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Leveraging FaceBase to Accelerate Understanding of Craniofacial Development, Disorders, and Conditions

Translational Genomics Research Branch
Division of Extramural Research 


The goal of this initiative is to encourage the broader craniofacial research community to utilize FaceBase resources and to foster new projects that build on FaceBase data. This initiative extends the logic behind the NIDCR FOAs for small grants for analyzing existing data and developing statistical methods (PAR-09-182, PAR-10-041) to FaceBase.



The FaceBase Consortium was developed to accelerate understanding of the genetic basis of craniofacial development and the etiology of craniofacial diseases and disorders through creation of a public resource for data from experimental models and human studies and the integration of those data.  With its initial focus on mid-face development and cleft lip and palate, the FaceBase FOA  attracted a talented group of investigators with diverse projects, all of whom are contributing their data to the FaceBase resource.  Most of these projects are producing large datasets that can be productively analyzed further by researchers outside of the consortium.  These datasets include:

  • microarray and RNA-seq measurements of gene and miRNA expression across different parts of the developing face and different developmental stages
  • genotype and morphometric data relevant to normal human facial shape in several ethnic populations
  • genotype and morphometric data relevant to craniofacial shape in mice
  • resequencing data from a GWAS follow-up study
  • ChIP-Seq data on genetic enhancers active during craniofacial development.

Researchers outside of the consortium have also expressed their willingness to share their datasets with the wider scientific community through the FaceBase hub. 

Examples of potential research topics could include but are not limited to:

  1. Data-mining projects that integrate data across FaceBase datasets and other existing datasets;
  2. Projects that use FaceBase data as preliminary data to target genomic areas for further study;
  3. Projects that combine data-mining to identify targets or pathways with hypothesis-testing in experiments, such as systems biology approaches.


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This page last updated: February 26, 2014