Performance of Artificial Intelligence versus standard Genome-Wide Association Studies to identify molecular pathways in autoimmune diseases for precision medicine across ethnicities.

About the Project

Background: Genome-Wide Association Studies (GWAS) were successful in identifying 100s of genetic variants associated with autoimmune disease susceptibility. However, the value of Polygenic Risk Scores (PRS) for clinical medicine is limited. When considered in aggregate, genetic variants identified so far explain only a small proportion of genetic liability to disease (missing heritability), because of methodological limitations. Also, traditionally, GWAS were performed in individuals of a single ethnicity (or ancestry). The impact of ancestry-specific associations on molecular and cellular pathways are unknown.

Therefore, complex disease genetics faces today three major challenges: 1) the development of new artificial intelligence (AI)-based methodologies to fully capture the genetic architecture of autoimmune disease; 2) explain disease heterogeneity by translating long lists of susceptibility polymorphisms into cell type-specific molecular pathways to define disease endotypes for precision medicine; 3) understand how genetic differences between ancestries impact on cell type-specific molecular pathways, disease aetiology and outcome.

Research question: To identify ancestry-specific cell subsets and their intracellular pathways under the control of genetic polymorphisms conferring susceptibility to autoimmune diseases.

Methods and objectives:

Objective 1 - comparative performance of AI algorithms over traditional statistical approaches across ethnicities:

Recent large GWAS have identified genetic susceptibility polymorphisms associated with rheumatoid arthritis (RA) [1], Psoriatic Arthritis (PsA) [2] and myositis [3]. We will use cutting-edge artificial intelligence (AI) network algorithms [4,5] to identify sets of polymorphisms associated with each disease. The performance of various AI tools over standard statistical approaches will be assessed by their capacity to identify known variants. New variants will be validated in external datasets (collaborators). Analysis will be conducted in different ethnic groups.

Objective 2, mapping genetic polymorphisms to genes, pathways and cell types across ethnicities:

Known and newly identified susceptibility polymorphisms will be mapped to genes, genes to pathways, and pathways to cell types, using publicly available resources (including expression/splicing/protein quantitative trait loci, transcription factor binding, chromatin conformation, epigenetic marks). We will assess if and which cellular pathways are ancestry-specific.

Objective 3, experimental in vitro validation of cellular quantitative trait loci (optional):

Experimental validation in the wet lab will be performed by testing the association of a pathway-specific PRS with specific cellular functions (i.e. a T-cell receptor signalling pathway PRS will be tested for its association with IL-17 cytokine expression in T cells). A set of > 100 healthy volunteers peripheral blood mononuclear cells is available for this project (including genome-wide genotypes). Specific pathways will be stimulated in vitro (for example anti-CD3/CD8 beads for TCR signalling) and effector function (i.e. cytokine production) will be quantified by multiparameter flow cytometry.

Training opportunities: This is an exciting opportunity for a highly motivated student to join a vibrant and dynamic research environment and a great training opportunity in AI, bioinformatics, statistical genetics. This project also offers the possibility for interested applicants to be trained in wet lab techniques. The supervisory team offers expertise for 1-to-1 training in all areas, in addition to regular University training programmes.

Entry Requirements

Applicants are expected to hold (or about to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in bioinformatics, information technology, informatics, biostatistics or relevant subject area. Applicants are expected to have a strong interest and background in command line programming and bioinformatics. No prior knowledge in immunology/rheumatology is required (although it will be an advantage). No prior skills in flow cytometry or wet lab is required (but would be an advantage for candidates choosing the optional experimental validation step (Objective 3).

Application Guidance

Candidates must contact the primary supervisor before applying to discuss their interest in the project and assess their suitability.

Apply directly via this link: https://tinyurl.com/ycweuusx or on the online application portal, select Bioinformatics PhD as the programme of study.

Please ensure that your application includes all required supporting documents:

• Curriculum Vitae (CV)
• Supporting Statement
• Academic Certificates and Transcripts

Incomplete or late applications will not be considered.Further details are available on our website.

Funding Notes

This 4 year PhD project is for self funded students.

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.