Does 3D Phenotyping Yield Substantial Insights in the Genetics of the Mouse Mandible Shape?
Nicolas NavarroAli Murat MagaPublished in: G3 (Bethesda, Md.) (2016)
We describe the application of high-resolution 3D microcomputed tomography, together with 3D landmarks and geometric morphometrics, to validate and further improve previous quantitative genetic studies that reported QTL responsible for variation in the mandible shape of laboratory mice using a new backcross between C57BL/6J and A/J inbred strains. Despite the increasing availability of 3D imaging techniques, artificial flattening of the mandible by 2D imaging techniques seems at first an acceptable compromise for large-scale phenotyping protocols, thanks to an abundance of low-cost digital imaging systems such as microscopes or digital cameras. We evaluated the gain of information from considering explicitly this additional third dimension, and also from capturing variation on the bone surface where no precise anatomical landmark can be marked. Multivariate QTL mapping conducted with different landmark configurations (2D vs. 3D; manual vs. semilandmarks) broadly agreed with the findings of previous studies. Significantly more QTL (23) were identified and more precisely mapped when the mandible shape was captured with a large set of semilandmarks coupled with manual landmarks. It appears that finer phenotypic characterization of the mandibular shape with 3D landmarks, along with higher density genotyping, yields better insights into the genetic architecture of mandibular development. Most of the main variation is, nonetheless, preferentially embedded in the natural 2D plane of the hemi-mandible, reinforcing the results of earlier influential investigations.
Keyphrases
- high resolution
- low cost
- high throughput
- genome wide
- mass spectrometry
- high density
- escherichia coli
- healthcare
- copy number
- tandem mass spectrometry
- metabolic syndrome
- gene expression
- health information
- postmenopausal women
- bone mineral density
- skeletal muscle
- body composition
- insulin resistance
- genetic diversity
- data analysis