Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

The human face is a critical feature that enables to distinguish individuals. The shape of the human face is mainly determined by genetics, probably involving development, genes, and regulatory elements. However, the genomics of facial variation is poorly understood. Therefore, by studying facial development using 3D facial images and genomics, my long-term goal is to uncover genotype-phenotype connections of the human face and the principles of genetic and epigenetic regulations in human facial traits.
The facial biology community including FaceBase (A Resource for Facial Researchers) has generated and will generate extensive data sets. These include genome-wide gene expression and gene regulatory landscapes for human/mouse facial anatomic regions and their developmental precursors, 3D facial images and SNPs (single-nucleotide polymorphisms) for thousands of healthy individuals. The lab at the University of Calgary is one of a dozen labs in the world to generate 3D human facial data. However, it is challenging to integrate and interpret these data to further get insight into the genetic understanding of normal-range variations of the human face.
At the moment, 3D facial modeling is focusing on global facial features, and the facial GWAS (genome-wide association study) analysis ignores the developmental context of facial traits. We hypothesize that a facial trait is determined by the gene regulatory networks (GRNs) and the signaling networks (SNs) of the developmental precursors of that trait. Therefore, a same phenotypic change of a trait can be caused by different SNPs in these networks. Therefore, my short-term goals of this research program are (i) to develop computational tools to enhance 3D facial analysis and network modeling, and (ii) to develop a new GWAS framework (i.e., DevNetGWAS) which integrates the GRNs/SNs of a facial trait’s developmental precursors into the GWAS analysis for significantly improving the GWAS statistical associations, and inferring genetic mechanisms of that facial trait.
To reach these short-term goals, I have proposed five projects with the following objectives: (i) to construct developmental precursors-specific SNs and GRNs of a facial trait with the guidance of the anatomic ontology, (ii) to develop new algorithms for modeling facial anatomic regions such as the lip in a more detailed manner, (iii) to develop new algorithms for modeling the facial trait’s developmental precursors-specific networks, (iv) developing a facial trait’s developmental context-dependent GWAS (i.e., DevNetGWAS) framework, and (v) to apply the DevNetGWAS to the 3D facial GWAS data for identifying and experimentally validating genetically associated genes and network modules for a facial trait (eg, the lip). These tools and the DevNetGWAS framework will pave the way for the understanding of facial genomics and advance the field of human trait GWAS analysis.