Risk of common cancers in diverse populations

external supervisor

Kyriaki Michailidou, Cyprus Institute of Neurology and Genetics

background

Several known genetic, lifestyle, and environmental factors associate with risks of common cancer sites (e.g. breast, ovarian, prostate, colorectal, gastric). However, important differences in the prevalence and patterns of such risk factors exist across populations globally.

Comparative characterisation, understanding, and prediction of risk require large scale data ideally from diverse population-based cohort studies with prospective follow up for cancer incidence and mortality.

The aim of this project is to explain and predict risk of common cancers in over 1.6 million adults from diverse populations. The project will be developed according to the student’s interests and may include:

• estimating the risks associated with pathogenic variants in genes associated with hereditary cancer syndromes.
• fine-mapping and other approaches to understand the biological pathways between single nucleotide variants and cancer-specific risk.
• using genetic, -omic, clinical and observational data for predicting risk of specific cancers, including assessment of gene-environment interactions. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The project will provide experience in handling and analysing large-scale genetic and other data. Training in statistics, genetic epidemiology and research methods will be available. It is anticipated that the project will lead to some publications.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The student will be based at the Big Data Institute Building. There are excellent facilities and a world-class community of population health, data science, cancer epidemiology, and genomic medicine researchers. There will be in-house training in epidemiology, statistics, and genetics and opportunities for collaboration with international consortia. There may be opportunities to visit the Cyprus Institute of Neurology and Genetics.

PROSPECTIVE  STUDENT

The ideal candidate will have a Master's degree in a relevant area (e.g. statistics/epidemiology/genetics) or a bachelor’s degree and some work experience.