Genome-wide association study of multiethnic nonsyndromic orofacial cleft families identifies novel loci specific to family and phenotypic subtypes.

Nonsyndromic orofacial clefts (OFCs) are among the most common craniofacial birth defects worldwide, and known to exhibit phenotypic and genetic heterogeneity. Cleft lip plus cleft palate (CLP) and cleft lip only (CL) are commonly combined together as one phenotype (CL/P), separately from cleft palate alone. In comparison, our study analyzes CL and CLP separately. A sample of 2218 CL and CLP cases, 4537 unaffected relatives of cases, and 2673 pure controls with no family history of OFC were selected from the Pittsburgh Orofacial Cleft (Pitt-OFC) multiethnic study.genome-wide association studies were run for seven specific phenotypes created based on the cleft type(s) observed within these families, as well as the combined CL/P phenotype. Five novel genome-wide significant associations, 3q29 (rs62284390), 5p13.2 (rs609659), 7q22.1 (rs6465810), 19p13.3 (rs628271), and 20q13.33 (rs2427238), and nine associations (p ≤ 1.0E-05) within previously confirmed OFC loci-PAX7, IRF6, FAM49A, DCAF4L2, 8q24.21, ARID3B, NTN1, TANC2 and the WNT9B:WNT3 gene cluster-were observed. We also found that single nucleotide polymorphisms within a subset of the associated loci, both previously known and novel, differ substantially in terms of their effects across cleft- or family-specific phenotypes, indicating not only etiologic differences between CL and CLP, but also genetic heterogeneity within each of the two OFC subtypes.

Genome-wide Enrichment of De Novo Coding Mutations in Orofacial Cleft Trios.

Although de novo mutations (DNMs) are known to increase an individual's risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 child-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to an individual's OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

Genetic Analyses of Enamel Hypoplasia in Multiethnic Cohorts.

Enamel hypoplasia causes reduction in the thickness of affected enamel and is one of the most common dental anomalies. This defect is caused by environmental and/or genetic factors that interfere with tooth formation, emphasizing the importance of investigating enamel hypoplasia on an epidemiological and genetic level. A genome-wide association of enamel hypoplasia was performed in multiple cohorts, overall comprising 7,159 individuals ranging in age from 7-82 years. Mixed-models were used to test for genetic association while simultaneously accounting for relatedness and genetic population structure. Meta-analysis was then performed. More than 5 million single-nucleotide polymorphisms were tested in individual cohorts. Analyses of the individual cohorts and meta-analysis identified association signals close to genome-wide significance (P < 510-8), and many suggestive association signals (510-8 < P < 510-6) near genes with plausible roles in tooth/enamel development. The strongest association signal (P = 1.5710-9) was observed near BMP2K in one of the individual cohorts. Additional suggestive signals were observed near genes with plausible roles in tooth development in the meta-analysis, such as SLC4A4 which can influence enamel hypoplasia. Additional human genetic studies are needed to replicate these results and functional studies in model systems are needed to validate our findings.

Dental anomaly detection using intraoral photos via deep learning.

Children with orofacial clefting (OFC) present with a wide range of dental anomalies. Identifying these anomalies is vital to understand their etiology and to discern the complex phenotypic spectrum of OFC. Such anomalies are currently identified using intra-oral exams by dentists, a costly and time-consuming process. We claim that automating the process of anomaly detection using deep neural networks (DNNs) could increase efficiency and provide reliable anomaly detection while potentially increasing the speed of research discovery. This study characterizes the use of` DNNs to identify dental anomalies by training a DNN model using intraoral photographs from the largest international cohort to date of children with nonsyndromic OFC and controls (OFC1). In this project, the intraoral images were submitted to a Convolutional Neural Network model to perform multi-label multi-class classification of 10 dental anomalies. The network predicts whether an individual exhibits any of the 10 anomalies and can do so significantly faster than a human rater can. For all but three anomalies, F1 scores suggest that our model performs competitively at anomaly detection when compared to a dentist with 8 years of clinical experience. In addition, we use saliency maps to provide a post-hoc interpretation for our model's predictions. This enables dentists to examine and verify our model's predictions.

Tooth Agenesis Patterns in Orofacial Clefting Using Tooth Agenesis Code: A Meta-Analysis.

Individuals with orofacial clefting (OFC) have a higher prevalence of tooth agenesis (TA) overall. Neither the precise etiology of TA, nor whether TA occurs in patterns that differ by gender or cleft type is yet known. This meta-analysis aims to identify the spectrum of tooth agenesis patterns in subjects with non-syndromic OFC and controls using the Tooth Agenesis Code (TAC) program. An indexed search of databases (PubMed, EMBASE, and CINAHL) along with cross-referencing and hand searches were completed from May to June 2019 and re-run in February 2022. Additionally, unpublished TAC data from 914 individuals with OFC and 932 controls were included. TAC pattern frequencies per study were analyzed using a random effects meta-analysis model. A thorough review of 45 records retrieved resulted in 4 articles meeting eligibility criteria, comprising 2182 subjects with OFC and 3171 controls. No TA (0.0.0.0) was seen in 51% of OFC cases and 97% of controls. TAC patterns 0.2.0.0, 2.0.0.0, and 2.2.0.0 indicating uni- or bi-lateral missing upper laterals, and 16.0.0.0 indicating missing upper right second premolar, were more common in subjects with OFC. Subjects with OFC have unique TA patterns and defining these patterns will help increase our understanding of the complex etiology underlying TA.