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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.


Workflow for the bioinformatics pipeline.
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Figure 6: Workflow for the bioinformatics pipeline.

Mentions: Candidate gene lists from each QTL interval were separately submitted to the gene enrichment toolkit, along with our training list of known craniofacial genes. The total training list consisted of 102 autosomal genes (Supplemental Table 2) that are known to be involved with craniofacial development and/or craniofacial disorders and was compiled by two of us (MLC and TCC). Because Toppgene uses a resampling approach (permutation test) to assess the significance of each gene, there can be potential issues due to randomness of the sampling of the genome. Therefore, candidate lists from each SSH.qtl interval were submitted to the Toppgene tool ten times. For a gene to be considered a strong craniofacial candidate, it needed to be present in all ten iterations with a significance value of 0.01 or lower. From this list, only the genes that are known to harbor exonic non-synonymous single nucleotide variants between A/J and C57BL/6J strains were retained. This information was obtained from the Wellcome Trust Mouse Genome SNP data. The list with exonic variants was submitted to the Ensembl Variant Effect Predictor (VEP) to measure the effect of variants. The resultant list contained 16 candidates with high SIFT scores (Table 2). The workflow as well as the number of candidate genes remaining at each step is provided in Figure 6.


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)

Workflow for the bioinformatics pipeline.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Workflow for the bioinformatics pipeline.
Mentions: Candidate gene lists from each QTL interval were separately submitted to the gene enrichment toolkit, along with our training list of known craniofacial genes. The total training list consisted of 102 autosomal genes (Supplemental Table 2) that are known to be involved with craniofacial development and/or craniofacial disorders and was compiled by two of us (MLC and TCC). Because Toppgene uses a resampling approach (permutation test) to assess the significance of each gene, there can be potential issues due to randomness of the sampling of the genome. Therefore, candidate lists from each SSH.qtl interval were submitted to the Toppgene tool ten times. For a gene to be considered a strong craniofacial candidate, it needed to be present in all ten iterations with a significance value of 0.01 or lower. From this list, only the genes that are known to harbor exonic non-synonymous single nucleotide variants between A/J and C57BL/6J strains were retained. This information was obtained from the Wellcome Trust Mouse Genome SNP data. The list with exonic variants was submitted to the Ensembl Variant Effect Predictor (VEP) to measure the effect of variants. The resultant list contained 16 candidates with high SIFT scores (Table 2). The workflow as well as the number of candidate genes remaining at each step is provided in Figure 6.

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.