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Assessing structural variation and the non-coding genome in prostate cancer

2025/11

background

Each year in the UK, around 10,000 men are diagnosed with advance stage prostate cancer which is an alarming number. We need to better understand what drives prostate cancer development. From large consortiums such as PRACTICAL, we have identified many genomic regions that associate with the risk of prostate cancer. Little is known how structural variants (SV) may impact upon these known regions. Additionally, as many of these regions point towards the non-coding regions of the genome, we need to further understand how these variants impact prostate cancer risk.

While these specific aims are preliminary until discussion with interested students, the aims of this project could include:

  1. Identify and characterise germline SV with risk of prostate cancer using the UK Biobank and the EPIC-Oxford prostate cohort. Perform stratification analysis to identify SV related to aggressive subtypes of prostate cancer.
  2. Leverage findings from genome-wide association studies to identify non-coding functional variants in linkage disequilibrium with sentinel variants, including but not limited to methylation of GpC sites, imprinted domains, miRNA and their binding sites.
  3. The impact of both aims 1 and 2 on the somatic landscape of prostate cancer. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience of working in genomic epidemiology and will work with big dataset such as the UK Biobank. Additionally, students will develop skills analysing data using local the HPC and cloud technologies using DNAnexus. The student will be working with data from different technologies including nanopore data. The ideal student will also be working with data from the TGCA and Genomics England. 

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Individuals will receive computational training and will be expected to present results at national/international conferences and meetings. 

PROSPECTIVE  STUDENT

The ideal candidate will have a Master's degree in a relevant area (bioinformatics/statistics/epidemiology/biomedical sciences)

Supervisors

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