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


About the research

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).

Training Opportunities

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.

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide-range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

As well as the specific training detailed above, students will have 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.


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