MRC DiMeN Doctoral Training Partnership: Use of data science to elucidate functional mechanisms of inherited genetic variants of apoptosis genes affecting breast cancer susceptibility

Use of data science to elucidate functional mechanisms of inherited genetic variants of apoptosis genes affecting breast cancer susceptibility

Breast cancer is the most frequently diagnosed cancer in women in the United States and Europe. Knowledge of germline risk variants holds promise for improved prediction, early diagnosis, targeted therapies, and prevention. This began two decades ago with the identification of the high-risk breast and ovarian cancer genes BRCA1 and BRCA2, which led to the use of PARP-inhibitors for treatment of BRCA1/2 mutated cancers. In addition, hundreds of common inherited variants (single nucleotide polymorphisms or SNPs) are known to contribute to breast cancer risk. However, it remains a major challenge to identify the functional risk variants and their mechanism of action. We previously demonstrated that breast cancer is significantly associated with a number of genetic variants on chromosome 2q33 (1). Furthermore, the mechanisms of these associations may be related to local gene expression of genes such as CASP8, CASP10 and CFLAR, which are all involved in programmed cell death (apoptosis), a process that is highly disregulated in cancer (2).

The aim of this project is to test the hypothesis that inherited variants at 2q33 and other apoptosis-related loci affect risk in breast cancer through distinct but related mechanisms modulating apoptosis. The student will use large datasets that are already available to perform statistical functional mapping at these loci, and will determine the possible mechanisms of action of the risk variants, using publicly available and collaborative functional annotation data sets.

Objectives:
1. To analyse genome-wide genotype data for apoptosis genes from the Breast Cancer Association Consortium (BCAC), comprising over 120,000 cases and 105,000 controls. Statistical mapping will use imputation and stepwise and penalised logistic regression, with more novel haplotype-mining approaches.
2. Lists of credible risk variants identified in Objective 1 will be intersected with functional data types, including published and publicly available expression QTL data and epigenetic data. Additional collaborative RNAseq and 3D chromatin conformation data are also available. These analyses will identify the plausable functional regulatory variants and their putative target genes.
3. The above techniques will be applied to up to 15 apoptosis gene regions, depending on progress. Given the large BCAC data set it will be possible to examine statistical interactions between the target genes identified in objective 2. These analyses will yield a pathway-wide picture of the mechanisms of action of apoptosis-related variants, which could also feed into mathematical models of apoptosis.
4. A number of relevant breast cancer loci are already established but we also have access to relevant data in other cancers, providing contingency plans if needed.

The student will have the opportunity to work in a leading cancer genetics laboratory (https://www.sheffield.ac.uk/oncology-metabolism/staff/acox) with a track record of PhD students who have gone on to successful post-doctoral research. We work with bioinformatics experts in the University of Sheffield Bioinformatics Hub (http://bioinformatics.group.shef.ac.uk), and the Sheffield Institute for Nucleic Acids (SInFoNIA; http://genome.sheffield.ac.uk). The consortium collaboration provides excellent research and networking opportunities.