Limits...
Quantitative trait loci affecting the 3D skull shape and size in mouse and prioritization of candidate genes in-silico.

Maga AM, Navarro N, Cunningham ML, Cox TC - Front Physiol (2015)

Bottom Line: However, they account for significant amount of variation in some specific directions of the shape space.Many QTL have stronger effect on the neurocranium than expected from a random vector that will parcellate uniformly across the four cranial regions.On the contrary, most of QTL have an effect on the palate weaker than expected.

View Article: PubMed Central - PubMed

Affiliation: Division of Craniofacial Medicine, Department of Pediatrics, University of Washington Seattle, WA, USA ; Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute Seattle, WA, USA.

ABSTRACT
We describe the first application of high-resolution 3D micro-computed tomography, together with 3D landmarks and geometric morphometrics, to map QTL responsible for variation in skull shape and size using a backcross between C57BL/6J and A/J inbred strains. Using 433 animals, 53 3D landmarks, and 882 SNPs from autosomes, we identified seven QTL responsible for the skull size (SCS.qtl) and 30 QTL responsible for the skull shape (SSH.qtl). Size, sex, and direction-of-cross were all significant factors and included in the analysis as covariates. All autosomes harbored at least one SSH.qtl, sometimes up to three. Effect sizes of SSH.qtl appeared to be small, rarely exceeding 1% of the overall shape variation. However, they account for significant amount of variation in some specific directions of the shape space. Many QTL have stronger effect on the neurocranium than expected from a random vector that will parcellate uniformly across the four cranial regions. On the contrary, most of QTL have an effect on the palate weaker than expected. Combined interval length of 30 SSH.qtl was about 315 MB and contained 2476 known protein coding genes. We used a bioinformatics approach to filter these candidate genes and identified 16 high-priority candidates that are likely to play a role in the craniofacial development and disorders. Thus, coupling the QTL mapping approach in model organisms with candidate gene enrichment approaches appears to be a feasible way to identify high-priority candidates genes related to the structure or tissue of interest.

No MeSH data available.


Landmarks used in the study.Green: lateral face, red: dorsal face, black: neurocranium, blue: palate. Points with two colors are assigned to both regions.
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Figure 1: Landmarks used in the study.Green: lateral face, red: dorsal face, black: neurocranium, blue: palate. Points with two colors are assigned to both regions.

Mentions: All animals were imaged at the Small ANimal Tomographic Analysis (SANTA) Facility at Seattle Children's Research Institute using high-resolution microcomputed tomography (model 1076; Skyscan, Belgium) employing a standardized imaging protocol (18 μm spatial resolution, 0.5 Al filter, 55 kV, 420 ms exposure, 3 frame averaging). Reconstructed image stacks were loaded into 3D Slicer (http://www.slicer.org) and rendered in 3D. A random subset of 50 samples was landmarked twice using an initial set of 55 skull landmarks. We calculated the difference in the coordinates of matching landmarks from the two sets (i.e., observer error) and removed those that consistently exceed an arbitrary cut of 7 voxels (0.125 mm). Based on these results, two landmarks were dropped from the set. The remaining samples were landmarked only once for efficiency. Figure 1 shows the final set of landmarks used in the study.


Quantitative trait loci affecting the 3D skull shape and size in mouse and prioritization of candidate genes in-silico.

Maga AM, Navarro N, Cunningham ML, Cox TC - Front Physiol (2015)

Landmarks used in the study.Green: lateral face, red: dorsal face, black: neurocranium, blue: palate. Points with two colors are assigned to both regions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374467&req=5

Figure 1: Landmarks used in the study.Green: lateral face, red: dorsal face, black: neurocranium, blue: palate. Points with two colors are assigned to both regions.
Mentions: All animals were imaged at the Small ANimal Tomographic Analysis (SANTA) Facility at Seattle Children's Research Institute using high-resolution microcomputed tomography (model 1076; Skyscan, Belgium) employing a standardized imaging protocol (18 μm spatial resolution, 0.5 Al filter, 55 kV, 420 ms exposure, 3 frame averaging). Reconstructed image stacks were loaded into 3D Slicer (http://www.slicer.org) and rendered in 3D. A random subset of 50 samples was landmarked twice using an initial set of 55 skull landmarks. We calculated the difference in the coordinates of matching landmarks from the two sets (i.e., observer error) and removed those that consistently exceed an arbitrary cut of 7 voxels (0.125 mm). Based on these results, two landmarks were dropped from the set. The remaining samples were landmarked only once for efficiency. Figure 1 shows the final set of landmarks used in the study.

Bottom Line: However, they account for significant amount of variation in some specific directions of the shape space.Many QTL have stronger effect on the neurocranium than expected from a random vector that will parcellate uniformly across the four cranial regions.On the contrary, most of QTL have an effect on the palate weaker than expected.

View Article: PubMed Central - PubMed

Affiliation: Division of Craniofacial Medicine, Department of Pediatrics, University of Washington Seattle, WA, USA ; Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute Seattle, WA, USA.

ABSTRACT
We describe the first application of high-resolution 3D micro-computed tomography, together with 3D landmarks and geometric morphometrics, to map QTL responsible for variation in skull shape and size using a backcross between C57BL/6J and A/J inbred strains. Using 433 animals, 53 3D landmarks, and 882 SNPs from autosomes, we identified seven QTL responsible for the skull size (SCS.qtl) and 30 QTL responsible for the skull shape (SSH.qtl). Size, sex, and direction-of-cross were all significant factors and included in the analysis as covariates. All autosomes harbored at least one SSH.qtl, sometimes up to three. Effect sizes of SSH.qtl appeared to be small, rarely exceeding 1% of the overall shape variation. However, they account for significant amount of variation in some specific directions of the shape space. Many QTL have stronger effect on the neurocranium than expected from a random vector that will parcellate uniformly across the four cranial regions. On the contrary, most of QTL have an effect on the palate weaker than expected. Combined interval length of 30 SSH.qtl was about 315 MB and contained 2476 known protein coding genes. We used a bioinformatics approach to filter these candidate genes and identified 16 high-priority candidates that are likely to play a role in the craniofacial development and disorders. Thus, coupling the QTL mapping approach in model organisms with candidate gene enrichment approaches appears to be a feasible way to identify high-priority candidates genes related to the structure or tissue of interest.

No MeSH data available.