Opportunities and Challenges in Functional Genomics Research in Osteoporosis: Report From a Workshop Held by the Causes Working Group of the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society on October 5th 2020 - Tobias, Jonathan H.; Duncan, Emma L.; Kague, Erika; Hammond, Chrissy L.; Gregson, Celia L.; Bassett, Duncan; Williams, Graham R.; Min, Josine L.; Gaunt, Tom R.; Karasik, David; Ohlsson, Claes; Rivadeneira, Fernando; et al. (Frontiers in Endocrinology, 2021)
The discovery that sclerostin is the defective protein underlying the rare heritable bone mass disorder, sclerosteosis, ultimately led to development of anti-sclerostin antibodies as a new treatment for osteoporosis. In the era of large scale GWAS, many additional genetic signals associated with bone mass and related traits have since been reported. However, how best to interrogate these signals in order to identify the underlying gene responsible for these genetic associations, a prerequisite for identifying drug targets for further treatments, remains a challenge. The resources available for supporting functional genomics research continues to expand, exemplified by ‘multi-omics’ database resources, with improved availability of datasets derived from bone tissues. These databases provide information about potential molecular mediators such as mRNA expression, protein expression, and DNA methylation levels, which can be interrogated to map genetic signals to specific genes based on identification of causal pathways between the genetic signal and the phenotype being studied. Functional evaluation of potential causative genes has been facilitated by characterisation of the ‘osteocyte signature’, by broad phenotyping of knockout mice with deletions of over 7000 genes, in which more detailed skeletal phenotyping is currently being undertaken, and by development of zebrafish as a highly efficient additional in vivo model for functional studies of the skeleton. Looking to the future, harnessing this expanding repertoire of tools to functionally annotate the array of genetic signals for osteoporosis-related phenotypes offers exciting opportunities for target identification, and ultimately the development of new treatments for osteoporosis.