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Statistical Genetics: Novel statistical method development for discovery and interpretation of genetic contribution to human cardiovascular and haematological traits

Project Description

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).

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 multivariate and rare variant collapsing modelling, sparsity inducing priors (e.g. spike and slab); functional enrichment modelling, statistical fine-mapping, phenotype imputation, MCMC, Variational Bayes.

We are now able to obtain huge amount of information about individual’s genetic makeup, which can be used to assess which positions of our DNA are responsible for causing disease (e.g. heart disease) or changes in disease risk factors (e.g. cholesterol levels, blood pressure) by comparing the genetic makeup and disease/disease risk factor measurements for many individuals. To improve the detection of such genetic changes and provide interpretation of the possible biological mechanisms through which the phenotypic changes occur, our aim is to develop new statistical methods and tools to address the underlying data complexity and scale to the ever-increasing data dimensionality.

We plan to 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 (common and rare) genetic changes that lead to changes in phenotypic traits, such as haemoglobin levels or cardiovascular disease; (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.

One area of keen interest is human haematopoiesis, where we aim to classify genetic variants according to which subset of blood cell traits they affect. To overcome the challenges associated with current rare variant association methods for example (e.g. SKAT, burden), we would like to explore flexible Bayesian approaches with biologically motivated priors derived from regulatory annotations (histone modifications, open chromatin, transcription factor binding, etc). Furthermore, given an observed association between (common and rare) genotype and a blood cell trait, our aim is to provide insight into the likely stage of hematopoietic differentiation it affects and the likely underlying causal mechanism. There will be opportunities here for the development of integrative approaches leveraging gene expression and reference epigenome maps for different blood cell types and their precursors (where available) (BLUEPRINT, ENCODE, Roadmap Epigenomics, GTEx). Finally, our aim would be to compare our results to haematological diseases, which can provide further insight into the general mechanisms of such complex (multi-genic) diseases.

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.

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.

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.

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.


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).

Iotchkova, V. et al. GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction. bioRxiv (2016).

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

Sharp, K., Iotchkova V., Marchini J. Sparse Bayesian modelling for multitrait genetic association studies. Annals of Applied Statistics Submitted (2018).

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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