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NCKIR9: Exploit the impact of disease-causing HLA variants on cellular phenotypes

   Kennedy Institute of Rheumatology

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  Dr Yang Luo, Prof John Todd  No more applications being accepted  Funded PhD Project (Students Worldwide)

Oxford United Kingdom Cell Biology Data Analysis Genetics Immunology

About the Project

The 4 Mb major histocompatibility (MHC) region on chromosome region 6p21.3, which contains the human leukocyte antigen (HLA) complex, plays a vital role in our immune system and is of great importance to many areas of medicine, from organ transplantation and disease diagnosis to cancer immunotherapy. In autoimmune diseases, for example, the HLA-DRB1 allele is highly associated with seropositive rheumatoid arthritis, while individuals with HLA-B*27 alleles are 100 times more likely to develop active ankylosing spondylitis. Despite recent progress in discovering the genetic associations between HLA and a vast number of immune-mediated traits, the molecular mechanisms behind these associations have yet to be unravelled. The MHC region is notoriously difficult to study because of its highly polymorphic nature and high linkage disequilibrium structure. Our expertise in computational biology will facilitate accurate HLA inferencing in large, ancestrally diverse groups (Luo et al. 2021), and the quantification of HLA expression in given individuals.

This project will focus on pinpointing the cellular contexts in which disease-causing HLA variants affect gene expression. Powered by the integration of advanced next generation sequencing techniques, such as single cell RNA-seq, ATAC-seq and CITE-seq (Trzupek et al. 2021), we will perform the single-cell QTL analysis within the HLA region and develop a cell type-specific lens to be able to understand the interplay between HLA genetics and gene expression in immune-mediated disease context.


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 and Wellcome Trust Centre of Human Genetics, 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/Snakemake, and the use of high-performance compute clusters. The student will gain expertise in analysing cutting-edge sequencing datasets including single cell RNA-, ATAC- and CITE-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.


1. 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)
2. Trzupek, D. et al. Single-cell multi-omics analysis reveals IFN-driven alterations in T lymphocytes and natural killer cells in systemic lupus erythematosus. Wellcome Open Res. 6, 149 (2021)
3. Gutierrez-Arcelus, M. et al. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nature Genetics 52, 247–253 (2020)
4. Trzupek, D. et al. Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis. Genome Med. 12, 55 (2020)
5. D’Antonio, M. et al. Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease. Elife 8, (2019)
KEYWORDS: HLA, autoimmune diseases, eQTL, single cell RNA-sequencing, single cell ATAC sequencing
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