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.


Positions of skull size QTL (gray) and skull shape QTL (black) on genetic map. Skull centroid size QTL (SCS.qtl) are abbreviated as “q” and are left to the chromosome map. Skull shape QTL (SSH.qtl) are as abbreviated as “Q” and are to the right of the chromosome map. Vertical bars indicate the Bayesian estimate of the confidence interval of QTL.
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Figure 2: Positions of skull size QTL (gray) and skull shape QTL (black) on genetic map. Skull centroid size QTL (SCS.qtl) are abbreviated as “q” and are left to the chromosome map. Skull shape QTL (SSH.qtl) are as abbreviated as “Q” and are to the right of the chromosome map. Vertical bars indicate the Bayesian estimate of the confidence interval of QTL.

Mentions: Our interval mapping has identified seven QTL responsible for the variation in skull centroid size (SCS.qtl). Location, nearest marker, and the confidence intervals for the identified loci, and the QTL effect are provided in Table 1. The size of the confidence intervals for these loci is highly variable; when converted to genomic location, they vary from 13 to 89 MB. The location of the SCS.qtl peaks are shown on Figure 2. SCS.qtl 0.7 on chromosome 13 shows the largest effect for increased skull size, whereas SCS.qtl 0.3 on chromosome 5 is the only QTL related to reduced skull size.


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)

Positions of skull size QTL (gray) and skull shape QTL (black) on genetic map. Skull centroid size QTL (SCS.qtl) are abbreviated as “q” and are left to the chromosome map. Skull shape QTL (SSH.qtl) are as abbreviated as “Q” and are to the right of the chromosome map. Vertical bars indicate the Bayesian estimate of the confidence interval of QTL.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Positions of skull size QTL (gray) and skull shape QTL (black) on genetic map. Skull centroid size QTL (SCS.qtl) are abbreviated as “q” and are left to the chromosome map. Skull shape QTL (SSH.qtl) are as abbreviated as “Q” and are to the right of the chromosome map. Vertical bars indicate the Bayesian estimate of the confidence interval of QTL.
Mentions: Our interval mapping has identified seven QTL responsible for the variation in skull centroid size (SCS.qtl). Location, nearest marker, and the confidence intervals for the identified loci, and the QTL effect are provided in Table 1. The size of the confidence intervals for these loci is highly variable; when converted to genomic location, they vary from 13 to 89 MB. The location of the SCS.qtl peaks are shown on Figure 2. SCS.qtl 0.7 on chromosome 13 shows the largest effect for increased skull size, whereas SCS.qtl 0.3 on chromosome 5 is the only QTL related to reduced skull size.

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.