LTBP2).
Independent studies from two different populations (Japanese and Hau Chinese)
identified single nucleotide polymorphism in the GHR, thus indicating the height of the
mandibular ramus is under genetic control (Yamaguchi, Maki et al. 2001; Zhou, Lu et al.
2005; Tomoyasu, Yamaguchi et al. 2009). While these studies support the genetic
control of mandibular morphology, they did not correlate these shape changes with Class
III malocclusion. Recently, multiple genome-wide linkage studies have revealed
chromosome loci and candidate genes that may confer susceptibility to Class III
malocclusion. Yamaguchi et al identified three loci (1p36, 6q25 and 19p13.2) in 90
sibling-pairs from a Korean and Japanese population suggestive of linkage for
mandibular prognathism (Yamaguchi, Park et al. 2005). Xue used these data to search
for susceptibility genes within the chromosome locus 1p36, and identified EPB41 as a
possible candidate gene involved in this phenotype (Xue, Wong et al. 2010). Additional
linkage loci were detected in a Chinese population on chromosome 4 (4p16.1) and
chromosome 14q24.3-31.2, suggesting that the candidate genes ellis-van creveld (EVC),
EVC2 and TGFB3 and LTBP2 may play a role (Li, Zhang et al. 2010; Li, Li et al. 2011).
Furthermore, in a Colombian sample, loci (1p22.1, 3q26.2, 11q22, 12q13.13, 12q23) and
candidate genes within the 12q23 region (IGF1, HOXC and COL2A1) were suggestive of
10
linkage for Class III malocclusion. Interestingly, the Class III phenotype in this Hispanic
population was primarily associated with maxillary deficiency as opposed to mandibular
prognathism (Frazier-Bowers, Rincon-Rodriguez et al. 2009). Taken together, these data
provide powerful evidence that Class III malocclusion is genetically linked to specific
loci that regulate maxillary or mandibular growth. Despite these informative studies
however, detection of human susceptibility genes for Class III malocclusion is at its
initial stages due to the limited phenotypes investigated. For example, none of these
findings have been replicated in populations of different ancestries and most investigated
the mandibular prognathism component of malocclusion. Thus, studies characterizing
distinct subtypes of Class III malocclusion phenotypes are needed to reduce genetic
heterogeneity associated with the multifactorial and polygenetic nature of this complex
disease.
Class III Malocclusion Morphology
While the aforementioned studies have improved our understanding of the genetic
nature of Class III malocclusion, the specific etiology remains unknown due to the
variability in craniofacial morphology contributing to the disease. It has been suggested
that no single morphologic trait can be isolated because of the skeletal heterogeneity of
this group of malocclusion (Williams and Andersen 1986). Thus, the first step in
identification of a causative gene is to define distinct subgroups of CIII phenotypes.
Broad diversity exists in the size and shape of the skeletal components contributing to
Class III malocclusion. Lateral cephalometric radiographs have been the standard record
used in studies attempting to quantify the underlying craniofacial morphology. Jacobson
identified various types of skeletal patterns and stated that Class III malocclusion can
result from 5 different component variables: 1. The mandible may be too large relative
to the maxilla, 2. The maxilla may be too small relative to the mandible, 3. The maxilla
may be retropositioned relative to the mandible, 4. The mandible may be positioned too
11
far forward relative to the maxilla, 5. A forward rotation of the mandible relative to the
cranium will cause the chin point to move into a horizontally more protrusive position
resulting in a prognathous mandible and a reduction in lower anterior facial height
(Jacobson, Evans et al. 1974). The most common pattern in his sample was a prognathic
mandible and a well positioned maxilla, with only a quarter presenting with maxillary
deficiency. However, noticeable differences in their cephalometric measurements,
especially ANB, suggested multiple factors or types of mandibular prognathism exist. In
addition to the maxillo-mandibular relationship, the Class III subjects presented with a
more obtuse gonial angle, a forward position of the glenoid fossa and a short anterior
cranial base. Various other studies have also implicated cranial base morphology usually associated with a short anterior cranial base length and a decreased angle between
the anterior and posterior cranial bases (i.e., saddle angle) - as a major contributing factor
in establishing the antero-posterior relationship of the jaws (Sanborn 1955; Hopkin,
Houston et al. 1968; Kerr and Adams 1988; Battagel 1993; Battagel 1994). In a study of
Class III adults, Sanborn found that 45.2% of his sample presented with mandibular
protrusion, 33% presented with maxillary retrusion and only 9.5% presented with a
combination of mandibular protrusion and maxillary retrusion (Sanborn 1955). In
contrast, Guyer’s sample showed that less than 20% had pure mandibular prognathism
while 25% had pure maxillary skeletal retrognathism, and 22% had a combination of the
two (Guyer, Ellis et al. 1986). Ellis and McNamara also identified phenotypic
heterogeneity in the clinical severity of their sample of 302 Class III adults. They noted
the most common combination of variables was a protrusive mandible, retrusive maxilla,
long lower face height, protrusive maxillary incisors and retrusive mandibular incisors.
The majority (30.1%) presented with a combination of mandibular skeletal protrusion and
maxillary skeletal retrusion (Ellis and McNamara 1984). The prevalence of maxillary
skeletal retrusion (19.5%) in their sample was slightly less than Guyer’s but greater than
12
most others, which tend to cite mandibular skeletal protrusion as the principal anomaly
(Sanborn 1955; Dietrich 1970; Jacobson, Evans et al. 1974).
Phenotypic diversity in the craniofacial morphology contributing to Class III
malocclusion has also been shown to exist among children as well as different ethnic
groups (Ngan, Hagg et al. 1997; Singh, McNamara et al. 1998; Ishii, Deguchi et al. 2002;
Bukhary 2005). Class III Caucasian children in the UK presented with an acute cranial
base angle, a short and retrusive maxilla and a long but prominent mandible, yet a wide
range of variability was noted. In order to compensate for the skeletal discrepancy, the
maxillary incisors proclined forward and mandibular incisor tipped back. Moreover, the
female subjects tended towards more horizontal development while the males exhibited a
more vertical growth pattern (Battagel 1993). A similar cephalometric study of Class III
Syrian children identified different combinations of skeletal aberrations (Mouakeh 2001).
These subjects presented with a significantly shorter anterior and posterior cranial base
and smaller than normal cranial base angle. The anterior face height was short with an
anteriorly positioned glenoid fossa. The maxilla was small and positioned posteriorly
while the mandible was large but positioned normally. Interestingly, the maxillary
incisors were not protrusive as is the typical dental compensation usually present.
Instead, they were excessively upright and retruded whereas the mandibular incisors were
only slightly tipped lingually. In general, the majority of morphological studies of CIII
subjects have illustrated a short cranial base with an acute saddle angle, a retrusive
maxilla, a protrusive mandible, protrusive maxillary incisors and retrusive mandibular
incisors. From these data, it is apparent that Class III malocclusion does not exist in a
single form, but instead is a result of various combinations of morphologic components,
each responsible for the complex nature of this trait.
Evidence of the deviant Class III craniofacial pattern appears early in development
and becomes progressively more severe with time (Miyajima, McNamara et al. 1997). In
a study of 2074 untreated Class III Japanese female subjects, the position of the maxilla
13
was retrusive initially and remained retrusive over time. In contrast, the mandible began
protrusive and continued to grow forward with time, further increasing the imbalance
between the upper and lower jaws. Also, the lower anterior face height and mandibular
plane increased at each developmental stage. This difference in severity with increasing
age is especially evident in males. In a study of 1094 lateral cephalometric records of
Class III individuals beyond age 13, male subjects presented with significantly larger
linear dimensions of the maxilla, mandible and the anterior facial heights when compared
with females during the circumpubertal and postpubertal periods (Baccetti, Reyes et al.
2005). In summary, these studies provide striking evidence that the Class III pattern
establishes early in development without the possibility of self-correction and can exist in
any number of skeletal and dentoalveolar combinations as well as differ based on age,
gender or ethnicity. Recently, studies have used multivariate analyses in order to identify
clinically distinct subgroups of Class III malocclusion in an effort to reduce this inherent
heterogeneity.
Multivariable methods such as principal component analysis and cluster analysis
applied to data from cephalometric radiographs have provided insight into treatment
outcomes, growth prediction and characterization of Class III malocclusion phenotypes.
Principal component analysis accounts for the overall morphological variation in the
craniofacial complex (Landauer 1962; Liebgott 1977), while cluster analysis aims to
identify relatively homogeneous groups of cases based on these selected characteristics.
Unlike traditional cephalometric analyses that use discrete linear and or angular
dimensions, multivariate techniques simultaneously evaluate the component dimensional
arrays and result in various subcategories of craniofacial forms of malocclusion
(Finkelstein, Lavelle et al. 1989). Procrustes analyses can be performed on the data in
order to correct errors associated with different geometric sizes (i.e., a larger individual
may display a greater absolute length compared to a smaller subject when in fact the
14
length may be smaller if normalized for size) (Rohlf and Slice 1990; Marcus, Corti et al.
1996; Singh 1999).
Cluster analysis has been used to investigate the nature of morphological changes
of the craniofacial skeleton induced by orthopedic treatment in growing patients with
mandibular prognathism. Using multivariate analysis of cephalometric data before and
after chin cup therapy, Lu et al indicated that the orthopedic correction of the Class III
profile was due to changes in the mandbular position (downward and backward rotation)
rather than changes in the mandibular dimension (Lu, Tanne et al. 1993). Their results
suggested that perhaps treatment decisions should be based on balancing craniofacial
form rather than treating to cephalometric norms, which remains the current standard of
care. In addition, Tahmina investigated the morphologic features of the craniofacial
skeleton in Class III subjects treated with chin cup and identified cephalometric
determinants that discriminate between stable and unstable treatment outcomes
(Tahmina, Tanaka et al. 2000). Subjects whose mandible exhibited significant forward
growth with a large gonial angle and an upward-and-forward rotation were highly
associated with unsatisfactory treatment outcomes after pubertal growth. Collectively,
these authors propose using multivariable methods to establish a detailed appraisal of the
specific morphologic changes induced by therapy in order to improve treatment
efficiency and effectiveness for Class III patients.
In addition to the evaluation of treatment outcomes within Class III individuals,
cluster analysis has been used to differentiate between favourable and unfavourable
growers. In a study by Abu Alhaija and Richardson in 2003, three clinically
distinguishable clusters were produced from a sample of 115 Class III adolescent
individuals (mean age 11.6 +/- 1.7 years for the 59 females and 12.7 +/- 1.3 years for the
56 males) (Abu Alhaija and Richardson 2003). The first cluster demonstrated a large
intermaxillary discrepancy representing a horizontal type of Class III case. The third
cluster had a moderate intermaxillary discrepancy and represented the vertical/long face
15
type of case. The second cluster presented with the least severe intermaxillary
discrepancy, representing an intermediate horizontal/vertical type of Class III case. After
applying a discriminant function analysis to each of the clinically distinguishable clusters,
prediction of good or poor growth - determined on the basis of the change in Wits
measurement (cut-off point of Wits = 2.5) - was greater than 85% using predictive
variables such as: mandibular length, saddle angle, posterior cranial base length and
vertical position of PNS in Cluster 1; mandibular length, saddle angle and lower incisor
inclination in Cluster 2; and lower incisor position and inclination, vertical distance
between gonion and point A, overbite, angle of convexity (SAB), saddle angle, soft tissue
maxillary prominence and vertical position of nasion in Cluster 3. The authors concluded
that patients must be allocated to the appropriate cluster in order to increase the accuracy
of growth prediction, which ultimately results in identification of those most likely to
benefit from treatment. Likewise, identification of the appropriate clusters of Class III
individuals is required in order to increase the accuracy of genetic studies.
Mackay et al (1992) studied morphologic variation in craniofacial form using cluster
analysis in 50 severe, non-growing Class III cases requiring surgical correction and
identified 5 subgroups (Figure 1) (Mackay, Jones et al. 1992). The majority of subjects
presented with mandibular prognathism while only 14% had maxillary retrognathism.
Increased lower face height was also highly present in 58% of cases. Cluster 1
demonstrated a retrognathic and short maxilla, as measured from the anterior nasal spine
(ANS) to the posterior nasal spine (PNS), with a slightly prognathic mandible and
retroclined lower incisors. Cluster 2 had a normal maxilla and slightly prognathic
mandible with a reduced maxillo-mandibular plane angle and lower face height. Cluster
3 demonstrated a normal maxilla and a very prognathic mandible with increased body
length, as measured from Gonion (Go) to Menton (Me). Also, the lower incisors in this
cluster were markedly retroclined. Cluster 4 had a normal maxilla with a slightly
prognathic mandible, similar to Cluster 2. The face height and mandibular body length
16
differed, however, with this group showing increased dimensions. Finally, Cluster 5
illustrated an average maxilla and moderately prognathic mandible with a moderately
increased mandibular body length. The upper incisors were markedly proclined as well.
The findings from this early study using multivariable techniques provide novel evidence
that different forms of Class III malocclusion exist and can successfully be divided into
groups based on similar phenotypes.
Hong and Li used cluster analysis to illustrate that different patterns of skeletal
architecture - beyond the current simple classification based on the position of the
maxilla, mandible, dentoalveolar units and vertical relationships - contribute to the
development of the Class III deformity (Hong and Yi 2001). They identified 7 clusters in
their Asian sample of 106 untreated Class III subjects with a mean age of 21-years-old
(range 16-32). Any subject with a history of previous orthodontic treatment, trauma,
temporomandibular disorder (TMD), congenital anomalies or missing teeth mesial to the
permanent second molars were excluded. Their clusters illustrated that in addition to the
facial bones and dentition, the cranial base, cranial vault and the cervical spine were also
involved in different but specific architectural patterns.
The most recent and directly applicable article carried out by Bui et al in 2006
characterized Class III malocclusion phenotypes using cluster analysis and principal
component analysis of 67 cephalometric variables derived from 309 Class III subjects
(Bui, King et al. 2006). Their study group included a wide age range (average age of
19.10 +/- 9.89; range 5.92 – 56.25) and was ethnically diverse consisting of 73%
Caucasians, 17% African Americans, 5% Asians, 3% Hispanics and 2% other. Only
subjects with previous orthodontic treatment, congenital abnormalities, trauma or
incomplete or undiagnostic cephalograms were excluded (Table 1). Five clusters were
identified representing distinct subgroups of Class III malocclusion (Figure 2; Table 2).
Cluster 1 represented subjects with mandibular prognathism and increased
vertical face height. Subjects in Cluster 2, on the other hand, exhibited maxillary
17
deficiency and decreased vertical dimensions. Maxillary deficiency was also present in
Cluster 3, yet the vertical measures and most of the mean variables deviated in the
opposite direction. Cluster 4 represented the mildly mandibular prognathic subphenotype,
while Cluster 5 was defined by borderline Class III individuals, with evidence of an
increased mean for most variables. The vertical face height in Cluster 4 and Cluster 5
was normal.
In addition to the spectrum of phenotypic variation evidenced by the clusters, 5
principal components were revealed (Table 3). These principal components explained
67% of variation within the sample and represented anteroposterior and vertical
dimensions rather than specific craniofacial structures. For example, principal
component 1 explained 22% of the variance alone and consisted of sagittal parameters
that primarily describe the anterior part of the face (ANB and facial taper). The second
principal component explained 15% of the variance and was significant for vertical
measurements (lower face height, mandibular plane angle) as well as variables to
describe the lower incisor position. Principal component 3 explained 14% of the variance
and was comprised of variables related in both anteroposterior and vertical dimensions.
They noted that only 6% of the variation within the Class III sample was explained by the
traditional cephalometric variables used to identify Class III subjects such as overjet and
Wits. As such, they argued that more important variables such as the saddle angle should
be used in cephalometric analyses during diagnosis and treatment planning for these
patients. Based upon these combined data, the authors suggested that different genes
may be involved in controlling dimensions versus structures and questioned our 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, thereby preventing full expression of the genotype and resulting malocclusion
phenotype. In addition, the very small numbers of ethnicities represented may not be
18
large enough to be statistically meaningful. Still, substantiation that Class III
malocclusion exists in morphologically diverse patterns that can be classified into
subphenotypes using multivariable methods such as cluster and principal component
analysis was provided.
While previous studies have contributed to our understanding of the inheritance of
the Class III phenotype, there are still significant gaps in knowledge of the specific
genetic contribution. To date, no study has comprehensively defined clinically distinct
Class III malocclusion subtypes in a large sample of Caucasian adults with complete
erupted permanent dentitions (except for 3rd molars) whose growth was finalized at time
of initial records. Thus, the purpose of our study was to characterize Class III
malocclusion phenotypes as a necessary first step toward discovering the genetic etiology
of this debilitating disease. Other institutions can expand this protocol to different
population groups, ultimately translating into improved patient treatment and quality of
life worldwide.
19
Figure 1
Mean Shapes for each Cluster from Mackay 1992
20
Table 1
Eligibility Criteria and Descriptive Statistics from Bui 2006
21
Figure 2
Representative Tracing from each Cluster from Bui 2006
22
Table 2
Table 3
Summary of Cluster Analysis from Bui 2006
Summary of Principal Component Analysis from Bui 2006
23
MATERIALS AND METHODS
The study protocol was reviewed and approved by the Institutional Review Board
at the University of Iowa.
Sample
The study sample included adult class III patients who were seeking treatment at
the University of Iowa Orthodontic Graduate Clinic, University of Iowa Hospital
Dentistry Clinic or surrounding area Private Practice Clinics. The sample consisted of
292 Caucasian adult subjects (126 male, 166 female; age range 16-57 years) who met our
eligibility criteria (Table 4), which selected for moderate to severe Class III malocclusion
in accordance with previous studies (Abu Alhaija and Richardson 2003; Bui, King et al.
2006; Cruz, Krieger et al. 2008; Frazier-Bowers, Rincon-Rodriguez et al. 2009). The
original sample included 311 individuals; eighteen of non-Caucasian race were excluded
due to lack of power and one additional subject was found to be ineligible based on
inclusion criteria. Our sample was unique in that only adult subjects whose growth was
95% complete at the time of initial records were studied, in an effort to ensure full
expression of their untreated developing malocclusion phenotype. Individuals presenting
with at least two of the following criteria were included: ANB < 0, Overjet <0 (at least
an edge-to-edge relationship or an anterior cross bite), Wits measurements of < -1.0 for
males, < 0 for females (Jacobson 1975), Angle Class III molar or canine relationship on
at least one side and determination of a concave profile. Profile convexity or concavity
was determined by measuring the internal angle between a line from the nose bridge to
the base of the upper lip and a line from the base of the upper lip to the chin. A convex
profile was indicated by a smaller angle and a forward-positioned upper jaw relative to
the chin. A concave profile was indicated by a larger angle and a backward-positioned
upper jaw. Subjects presenting with any one of the following criteria were excluded:
history of facial trauma, previous orthodontic treatment, the presence of facial
24
syndromes, missing or poor quality records, missing teeth other than 3rd molars, impacted
teeth or retained primary teeth. These stringent eligibility criteria were chosen to
minimize heterogeneity and increase our explanatory power of the variation observed
(Table 4).
Exclusion Criteria
*at least 2 or more required
Inclusion Criteria
*at least 2 or more required
Adult (female > 16, male > 18)
History of severe facial trauma
ANB < 0
Previous orthodontic treatment
Overjet < 0
*at least edge-to-edge or anterior crossbite
Presence of facial syndromes
Wits (female < 0, male < -1)
Missing or poor quality records
Angle CIII molar or canine relationship
on at least one side
Missing teeth other than 3rd molars
Concave profile
Impacted or retained primary teeth
Table 4
Eligibility Criteria
Cephalometric Procedure
2D pre-treatment lateral cephalometric films of 292 Class III adults were digitized
using Dolphin Imaging, version 11.0 (Dolphin Imaging Systems, Chatsworth, Calif). 63
cephalometric measurements were taken (Table 5 & 6) representing distance (mm),
degree, percentage and difference measures between cephalometric landmarks, which
25
were derived from commonly used lateral cephalometric analyses (Steiner 1953; Bishara
1981; McNamara 1984; Bui, King et al. 2006). The lateral cephalometric radiographs
were taken with the frankfort horizontal plane (Po-Or) parallel to the floor. Data were
obtained from two different sources (film and digital radiographs). All films taken on
conventional/analog cephalometric units from either the College of Dentistry Graduate
Orthodontic Clinic or the Hospital Dentistry Clinic were scanned into Dolphin with a
100mm ruler and corrected for magnification by 12% and 13%, respectively. Distance
measures for film radiographs were scaled (multiplied by 0.8929 for 12% magnified
cephs from the College of Dentistry Graduate Clinic and 0.8850 for 13% magnified cephs
from Hospital Dentistry Clinic) to match the digital radiographs in size. The remaining
digital radiographs were not corrected for magnification (Cohen 2005). In order to
reduce landmark identification errors, all scanned analog films were traced twice and the
average value for each variable was used in data analysis (Baumrind and Frantz 1971).
Method Error
The reliability of the location of craniofacial landmarks and resulting calculation
of craniofacial measurements was determined by means of inter-rater and intra-rater
methods using Intraclass Correlation and Difference Testing. 15 random cephalometric
radiographs were traced by two different raters (K.V. and L.M.) for inter-rater reliability
and traced two times at least three weeks apart by the same rater (K.V.) for intra-rater
reliability. All analyses were performed using SAS for Windows (v9.2, SAS Institute
Inc, Cary, NC, USA), and a type I error of 0.05 was assumed.
Intraclass Correlation
A one-way ANOVA model was created for each variable with groups defined by
participant ID. The residuals from each model were examined for normality using the
Shapiro-Wilk test. In the cases where the normality assumption was validated, the
26
Intraclass Correlation by Shrout and Fleiss was used to assess agreement (Zar 1999). The
Intraclass Correlation is a parametric procedure used when data is paired but it is
impossible to assign one variable independent and the other dependent. The Intraclass
Correlation gives the proportion of variance attributable to between group differences,
and the null hypothesis for significance testing is that this coefficient is equal to zero.
For the variables in which the residuals from the ANOVA model were not normally
distributed, a non-parametric intraclass correlation by Rothery was used to assess
agreement (Rothery 1979). As with the parametric analog, the Rothery intraclass
correlation is a ratio involving the variance within groups and variance between groups;
however, it is based on the overall ranks of the data rather than actual values freeing it
from the assumption of normality.
The Intraclass Correlation results for inter-rater reliability ranged from 0.8594 to
0.9987, with only 4 variables < 90%. The Rothery intraclass correlation was used for 14
of the 63 variables. The Intraclass Correlation results for intra-rater reliability ranged
from 0.9021 to 0.9999, with only 2 variables < 94%. The Rothery intraclass correlation
was used for 17 of the 63 variables. In general, inter- and intra-rater reliability is deemed
acceptable with values above 85%. Thus, excellent agreement between the two measures
for all 63 variables was achieved.
Difference Testing
The difference was calculated for each pair of measurements. The normality of
each difference variable was tested using the Shapiro-Wilk test under the null hypothesis
that the distribution is normal. Difference variables shown to be normal were tested
using the paired t-test under the null hypothesis that the mean difference between the two
measures was equal to zero. For variables in which the distributions of the difference
were shown to be non-normal, a Wilcoxon Signed-Rank test was used to determine if the
27
median difference between the measurements from the two measures was equal to zero
(assuming symmetry).
For inter-rater reliability, 16 significant differences were found to exist between
the two sets of measures. The paired t-test showed the mean difference between
measurements was non-zero for 11 normally distributed measures (ArGomm, CoGnmm,
GoPgmm, IdPgMPDeg, LLipEplanemm, LLipSTNPerpmm, NGnGoDeg, PgNBmm,
SGoNMePerc, SNGoGnDeg, ULipEPlanemm). The Wilcoxon Signed Rank test showed
significant differences in the dual measures of 5 not normally distributed variables
(CoANSmm, OBmm, STPgSTNPerpmm, U1SNDeg, ULipSTNPerpmm). The median
difference was greater than 0.5 for only 7 of the 16 variables.
For intra-rater reliability, 5 significant differences were found to exist between the
two sets of measures. The paired t-test showed the mean difference between
measurements was non-zero for 3 normally distributed variables (IdPgMPDeg,
LLipEplane and Overjetmm). The Wilcoxon Signed Rank test showed significant
differences in the dual measures of 2 not normally distributed variables (CoAmm and
SNA). The median difference for CoAmm was 0.5; the median difference for SNA was
0.1.
After examining variables with significant differences, outliers were identified
and techniques utilized to improve reliability to acceptable values.
In general,
cephalometric measurements within 0.5-1 are acceptable within the literature due to the
inherent difficulty in landmark location.
Statistical Analysis
Due to the large amount of variables collected, data reduction methods such as
Principal Component Analysis (PCA) and Cluster Analysis (CA) were used. The goal of
these statistical tests is to identify the most homogeneous groups of individuals
representing distinct Class III phenotypes in an effort to reduce genetic heterogeneity.
28
Characterization of distinct Class III malocclusion phenotypes is necessary for future
quantitative and qualitative trait association mapping genetic studies in orthodontics. In
medicine for example, PCA and CA have been used effectively to develop empiric
phenotypes for genetic mapping of complex traits such as atherogenic dyslipedemia
(Wilcox, Wyszynski et al. 2003), cardiovascular disease (Cox, Bellis et al. 2009), bone
size traits (Tan, Liu et al. 2008), schizophrenia (Lin, Liu et al. 2009) and age-related
hearing impairments (Van Laer, Huyghe et al. 2010).
Data were standardized using a linear model for age, gender and appropriate
interactions. A separate model was fit for each of the 63 cephalometric measures using
standard regression multiple regression methods. In all, four different configurations of
covariate adjustment were used among the 63 models: all included an adjustment for
gender, and some also required age adjustment, and other an additional consideration of
gender by age interaction, i.e., different age adjustment for each gender. Model
diagnostic procedures were performed on all standardization models and assumptions
were validated. The studentized (normalized) residuals were extracted from these models
and used as the standardized data for the principal component analysis. All analyses
were performed using SAS 9.2 (Cary, NC) with a 0.05 level of significance.
Principal Component Analysis
PCA is a data reduction method in which an initial group of variables is reduced
into a smaller set that retains maximum variation information and comprises a better
representation of the population (Guadagnoli and Velicer 1988; Shlens 2005). PCA is a
multivariate technique often used for quantitative data reduction prior to regression or
cluster analysis. PCA decomposes the correlations of a set of variables into orthogonal
linear combinations of these variables called components (Lindeman et al., 1980).
Components are the eigenvectors of the correlation matrix and the 63 components were
sorted in descending order by eigenvalues which represent the variances of the
29
components (Kleinbaum, Kupper et al. 1988). The information captured by the
components decreases with the component order. The first component is the most
informative, followed by the second component, and so forth. Each component has
scoring coefficients or weights for the included variables used to construct a linear index
that reflects a phenotypic axis of variation in the variables (Shlens 2005). Calculated
using weights of all 63 original variables, the standardized principal component scores
were extracted for each subject ensuring that the variances were standardized prior to
employing the clustering algorithm.
Cluster Analysis
Cluster Analysis is a multivariate method of classifying individuals into groups
based on degrees of similarity (Whartin 1984; McLachlan 1992; Kim and Abraham
2008). Standardized PCA scores were the basis for the formation of clusters defining
subphenotypes of Class III malocclusion. Criterion-based model selection methods were
used to determine the cluster configuration that illustrated the most distinct clusters
graphically. After principal components were extracted from the PCA procedure for each
subject, the eigenvectors were divided by the square roots of the eigen values to produce
scores with unit standard deviations (standardized data). These scores were the basis for
the formations of clusters defining subphenotypes. Cluster analysis was performed via a
partitional cluster analysis of extracted principal components using SAS 9.3 statistical
software with methods based on the leader (Hartigan 1975) and the k-means (Macqueen
1967) algorithms using the method of Anderberg (Anderberg 1973) called nearest
centroid sorting. The process initiates with the selection of cluster seeds based on an
estimate of cluster means or centroids; each subject is then placed in the cluster of the
nearest centroid according to Euclidean distance measures. Recalculation of centroids
and iterative assignments of subjects to the nearest clusters continues until the minimum
of the sum of squared Euclidean distances between subjects and cluster means is
30
accomplished. The final cluster assignments are achieved when the algorithm converges
(i.e., no further changes occur in cluster centroids).
To visualize the cluster analysis results, a canonical discriminant analysis was
performed and scored canonical variables were computed. The purpose of canonical
discriminant analysis is to identify axes (i.e., in this case, the (k-1) axes for k clusters)
that best separate the clusters. The canonical discriminant procedures results in linear
combinations of the standardized principal components that summarize between-cluster
variation similar to the way in which principal components summarize total variation.
These linear functions are uncorrelated and define a (k-1) space that best separates the
projections of the k groups into that space.
Canonical discriminant analysis (Hotelling 1936) transforms the standardized
principal component scores used in the cluster analysis so that the pooled within-cluster
covariance matrix is an identity matrix. Cluster means are then computed for the
transformed variables. Finally, a principal component analysis is performed on the
means, weighting each mean by the number of observations in the class (SAS 9.3 Proc
FASTCLUS documentation). The eigenvalues are equal to the ratio of between-cluster
variation to within-cluster variation in the direction of each resulting principal
component; the analogy to principal components analysis is evident.
The first canonical variable, or canonical component, is the linear combination of
the variables that has the highest possible multiple correlation with the groups (clusters).
This maximal multiple correlation is called the first canonical correlation, and the
coefficients of the linear combination are the canonical coefficients or canonical weights.
The second canonical variable is obtained by finding the linear combination uncorrelated
with the first canonical variable that has the highest possible multiple correlation with the
groups. This process can be repeated until the number of canonical variables is equal to
the original number of variables, or the number of classes minus one, whichever is
smaller. The scored canonical variables are used in this study to plot pairs or triads of
31
canonical variables in order to aid visual interpretation of cluster differences. R statistical
program was used in conjunction with the rgl package to produce three-dimensional
graphs of the data.
The k-means clustering algorithm is sensitive to extreme values as a consequence
of the least squares condition; however no subjects in this dataset appeared to be extreme
observations. The clustering algorithm was performed separately for a range of number
of clusters, from 3 to 7 clusters. The criterion based methods of pseudo F statistic,
approximate expected over-all R2, and cubic clustering criterion (valid because of the
uncorrelated nature of principal components); as well as data visualization techniques of
scored canonical variables were used to determine the appropriate number of clusters. Of
the range of clusters considered, the five cluster model best optimized the criterion and
presented the most distinct clusters graphically. Cluster validation was performed by
locating subjects closest to the final cluster means and examining the subject’s
cephalometric data and profile to ensure that clusters represented distinct clinical
phenotypes. All analyses used SAS 9.3 with a 0.05 level of significance.
32
Cranial Base
Intermaxillary
Dental
Saddle/Sella Angle (SN-Ar) (º)
ANB (º)
U1 - SN (º)
Ant Cranial Base (SN) (mm)
Facial Plane to AB (AB-NPg)
(º)
U1 - NA (º)
Post Cranial Base (S-Ar) (mm)
Facial Plane to SN (SN-NPg) (º)
U1 - NA (mm)
Midface Length (Co-A) (mm)
U1 - FH (º)
P-A Face Ht (S-Go/N-Me) (%)
IMPA (L1-MP) (º)
SNA (º)
Y-Axis (N-S-Gn) (º)
L1 - NB (º)
Convexity (NA-APg) (º)
Mx/Md Diff (Co-Gn - Co-ANS)
(mm)
L1 - NB (mm)
N-A || HP (mm)
Wits Appraisal (AO-BO) (mm)
L1 Protrusion (L1-APg) (º)
A to N Perp (FH) (mm)
Ant Face Ht (N-Me) (mm)
L1 Protrusion (L1-APg) (mm)
Mx Unit Length (Co-ANS)
(mm)
Upper Face Ht (N-ANS) (mm)
FMIA (L1-FH) (º)
Lower Face Ht (ANS-Me) (mm)
Interincisal Angle (U1-L1) (º)
Nasal Ht (N-ANS/N-Me) (%)
UADH (U1-PP) (mm)
SNB (º)
PFH:AFH (Co-Go/N-Me) (%)
LADH (L1-MP) (mm)
Facial Angle (FH-NPg) (º)
FMA (FH-MP) (º)
UPDH (U6-PP) (mm)
Gonial/Jaw Angle (Ar-Go-Me)
(º)
SN - GoGn (º)
LPDH (L6 - MP) (mm)
Chin Angle (Id-Pg-MP) (º)
Occ Plane to SN (º)
Overjet (mm)
Ramus Height (Ar-Go) (mm)
Occ Plane to FH (º)
Overbite (mm)
Length of Mn Base (Go-Pg)
(mm)
FH - SN (º)
Maxilla
Mandible
Facial Taper (N-Gn-Go) (º)
Soft Tissue
Articular Angle (S-Ar-Go) (º)
Upper Lip to E-Plane (mm)
N-B || HP (mm)
Lower Lip to E-Plane (mm)
N-Pg || HP (mm)
U Lip to ST N Perp (FH) (mm)
B to N Perp (FH) (mm)
L Lip to ST N Perp (FH) (mm)
Pg to N Perp (FH) (mm)
ST Pg to ST N Perp (FH) (mm)
Mn Unit Length (Co-Gn) (mm)
Pg - NB (mm)
Post Facial Ht (mm) (Co-Go)
Table 5
63 Cephalometric Variables
33
Landmark
Description
Anatomic Porion (Po)
Orbitale (Or)
Sella (S)
Nasion (N)
ST Nasion (N')
Tip of Nose (Pronasale)
On scanned cephs, locate porion distal to the most superior surface of
the Mn condyle (determines the vertical location) at the posterior
border of the cephalostat ear rod (determines the AP location).
The lowest point on the inferior rim of the orbit.2 When both right
and left orbitale outlines are visible, orbitale is located at the bisection
of the two orbit outlines.
The geometric center of the bony outline of the sella turcica
(hypophyseal or pituitary fossa) of the sphenoid bone. 2 3 4
The most anterior point on the frontonasal suture in the midsagittal
plane. 2 Intersection of the internasal suture with the nasofrontal
suture. 4
The point of greatest concavity in the midline between the forehead
and the nose. 2
The most prominent or anterior point of the anterior curve of the nose.
2, 4
Upper Lip
Lower Lip
ST Pogonion (Pog')
B Point (B)
(Supramentale)
Pogonion (Pog)
A point indicating the mucocutaneous border of the upper lip.
Usually the most anterior point on the curve of the upper lip.1, 4
The median point on the lower margin of the lower membranous lip.
Usually the most anterior point on the curve of the lower lip. 1, 4
The most prominent or anterior point on the ST chin in the
midsagittal plane. 2, 4
Located at the most posterior point on the shadow of the anterior
border of the mandible near the apex of the central incisor root. The
deepest/most posterior midline point in the concavity of the mandible
between infradentale (the most superior point on the alveolar bone
overlying the mandibular incisors) and Pogonion. 2 , 3
Located at the most anterior point on the bony chin. 1, 2 The most
anterior point on the symphysis of the mandible in the median plane.
3, 4
Anatomical Gnathion
(Gn)
Menton (Me)
Gonion (Go)
Articulare (Ar)
Condylion
A point located by taking the midpoint between the anterior
(pogonion) and inferior (menton) points of the bony chin. 2, 4 The
lowest, most anterior midline point on the symphysis of the
mandible.3
Located at the most inferior point on the shadow of the chin. 1 The
most inferior point on the symphysis of the mandible in the median
plane. 2 , 3 , 4
A point on the curvature of the angle of the mandible located by
bisecting the angle formed by lines tangent to the posterior ramus and
the inferior border of the mandible. 2, 3
A point at the junction of the posterior border of the ramus and the
inferior border of the posterior cranial base (occipital bone). 2
Most posterior superior point of the condyle.4
Table 6
Cephalometric Landmark Definitions
34
Table 6
A Point (A) (Subspinale)
Anterior Nasal Spine
(ANS)
Posterior Nasal Spine
(PNS)
U6 Occlusal
L6 Occlusal
Distal U6
Mesial U6
Distal L6
Mesial L6
L1 Labial Gingival
Border
L1 Tip
L1 Root
U1 Tip
U1 Root
References
Continued
The most posterior midline point/deepest point in the concavity of the
maxilla, between the ANS and the prosthion (the most inferior point
on the alveolar bone overlying the maxillary incisors). 2, 4 The point
in the median sagittal plane where the lower front edge of the anterior
nasal spine mets the front wall of the maxillary alveolar process.3
The anterior tip of the sharp bony process of the maxilla at the lower
margin of the anterior nasal opening. 2, 4
The posterior spine of the palatine bone constituting the hard palate. 2,
4
A process formed by the united, projecting medial ends of the
posterior borders of the horizontal plars of the two palatine bones. 3
When PNS not visible, use PTM point to determine AP position and
palatal plane to determine vertical position. 5
Mesial buccal cusp tip of the maxillary left (most superior, distal)
molar 4
Mesial buccal cusp tip of the mandibular left (most superior,
distal)molar 4
Distal surface of the upper first molar, perpendicular to the occlusal
plane 4
Mesial surface of the upper first molar, perpendicular to the occlusal
plane 4
Distal surface of the lower first molar, perpendicular to the occlusal
plane 4
Mesial surface of the lower first molar, perpendicular to the occlusal
plane 4
Labial cemento-enamel junction (CEJ) of the most prominent lower
central incisor 4
Incisal tip of the most prominent lower central incisor 4
Root apex of the most prominent lower central incisor 4. When apex
not visible, locate vertical position by using "ideal" anatomical apex
location and adjust the AP position based on achieving the most ideal
angulation 5.
Incisal tip of the most prominent upper central incisor 4
Root apex of the most prominent upper central incisor 4. When apex
not visible, locate vertical position by using "ideal" anatomical apex
location and adjust the AP position based on achieving the most ideal
angulation 5.
1-Staley/Reske Course Manual
2-Jacobson, Radiographic Cephalometry
3-Southard Handout
4-Dolphin Definitions
5-Modifications for M63
35
RESULTS
The results of the principal components analysis revealed that six principal
components accounted for 81.17% of the total variance in the data (Figure 3 & 4; Table
7). The first six principal components were selected because they explained the most
variation in the data set and were specific in their anatomic explanation. The six
eigenvectors are orthogonal and uncorrelated (these are distinct properties), representing
perpendicular directions in space. The quartile figures illustrate the representative
profiles from the low to high extremes, which provide visual confirmation of the
interpretation of each principal component (Figure 6, 8, 10, 12, 14 & 16). The
cephalometric variables contributing the most to each principal component are
individually listed (Table 8, 9, 10, 11, 12 & 13) and summarized together in Table 14.
Principal component 1 refers to the antero-posterior position of the mandible in
relationship to the cranial base and explains 23.7% of the variation (Figure 5 & 6; Table
8). Principal component 2 refers to the maxillo-mandibular horizontal and vertical size
discrepancies and explains 17.3% of the variation (Figure 7 & 8; Table 9). Principal
component 3 refers to the position and inclination of the lower incisor and its effect on
lower lip protrusion and explains 13.3% of the variation (Figure 9 & 10; Table 10).
Principal component 4 refers to lower incisor angulation, facial taper and variation in
maxillo-mandibular discrepancies and explains 12.0% of the variation (Figure 11 & 12;
Table 11). Principal component 5 refers to variation in the upper incisor and the
maxillary horizontal position and explains 8.3% of the variation (Figure 13 & 14, Table
12). Principal component 6 refers to variation in the cranial base and explains 6.7% of
the variation (Figure 15 & 16; Table 13). The remaining PCs consisted of multiple
variables.
The cluster analysis resulted in the identification of five sub-phenotypes within
Class III subjects (Figures 18-25). The preliminary cluster analysis revealed three to
36
seven clusters of CIII phenotypes based upon the cephalometric measurements (Figure
17). The model with three clusters was too simplistic while the seven cluster model
contained redundant information. Although the cluster validation graph showed the ideal
statistical criteria at four clusters, an important CIII phenotype – the vertical subtype –
was not represented; thus, a five cluster model was selected because it yielded the most
spacially distinct and clinically meaningful subphenotypes that were acceptable
statistically (Table 15). Cluster 5 (severely retrusive maxilla, normal mandible) was the
central cluster and contained the most observations (n=86); however, cluster 4 (normal
maxilla, severely protrusive mandible) had the largest standard deviation (spread of
observations). Cluster 4 also had the fewest observations (n=44). The centroid of each
cluster is illustrated in Figure 26. A core of 4 individuals closest to each cluster centroid
is illustrated graphically in Figures 27-31. Cluster 1 subjects presented with a concave
profile, acute saddle angle, short anterior cranial base length, slightly retrusive maxilla,
slightly protrusive mandible, slightly flat mandibular plane, increased anterior facial
height, normal ramus height, normal upper incisor inclination, retrusive lower incisors
and retrusive upper and lower lips (Figure 27). Cluster 2 subjects presented with a
straight profile, acute saddle angle, short anterior and posterior cranial base lengths,
moderately retrusive maxilla, slightly protrusive mandible, normal mandibular plane,
decreased anterior facial height, a short ramus height, protrusive upper incisors, normal
lower incisors and retrusive upper and lower lips (Figure 28). Cluster 3 subjects
presented with a slightly convex profile, normal saddle angle, long anterior and posterior
cranial base lengths, normal maxilla, protrusive mandible expressed vertically, steep
mandibular plane, increased anterior facial height, long ramus height, normal upper
incisor inclination, protrusive lower incisors and protrusive lower lips (Figure 29).
Cluster 4 subjects presented with a concave profile, acute saddle angle, short anterior and
posterior cranial base lengths, normal maxilla, severely protrusive mandible, normal
mandibular plane, slightly short ramus height, protrusive upper incisors, retrusive lower
37
incisors and a retrusive upper lip and protrusive lower lip (Figure 30). Cluster 5 subjects
presented with a straight profile, normal saddle angle, slightly short anterior and posterior
cranial base lengths, severely retrusive maxilla, normal mandible, normal mandibular
plane, increased lower anterior facial height, short ramus height, normal upper incisor
inclination, slightly protrusive lower incisors and a retrusive upper lip and normal lower
lip (Figure 31). Clusters 1 and 2 represent borderline CIII phenotypes with a
combination of mild maxillary retrognathism and mandibular prognathism. Cluster 3
corresponds with the vertical CIII phenotype, while Cluster 4 and Cluster 5 represent the
severely mandibular prognathic and severely maxillary retrognathic phenotypes,
respectively. Complete descriptions of clusters are given in Table 16.
38
Figure 3
Figure 4
Scree Plots
Component Scores 95% Prediction Ellipse
39
Table 7
Principal Component Eigenvalues
40
Figure 5
PC1 Extremes
Principal component 1 refers to the antero-posterior position of the mandible in relationship to the
cranial base and explains 23.7% of the variation.
Figure 6
PC1 Quartiles
Figure 6 represents the spectrum of profiles from low to high extreme including the quartiles for
PC1. Notice how the degree of mandibular prognathism increases from left to right thus providing
visual confirmation of our interpretation of PC1.
41
Table 8
PC1 Cephalometric Variables
42
Figure 7
PC2 Extremes
Principal component 2 refers to the maxillo-mandibular horizontal and vertical (posterior face
height) size discrepancies and explains 17% of the variation.
Figure 8
PC2 Quartiles
Figure 8 represents the spectrum of profiles from low to high extreme including the quartiles for
PC2. Notice how the mandibular, maxillary and ramus lengths increase from left to right thus
providing visual confirmation of our interpretation of PC2.
43
Table 9
PC2 Cephalometric Variables
44
Figure 9
PC3 Extremes
Principal component 3 refers to the position and inclination of the lower incisor and its effect on
lower lip protrusion and explains 13% of the variation.
Figure 10 PC3 Quartiles
Figure 10 represents the spectrum of profiles from low to high extreme including the quartiles for
PC3. Notice how the lower incisor position and lower lip protrusion increase from left to right
thus providing visual confirmation of our interpretation of PC3.
45
Table 10 PC3 Cephalometric Variables
46
Figure 11 PC4 Extremes
Principal component 4 refers to lower incisor angulation, facial taper and variation in maxillomandibular discrepancies and explains 12% of the variation.
Figure 12 PC4 Quartiles
Figure 12 represents the spectrum of profiles from low to high extreme including the quartiles for
PC4. Notice how the lower incisor angulation and maxilla and mandible lengths increase while
the facial taper decreases from left to right thus providing visual confirmation of our interpretation
of PC4.
47
Table 11 PC4 Cephalometric Variables
48
Figure 13 PC5 Extremes
Principal component 5 refers to variation in the upper incisor and the maxillary horizontal position
and explains 8% of the variation.
Figure 14 PC5 Quartiles
Figure 14 represents the spectrum of profiles from low to high extreme including the quartiles for
PC5. Notice how maxillary position becomes more protrusive while the upper incisor angulation
decreases from left to right thus providing visual confirmation of our interpretation of PC5.
49
Table 12 PC5 Cephalometric Variables
50
Figure 15 PC6 Extremes
Principal component 6 refers to variation in the cranial base and explains 6% of the variation.
Figure 16 PC6 Quartiles
Figure 16 represents the spectrum of profiles from low to high extreme including the quartiles for
PC6. Notice how the cranial base changes from left to right thus providing visual confirmation of
our interpretation of PC6.
51
Table 13 PC6 Cephalometric Variables
52
Principal
Component
Variance
Explained
1
2
3
4
5
6
0.2374
0.1729
0.1325
0.1199
0.0825
0.0665
0.4103
0.5428
0.6627
0.7452
0.8117
Facial
Plane to
SN
(SN-NPg)
(º)
Mn Unit
Length (CoGn) (mm)
L1
Protrusio
n (L1APg)
(mm)
IMPA
(L1-MP)
(º)
U1 NA (º)
FH - SN
(º)
N-Pg || HP
(mm)
Post Facial
Ht (mm)
(Co-Go)
L1 - NB
(mm)
Mx/Md
Diff (CoGn - CoANS)
(mm)
U1 NA
(mm)
Saddle/S
ella
Angle
(SN-Ar)
(º)
Y-Axis
(N-S-Gn)
(º)
Midface
Length (CoA) (mm)
Lower
Lip to ST
N Perp
(FH)
(mm)
Chin
Angle (IdPg-MP)
(º)
A to N
Perp
(FH)
(mm)
Occ
Plane to
FH (º)
L1 - NB
(º)
Wits
Appraisal
(AO-BO)
(mm)
SNA
(º)
Upper
Lip to ST
N Perp
(FH)
(mm)
N-A ||
HP
(mm)
Lower
Lip to ST
N Perp
(FH)
(mm)
Cumulative
Variance
Variables
N-B || HP
(mm)
Mx Unit
Length (CoANS) (mm)
SNB (º)
Ramus
Height
(Ar-Go)
(mm)
Pg - NB
(mm)
Facial
Taper (NGn-Go) (º)
Table 14 Summary of Principal Component Analysis
53
Figure 17 Cluster Validation Criteria
54
Figure 18 Plots of CIII Clusters – Centroids Labeled (Can 1 & 2)
55
Figure 19 Plots of CIII Clusters – Centroids Labeled (Can 1 & 3)
56
Figure 20 Plots of CIII Clusters – Centroids Labeled (Can 1 & 4)
57
Figure 21 Plots of CIII Clusters – Centroids Labeled (Can 2 & 3)
58
Figure 22 3-D Plot of 5 Clusters of CIII Malocclusion Subjects
59
Figure 23 3-D Plot of 5 Clusters of CIII Malocclusion Subjects
60
Figure 24 3-D Plot of 5 Clusters of CIII Malocclusion Subjects
61
Figure 25 3-D Plot of 5 Clusters of CIII Malocclusion Subjects
62
Table 15 Summary of Cluster Analysis
63
Figure 26 Cluster Centroids
Clusters 1 and 2 represent borderline CIII phenotypes with a combination of mild maxillary
retrognathism and mandibular prognathism. Cluster 3 corresponds with the vertical CIII
phenotype. Cluster 4 and Cluster 5 represent the severely mandibular prognathic and severely
maxillary retrognathic pheontypes, respectively.
64
Figure 27 Cluster 1 Centroid, Description and Core
Cluster 1 represents a borderline CIII phenotype with a slightly retrusive maxilla and slightly
protrusive mandible contributing to the malocclusion, with a slightly reduced vertical dimension.
65
Figure 28 Cluster 2 Centroid, Description and Core
Cluster 2 also represents a borderline CIII phenotype yet differs from Cluster 1 in that the maxilla
is moderately retrusive with a normal vertical dimension.
66
Figure 29 Cluster 3 Centroid, Description and Core
Cluster 3 corresponds with the vertical CIII phenotype presenting with a steep mandibular plane
and long cranial base.
67
Figure 30 Cluster 4 Centroid, Description and Core
Cluster 4 represents the severe mandibular prognathic phenotype with a very protrusive mandible
contributing to the malocclusion.
68
Figure 31 Cluster 5 Centroid, Description and Core
Cluster 5 corresponds with the severe maxillary retrognathic phenotype with a retrusive maxilla
contributing to the malocclusion.
69
Table 16 Summary of Cluster Descriptions
70
DISCUSSION
Our data were standardized using a linear model for age and gender as well as
appropriate interactions to prevent errors associated with using only age and gender for
data normalization as was carried out via Bui et al (Bui, King et al. 2006). The most
highly correlated variables were identified in an attempt to eliminate redundant data often
contained within cephalometric analyses as well as to highlight meaningful variables that
may commonly be overlooked or missed altogether.
The principal component analysis reduced a large data set of 63 cephalometric
variables into six principal components, which captured 81% of variation within our
sample. In a similar study, Bui identified five principal components explaining only 67%
of their sample variance. The ability to capture an additional 14% of variation could be
attributed to our homogeneous sample as well as our more stringent eligibility criteria.
For example, Bui’s sample included small, racially diverse groups (Table 1) compared to
our large solely Caucasian group. Furthermore, by including only adult subjects whose
growth was complete at the time of initial records, full expression of the malocclusion
phenotype was ensured. Bui’s sample age ranged from 5.92-56.25, which could bias
interpretation of the results since cephalometric measurements obtained on a CIII patient
at age 10 will be completely different at age 25 once growth is complete. In addition, any
subject who presented with missing or impacted teeth was excluded from our study in an
attempt to further reduce confounding variables as early tooth loss can result in Class III
malocclusion irrespective of the patient’s genotype. Despite differences in sample
composition between the two studies, the principal components were similar in terms of
the most informative cephalometric variables. In both studies, principal component 1
represented sagittal parameters such as the facial plane to SN and the facial angle.
Principal components 2 and 3 consisted mostly of vertical and antero-posterior measures
as well as lower incisor and lower lip position. Together, approximately half of the
71
variation in both samples was explained by the heavily weighted variables in these three
components. Interestingly, the maxillary and upper incisor position was not captured in
the Bui principal component analysis to the same extent as ours (PC5, explaining 8%).
Perhaps geographic differences between our populations (North Carolina versus Iowa)
account for these conflicting results.
The six principal components were used as the basis for the formation of clusters
defining subphenotypes of Class III malocclusion in our study. Instead of using standard
PCA scores, Bui used normalized cephalometric values to determine their clusters. Other
studies have employed different methods such as the centroid method used by Mackay or
the Delaire analysis used by Hong and Yi (Mackay, Jones et al. 1992; Hong and Yi 2001)
to evaluate craniofacial morphology, which may account for the slightly different results
between studies.
Determination of the number of clusters is subjective and can result in variability
between studies. Models using three to seven clusters were tested in our sample to
determine the model that best optimized the criterion and presented the most spacially
distinct clusters graphically. Additional cluster validation was performed by locating
subjects closest to the final cluster means and examining the subjects’ cephalometric data
to ensure that each cluster represented clinically meaningful CIII phenotypes. We
selected five clusters, which is in accordance with previous studies. Bui and Mackay
identified five cluster groups, while Abu Alhaija and Richardson identified three clusters
and Hong and Yi identified seven (Abu Alhaija and Richardson 2003). Our description
of the representative facial types in each cluster is more complex than Bui’s and Abu
Alhaija’s, which included variation in only three components: maxillary position,
mandibular position and vertical dimensions. While is tempting to oversimplify the
facial morphology in this way, it prevents using modern complex statistical tests such as
PCA and CA to their full potential. By including additional morphological features that
contribute to the CIII malocclusion phenotype such as cranial base dimensions, incisor
72
angulation and lip posture, as also suggested by Hong and Li (Hong and Yi 2001), a more
sophisticated diagnosis can be obtained.
Our findings tend to confirm differences in craniofacial dimensions contributing
to malocclusion versus actual structures as supported by Bui and Lu (Lu, Tanne et al.
1993). As a result, treatment aimed at achieving cephalometric norms for individual
skeletal components is likely to produce unsuccessful outcomes and therefore should be
replaced by treatment aimed at balancing craniofacial morphology instead. Moreover,
our results provided additional support that other cephalometric variables may be more
important for evaluating the morphological characteristics of CIII subjects as compared
to commonly used cephalometric variables such as ANB, OJ and Wits. These
measurements were not identified as the most highly informative variables in the CIII
data set as was expected. While direct comparison of our results to similar studies within
the literature is limited due to small sample sizes, differences in age, ethnicity and
malocclusion severity (Mackay, Jones et al. 1992; Lu, Tanne et al. 1993; Hong and Yi
2001; Abu Alhaija and Richardson 2003), similarity between these studies is encouraging
as it indicates an underlying skeletal structure in the subphenotypes of subjects with Class
III malocclusion in diverse population samples.
Limitations of the Study
As with any research project, there are several inherent limitations that exist for
this study. Our cephalometric measurements were obtained from two dimensional
radiographs, which present an obvious challenge in interpreting the true threedimensional facial morphology. However, until three-dimensional records obtained from
cone beam computed tomography become the standard of care in orthodontics, studies
will continue to use their existing 2D records to promote research. Furthermore, because
our records were obtained from CIII individuals who chose to seek treatment, a sample
bias is likely present. Subjects who present for correction of their malocclusion may
73
represent a more severe phenotype than occurs within the normal population. Therefore,
our results may not be generalized to the entire US population. In addition, because we
only included Caucasians, these results will not apply to other ethnic groups. The effects
of age and sex on the resulting phenotype were not tested for in this study since data were
normalized using a linear model. Because sexual dimorphism of the CIII trait has been
reported in the literature (Baccetti, Reyes et al. 2005), further studies should report on the
effects of these important factors. Finally, subjectivity is inherent in the determination of
the ideal number of principal components and cluster groupings, thus other institutions
may obtain different results depending on their interpretation of the data and their
definition of “spacially distinct” and “clinically meaningful” phenotypes.
Clinical Application
While these data do not directly apply to the clinical management of CIII patients
today, they serve as a critical step in the identification of the genetic etiology of CIII
malocclusion tomorrow. Using these methods, subjects presenting for initial records can
be characterized into specific subphenotypes and their treatment decisions altered
accordingly. A priori knowledge of the pattern of craniofacial growth could virtually
eliminate years of unsuccessful treatment or encourage compliance for those selected
cases who would most likely benefit from early intervention. Hence, this study allows
limitless possibilities in the prevention and treatment effectiveness resulting in improved
quality of life on an individual basis and population-based cost effectiveness as well.
Future Projects
This study will set the standard for other institutions to follow in their specific
patient populations. As phenotypic information from different ethnic groups becomes
available, institutions can collaborate and increase sample sizes that will improve the
likelihood of identifying a causative gene for CIII malocclusion. Moreover, replication
74
of these data using cephalometric measurements obtained from three-dimensional
computed tomography will provide additional information that potentially could reduce
heterogeneity to a point that what was once thought too complex or even impossible. In
the future, the addition of environmental data and genotypic information to these results
will pave the way for the eventual identification of the genetic etiology of CIII
malocclusion.
75
SUMMARY AND CONCLUSIONS
Malocclusion is a complex trait that has a profound negative effect worldwide.
Studies have demonstrated that environmental and genetic factors confer susceptibility to
abnormal facial growth leading to severe Class III malocclusion. While informative,
these studies have failed to identify a human susceptibility gene due to the lack of a welldefined malocclusion phenotype. Thus, our study aimed at addressing the current gap in
knowledge by characterizing Class III malocclusion phenotypes into distinct subgroups to
reduce genetic heterogeneity.
The current project generated 6 principal components of various multivariate
traits as well as 5 cluster groups with the most homogeneous Class III individuals. The
results from this study will provide the necessary impetus for future gene mapping /
whole genome association studies that aim to characterize malocclusion risk factor
quantitative trait loci. Ongoing studies at the University of Iowa College of Dentistry are
using these data to target individuals for collection of DNA, environmental data and
quality of life information. Integration of genetic and environmental data will eventually
lead to identification of the specific genetic variants and environmental offenders that
predispose to disproportionate craniofacial growth and Class III malocclusion.
Discovery of the genetic etiology of malocclusion will have a huge impact on
orthodontic patient care worldwide via novel and improved therapy and prevention
approaches. Genetic counseling will allow identification of individuals at risk and of
those most likely to benefit from specific treatment modalities. In the future, gene
therapy will be capable of reestablishing harmony in the growing face, ultimately
translating into improved quality of life for individuals affected with this severe and
debilitating disease.
76
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