Deciphering the genetic architecture of common cancers using statistical and machine learning methods
Kyriaki Michailidou, Cyprus Institute of Neurology and Genetics
It is estimated that approximately 10% of cancers are caused by inherited genetic variants. There exist several known genetic variants associated with risks of common cancer sites (e.g. breast, ovarian, prostate, colorectal, gastric), and these range from rare high penetrance pathogenic variants to common single nucleotide variants associated with modest differences in disease risk and unclear biological pathways to cancer development.
Characterisation and understanding of the risks associated with such variants require large scale data ideally from population-based cohort studies with prospective follow up for cancer incidence.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
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.
- classification of variants in such genes with respect to their pathogenicity.
- fine-mapping and other approaches to understand the biological pathways between single nucleotide variants and cancer.
- use of genetic and other data for predicting risk of cancer.
- proteomics and other -omic datasets
- pathway analyses and network analyses
- the project may be focused on methodology for addressing such objectives, the application, or a mixture of the two.
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 may be opportunities to visit the Cyprus Institute of Neurology and Genetics.
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.