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  • Project No: NC-12
  • Intake: 2024 KIR Non Clinical


Proteins are the essential functional units in human metabolism, governing biological processes and forming the basis for therapies and interventions across a wide spectrum of health and disease. Recent technical advances in proteomic profiling, employing antibodies (such as Olink) or aptamer-based affinity reagents (such as SomaScan), allow high-throughput measurements of thousands of proteins. These advances enable the exploration of links between naturally occurring sequence variation in the human genome (SNPs) and protein levels (pQTL), many of which colocalize with association signals for common human diseases. By integrating existing genome-wide association studies, gene expression, and recently available proteomic data, it will become feasible to identify disease-critical variants, genes, proteins, cells, and tissues.

This project aims to investigate disease- and tissue-specific pQTLs in the context of osteoarthritis (OA). We will use newly generated proteomic data generated by the “Synovial fluid To Detect Endotypes by unbiased Proteomics in OA” consortium (STEpUP OA). This initiative is an international effort to perform proteomic analyses over 7,000 proteins in approximately 2,000 synovial fluid samples taken from 1,650 individuals with OA or at risk of OA after acute joint injury. We will integrate this with publicly available large plasma-based proteomic datasets such as the Fenland study (N = 12K) and the UK Biobank (N = 53K).

We will develop novel statistical methods to harness the wealth of genetic and proteomics data now available to help answer important questions ranging from whether OA is cartilage related to whether plasma or synovial fluid are more important for disease etiology. Through integrative analyses of genetic, expression, proteomic, and clinical data, this approach promises an improved understanding of OA pathology in a context-specific framework.



Proteomics, Osteoarthritis, statistical methods, genomics



This project is well suited to a student with a background in statistical genetics, or a background in statistical modelling or machine learning who is interested in developing applied knowledge in the biological sciences.

The successful candidate will be benefit from supervision by a team of scientists with key expertise in statistical genetics, immunology, and single cell genomics. You will be based in the Kennedy Institute of Rheumatology, world-leading centres in genomics and inflammatory biology. Training will be provided in data science techniques including statistical data analysis and visualisation with R, the writing of computational pipelines with Python/NextFlow, and the use of high-performance compute clusters. The student will gain expertise in analysing cutting-edge sequencing datasets including whole genome, RNA and proteomic sequencing.

The Kennedy Institute is a world-renowned research centre and has a vibrant PhD program with weekly journal club, seminars, student symposia, weekly internal institute presentations and training. A core curriculum of lectures will provide a solid foundation of a broad range of subjects including data analysis, statistical methods, and immunology summer school. In additional to institutional support, the successful applicant will benefit from being part of the University of Oxford college system. Students will also have the opportunity to work closely with both computational and experimental scientists.



Luo, Y. et al. A high-resolution HLA reference panel capturing global population diversity enables multi-ethnic fine-mapping in HIV host response. Nature Genetics (2021)

Zhu et al. Variants in ALDH1A2 reveal an anti-inflammatory role for retinoic acid and a new class of disease-modifying drugs in osteoarthritis. Science Translational Medicine (2022)

Nathan et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature (2022)

Pietzner et al. Mapping the proteo-genomic convergence of human diseases. Science (2021)



Computational biology, statistical modelling, proteomics, osteoarthritis