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Genetic Epidemiology of Cardiovascular Disease: Studying genetic variation in cardiomyopathy and coronary artery disease across the entire allele frequency spectrum in order to identify causative genes and susceptibility loci.

Project Description

Studying genetic variation in cardiomyopathy and coronary artery disease across the entire allele frequency spectrum in order to identify causative genes and susceptibility loci.

Patients suffering cardiovascular diseases such as cardiomyopathy and coronary artery disease (CAD) tend to cluster in families due to underlying monogenic or polygenic architectures respectively. We study genetic variation in these diseases across the entire allele frequency spectrum in order to identify causative genes and susceptibility loci. We work closely with departmental colleagues who use functional genomic and biochemical techniques to study the underlying pathogenic molecular and cellular mechanisms. We led the CARDIoGRAMplusC4D meta-analysis consortium (~185,00 cases and controls, 48 research groups working in 4 continents) to identify ten novel CAD susceptibility genes (Nature Genetics 2015), discoveries that refocused attention on pathophysiological processes within coronary vessel walls. We recently completed an interim data-mining experiment of the UK Biobank to gain further genetic insights into coronary disease genetic architecture (Nature Genetics 2017). We have also developed and applied novel methods that resolve genetic heterogeneity patterns that were overlooked in GWAS meta-analysis (PLoS Genetics 2017). We are also analysing high-throughput sequencing data of thousands of cardiomyopathy patients collected in Oxford, work that aims to expand the catalogue of clinically actionable genes for monogenic cardiomyopathies (e.g. Walsh et al. 2017).

There will opportunities to develop and apply research methodologies in statistical genetics and bioinformatics. Students will attain fluency in programming in at least one high-level statistical analysis package (e.g. R, Stata). Projects are based in the Wellcome Centre for Human Genetics, which has high-performance computer facilities and a thriving community of statistical geneticists and bioinformaticians who enjoy focussed seminars and workshops.

As well as the specific training detailed above, students will have access to high-quality training in scientific and generic skills, as well as access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

Funding Notes

Our main deadline for applications for funded places has now passed. Supervisors may still be able to consider applications from students who have alternative means of funding (for example, charitable funding, clinical fellows or applicants with funding from a foreign government or equivalent). Prospective applicants are strongly advised to contact their prospective supervisor in advance of making an application.

Please note that any applications received after the main funding deadline will not be assessed until all applications that were received by the deadline have been processed. This may affect supervisor availability.


CARDIoGRAMplusC4D Consortium. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015 47(10):1121-30
Nelson CP et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 49(9):1385-1391
Magosi LE et al. Identifying systematic heterogeneity patterns in genetic association meta-analysis studies. PLoS Genet. 2017 13(5):e1006755
Walsh et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017 19(2):192-203

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

FTE Category A staff submitted: 238.51

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

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