Dataset Information

Exploring the Underlying Genetics of Craniofacial Morphology through Various Sources of Knowledge.

ABSTRACT: The craniofacial complex is the billboard of sorts containing information about sex, health, ancestry, kinship, genes, and environment. A thorough knowledge of the genes underlying craniofacial morphology is fundamental to understanding craniofacial biology and evolution. These genes can also provide an important foundation for practical efforts like predicting faces from DNA and phenotype-based facial diagnostics. In this work, we focus on the various sources of knowledge regarding the genes that affect patterns of craniofacial development. Although tremendous successes recently have been made using these sources in both methodology and biology, many challenges remain. Primary among these are precise phenotyping techniques and efficient modeling methods.

SUBMITTER: Roosenboom J

PROVIDER: S-EPMC5178329 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring the Underlying Genetics of Craniofacial Morphology through Various Sources of Knowledge.

Roosenboom Jasmien J Hens Greet G Mattern Brooke C BC Shriver Mark D MD Claes Peter P

BioMed research international 20161208

The craniofacial complex is the billboard of sorts containing information about sex, health, ancestry, kinship, genes, and environment. A thorough knowledge of the genes underlying craniofacial morphology is fundamental to understanding craniofacial biology and evolution. These genes can also provide an important foundation for practical efforts like predicting faces from DNA and phenotype-based facial diagnostics. In this work, we focus on the various sources of knowledge regarding the genes th ...[more]

PMID: 28053980

Similar Datasets

Project description:Using eight inbred founder strains of the mouse Collaborative Cross (CC) project and their reciprocal F1 hybrids, we quantified variation in craniofacial morphology across mouse strains, explored genetic contributions to craniofacial variation that distinguish the founder strains, and tested whether specific or summary measures of craniofacial shape display stronger additive genetic contributions. This study thus provides critical information about phenotypic diversity among CC founder strains and about the genetic contributions to this phenotypic diversity, which is relevant to understanding the basis of variation in standard laboratory strains and natural populations. Craniofacial shape was quantified as a series of size-adjusted linear dimensions (RDs) and by principal components (PC) analysis of morphological landmarks captured from computed tomography images from 62 of the 64 reciprocal crosses of the CC founder strains. We first identified aspects of skull morphology that vary between these phenotypically 'normal' founder strains and that are defining characteristics of these strains. We estimated the contributions of additive and various non-additive genetic factors to phenotypic variation using diallel analyses of a subset of these strongly differing RDs and the first eight PCs of skull shape variation. We find little difference in the genetic contributions to RD measures and PC scores, suggesting fundamental similarities in the magnitude of genetic contributions to both specific and summary measures of craniofacial phenotypes. Our results indicate that there are stronger additive genetic effects associated with defining phenotypic characteristics of specific founder strains, suggesting these distinguishing measures are good candidates for use in genotype-phenotype association studies of CC mice. Our results add significantly to understanding of genotype-phenotype associations in the skull, which serve as a foundation for modeling the origins of medically and evolutionarily relevant variation.

Project description:Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N?=?597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose.

Dataset Information

Exploring the Underlying Genetics of Craniofacial Morphology through Various Sources of Knowledge.

Publications

Exploring the Underlying Genetics of Craniofacial Morphology through Various Sources of Knowledge.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets