Kennedy Trust Prize Studentships
Can we predict osteoarthritis? Investigating its genetic risk after knee joint injury
Osteoarthritis (OA) affects 9 million people in the UK and is the main reason for costly total joint replacement. Knee OA is the commonest form, but we still have much to understand about its initiation. OA is associated with changes in many joint tissues including cartilage and bone and typically occurs later in life. This type of OA is difficult to predict and diagnose at its early stages. Developing pharmacological interventions to prevent or slow disease has therefore proved challenging. A subgroup of knee OA (~12%) is so-called ‘post-traumatic’ OA, initiated by a significant joint injury. It typically affects younger individuals in their 20s to 40s, where surgical options are lacking, impacting (and often ending careers of) amateur and professional athletes. ~50% of those with substantial acute knee injuries such as anterior cruciate ligament (ACL) tears will develop a post-traumatic OA phenotype within 5-10 years of their injury1. We do not understand why some individuals make a full recovery from their injury, whilst others go on to develop progressive symptomatic OA, though our work suggests that change in certain proteins within the joint might be associated with either damage or successful repair2. The genetics of later-onset, established OA have been well-studied in large international consortia (arcOGEN,3) but the genetics of post-traumatic OA remain largely un-investigated. Studying those exposed to joint trauma is a useful ‘mechanistic niche’ for finding a foothold on initiating processes of this challenging disease. In preliminary studies, we found an apparent association between one well-established OA variant and clinical outcome after knee injury (unpublished data).
Our hypothesis is that post-traumatic OA is a phenotype which results from genotype-environment interaction, with disease onset requiring both an environmental trigger (the injury) and a degree of genetic susceptibility. During this project, you will investigate the role of genetics in post-traumatic OA of the knee, in order to establish:
- Is there a genetic basis for risk of post-traumatic OA?
- s genetic risk of post-traumatic OA the same, or different to other, ‘non-traumatic’ OA?
- How does genetic risk for post-traumatic OA manifest itself? (e.g. through inflammatory or repair signalling molecules or pathways, or through changes in underlying bone or cartilage structure)
You will use two important (and very different) types of dataset associated with genotypic data. This first are highly phenotyped cases as part of Osteoarthritis and Sporting Knee Injury: Genomic association with Risk (OSKGAR):a newly established international consortium for the purpose of these studies. This brings together for the first time data from >1000 highly phenotyped individuals with acute knee joint injury, with longitudinal outcome data (led by Fiona Watt). The consortium includes our local prospective cohorts: the Knee Injury Cohort at the Kennedy (KICK)study and the Oxford Knee Injury Cohort (OxKIC), as well as similar cohorts from Sweden, Netherlands, USA and Canada. The second are extremely large population datasets: UK BioBank(UKBB)and the Genetics of Osteoarthritis(GO)Consortium (led by collaborator Ele Zeggini, Sanger Institute Cambridge). Including ~31K and ~61K with knee OA respectively, ~7000 cases will have post-traumatic knee OA, sufficient to power genome-wide association studies. You will compare genomic associations of these cases with those exposed to knee injury without OA, those with non-traumatic OA and healthy OA-free, injury-free controls.
You will be based within a multi-disciplinary team in the Arthritis Research UK Centre for Osteoarthritis Pathogenesis at the world-leading Kennedy Institute of Rheumatology which aims to translate scientific findings to patient benefit. Full training is given in genomic analytical techniques as well as the molecular biology techniques involved in the project. A core curriculum in the first term provides foundation in musculoskeletal sciences, immunology and data analysis. You will develop and validate techniques for identifying cases of injury and of post-traumatic OA in the large datasets by using routine clinical Electronic Patient Record data (including hospital and newly available primary care data) and self-reported phenotypes. You will learn the latest theories and methods in complex trait genetics, such as polygenic risk prediction and LD score regression, and learn how to apply them to real data. Studies will start in UKBB, moving to a combined larger dataset (GO including UKBB), testing key findings in OSKGAR. Once you have identified candidate genetic risk variants for post-traumatic OA, you will use a variety of datasets to investigate the functional impact of these variants on gene expression (i.e. expression Quantitative Trait Loci, or eQTLs, some from cohort whole blood RNA data), protein levels (pQTLs, both in blood and in the synovial fluid of the joint) and on other whole-body phenotypes (such as height, weight, cartilage thickness). There will be an expectation that you will present your data at regular Centre meetings, national and also international meetings.
The ultimate aim is to discover biomarkers that will allow us to predict OA risk at an early stage, identify new pathways for investigation of the biology of joint injury and repair and develop new treatments for this common disease.
1. Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. The American journal of sports medicine. 2007;35(10):1756-69.
2. Watt FE,Paterson E,Freidin A,Kenny M,Judge A,Saklatvala J,Williams A,Vincent TL. Acute molecular changes in synovial fluid following human knee injury are associated with early clinical outcomes. Arthritis Rheumatol.2016; 68: 2129-2140.
3. Zengini, E., et al., Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet, 2018. 50(4): 549-558.
Genes, Genetics, Epigenetics and Genomics; Musculoskeletal Science; Bioinformatics, Statistics and Computational Biology; Translational Medicine and Medical Technology; Ageing, Geratology and Degenerative Diseases
Contact: Dr Fiona Watt, Kennedy Institute, University of Oxford