Prediction of osteoporosis candidate genes by computational disease gene identification strategy

Abstract

Osteoporosis is a complex disease with strong genetic component. To date, more than twenty genome wide linkage scans across multiple populations have been launched to hunt for osteoporosis susceptibility genes. Some significant or suggestive chromosomal regions of linkage to bone mineral density have been identified and replicated in genome-wide linkage screens. However, identification of key candidate genes within these confirmed regions is challenging. Now some bioinformatics tools are available for disease gene identification. These tools use information extracted from public online databases, such as sequence data, medical literature, gene ontology and function annotation, and information on biology, function and gene expression. In this study we used five freely available bioinformatics tools (Prioritizer, Geneseeker, PROSPECTR and SUSPECTS (PandS), Disease Gene Prediction (DGP) and Endeavour) to analyze the thirteen well replicated osteoporosis susceptibility loci (1p36, 1q21-25, 2p22-24, 3p14-25, 4q25-34, 6p21, 7p14-21, 11q14-25, 12q23-24, 13q14-34, 20p12, 2q24-32 and 5q12-21) and identify a subset of most likely candidate osteoporosis susceptibility genes that are largely involved in TGF-β signaling, GM-CSF signaling, axonal guidance signaling, PPAR signaling, and Wnt/β-catenin signaling pathway. The list of most likely candidate genes and the associated pathway identified might assist researchers in prioritizing candidate disease genes for further empirical analysis and understanding of the pathogenesis of osteoporosis.