Dissecting the Complexity of Skeletal-Malocclusion-Associated Phenotypes: Mouse for the Rescue.
Iqbal M LoneOsayd ZohudAysar NashefChristian KirschneckPeter ProffNezar WattedFuad A IraqiPublished in: International journal of molecular sciences (2023)
Skeletal deformities and malocclusions being heterogeneous traits, affect populations worldwide, resulting in compromised esthetics and function and reduced quality of life. Skeletal Class III prevalence is the least common of all angle malocclusion classes, with a frequency of 7.2%, while Class II prevalence is approximately 27% on average, varying in different countries and between ethnic groups. Orthodontic malocclusions and skeletal deformities have multiple etiologies, often affected and underlined by environmental, genetic and social aspects. Here, we have conducted a comprehensive search throughout the published data until the time of writing this review for already reported quantitative trait loci (QTL) and genes associated with the development of skeletal deformation-associated phenotypes in different mouse models. Our search has found 72 significant QTL associated with the size of the mandible, the character, shape, centroid size and facial shape in mouse models. We propose that using the collaborative cross (CC), a highly diverse mouse reference genetic population, may offer a novel venue for identifying genetic factors as a cause for skeletal deformations, which may help to better understand Class III malocclusion-associated phenotype development in mice, which can be subsequently translated to humans. We suggest that by performing a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with skeletal deformation and Class III malocclusion characterization/phenotypes, including mandibular basic bone, gum, and jaw, in the CC mouse population, we expect to better identify genetic factors and better understand the development of this disease.
Keyphrases
- genome wide
- genome wide association study
- long noncoding rna
- dna methylation
- mouse model
- copy number
- high resolution
- healthcare
- risk factors
- mental health
- poor prognosis
- type diabetes
- metabolic syndrome
- machine learning
- systematic review
- bone mineral density
- body composition
- risk assessment
- big data
- mass spectrometry
- deep learning
- genome wide association
- human health
- bone loss
- high fat diet induced
- nucleic acid