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Background

Chronic kidney disease (CKD) is an important and established risk factor for vascular disease. Understanding the genetic determinants of CKD 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.

The Study of Heart and Renal Protection randomised trial involves ~9,500 individuals with established CKD followed-up for an average of 5 years. These individuals have been extensively phenotyped and new genome-wide data in ~5,500 European individuals will be available in early 2016. The UK Biobank study also provides extensive genetic and biochemical data (including measures of renal function) in 500,000 individuals from the general population. Using data from both of these studies, this project will offer the unique opportunity to discover new genetic determinants of kidney function and CKD using hypothesis-free genome-wide approaches as well as to examine the impact of candidate genes for renal function and CKD. In addition, you will examine how these genetic factors relate to known risk factors (e.g. LDL-cholesterol [‘bad’ cholesterol]) and vascular disease outcomes.

Relevant 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.
  • Kottgen et al. New loci associated with kidney function and chronic kidney disease. Nature Genetics. 2010; 42(5):376-84.
  • UK Biobank http://www.ukbiobank.ac.uk/‎

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 an experienced multi-disciplinary team of statisticians, clinicians, statistical geneticists and programmers.

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.

Supervision

In addition to the supervisors listed above, this project will be co-supervised by Dr Natalie Staplin at NDPH.

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 desirable prior qualifications are an MSc in Applied Statistics/Bioinformatics or Biology/Genetics with a strong statistical/programming component. An understanding of genetic epidemiology would also be extremely advantageous.

Supervisors

  • Jemma Hopewell
    Jemma Hopewell

    Associate Professor & Senior Scientist in Genetic Epidemiology and Clinical Trials

  • Colin Baigent
    Colin Baigent

    Professor of Epidemiology and Director, MRC PHRU

Projects