A GWAS to Identify Genetic Loci Associated with Normal Molar Size Variation in Mice

Objectives: Our goal is to investigate the underlying genetics of the relationship between molar and jaw size in mice using quantitative approaches. We are conducting an exploratory genome-wide association study to identify loci responsible for normal development of molar and jaw size in mice. Understanding the genetics of normal molar size variation in mice is important as human and mouse genomes share 99% of their encoded sequences. The evolutionary conservation of large linkage groups between the species, with respect to the nature of the encoded genes, is an asset in identifying potentially corresponding homologous mutations and disease genes. There is a critical gap in our knowledge of how these genes act on pathways and processes. Identifying the genetic basis of natural variation in mice has significant scientific value for improving understanding of human genetics.
Methods: 50 reference inbred strains with published genotypic data are acquired from Jackson Laboratory. Litters are bred on-site to minimize variation due to environmental perturbation and to acquire prodigy of the same age. The craniodental phenotype of the litters is acquired in 3D using high-resolution microCT. We use a combination of image and landmark-based phenotypic characterization methods to capture the 3D shape and size of molars, as well as jaw lengths, from these reference strains. The resultant phenotypes will be subjected to normalization with respect to body, skull and jaw size. We will use the efficient linear mixed-model as implemented in the GEMMA software to identify variants reaching genome-wide significance and conduct a bioinformatics analysis to identify potential causal variants within the identified interval.
Results: Our provisional results with a subset of inbred strains, as well as prior publications, indicate multiple genomic intervals associated with molar size in mice.
Conclusions: At the time of full analysis we expect to validate these tentative loci and identify candidate genes through bioinformatics analysis.