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Building the skull – normal and abnormal development

Project Description

Using a combination of patient samples and mouse models, we study the causes and developmental origins of skull malformation. The work ranges from scanning human genome sequences for new mutations, to use of genome editing and single cell transcriptomics to model the developmental causes of these malformations in mice. Projects on offer would particularly appeal to students interested in genetics, genomics and developmental mechanisms and for whom clinical application is a key motivator. There will be particular opportunities to learn core bioinformatics skills, perform genome editing and explore mouse models to understand disease mechanisms.

Our work is focused on the causes of skull malformations, particularly craniosynostosis, the premature fusion of one or more sutures separating the bones of the skull vault. A complex network of developmental mechanisms is involved in patterning and maintaining this complex system of bones, and a variety of genetic mutations can affect these processes to cause serious skull malformations. Oxford is a leading national referral centre in the surgical treatment of these malformations, enabling us study the entire process by which these arise from patient to mutation, and from mouse model to molecular pathogenesis.

For the study of clinical samples, massively parallel genome sequencing has revolutionised the identification of Mendelian disease genes. Our group has identified many new human disease genes using this approach and the UK’s Genomics England 100,000 Genomes project ( and our own genome analysis programme will provide further opportunities for discovery during the course of the studentship. To investigate pathophysiology, we model carefully selected mutations in mice. We still know very little about what happens biologically in the cranial sutures themselves: these structures must achieve a delicate balancing act of enabling growth of new bone at the margins of the suture, whilst also ensuring that the mid-part of the suture remains open along its entire length. We explore how the suture works by mapping out the cellular hierarchy of activities from undifferentiated stem cell to fully formed osteoblast, comparing results between normal sutures and those from mice with targeted mutations.

In clinical genome analysis, you could follow through the entire experimental process from choice of DNA sample to identification of a new disease gene, learning how DNA is processed for sequencing and how the raw sequence is filtered. Once the vast majority of sequence variants have been excluded, you will use a combination of bioinformatics tools, biological knowledge and analysis of the literature to identify the most likely candidate changes. You will then verify these in the original samples and, for the strongest candidate mutations, analyse the DNA sequence of a large number of patient samples to seek independent support. For mouse model analysis, you could target specific mutations using CRISPR/Cas9 genome editing and analyse the phenotypes of mutant mice using single cell transcriptomics, epigenetic signatures, analysis of protein levels and activation, and phenotyping methods such as micro-CT scanning.

You will learn how to use a wide range of web resources to interpret genomic information from human, mouse and other species. For experimental work you will be exposed to a wide variety of techniques including cell culture, fluorescence-activated cell sorting (FACS), microscopy, single cell transcriptomics and next generation sequencing, as well as basic molecular biology methodologies for analysing DNA, RNA and protein, all of which are essential for functional characterisation of mutations. If working on mice you will obtain your own personal license and undergo training in husbandry and experimental analysis.

Acquisition of bioinformatics skills is central to progress in biology, whether applied to genome sequence analysis, single cell transcriptomics, or epigenomics. Full support will be available through the Centre for Computational Biology. For clearly defined projects requiring advanced bioinformatics skills there will be the opportunity for more intensive training through the in-house Computational Genomics: Analysis and Training (CGAT) programme.

Students will be enrolled on the MRC Weatherall Institute of Molecular Medicine DPhil Course, and generic skills training is also offered through the Medical Sciences Division’s Skills Training Programme and students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.

All 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 WIMM.

Funding Notes

Funding for this project is available to scientists through the RDM Scholars Programme, which offers funding to outstanding candidates from any country. Successful candidates will have all tuition and college fees paid and will receive a stipend of £18,000 per annum.
For October 2020 entry, the application deadline is 10th January 2020 at 12 noon (midday).
Please visit our website for more information on how to apply.


Twigg SRF, et al (2013). Reduced dosage of ERF causes complex craniosynostosis in humans and mice, and links ERK1/2 signalling to regulation of osteogenesis. Nature Genet 45:308-313.

Twigg, SRF & Wilkie AOM (2015). A genetic-pathophysiological framework for craniosynostosis. Am J Hum Genet 97:359-77.

Twigg SRF, et al (2015). Gain-of-function mutations in ZIC1 are associated with coronal craniosynostosis and learning disability. Am J Hum Genet 97:378-388.

Miller KA, Twigg SRF, et al (2017). Diagnostic value of exome and whole genome sequencing in craniosynostosis. J Med Genet 54: 260-268.

Goos JAC, (15 authors), Twigg SRF (2019). A de novo point mutation in BCL11B leads to loss of interaction with the NuRD and PRC2 transcriptional complexes and is associated with craniosynostosis. Hum Mol Genet 28:2501-2513.

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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