Osteoporosis (OP) is an often asymptomatic multi-factorial condition that is characterized by a progressive loss of bone mass resulting in increased fracture (FX) risk and reduced lifespan(1). It represents a significant public health burden that affects an estimated 2.2 million Australians and results in 20,000 hip fractures annually, with direct and indirect disease-related costs estimated at $7.4 billion per year(2). Due to the insidious nature of this disease, individuals who are most at risk of OP are often only identified once they present with low trauma FX. The situation is further exacerbated as most pharmacological treatments function as anti-resorptives that halt further bone loss, but fail to fully restore bone quality. Only one osteoanabolic drug is presently approved by the United States Food and Drug Administration, however this compound is far from ideal as it requires daily administration via injection to ensure adequate bone formation(3). Consequently, there is considerable scope for identifying novel osteoanabolic pathways that could in principle be targeted by new and existing pharmacotherapies to build bone mass before clinical sequelae develop.
The goal of this PhD is to combine statistical and molecular genetics approaches to identify and assess the therapeutic potential of OP drug targets.
This PhD encompasses three aims:
(a) Achieving major advances in understanding the genetics of OP by performing the largest whole-exome and genome sequencing study of OP in ~500,000 subjects from The UK Biobank Study(4).
(b) Identifying and prioritising candidate genes for future functional validation as drug targets by: (i) identifying novel low frequency, loss of function variants in protein coding regions of OP associated loci(5), (ii) screening identified candidates for predicted adverse effects by mining phenome-wide association data across >2000 traits and diseases(6), and (iii) screening implicated genes in my collaborators’ established in vivo mouse(7) and zebrafish skeletal phenotyping programs(8).
(c) Identifying novel molecular biomarkers that are causally related to OP, and that represent excellent opportunities for drug repositioning, by performing Mendelian randomization analysis of thousands of proteomic and metabolic biomarkers in hundreds of thousands of individuals(9).
References
1. Kanis, J.A. et al. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 24, 23-57 (2013). 2. Sambrook, P.N. et al. Preventing osteoporosis: outcomes of the Australian Fracture Prevention Summit. Med J Aust 176 Suppl, S1-16 (2002). 3. Baron, R. & Hesse, E. Update on bone anabolics in osteoporosis treatment: rationale, current status, and perspectives. J Clin Endocrinol Metab 97, 311-25 (2012). 4. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203-209 (2018). 5. Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol 30, 317-20 (2012). 6. Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nature Communications 9(2018). 7. Freudenthal, B. et al. Rapid phenotyping of knockout mice to identify genetic determinants of bone strength. Journal of Endocrinology 231, R31-R46 (2016). 8. Hammond, C.L. & Moro, E. Using transgenic reporters to visualize bone and cartilage signaling during development in vivo. Front Endocrinol (Lausanne) 3, 91 (2012). 9. Evans, D.M. et al. Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates. Plos Genetics 9(2013).
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