Title: Application of Genomic Resources and Gene Expression Profiles to Identify Genes That Regulate Bone Density
Volume: 4
Issue: 1
Author(s): W- K. Gu, X- M. Li, B. A. Roe, K- H. William Lau, B. Edderkaoui, S. Mohan and D. J. Baylink
Affiliation:
Abstract: Inadequate bone density is the strongest determinant of subsequent osteoporotic fracture. More than 70% of the variability in human bone density has been attributed to genetic factors. Therefore, the identification of genes regulating peak bone density represents a major advance in both the understanding of pathways that regulate bone density and the pathogenesis of diseases such as osteoporosis. Although association studies have revealed many candidate genes, the exact roles of these genes in the regulation of bone density are not clearly defined. Recently, a large number of bone density quantitative trait loci (QTLs) have been identified using mouse models and human populations. However, none of the genes responsible for these QTLs have been identified. Thus, the regulation of bone density is likely far more complicated than previously anticipated. Over the next decade, DNA microarrays, combined with sophisticated informatics and genomic databases, will provide a new generation of molecular tools for the identification and functional studies of genes responsible for bone density. This review intends to provide an update on the application of genomic resources and gene expression profiles to identify genes that regulate b one density. First, the progress and problems with association studies for QTL identification of bone density will be summarized. Then current resources of genomic sequences and ESTs that can be used for the identification of QTL genes will be discussed. Finally information on the 207 ESTs that are expressed in the bone and 39 ESTs that we have identified within the QTL regions will be presented. It is anticipated that this review will stimulate further studies on candidate genes that regulate bone density by taking advantage of the rapidly emerging genomic data.
