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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.


Contribution of each anatomical region to the variation in covariates and SSH.qtl. (A) Unstandardized (B) weighted by the number of landmarks in a given region. Low proportion of Dorsal Face appears to be an artifact to its low number of landmarks whereas the low proportion accounted by the palate is robust. Horizontal gray lines represent 95% intervals obtained from random vectors. Many QTL have a 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. For covariate or QTL specific breakdown of regional variation, see Supplemental Figure 4.
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Figure 5: Contribution of each anatomical region to the variation in covariates and SSH.qtl. (A) Unstandardized (B) weighted by the number of landmarks in a given region. Low proportion of Dorsal Face appears to be an artifact to its low number of landmarks whereas the low proportion accounted by the palate is robust. Horizontal gray lines represent 95% intervals obtained from random vectors. Many QTL have a 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. For covariate or QTL specific breakdown of regional variation, see Supplemental Figure 4.

Mentions: Our interval mapping has identified 30 QTL responsible for variation in skull shape (SSH.qtl). All autosomes harbor at least one, and in some cases up to three SSH.qtl (Chr 1 and 4). Location, nearest marker, and the confidence intervals for the identified SSH.qtl are provided in Table 2 and plotted on Figure 2. On average, the width of the confidence intervals was 5.4 cM (or 10.4 MB). Visualizations of each SSH.qtl are rendered in six anatomical views and are provided with online Supplemental Data. To obtain a broad sense of importance of anatomical regions involved in each SSH.qtl effect, we assigned each landmark to one of the four anatomical regions (neurocranium, dorsal face, lateral face, and palate), summed up the magnitude of displacement for each landmark in the region and visualized it as a proportion of the total magnitude of displacement. If a landmark sits on a boundary of these regions (e.g., landmark on the triple junction of sutures between the frontal, parietal and squamosal bones), the magnitude of the displacement is split equally between the bounding region (in this case lateral face and neurocranium). We also calculated a second set of ratios in which the proportions are normalized by the number of landmarks in a region. Figure 5, and also Supplemental Figure 4, show the contribution of each of these regions to a given SSH.qtl effect, as well as the additive covariates included in the analysis (size, directionality of the cross, and sex), on skull shape, with and without normalization. Even though both the neurocranium and palate have a similar number of landmarks assigned (Figure 1), it appears that the contributions of the neurocranium to the overall skull shape differences are consistently larger than those of the palate. These two regions present contributions that mostly differ (higher and lower respectively) from what can be expected from the random partitioning of landmarks (Figure 5). On the other hand, for both facial regions contributions are not different from what is expected under the hypothesis of pleiotropy, with only a few loci having stronger than expected contributions.


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)

Contribution of each anatomical region to the variation in covariates and SSH.qtl. (A) Unstandardized (B) weighted by the number of landmarks in a given region. Low proportion of Dorsal Face appears to be an artifact to its low number of landmarks whereas the low proportion accounted by the palate is robust. Horizontal gray lines represent 95% intervals obtained from random vectors. Many QTL have a 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. For covariate or QTL specific breakdown of regional variation, see Supplemental Figure 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Contribution of each anatomical region to the variation in covariates and SSH.qtl. (A) Unstandardized (B) weighted by the number of landmarks in a given region. Low proportion of Dorsal Face appears to be an artifact to its low number of landmarks whereas the low proportion accounted by the palate is robust. Horizontal gray lines represent 95% intervals obtained from random vectors. Many QTL have a 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. For covariate or QTL specific breakdown of regional variation, see Supplemental Figure 4.
Mentions: Our interval mapping has identified 30 QTL responsible for variation in skull shape (SSH.qtl). All autosomes harbor at least one, and in some cases up to three SSH.qtl (Chr 1 and 4). Location, nearest marker, and the confidence intervals for the identified SSH.qtl are provided in Table 2 and plotted on Figure 2. On average, the width of the confidence intervals was 5.4 cM (or 10.4 MB). Visualizations of each SSH.qtl are rendered in six anatomical views and are provided with online Supplemental Data. To obtain a broad sense of importance of anatomical regions involved in each SSH.qtl effect, we assigned each landmark to one of the four anatomical regions (neurocranium, dorsal face, lateral face, and palate), summed up the magnitude of displacement for each landmark in the region and visualized it as a proportion of the total magnitude of displacement. If a landmark sits on a boundary of these regions (e.g., landmark on the triple junction of sutures between the frontal, parietal and squamosal bones), the magnitude of the displacement is split equally between the bounding region (in this case lateral face and neurocranium). We also calculated a second set of ratios in which the proportions are normalized by the number of landmarks in a region. Figure 5, and also Supplemental Figure 4, show the contribution of each of these regions to a given SSH.qtl effect, as well as the additive covariates included in the analysis (size, directionality of the cross, and sex), on skull shape, with and without normalization. Even though both the neurocranium and palate have a similar number of landmarks assigned (Figure 1), it appears that the contributions of the neurocranium to the overall skull shape differences are consistently larger than those of the palate. These two regions present contributions that mostly differ (higher and lower respectively) from what can be expected from the random partitioning of landmarks (Figure 5). On the other hand, for both facial regions contributions are not different from what is expected under the hypothesis of pleiotropy, with only a few loci having stronger than expected contributions.

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.