Abstract
Osteoporosis is a highly prevalent disease, characterized by reduced bone strength and an increased susceptibility to bone fractures, with over 10 million affected individuals in the U.S. alone. As populations age more successfully, the prevalence of osteoporosis is expected to rise; therefore, understanding the genetic basis of bone strength and related traits is of the utmost importance to the development of therapeutic interventions aimed at reducing the societal burden of osteoporosis. To this end, over the last decade, geneticists have performed genome-wide association studies (GWASs) of bone mineral density (BMD) in order to gain insight into the genetics of osteoporosis. These studies have been very successful, identifying over 1,100 independent associations to date. However, efforts to understand the genetics of bone and to discover actionable therapeutic targets have been limited due to two main shortcomings of BMD GWASs. First, GWASs in the bone field have almost exclusively focused on BMD as a trait. While BMD is a clinically relevant predictor of osteoporosis, it only explains part of the variance in bone strength. Second, progress has been limited due to the inherent difficulties in identifying the causal genes that underlie GWAS associations.
Here, we address these limitations by utilizing systems genetics approaches. Using a novel mouse population, the Diversity Outbred, we perform a GWAS of 55 bone traits and identify putatively causal genes underlying some of the GWAS associations. Furthermore, we utilize systems genetics approaches in order to inform existing BMD GWASs. The work presented in this dissertation provides a resource that will increase our understanding of the genetics of bone, and presents methodological techniques that are applicable across myriad complex traits.
