Candidate Gene Analysis/Nonsyndromic Craniosynostosis

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Craniosynostosis, the premature closure of one or more cranial sutures, occurs in approximately 1 in 2000 live births. More than 85% of all craniosynostosis cases are thought to be nonsyndromic, i.e., not associated with other congenital anomalies or with known mutations in the genes causing craniosynostosis syndromes. Identification of genes causing nonsyndromic craniosynostosis will allow better understanding of the complex process of calvarial formation and will open new venues for study of normal and abnormal development of the skull. Single nucleotide polymorphisms (SNPs) are the most common type of genetic markers and their use has become a major tool for the detection of genetic linkage and disease associations. New genotyping technologies that are available to us allow rapid and highly sensitive genotyping of a large set of SNPs in or near candidate genes. We propose to perform genetic association and linkage analysis on a group of candidate genes selected on the basis of their biologic function, expression pattern, or observed phenotype. Our group has identified candidate genes, characterized SNPs and has already successfully genotyped 48 families with craniosynostosis. Associations with several candidate genes have been established. A panel of SNPs validated in our laboratory will be applied on a set of DNA samples from more than 200 well-characterized craniosynostosis families. The SNP genotypes will allow comparison of allele frequencies, linkage disequilibrium patterns, and haplotypes at the candidate gene loci. The specific aims for this study are: 1) to perform SNP genotype analysis of a large set of DNA samples for 750-1000 SNPs located within or near 50 craniosynostosis candidate genes; 2) to perform case-parent trio based allelic and haplotype Transmission Disequilibrium Tests (TDTs) to test for associations in the presence of linkage between marker loci and nonsyndromic craniosynostosis; and 3) to test for gene-gene interaction using conditional logistic regression analysis. This new information will lead to better strategies for diagnosis and management of nonsyndromic craniosynostosis.