Genomics to inform drug discovery for atopic dermatitis

PhD project (3/4 yr research project leading to independent research at the doctorate level)

Lavinia Paternoster, Tom Gaunt

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Atopic dermatitis (eczema) is a common chronic skin disease with prevalence rates of 10-20% and heritable estimates of up to 90%. 31 loci associated with AD case/control status have been identified through genome-wide association studies (GWAS) >377,000 individuals. Follow-up of these genetic findings is considered an effective strategy to identify potential drug targets. However, though identification of such variants might be informative for prevention of disease, it is unclear how useful they will be for understanding how to treat the condition. A better study design to inform design of treatments might be to find genetic variants for AD progression. This approach would identify targets that if acted on with drugs, might slow or prevent the progression of disease.

Aims & objectives

Conduct GWAS of progression traits for atopic dermatitis (such as persistence of the disease) to identify loci associated with these traits and therefore informative for drug discovery.

Follow-up these loci using multi-omic integrative data approaches to finemap the loci and identify the likely gene target.


1. Recruitment of cohorts with available data
2. Generate analysis plan and scripts
3. Conduct statistical association analyses
4. Follow-up variants identified using publicly available multi-omic resources (expression, epigenome and proteomic data) and bioinformatics approaches

There is potential for engaging with industry and/or experimental biologists to validate findings.

This project will be predominantly statistical and computational in nature. Analyses will be conducted using R and some bespoke software, operated in a Linux system. Therefore some coding will be essential.


Paternoster L et al. 2015. Multi-ancestry GWAS of 21,000 cases & 95,000 controls identifies new risk loci for atopic dermatitis. Nat Genet 47, 1449-56.

Paternoster L et al. 2017. Genetic epidemiology & Mendelian randomization for informing disease therapeutics. PLoS Genetics 13:e1006944

Created on Oct. 17, 2017, 3:32 p.m.

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