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Investigating the genetic overlap between eczema and other diseases

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  • Full or part time
    Dr Paternoster
    Prof Henderson
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

Rationale
Many of the genetic loci identified as associated with eczema in genome-wide association studies have previously been associated with other atopic conditions (such as asthma and allergy) or autoimmune diseases (such as psoriasis and inflammatory bowel disease, IBD). There are also epidemiological observations that eczema is associated with conditions such as obesity and depression, though the causal nature of these associations have yet to be established. This project will use large datasets and state-of-the-art statistical genetic methodology to fully assess the genetic overlap between eczema and several conditions. Increased understanding of the pathological mechanisms could lead to improved diagnosis, treatment and management of these common and debilitating health outcomes.

Aims & Objectives
- Quantify the genetic covariance between eczema and other diseases (e.g. other atopic conditions, psoriasis and IBD)
- Investigate which genetic polymorphisms overlap between eczema and other traits and determine whether the effects are in the same or opposite directions
- Where there is evidence of high genetic covariance, carry out genome-wide association studies combining multiple diseases
- Determine if eczema is causally related to later health outcomes, such as obesity and depression

Methods
This PhD will involve using state-of-the-art statistical methods to investigate the genetic relationship between eczema and other diseases. It will involve the analysis of extremely large datasets (with millions of genetic variants and tens of thousands of individuals). The methods will include (but are not limited to):

- Genome-wide association analysis
- Genome-wide allelic score analysis
- Meta-analysis
- Bivariate GCTA GREML analysis
- Mendelian randomisation

The large-scale nature of the datasets will require the use of Unix computing and adaptable statistical software such as R or Stata.

References

Cookson 2004. The immunogenetics of asthma and eczema. Nat Rev Immunol 4:978-88
Murray et al. 2011. Body mass index in young children and allergic disease. Clin Exp Allergy 41:78-85
Paternoster et al. 2012. Meta-analysis of genome-wide association studies for atopic dermatitis. Nat Genet 44:187-92

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