PROJECT SUMMARY/ABSTRACT Osteoporosis can be defined as the progressive loss of bone mass and strength with age, leading to increased risk of fragility fracture. Osteoporotic fracture and fracture-related traits, such as bone mineral density (BMD), are highly heritable and Genome-wide association studies (GWAS) for BMD have identified over 1100 associations for the phenotype of BMD. Further, there are many mono-allelic conditions, such as osteogenesis imperfecta, that lead to low BMD and low-trauma fractures in children. Bone is in a constant state of remodeling, with formation mediated by the osteoblast and resorption by the osteoclast and when these processes remain balanced, there is no net change in BMD. Imbalances in remodeling results in the loss of bone seen in osteoporosis, but a GWAS done for BMD cannot determine which of these physiological processes are affected by each locus. All current fracture prevention therapies focus on tipping the remodeling balance away from bone loss. There are three bone anabolic therapies approved by the FDA, but each of these has black box warnings, each can only be used for a limited time (1 to 2 years respectively) and none of them can be used in children. We have shown in previous work that bone mineralization by the osteoblast is a highly heritable, complex genetic trait and that genetic mapping for the absolute amount of mineralization possible yields information that is complementary to that identified by GWAS for BMD. However, the osteoblast is a highly regulated, complex cell that undergoes an as of yet incompletely described differentiation process, must be able to migrate to the site of bone remodeling, must be able to produce the proteinaceous extracellular matrix of bone and then must be able to execute mineralization. The goal of this application is to identify the key genes and pathways that control these aspects of osteoblastogensis and osteoblast function. In Aim 1, we will map high-resolution quantitative trait loci (QTL) for osteoblast maturation, migration and rate of mineral apposition. In Aim 2, we will use cutting edge Bayesian network analyses based on single cell RNA seq and single cell ATAC seq to define master control genes of various stages of osteoblast development. In Aim 3 we conduct functional follow up on genes found via our preliminary analyses that control the late stages of osteoblast function. We expect that this comprehensive and complementary approach to identify key genes for osteoblastic processes will provide critical insight into how bone is formed by the osteoblast. More importantly, the genes that we identify will serve as potential therapeutic targets capable of increasing bone formation in the setting of osteoporosis and in other formation disorders.