2024-25 Project (Southgate & Pittman)

Multi-dimensional genomics to determine genetic risk factors for cluster headache



Dr Laura Southgate at SGUL
Email: lasouthg@sgul.ac.uk


Dr Alan Pittman at SGUL
Email: apittman@sgul.ac.uk


Project Summary

Cluster headache is a debilitating and incurable pain condition with a likely genetic predisposition. Sufferers experience regular daily episodes of extreme pain, often for months without remission. We recently completed a genome-wide association study (GWAS) and meta-analysis, which identified multiple disease-associated loci (O’Connor et al. 2021; Winsvold et al. 2023). Whilst these GWAS studies have been incredibly valuable, interpreting the functional context of non-coding variants remains challenging. Therefore, no major causal gene has yet been detected. This project seeks to use cutting-edge 3D genomics analysis to identify regulatory elements and potential risk genes for cluster headache. Candidate genes will be validated using next-generation sequencing analysis and in vitro functional assays. These studies will identify key pathways and regulatory mechanisms in the aetiology of cluster headache, providing an important framework for the development of novel therapies and future pharmacogenetic studies.  This project is highly relevant to students who are interested in applying bioinformatic and molecular biology approaches to better understanding human health and disease.

Project Key Words

Bioinformatics; Genomics; Headache; Neurology

MRC LID Themes

  • Global Health = No
  • Health Data Science = Yes
  • Infectious Disease = No
  • Translational and Implementation Research = No


MRC Core Skills

  • Quantitative skills = Yes
  • Interdisciplinary skills = No
  • Whole organism physiology = No

Skills we expect a student to develop/acquire whilst pursuing this project

This project will equip the student with a range of versatile skills including bioinformatics and data science skills (e.g. R, Python).


Which route/s is this project available for?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • SGUL – MSc Genomic Medicine
  • SGUL – MRes/MSc Translational Medicine

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes


Particular prior educational requirements for a student undertaking this project

  • SGUL’s standard institutional eligibility criteria for doctoral study.
  • Applicants must have obtained, or be about to obtain, an MSc/MRes or BSc (2:1 or above) in biological sciences, molecular biology, genetics, bioinformatics or a related life sciences field. Candidates with prior experience in bioinformatics, computational biology or systems biology are particularly encouraged to apply. Applicants should have effective communication skills and be willing to travel internationally to attend meetings with collaborators.

Other useful information

  • Potential CASE conversion? = No


Scientific description of this research project

Cluster headache (CH) also known as ‘suicide headache’, is an incurable and frequently mis-diagnosed circadian pain condition. Our European CH consortium recently completed genome-wide association studies, which revealed significant association to single nucleotide polymorphisms (SNPs) at multiple loci, including some previously implicated in migraine (O’Connor et al. 2021; Winsvold et al. 2023). However, no major causal gene has yet been detected, likely due to locus heterogeneity.    

This project will evaluate the genetic risk factors and regulatory mechanisms underlying cluster headache, with potential to detect novel causal gene(s) leading to improved diagnosis and understanding of this debilitating condition.   

1. Project objectives: 
a) Use publicly available high-throughput chromatin conformation capture (Hi-C) maps to identify topologically associated domains (TADs) with lead SNPs at the previously identified association loci in cluster headache 
b) Examine existing exome sequencing data to detect candidate gene variants at risk loci and related TADs 
c) Use in vitro assays to experimentally validate identified candidate genes   

2. Techniques to be used: 
a) Genetic analysis of genomic data from patients, relatives and control individuals (GWAS, exome sequencing, Hi-C) 
b) Bioinformatics and data science skills (R, Python, statistics) 
c) Molecular biology techniques (gene knockdown, cell culture, qPCR, in vitro assays)   

3. Confirmed availability of any required databases or specialist materials  Existing genotyping and sequencing data from patients with cluster headache are available in our in-house database and through the European cluster headache genetics consortium (https://www.clusterheadachegenetics.org). Additional sequence data will be accessed through Genomics England (GeL) and the UK Biobank. Dr Southgate and Dr Pittman are current members of GeL Clinical Interpretation Partnerships. This research project will be registered accordingly and an application will be made for access to UK Biobank data via their research analysis pipeline (cost: £675 for 4 years).  

Similarly, Hi-C maps and other 3D genomics datasets will be accessed via publicly available repositories (e.g. The 3D Genome Browser: http://3dgenome.fsm.northwestern.edu; Juicebox: https://www.aidenlab.org/juicebox) and applications for access will be submitted, where required.   

4. Potential risks to the project and plans for their mitigation 
As this project will be using existing, publicly available datasets, the potential risks remain relatively low. Whilst the focus will be on identifying genetic risk factors through an analysis of regulatory regions in cis and trans with reported association signals, it remains possible that this approach may prove unfeasible. Nonetheless, this project has a great degree of flexibility and the above plan represents only a fraction of the possible avenues for exploration of these data. Other options include:
a) novel gene identification and pathway enrichment analysis using existing exome sequence data;
b) in-depth interrogation of rare and common variation in candidate genes;
c) investigation of genotype-phenotype correlations using the health data in GeL and UK Biobank.

Further reading

(Relevant preprints and/or open access articles)

  • 10.1002/ana.26743
  • 10.1002/ana.26150

Additional information from the supervisory team

  • The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.
    Southgate-Pittman Recording