Sebastian May-Wilson

Thesis title: Using integrative omics to disentangle causal relationships between tissue-specific pathways and coronary artery disease


I completed a BSc in Molecular Genetics at the University of Edinburgh, graduating in 2015. During this time I worked and volunteered in a number of different laboratories including the labs of Prof. Joanna Wilson (University of Glasgow), Prof. Jeremy Mottram (now University of Leeds) and Prof. Nick Gilbert (University of Edinburgh) working in a variety of fields in genetics, respectively: Epstein-Barr Virus, Trypanosomes and epigenetic modification.

I went on to complete an MRes at the University of Glasgow, studying in the program of Integrative Mammalian Biology, graduating in 2016. This involved research projects in the field of molecular genetics, specifically involving work with mice, working on collagen IV disease with Dr. Tom van Agtmael and colorectal cancer in the lab of Prof. Owen Sansom.

Prior to beginning my PhD at, and returning to, the University of Edinburgh, I also spent 2 years working in a bioinformatics laboratory at the Institute of Cancer Research in London, working on the functional annotation of a colorectal cancer GWAS.


BSc (2015, Molecular Genetics, Edinburgh)

MRes (2016, Integrative Mammalian Biology, Glasgow)

PhD (Ongoing)

Research summary

My research interests are broad, but I have a fascination with genetic and epigenetic control in its many shapes and forms and how dysregulation leads to disease. I am particularly interested in work with large datasets such as work involving multi-omics and GWAS, and how these tools can be used to help expand biological understanding of human genetics and potentially lead to the development of targetted therapies. This includes an interest in cancer genetics, genetic engineering with the use of CRISPR and also population and quantitative genetics with the use of proteomics and transcriptomics datasets.

Project activity

One of the aims of personalised medicine is to divide patients with the same disease into different groups based on their genotypes in order improve treatment choices and patient management. One of the largest limitations to this is that although many genetic variants associated to many complex phenotypes have been identified, how they relate to each other and in which tissue they carry out their function is still in many cases unknown. Knowing which pathways in which tissue affect a disease would help to identify new drugs but would also allow us to understand the differences between each patient which apparently have the exact same disease, making personalised medicine much closer than today. A possible approach to achieve this goal is to combine the genetic variants which underlie the expression of genes in a specific pathway in a specific tissue to predict a target close to its physiological function (i.e. a measured protein) and use Mendelian randomisation to verify the causal effect of the pathway on common disorders. 

The aims of my project have therefore involved several steps:

  1. The creation of polygenic risk scores (PRS) for genes from transcriptomics data
  2. Combining PRS for genes into pathway scores PRSPathway and create an estimate of overall pathway functionality
  3. Conduct a PheWAS of predicted pathways and various traits in UKBB to discover associations
  4. Creation of an R package to allow reproducibility of this work and allow others to perform the same analysis

In the press

I have presented a summary of my work (as of halfway through my 2nd year) at the 2020 EMGM conference, which can be viewed here (timestamp - 31:17):

For an up-to-date view of my publications please check my ORCID page.


Pro-inflammatory fatty acid profile and colorectal cancer risk: A Mendelian randomisation analysis

19 Aug 2017


Variants associated with HHIP expression have sex-differential effects on lung function - [Under peer review]

01 Jun 2020