Our understanding of the gene interactions that underlie molecular pathogenesis is expanding rapidly, yet despite continuing technical advances in the area, genetic studies of common diseases remain costly and time consuming. Initial linkage and/or genome scan studies are rarely definitive, and require further focused genetic follow-up and/or validation studies often on additional patient cohorts. Current solutions include sample pooling to defray the considerable genotyping costs of whole genome studies, or reduced and selective focal candidate gene approaches. The success of the latter, however, depends on knowledge of the disease and pathways, which is growing but still limited. Our goal is to determine whether simulating the biological pathways of putative candidate genes prior to testing those candidates, can enable us to more accurately target higher-likelihood candidates and reduce both the cost and time involved in identifying the genes associated with disease, as well as to define the likely role of these targets in a future diagnostic or therapeutic setting. We will test this in a genetic study of Osteoporosis aimed at identifying the gene(s) underlying the observed linkage to chromosome 1p36. Osteoporosis is a disease of reduced bone mineral density (BMD) that is associated with increased risk of bone fracture and for which the treatment and care of hip fractures exceeds $9 billion annually. Numerous twin and family studies have shown that as much as 80% of the individual risk in BMD is under genetic control. Moreover the genetic basis of the disease is likely to be polygenic, involving multiple gene products implicated in both bone modeling (growth) and remodeling (loss and gain). Our goals in this phase of the study are 1) to evaluate all genes in the linkage region 1p36 in a novel biological modeling and simulation platform, "BoneFusion", for their impact and importance in bone remodeling, 2) to genotype SNPs in the genes identified as high value candidates in order to identifying associations between the gene candidates and osteoporosis, and test the model, and 3) to further use the BoneFusion computer simulation to define the potential of any genes showing strong association with the disease, as a therapeutic target and/or diagnostic marker
