Assessing the impact of somatic mosaicism on the blood proteome and cancer risk

BACKGROUND

As cells age, they inherit somatic events from their precursor cells. Large structural variations (SVs), such as mosaic deletions of entire chromosomes (e.g., Y chromosome loss), mosaic copy number alterations, and complex rearrangements like translocations, can profoundly impact gene expression and the blood proteome. These events are also implicated in cancer formation.

Specific aims of the project:

1. Identify somatic variation: utilise whole genome sequencing and Nanopore sequencing to identify somatic variations in large cohorts, such as the UK Biobank. Evaluate the impact of somatic variation on cancer risk factors and related phenotypic traits, including blood counts and hormone levels.
2. Assess the impact of somatic variation on the blood proteome: investigate the influence of somatic variation on the blood proteome in 50,000 individuals within the UK Biobank, with validation in other cohorts, such as The European Prospective Investigation into Cancer and Nutrition (EPIC).
3. Identify genetic susceptibility: determine genetic susceptibility to increased somatic burden in non-tumour tissue. Explore how genetic predisposition to somatic variation affects the blood proteome as individuals age.
4. Characterise tumour-related somatic variation: Assess the association of somatic variation identified in aims 1, 2, and 3 with tumour characteristics. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will develop advanced computational skills, with a focus on bioinformatics. This will include proficiency in Python, R, and Bash, particularly in the context of high-performance computing. Through this project, they will contribute to our understanding of how somatic variation influences blood protein abundance. The work will involve processing data from array technologies, Olink, Somalogic, and sequencing platforms. Additionally, the project will explore the potential etiological role of these somatic variants in cancer risk. By the end of this DPhil, the individual will be a skilled genomic epidemiologist.

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).