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Cardiovascular health in patients with inflammatory joint diseases: a genetic approach to understanding excess mortality


Project Description

Patients with chronic inflammatory joint diseases, such as rheumatoid arthritis and psoriatic arthritis, have a higher prevalence of cardiovascular disease (CVD) which is the major contributory factor to the observed early mortality in these patient groups. The risk factors for CVD are well understood in the general population and screening tools are routinely used to estimate this risk and guide medical interventions. However, the screening tools are not effective for estimating CVD risk in patients with inflammatory joint diseases due to the differing contribution from traditional risk factors and from disease-specific factors. Over recent years our understanding of the genetic basis for many traits has rapidly accelerated to the point where the development and application of genetic based prediction tools are showing promise. We hypothesise that genetics can help us understand the basis for the increased prevalence of CVD in patients with inflammatory joint disease by providing more accurate models of risk, estimating causal relationships and exploring overlapping genetic associations and pathways. Understanding these factors will help us address these important health inequalities suffered by patients with inflammatory joint diseases.

We hypothesise that the increased risk of CVD observed in patients with inflammatory joint diseases can be accurately estimated using large-scale genetic data to develop an effective screening tool. We will further investigate the increased prevalence by estimating the causal relationship between CVD and RA and by exploring overlapping genetic associations and pathways.

Objectives:

1. We will develop a multi-variable predictive model for CVD in patients with RA based on the heritable components of known traditional risk factors (diabetes, hypertension, and non-HDL cholesterol), CVD (atherosclerotic processes) and RA (disease-specific processes).

2. Perform a Mendelian randomisation study to test the causal relationship between RA and CVD.

3. Estimate the overall genetic correlation between RA and CVD and fine map genetic loci shared between RA and CVD.

Training/techniques to be provided:

• Statistical methods for the analysis of genetic data
• Biostatistics and epidemiological methodology
• Machine learning and predictive modelling
• High performance computing and statistical programming

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk.

Funding Notes

Candidates are expected to hold (or be due to obtain) a minimum upper-second (or equivalent) class undergraduate degree in genetics, bioinformatics or a relevant subject, and will have strong statistical skills. A Masters degree in a related subject and/or relevant research experience is desirable.

This project has a Standard fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

Informal enquiries may be made directly to the primary supervisor.

References

1 Bowes J, Ashcroft J, Dand N, et al. Cross-phenotype association mapping of the MHC identifies genetic variants that differentiate psoriatic arthritis from psoriasis. Ann Rheum Dis 2017;76.

2 Bowes J, Budu-Aggrey A, Huffmeier U, et al. Dense genotyping of immune-related susceptibility loci reveals new insights into the genetics of psoriatic arthritis. Nat Commun 2015;6:6046.

3 Hinks A, Bowes J, Cobb J, et al. Fine-mapping the MHC locus in juvenile idiopathic arthritis (JIA) reveals genetic heterogeneity corresponding to distinct adult inflammatory arthritic diseases. Ann Rheum Dis 2017;76:765–72.

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