Recent Advances in the Genetics of Osteoporosis

The last few years has seen considerable advances in our understanding of genetic influences on osteoporosis, leading to the identification of important new pathways contributing to bone mass, strength and fragility, and new potential drug targets for osteoporosis therapies. These have resulted from a range of methodological advances, the application of which to osteoporosis genetics will be reviewed in this Research Topic. In the case of familial causes of osteoporosis and other bone mass disorders, identification of causative genes has been considerably helped by the advent of low cost whole genome sequencing methods. In terms of genome wide association studies (GWAS) to identify genetic influences on common forms of osteoporosis, one of the most important methodological advances has been the availability of multiple large cohorts to provide sufficient statistical power. Since multiple skeletal characteristics contribute to fracture risk, further insights have been obtained by examining genetic influences on skeletal phenotypes beyond conventional hip and lumbar spine bone mineral density (BMD). A further challenge is to establish how the plethora of newly identified genetic signals alter gene function, which this Research Topic also examines by reviewing the application of in silico analysis and mouse screens to the outputs of osteoporosis GWAS studies. Other emerging areas in osteoporosis genetics, covered by this Research Topic, include the application of GWAS findings to examine causal inference in osteoporosis, and genetic influences on sclerostin, the target for recently developed anti-sclerostin treatment for osteoporosis.

(i) Familial bone mass disorders:
• New causes of familial osteoporosis
• Genetic architecture of high bone mass disorders

(ii) Beyond BMD measures:
• Use of heel ultrasound to explore the genetic architecture of osteoporosis
• Use of pQCT & HRpQCT scans to explore the genetic architecture of osteoporosis
• Genetic architecture of fracture risk

(iii) Functional studies:
• Bioinformatics tools
• Resources from bone cells and tissues
• Murine models
• Zebrafish

(iv) Use of genetics to explore causal inference in osteoporosis

(v) Genetic influences on sclerostin