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Background

Chronic kidney disease (CKD) is a common condition affecting 10% of the US population, and an established risk factor for cardiovascular disease. Understanding the genetic determinants of CKD progression and how they relate to known risk factors and disease outcomes is important for our biological understanding of kidney disease as well as the development of appropriate treatment strategies.

Genome-wide data in ~3,500 European individuals with established CKD from the Study of Heart and Renal Protection randomised trial are recently available, along with extensive phenotyping data (including serial measurements of creatinine). However, analyses of progression in populations with existing disease can be subject to a form of selection bias known as index event bias. This project will offer the unique opportunity to discover new genetic determinants of CKD progression using hypothesis-free genome-wide approaches, whilst also investigating the relevance of index event bias in such analyses.

There is also scope to utilise genetic epidemiological approaches, such as Mendelian randomization, to assess the genetic overlap between CKD progression and cardiovascular diseases (atherosclerotic and non-atherosclerotic cardiovascular diseases, including heart failure) using data from large-scale GWAS consortia and the UK Biobank study (which has extensive genetic and biochemical data available in 500,000 individuals from the general population).

References

Baigent et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011; 377(9784):2181-2192

Parsa et al. Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study. JASN Mar 2017; 28 (3) 923-934.

Dudbridge et al. Adjustment for index event bias in genome-wide association studies of subsequent events. Nat Commun 10, 1561 (2019).

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

You will gain experience of statistical and genetic epidemiology and handling large datasets, learn about renal disease biomarkers and progression, and develop research planning and design skills. You will learn from a collaborative team of statisticians, clinicians and genetic epidemiologists.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Additional training in statistical programming and cardiovascular and renal epidemiology will be provided. The successful applicant will have opportunities to attend and present work at relevant meetings.

Prospective candidate

The project involves statistical analysis of big data, including genetic, clinical trial, and UK Biobank data, and requires previous statistical programming training/experience (e.g. R, SAS) and an interest in developing these skills. Examples of suitable prior qualifications are an MSc in Applied Statistics/Bioinformatics or related field with a strong statistical/programming component. An understanding of genetic epidemiology would also be extremely advantageous.



 

Supervisors