Variant analyses of candidate genes in orofacial clefts in multi-ethnic populations

Family-based GWAS for dental class I malocclusion and clefts

BACKGROUND

Individuals born with cleft lip and/or palate who receive corrective surgery regularly have abnormal growth in the midface region such that they exhibit premaxillary hypoplasia. However, there are also genetic contributions to craniofacial morphology in the midface region, so although these individuals appear to have Class III skeletal discrepancy, their molar relationship may be Class I. Past genome-wide association studies (GWASs) on skeletal Class II and III malocclusion suggested that multiple genetic markers contribute to these phenotypes via a multifactorial inheritance model, but research has yet to examine the genetic markers associated with dental Class I malocclusion. Thus, our goal was to conduct a family based GWAS to identify genes across the genome that are associated with Class I malocclusion, as defined by molar relations, in humans with and without clefts.

METHODS

Our cohort consisted of 739 individuals from 47 Filipino families originally recruited in 2006 to investigate the genetic basis of orofacial clefts. All individuals supplied blood samples for DNA extraction and genotyping, and a 5,766 single nucleotide polymorphism (SNP) custom panel was used for the analyses. We performed a transmission disequilibrium test for participants with and without clefts to identify genetic contributors potentially involved with Class I malocclusion.

RESULTS

In the total cohort, 13 SNPs had associations that reached the genomic control threshold (p < 0.005), while five SNPs were associated with Class I in the cohort of participants without clefts, including four associations that were identified in the total cohort. The associations for the SNPs ABCA4 rs952499, SOX1-OT rs726455, and RORA rs877228 are of particular interest, as past research found associations between these genes and various craniofacial phenotypes, including cleft lip and/or palate.

CONCLUSIONS

These findings support the multifactorial inheritance model for dental Class I malocclusion and suggest a common genetic basis for different aspects of craniofacial development.

The presence and distribution of various genes in postnatal CLP-affected palatine tissue

BACKGROUND

Worldwide cleft lip with or without a cleft palate (CL/P) is the most common craniofacial birth defect. Apart from changes in facial appearance, additionally affected individuals often suffer from various associated comorbidities requiring complex multidisciplinary treatment with overall high expenses. Understanding the complete pathogenetic mechanisms of CL/P might aid in developing new preventative strategies and therapeutic approaches, help with genetic counselling, and improve quality of life. Many genes have been associated with the development of orofacial clefts; however, the majority require further research. Based on the role of PAX7, PAX9, SHH, SOX3, WNT3A, and WNT9B in orofacial development, the intention was to use chromogenic in situ hybridization to detect the six genes in postnatal CLP-affected palatine tissue and compare their distribution within the tissue samples.

RESULTS

Statistically significant differences in the distribution of PAX7, PAX9, WNT3A, and WNT9B were observed. In total, 19 pairs of moderate to very strong positive correlations were noted.

CONCLUSIONS

Changes in the cleft-affected palatine epithelium primarily seem to be associated with the PAX7 gene; however, PAX9, WNT3A, WNT9B, and SOX3 role seems to be more limited. Whilst connective tissue changes seem to depend on PAX7 only, SHH seems to participate individually and indistinctly. Numerous positive correlations reflect the complicating interactions of the pathways and their components in the orofacial cleft morphopathogenesis.

The Risk of Orofacial Cleft Lip/Palate Due to Maternal Ambient Air Pollution Exposure: A Call for Further Research in South Africa

Background

Despite being underreported, orofacial cleft lip/palate (CLP) remains in the top five of South Africa's most common congenital disorders. Maternal air pollution exposure has been associated with CLP in neonates. South Africa has high air pollution levels due to domestic burning practices, coal-fired power plants, mining, industry, and traffic pollution, among other sources. We investigated air pollutant levels in geographic locations of CLP cases.

Methods

In a retrospective case series study (2006-2020) from a combined dataset by a Gauteng surgeon and South African Operation Smile, the maternal address at pregnancy was obtained for 2,515 CLP cases. Data from the South African Air Quality Information System was used to calculate annual averages of particulate matter (PM) concentrations of particles < 10 µm (PM₁₀) and < 2.5 µm (PM_2.5). Correlation analysis determined the relationship between average PM_2.5/PM₁₀ concentrations and CLP birth prevalence. Hotspot analysis was done using the Average Nearest Neighbor tool in ArcGIS.

Results

Correlation analysis showed an increasing trend of CLP birth prevalence to PM₁₀ (CC = 0.61, 95% CI = 0.38-0.77, p < 0.001) and PM_2.5 (CC = 0.63, 95% CI = 0.42-0.77, p < 0.001). Hot spot analysis revealed that areas with higher concentrations of PM₁₀ and PM_2.5 had a higher proclivity for maternal residence (z-score = -68.2, p < 0.001). CLP birth prevalence hotspot clusters were identified in district municipalities in the provinces of Gauteng, Limpopo, North-West, Mpumalanga, and Free State. KwaZulu-Natal and Eastern Cape had lower PM₁₀ and PM_2.5 concentrations and were cold spot clusters.

Conclusions

Maternal exposure to air pollution is known to impact the fetal environment and increase CLP risk. We discovered enough evidence of an effect to warrant further investigation. We advocate for a concerted effort by the government, physicians, researchers, non-government organizations working with CLP patients, and others to collect quality data on all maternal information and pollutant levels in all provinces of South Africa. Collaboration and data sharing for additional research will help us better understand the impact of air pollution on CLP in South Africa.