**PLEASE NOTE – the deadline for requesting a funding pack from Darwin Trust has now passed and completed funding applications must be submitted to Darwin Trust by 19th January. We can still accept applications for this project from self-funding students.
Decades of research have shown that inherited mutations in key genes can trigger tumorigenesis. However, this evidence has been limited to low-frequency highly penetrant mutations, typically found in patients affected by cancer syndromes, which represent only a small fraction of all malignancies.
We have recently shown, instead, that high-frequency low penetrant mutations can mediate the risk of cancer in the broader population, using a new method, called BAGHERA, for estimating cancer heritability from GWAS data at gene-level resolution [1].
We are now looking at extending our statistical framework to understand the functional mechanisms mediated by genes associated with cancer heritability using integrating Quantitative Trait Loci (e.g. eQTL, mQTL). Moreover, we want to link cancer heritability genes to other lociå associated with susceptibility to other diseases and traits (e.g. BMI, height, diabetes, [2]), in order to identify comorbidities and new risk factors.
The student will be trained in advanced topics in statistical genetics, cancer biology, and in writing robust and reproducible analytical software and workflows.
The ideal candidate has a background in mathematics, synthetic biology, statistics or related field. He/she is strongly motivated to develop a competitive profile at the intersection of statistical genetics and cancer biology, while working in a fast-paced environment.
Stracquadanio lab: https://www.stracquadaniolab.org
Dr Stracquadanio’s Twitter: @DrStracquadanio
The School of Biological Sciences is committed to Equality & Diversity: https://www.ed.ac.uk/biology/equality-and-diversity
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