Prediction of osteoporosis candidate genes by computational disease-gene identification strategy

Abstract

Osteoporosis is a complex disease with a strong genetic component. To date, more than 20 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. We used five freely available bioinformatics tools (Prioritizer, GeneSeeker, PROSPECTR and SUSPECTS, Disease Gene Prediction, and Endeavor) to analyze the 13 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. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of most likely candidate osteoporosis susceptibility genes that are largely involved in transforming growth factor (TGF)-beta signaling, granulocyte-macrophage colony-stimulating factor (GM-CSF) signaling, axonal guidance signaling, peroxisome proliferator-activated receptor (PPAR) signaling, and Wnt/beta-catenin signaling pathway. Six nonoverlapping networks were generated by IPA 5.0 from 88 out of the 91 candidate genes. The list of most likely candidate genes and the associated pathway identified will assist researchers in prioritizing candidate disease genes for further empirical analysis and understanding the pathogenesis of osteoporosis.