This study aimed to identify genetic mutations associated with mandibular prognathism by analyzing the exomes of 951 individuals from the ClinSeq project who carried mutations in 20 genes known to be involved in prognathism development. Algorithmic methods were used to match variants in the study samples to clinical databases and identify likely causative variants. The analysis identified 7 single nucleotide variants within 4 genes matched to the ClinSeq data. Identifying individuals with prognathism-associated genotypes will enable their participation in further phenotyping studies to better understand genotype-phenotype correlations for the condition. This bioinformatic approach can also be applied to study other genetically-influenced disorders using ClinSeq data.

1. Abstract
Coy, Mishra, Biesecker, Lee, Sincan
Objective: Mandibular prognathism is a dentofacial deformity associated with a significant
Class III skeletal relationship and appears as a concave facial profile, commonly referred to as
“Habsburg jaw.” This condition occurs in approximately 1% of the US population, and the
dysmorphology may be severe enough to warrant surgical reconstruction in about 21% of the
cases. Understanding the influence of the suspected genetic mutations on the associated
development of facial structure will help researchers to capture the full spectrum of the
phenotype-genotype correlation and assist a clinicalteamto appropriately address a patient
under study, and the affected family members, by facilitating individualized treatment plans
through monitoring facial growth patters.
Methods: Our study sample is ClinSeq, an NHGRI project which aims to sequence 1500
participants in order to improve the way genetic information is stored, shared, and analyzed. In
our project, we have collected a subset of the presently sequenced individuals (n=951) who
have a mutation in at least one of 20 genes that has been extracted through literature review to
be involved in the development of prognathism. Algorithmic notations were constructed to
locate variant matches and provide clinical significance in our study sample against genomic
databases like ClinVar and HGMD. Using the Python-based Pandas module in the Jupyter
environment, the metadata was parsed and matched with refined parameters such as
chromosome, position, reference allele, and alternate allele in order to search for likely
causative variants.
Results: 7 single nucleotide variants within 4 of the genes were matched within the ClinSeq
subset.
Conclusions: The databases provide the researcher with additional genomic and valuable
molecular information to justify a correlation between the variants and mandibular
prognathism. The identification of individuals from the ClinSeq subset who possess the
genotype will lead to the matched individuals’ participation in NIDCR protocols involving
iterative phenotyping in which the individuals with the condition-associated genotypes are
monitored for clinical signs and symptoms of those conditions. Furthermore, this approach to
bioinformatically analyze the cohort’s exomes for secondary variants has the ability to be
applied to additional studies concerning other disorders with a genetic component while using
ClinSeq data.
Keywords: mandibular prognathism, genotype-phenotype, iterative phenotyping, exome,
genomic databases, secondary findings.