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Integrating human functional genomics, epigenomics and transcriptomics to accelerate drug discovery for precision medicine in asthma. Acronym: OxMAP (Oxford Multiomics Asthma Project)

  • Full or part time

    Prof J C Knight
  • Application Deadline
    Friday, January 10, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Lead supervisor: Dr Timothy Hinks, Nuffield Department of Medicine, University of Oxford
Co-supervisor: Prof Julian Knight, Nuffield Department of Medicine, University of Oxford
Commercial partner: Sensyne Health PLC, Schrödinger, Oxford

Asthma affects 350 million people worldwide, of whom 5-10% suffer severe, treatment-refractory disease. Asthma is clinically and biologically heterogeneous. For example, identifying a type-2 high, eosinophilic phenotype has enabled highly-effective precision medicine and accelerated drug discovery. The need to define other endotypes and pathways to expand opportunities for precision medicine is the foremost research priority in asthma in Europe1. These phenotypes include i)‘T2-low’ non-eosinophilic disease, ii) steroid-resistant ‘T2-high’ disease and iii) familial asthma associated with inflammatory bowel disease (IBD) or other specific immunological defects.

This project will capitalise on three specific opportunities: recent advances in genomics, epigenomics and single cell technologies; recruitment of a highly phenotyped patient cohort; and unique access to NHS patient-level longitudinal data.

To address this challenge the new collaboration will leverage the complementary capabilities of:-

• Oxford University: ongoing human immunology / bronchoscopy studies Dr Hinks’ laboratory; Prof Knight’s genomics expertise
• OUH NHS Trust: patient cohorts and patient-level data from the electronic patient record (EPR) under a recently agreed strategic research agreement
• Digital healthcare company Sensyne Health: bioinformatic, data integration and artificial intelligence expertise and discovery science


1. Whole genome sequencing of 500 highly phenotyped asthma patients, to be compared with controls available from the UK Biobank to identify novel associations of genetic variants with specific asthma phenotypes: specifically: i)‘T2-low’ non-eosinophilic asthma, ii) steroid-resistant ‘T2-high’ disease and iii) IBD-associated asthma.
2. Detailed transcriptomic and epigenomic analysis of airway samples from 60 selected, genetically-sequenced participants
3. To identify rare, highly-penetrant alleles in patient pedigrees with early-onset, severe eosinophilic asthma and other features of systemic immunological dysfunction.


• Whole genome sequencing on Illumina NovaSeq6000 platform of genomic DNA from peripheral blood (n=500)
• ePR data obtained from OUH ‘IORD’ database under existing strategic research agreement
• Bulk total RNA sequencing on airway epithelial cells (n=60)
• Epigenetic analysis of airway epithelial cells for accessible chromatin predictive of regulatory elements using ATACseq (n=60)
• Singe cell sequencing of endobronchial biopsies on 10x platform 10,000 cells/patient (n=60)

This partnership will take place under robust strategic research agreements between Sensyne, OUH and Oxford University. Given the complementary strengths of the three partners, this project will therefore foster a highly mutually synergistic collaboration which is strategic both to the academic and the non-academic partners. Specifically, the Respiratory Medicine Unit (RMU) and the Wellcome Centre for Human Genetics (WHG) are benefitting from receiving funding to perform the sequencing experiments on which this study depends, and on the expertise the Sensyne bioinformatics team have in integrating diverse datasets and applying artificial intelligence to analysis of the EPR data. Conversely Sensyne are dependent on the strength of the RMU in detailed patient stratification, in recruiting cohorts and obtaining and processing the bronchoscopy samples, and on the WHG wet-lab sequencing capabilities and expertise in analysing whole genome datasets.

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