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Novel statistical method development for discovery and interpretation of genetic contribution to human complex traits

 About the Research

Genetic and epidemiological research has generated a vast amount of data and expertise for evaluating the genetic contribution to human traits in health and disease. However, the genetic architecture of complex traits, affected by multiple genes, lifestyle and environmental factors, still remains largely unexplained. To this end, the availability of large-scale data presents enormous opportunities, however novel computational approaches are necessary for efficient data integration and analysis of different layers of information and will play an instrumental role in utilizing data to its full potential. The focus of our research group is on the development of novel statistical approaches aimed at understanding the genetic basis of complex traits (predominantly focusing on cardiometabolic and haematological phenotypes) and leveraging this information to learn more about the causal relationships between different traits (e.g. disease risk factors and disease or molecular layers and complex traits).

In particular, we use the power of UK Biobank data and Framingham and Jackson Heart studies, coupled with the development of novel computational methods to (a) identify and understand genetic changes that lead to changes in phenotypic traits, such as haemoglobin levels or blood disorders; (b) infer direction of causality between different disease and disease risk factor traits; (c) identify key regulatory markers with functional significance for gene regulation, human health and disease.

Of particular interest is the development of integrative approaches leveraging genetic, phenotypic, functional and regulatory data (in relevant cell types) [BLUEPRINT, ENCODE, Roadmap Epigenomics, GTEx projects] to provide insight into the general mechanisms of such complex (multi-genic) phenotypes.

Projects in the group would be suited to students with a quantitative background (e.g. Mathematics, Statistics, Computer Science) or relevant experience; with interest in novel statistical method development and keenness to work on genomic dataset analysis towards expanding knowledge of genetic predisposition to human traits. Key topics: Bayesian modelling, MCMC, Variational Bayes; association analysis, functional enrichment, fine-mapping, phenotype imputation, causal inference.

Training Opportunities

Students will be encouraged to enhance their training by attending either internal or external to the MRC WIMM and the University of Oxford courses relevant to their scientific topic.

Students will be enrolled on the MRC WIMM 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.

All MRC WIMM graduate students are encouraged to participate in the successful mentoring scheme of the Radcliffe Department of Medicine, which is the host department of the MRC WIMM. This mentoring scheme provides an additional possible channel for personal and professional development outside the regular supervisory framework. The RDM also holds 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.



Iotchkova V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet  51, 343-353 (2019).

Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).

Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).

Iotchkova, V. et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat Genet 48, 1303–1312 (2016).

Dahl, A. et al. A multiple-phenotype imputation method for genetic studies. Nat Genet 48, 466–472 (2016).

UK10K Consortium et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).

MRC WIMM Centre for Computational Biology© Martin Phelps





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