Background
Our laboratory is involved in genome-wide association studies (GWAS) to identify common variations underlying the risk of breast and ovarian cancers. The current challenge is in the functional interpretation of genetic association data. With this aim, we use a variety of computational approaches to define potential molecular mechanisms at GWAS loci and to generate specific hypotheses to guide further experimental work. Specific areas of interest include: • Analysis of high throughput sequencing data, such as ATAC-seq and HiChIP from primary breast samples and cultured cells. • Integration of genetic and functional genomics data to predict target genes at GWAS loci. • Mining of public epigenomic datasets such as those from the ENCODE and ROADMAP Consortia. • Identification of candidates for drug repositioning. • Analysis of CRISPR screen data. The project would suit a bioinformatics student with an interest in gene regulation. Students would work closely with dry and wet lab scientists to identify cancer genes and pathways, which might represent targets for future drug development.