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MRC DiMeN Doctoral Training Partnership: Application of data science to inherited genetic variants of apoptosis genes and susceptibility to breast cancer

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  • Full or part time
    Prof A Cox
    Dr A Droop
    Dr K Walters
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The identification of the breast and ovarian cancer genes BRCA1 and BRCA2 two decades ago has resulted in both improved risk assessment for patients and the use of PARP-inhibitors for treatment. In addition to highly penetrant mutations in genes like BRCA1 and BRCA2, hundreds of common inherited variants (single nucleotide polymorphisms or SNPs), identified through genome-wide association studies (GWAS), contribute to breast cancer susceptibility. However, in order to translate GWAS findings for patient benefit, we need to understand functional mechanisms behind the SNP associations with cancer. Thus, although almost 200 breast-cancer associated SNPs have been identified, the functional basis of the association has been determined for only a very few of these. Since most risk SNPs lie outside of protein-coding regions of the genome, they are likely to be acting through regulation of gene expression. We have shown that cancer-associated variants on chromosome 2q33 are related to local transcriptional regulation of genes such as CASP8, CASP10 and CFLAR. These genes are involved in programmed cell death (apoptosis), which is highly dysregulated in cancer.

We hypothesize that inherited variants at apoptosis-related gene regions affect risk of breast cancer through mechanisms that modulate the expression of apoptosis genes.

This is the first time a pathway-based approach has been applied to understanding the mechanism of action of breast cancer SNPs. Novel machine-learning and Bayesian statistical approaches will be explored.

Objectives and analytical approaches:
1. To use SNP data for 15 apoptosis gene regions to fully characterise the associations with breast cancer in each region (genetic fine-mapping).
We have SNP data from the Breast Cancer Association Consortium (BCAC), comprising over 120,000 cases and 105,000 controls. We will use standard univariate and multivariate stepwise and penalised logistic regression approaches to identify candidate causal SNPs. We will also apply neural network approaches to assess how these perform in comparison to the standard methods.

2. To identify the credible functional variants and their target genes in each region, and prioritise SNP-gene pairs for future laboratory-based functional studies.
We have access to various forms of functional genomic annotation data including publicly available expression QTL and ENCODE epigenetic data, and collaborative RNAseq and 3D chromatin conformation data. The functional annotations will be used in novel Bayesian approaches that incorporate prior functional information to identify candidate causal SNPs.

3. To examine statistical interactions between the gene regions and map these to functional networks to yield a pathway-wide mechanistic view of susceptibility.
Logistic framework and machine learning techniques will be used to examine interactions, both pairwise and higher-order, and these will be mapped onto the apoptosis pathway.

This is an interdisciplinary project that would suit a mathematician or statistician with an interest in biological applications, or a biologist/geneticist with an interest in learning the relevant statistical and computational approaches. The student will learn how to manage large datasets comprising -omics and health data. They will use frequentist and Bayesian statistical methods, and will gain high-performance computing skills. They will also receive training in machine learning and AI.


Web pages:
https://www.sheffield.ac.uk/oncology-metabolism/staff/acox
https://github.com/alastair-droop/
http://maths.dept.shef.ac.uk/maths/staff_info_337.html


Benefits of being in the DiMeN DTP:
This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.
We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.
Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards
Further information on the programme can be found on our website:
http://www.dimen.org.uk/

Funding Notes

Studentships are fully funded by the Medical Research Council (MRC) for 3.5yrs
Includes:
Stipend at national UKRI standard rate
Tuition fees
Research training and support grant (RTSG)
Travel allowance
Studentships commence: 1st October 2019.

To qualify, you must be a UK or EU citizen who has been resident in the UK/EU for 3 years prior to commencement. Applicants must have obtained, or be about to obtain, at least a 2.1 honours degree (or equivalent) in a relevant subject. All applications are scored blindly based on merit. Please read additional guidance here: https://goo.gl/8YfJf8
Good luck!

References

Relevant publications

Camp NJ et al. Cancer Res (2016) 76:1916
Droop AP Bioinformatics (2016) 32:1267
Spencer AV et al. Genet Epidemiol. (2016) 40:176-87

How good is research at University of Sheffield in Clinical Medicine?

FTE Category A staff submitted: 63.95

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities


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