Using epigenomic data to inform genome-wide association studies of bone mineral density

Osteoporosis is primarily an aging-related disease, characterized by decreased bone strength and increased fracture risk. The incidence of osteoporosis is increasing worldwide due to aging populations and is a major healthcare burden. For instance, osteoporosis-related healthcare costed China $9.45 billion USD in 2010, will likely double by 2035, and increase to $25 billion USD by 2050 (1). Bone mineral density (BMD) is used to predict fracture risk and is the primary clinical measurement used to diagnose osteoporosis. Genome-wide association studies (GWAS) of BMD have successfully identified many genetic loci that influence osteoporosis (2-12), but like most GWAS, these studies used stringent statistical significance thresholds to limit false positive results at the expense of false negatives.