You are here

A computation-driven approach to identifying heritable genomic risk factors in multiple-cases, early-onset breast cancer families

Description 
Since the discovery of the “high-risk” genes BRCA1 and BRCA2 two decades ago, breast cancer susceptibility research has identified many additional genetic variants associated with increased risk of disease. However, despite remarkable progress in recent decades, currently known genetic factors only explain ~50% of the familial risk of breast cancer. Most research is focused on identifying and validating additional genetic risk factors via the application of Next Generation Sequencing (NGS. e.g., whole-genome or exome sequencing) or high-density SNP arrays and the determination of a polygenic risk score (PRS). There is also emerging evidence that the presence of heritable epigenetic (rather than genetic) risk factors could account for the “missing heritability” of breast cancer. The complexity and diversity of factors that together create the wide spectrum of breast cancer risk certainly provide a partial explanation as to why the proportion of unexplained multiple-case breast cancer families has remained significant. However, this could also be due to the focus on restrictive single data-type study designs. While high-throughput “omics technologies” are becoming an integral part of cancer susceptibility research, historically each type of data has been considered independently. This project will involve a well-characterised population-based cohort of women diagnosed with breast cancer before the age of 40 years, and their families, for 15 years, for whom genomics and epigenomics data has been generated. By developing and applying innovative analytical and computational methods for multi-omic data-integration, this research will provide new opportunities for discovering genes or mechanisms underlying breast cancer susceptibility, which would have been missed by the analysis of a single data type and could explain a proportion of the “missing heritability” of breast cancer.
Essential criteria: 
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords 
cancer, breast cancer, genetics, epigenetic, genomics, bioinformatics, clinical translation, sequencing, NGS
School 
School of Clinical Sciences at Monash Health / Hudson Institute of Medical Research » Medicine - Monash Medical Centre
Available options 
PhD/Doctorate
Time commitment 
Full-time
Top-up scholarship funding available 
No
Physical location 
Monash Health Translation Precinct (Monash Medical Centre)
Co-supervisors 
Prof 
Melissa Southey

Want to apply for this project? Submit an Expression of Interest by clicking on Contact the researcher.